The Pennsylvania State University

The Graduate School

Department of Geography

SPECIES COEXISTENCE IN AN ALPINE FOREST IN CENTRAL JAPAN

A Dissertation in

Geography

Amanda Beatrice Young

 2016 Amanda Beatrice Young

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2016

The dissertation of Amanda B. Young was reviewed and approved* by the following:

Alan Taylor Professor of Geography Dissertation Advisor Chair of Committee

Andrew Carleton Professor of Geography

Erica Smithwick Associate Professor of Geography

Margot Kaye Associate Professor of Forest Ecology

Cynthia Brewer Professor of Geography Head of the Department of Geography

*Signatures are on file in the Graduate School

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ABSTRACT

In this dissertation, I examine Bond’s “slow seedling” hypothesis in an alpine forest system in central Japan. Bond (1989) developed this idea to explain the spatial partitioning of angiosperms and gymnosperms, in which gymnosperms are less responsive to resources like light and soil nutrients. Angiosperms prefer nutrient rich, dry soils in areas with relatively warm climates, relegating gymnosperms to the cool nutrient poor environments. Thus, gymnosperms typically dominate at high elevations and latitudes in environments such as treeline, with low temperatures and poorly developed soils.

The alpine treeline of central Japan is not composed of gymnosperms but an angiosperm,

Betula ermanii Cham., and is bounded by a subalpine forest dominated by Abies mariesii Mast. below and a mat of scrub pine Pinus pumila (Pall.) Regel above. The B. ermanii treeline is abrupt and has had no apparent upward movement over the past 50 years despite significant warming

(3ºC), indicating the ecosystem has long-term stability where gymnosperms and angiosperms coexist, though the dominance of each species is segregated by elevation. In this dissertation, I examine three drivers of the delineation of treelines - soil nutrients, the regeneration niche, and climate - from the subalpine forest through the B. ermanii treeline and into the P. pumila mat.

In Chapter 2, I assessed Bond’s “slow seedling” hypothesis by examining relationships between soil nutrients and tree importance values across the three forest bands using linear mixed-effect models. I found that the predictions from Bond’s hypothesis were inconsistently correct. For example, soil nitrogen was highest in the B. ermanii treeline on volcanic mountains, as I expected, but was similar across forest belts on non-volcanic mountains. This mixed evidence suggests lithology plays a role in driving local composition.

In Chapter 3, I explored differences in the regeneration niche across ontogeny (seedlings and trees) for B. ermanii and A. mariesii in the subalpine forest and treeline using a multivariate

iv niche overlap approach. Evidence that B. ermanii, with its broad habitat niche but narrow regeneration niche, contrasted with A. mariesii, which has a broad regeneration niche but a narrow habitat niche, largely supports Bond’s hypothesis. The broad regeneration niche of A. mariesii likely stems from the large investment in seedlings through advanced regeneration; however, many of these individuals do not successfully reach maturity. B. ermanii has a narrow regeneration niche limited by suitable conditions in high light on raised surfaces with low litter, though over ontogeny the habitat niche broadens.

In Chapter 4, I examined the influence of temperature, precipitation, PDSI on tree growth of B. ermanii and A. mariesii using a dendrochronological approach. B. ermanii responded positively to summer temperatures and negatively to summer precipitation, while A. mariesii had positive growth in the first year after warm and wet winters. 1.1% of the tree rings in B. ermanii were missing rings, possibly due to angiosperms’ responsiveness to changes in their environment.

Missing rings may result from disturbance events. Though no known insect outbreaks or fires occurred in the Japanese Alps, missing rings may have been caused by volcanic activity.

Understanding how and why the B. ermanii treeline exists is important for assessing responses to climate change as well as the cultural ecosystem services provided by alpine biodiversity. The climate-growth responses of B. ermanii and A. mariesii indicated that future warming, especially in winter, may reduce B. ermanii’s dominance at treeline. Warmer winter temperatures will likely increase A. mariesii’s growth and possibly reduce mechanical damage.

Though some A. mariesii trees occur in the B. ermanii treeline and P. pumila mat, they frequently have mechanical damage (tops are sheared off at snow height, needles show browning and mortality). If A. mariesii establishes at treeline and above, then the B. ermanii treeline may be at risk of being replaced. The competition with P. pumila, mechanical damage of the A. mariesii, longevity of B. ermanii, and elevated nitrogen in the B. ermanii treeline are all factors that allow

B. ermanii to persist in an environment where it is theoretically unexpected.

TABLE OF CONTENTS

List of Figures ...... v

List of Tables ...... vi

Acknowledgements ...... vii

Chapter 1 Introduction ...... 1

Theoretical Framework ...... 2 Climatic Limitations ...... 4 Landscape Interactions ...... 5 Geomorphology ...... 5 Disturbance ...... 6 Alpine forests of central Japan ...... 8 Climate ...... 8 Species ...... 9 Disturbance ...... 10 Summary ...... 11 References ...... 13

Chapter 2 Influence of topography and edaphic properties on broadleaf-deciduous versus evergreen conifers in a high elevation forest ...... 27

Introduction ...... 27 Methods ...... 30 Study Area Description ...... 30 Field Methods ...... 31 Statistical Analysis ...... 33 Results ...... 35 Topographic Properties ...... 35 Shrubs ...... 36 Light Environment ...... 37 Soil Nutrients ...... 37 Canopy Structure ...... 38 Predictors of Importance Values ...... 39 Discussion ...... 39 Canopy Structure ...... 40 Bedrock-Nutrients ...... 41 Treeline Elevation ...... 42 Nutrient Gradient...... 43 Microbial Activity ...... 44 Nutrient Deposition ...... 46 Allelopathy and Nitrogen Fixers ...... 47 Conclusions ...... 48 References ...... 50

Chapter 3 Influence of Forest Type and Ontogeny in Determining the Regeneration Niche in High Elevation Forests ...... 73

Introduction ...... 73 Methods ...... 76 Study Area ...... 76 Field Methods ...... 77 Seedling Measurements ...... 78 Tree/ Canopy Measurements ...... 79 Niche Overlap ...... 80 Species Overlap ...... 81 Subpopulation Overlap ...... 81 Results ...... 82 Species Overlap ...... 83 Subpopulation Overlap ...... 83 Discussion ...... 85 Ontogenetic Stage ...... 85 Forest Belt ...... 88 Species ...... 89 Ontogenetic specific parameters ...... 91 Conclusion ...... 92 References ...... 94

Chapter 4 Climate-growth Relationships in two forest species at their upper elevation limits in the Japanese Alps ...... 114

Introduction ...... 114 Study Area...... 117 Methods ...... 118 Lapse Rates of Temperature ...... 119 Field Sampling ...... 120 Chronology Development ...... 121 Climate-Growth Relationships ...... 122 Growth Variation...... 122 Results ...... 123 Chronology Comparison ...... 123 Climate-Growth Relationships ...... 124 Growth Variation...... 126 Discussion ...... 128 Temperature-Growth Relationships ...... 128 Precipitation-Growth Relationships ...... 131 PDSI-Growth Relationships ...... 134 Growth Variation...... 135 Conclusion ...... 137 References ...... 138

Chapter 5 Synthesis...... 174

Testing Bond’s Hypothesis ...... 174

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An explanation for the Betula ermanii treeline ...... 175 Future Directions ...... 177 Importance of this research in Japan ...... 179 Broad-leaf deciduous treeline ...... 181 References ...... 185

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LIST OF FIGURES

Figure 1-1. Number of publications from the ISI Web of Science (as of 2013) on treeline for various gymnosperm and angiosperm species in the northern and southern hemisphere. Search terms include: treeline, timberline, and individual genera from 2067 papers, 1137 papers included the term treeline or timberline and the species and continent was discernable...... 23

Figure 1-2. Annual mean temperature (oC) and total annual precipitation (mm) in Matsumoto, Japan over the past century...... 24

Figure 1-3. Species transitions from the subalpine forest, to a belt of Betula and a mat of pine at the upper elevations of the alpine forests in the Northern Japanese Alps. (Tsubakuro July 27th, 2013)...... 25

Figure 1-4. Repeat aerial photography from the 1960s (leaf-on) and 2000s (leaf-off) on Norikura at the upper boundary of the Betula belt (blue arrows) with the Pinus mat (yellow arrows)...... 26

Figure 2-1. Study mountains (red dots) are located within Chubu-Sangaku National Park (black polygon) which encompasses most of the Northern Japanese Alps. Surrounding the Northern Japanese Alps are four meteorological stations (black triangles)...... 61

Figure 2-2. Monthly total precipitation (mm) and mean temperature (°C) for each of the four meteorological stations surrounding the Northern Japanese Alps. Displayed as a hythergraph with the first initial of the month listed in the point...... 62

Figure 2-3. Box-whisker plots of the topographic (elevation, slope (%), and Topographic -1 Position Index [TPI]) and edaphic (nitrate [NO3-N; mg L ], ammonium [NH4-N; mg -1 -1 L ], phosphate [P2O2; mg L ], pH, total carbon [%] and total nitrogen [%]) parameters used in the linear mixed effects models. Data from transect points is divided based on bedrock (non-volcanic or volcanic) and forest belt (subalpine [SA; purple], Betula belt [BB; blue] and Pinus mat [PM; red]...... 63

Figure 3-1. Study sites (red dots) are located within Chubu-Sangaku National Park (black polygon) which encompasses most of the Northern Japanese Alps...... 101

Figure 3-2. Non-metric multidimensional scaling (nMDS) of the niche overlap (NO) distance matrix (A). Subpopulations abbreviated by their components (AM = A. mariesii [Blue], BE = B. ermanii [Green], S = Seedling [< 50 cm tall; circles], T = Tree [> 200 cm tall; triangles], BB = Betula belt, SA = Subalpine). Visual separation of the subpopulations by ontogeny ([B] seedlings and trees), forest belts ([C] subalpine and Betula belt), and species ([D] Abies and Betula). Samples sizes ranged from 22-78 individuals (BE.BB.T [50], BE.SA.T [20], BE.BB.S [29], BE.SA.S [41], AM.BB.T [36], AM.SA.T [78], AM.BB.S [22], AM.SA.S [59])...... 102

Figure 3-5. Variables for seedlings (Gap Light Index [A]) or trees (Wood Density [B], Bark Thickness [C], and AGE [D]) that were not included into NO analysis. Blues is

Abies and green is Betula, shades run from BB (dark) to SA (light). Samples sizes ranged from 22-78 individuals (BE.BB.T [50], BE.SA.T [20], BE.BB.S [29], BE.SA.S [41], AM.BB.T [36], AM.SA.T [78], AM.BB.S [22], AM.SA.S [59])...... 105

Figure 4-1. Study sites (red dots) are located within Chubu-Sangaku National Park (black polygon) which encompasses most of the Northern Japanese Alps. Surrounding the Northern Japanese Alps are four meteorological stations (black triangles)...... 151

Figure 4-3. Monthly total precipitation (mm) and mean temperature (oC) for each of the four meteorological stations surrounding the Northern Japanese Alps, displayed as a hythergraph...... 153

Figure 4-4. Standardized chronologies for each mountain and both species (no Betula chronlogy for KSM or NOW). Annual values (thin line) and 20 year spline (thick line) correspond to values on the left y-axis. Sample depth (grey shading) aligns with the values on the right y-axis. Verticl hashed line is where EPS values are stable or sample depth drops below 5 individuals...... 154

Figure 4-5. The first principal component from each of the principal component analysis done on the Betula chronologies and the one done on the Abies chronologies. Each of the individual chronologies were negatively loaded onto PC1, thus to make it comparable with the individual chronologies PC1 for both species was multiplied by -1...... 155

Figure 4-6. Percent of Abies (blue, AM-GC) and Betula (red, BE-GC) cores expressing a release (positive) or suppression (negative) in growth from 1700-2011. Missing rings occurred for both species (Betula, grey/red; Abies, black/blue) and sample size for Betula (red) and Abies (blue)...... 156

Figure 4-7. Percent of Betula (A) and Abies (B) cores expressing a release (positive) or suppression (negative) in growth from 1700-2011...... 157

Figure 4-8. Percent of Abies (blue) and Betula (red) cores expressing a release (positive) or suppression (negative) in growth from 1700-2011 for the three study sites that had both Abies and Betula chronologies (Chogatake [CHO], Sugorokudake [SUG], and Tsubakuro [TSU]). Missing rings for Betula, grey/red and the sample size for Betula (red) and Abies (blue)...... 158

Figure 4-9. Total number of missing rings per year from each of the Betula chronologies (A). Cores were divided between those that had portions removed (B) and complete cores (C) to examine the establishment year to number of missing rings. No missing rings are in red, with warm colors for few missing rings and cool colors for higher numbers of missing rings...... 159

Figure 4-10. Correlation (bars) and response functions (black dots) between PC1 for each species growth and temperature (red), precipitation (blue), and Palmer drought severity index (PDSI; green). Solid bars are significant correlations and black dots are significant response functions. Lower case month names are from the previous year and capitals are from the current year...... 160

Figure 5-1. Conceptual diagram of the alpine forest system in the Northern Japanese Alps. The forest transitions from an Abies mariesii (red shading) dominated subalpine forest, though a band dominated by Betula ermanii (green shading), and terminating in a scrub mat of Pinus pumila (orange shading). Density of Abies (red line) decreases with increasing elevation, while Pinus increases with elevation (orange line). Betula (green line) has a constant density from the subalpine forest, through the Betula belt and into the Pinus mat where it abruptly terminates...... 189

LIST OF TABLES

Table 2-1. Topographic (Topographic Wetness Index [TWI], Topographic Position Index [TPI], slope (°) and elevation (m)) and edaphic parameters (pH, ammonium (mg L- 1), nitrate (mg L-1), total carbon (%), total nitrogen (%), and phosphorus pentoxide (mg L-1)) of bedrock (non-volcanic and volcanic) in forest belts (Subalpine, Betula belt, Pinus mat) taken every 30m along transects. The number of points per forest belt by bedrock is denoted in parentheses next to the forest belt names. Values are the means (± standard error). Significant pairwise differences are indicated by letters within a row. (two-way ANOVA [df = 5,61], Tukey’s HSD, α =0.05)...... 64

Table 2-2. Vegetation cover (forb, shrub and canopy cover; [% cover classified into 6 categories (0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75%, 5: 75-95%, 6: 95-100%)]), light environment (Gap Light Index [GLI]) and litter depth (cm) of the bedrock (non- volcanic and volcanic) and forest belt (Subalpine, Betula belt, Pinus mat) taken every 10m along transects. The number of points per forest belt by bedrock is denoted in parentheses next to the forest belt names. Values are the means (± standard error). Significant pairwise differences are indicated by letters within a row. (two-way ANOVA [df =5,61], Tukey’s HSD, α =0.05)...... 65

Table 2-3. Percent occurrence of shrubs in different bedrock (non-volcanic and volcanic) and forest belts (Subalpine, Betula belt, Pinus mat). The number of points per forest belt by bedrock is denoted in parentheses next to the forest belt names...... 66

Table 2-4. Forest community structure across ontogeny (short seedlings < 50 cm, tall seedlings 50-100 cm, saplings 100-200 cm, trees > 200 cm) for the forest belts (Subalpine, Betula belt and Pinus mat). Units are stems per hectare (x), standard deviation (σ) and number of points at which individuals were recorded (N) ...... 67

Table 2-6. Variance of the random effects (%) and statistics of global models for species’ IV (Abies, Betula and Pinus). The performance of the global models is illustrated by the Akaike information criterion (AIC) and the AIC conditional (AICc), the R2 for only the fixed effects (R2m), and the R2 of the combined fixed and random effects (R2c)...... 69

Table 2-7. Top models, within two AIC (Akaike information criteria) of the best model (∆ AIC = 0) for the importance value of each species. AIC weight is the proportion that that submodel is the best model. Each importance value has 67 points...... 70

Table 2-8. Averaged top models and their summary results (standardized coefficients, unconditional standard error, 95% confidence interval and relative importance) for

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the importance value (IV) of each species (Abies mariesii [AM], Betula ermanii [BE], and Pinus pumila [PP]). Each importance value comprises 67 points...... 71

Table 3-1. Study area location information (Latitude and Longitude) and topographical description (elevation [m], aspect [o], and slope [o] for the sites in the Northern Japanese Alps...... 106

Table 3-2. Variables included in the niche overlap (NO) analysis. Variable type denotes if the data is a resource, categorical, continuous, proportional, measurement (same as continuous but only for positive data and log transformed) or binary (sensu Geange et al. 2011). Data type lists measurements possible for each variable. Seedling and Tree indicate if the variable was measured at the individual (I) or at the point (P) or not at all (-)...... 107

Table 3-3. Count of measured seedlings (< 50 cm tall) and trees (> 200 cm tall) for each of the five species found at the study sites...... 108

Table 3-4. Count of seedling (< 50 cm tall) and trees (> 200 cm tall) per species, split by forest belt and ontogenetic stage. The subalpine forest is dominated by fir and located below the Betula belt which is dominated by Betula...... 109

Table 3-5. Niche overlap (NO) between Abies and Betula with the full dataset statistically significant (p-values < 0.05) overlaps are noted with a *...... 110

Table 3-6. Niche overlap (NO) of the subpopulations [Abies (AM) and Betula (BE) across two forest belts: Betula belt (BB) and subalpine forest (SA) and two ontological states: Seedlings (S) and Trees (T)]. All values are significantly different, with a p-value < 0.05 with the Holm-Bonferroni correction method...... 111

Table 3-8. Significance between subpopulations (Abies [AM] and Betula [BE] across two forest belts: Betula belt [BB] and subalpine forest [SA] and two ontological states: Seedlings [S] and Trees [T]) and the environmental attributes. Environmental attributes included were topographic (slope; degrees), structural (crown-height ratio and crown-diameter ratio), establishment site (litter depth [cm], raised surface [cm], distance to conspecific [m]), vegetation cover (forb, shrub and canopy cover; [% cover classified into 6 categories [0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75%, 5: 75- 95%, 6: 95-100%]), light environment (Gap Light Index [GLI]), and wood properties (Density [cm3], bark thickness [cm], and tree age [years]. Values are the means (± standard error). Significant difference is indicated by difference in letters

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within a row. (ANOVA [df = 7,327; Slope-Canopy Cover], ANOVA [df = 3,147; GLI], ANOVA [df = 3, 175; Wood Density], ANOVA [df = 3,155; Bark thickness], and ANOVA [df = 3,162; Age], Tukey’s HSD, α =0.05)...... 113

Table 4-1. Location (latitude [ºN] and longitude [ºE]) and topographic environment (Elevation [m], aspect (º) and slope[º]) of the mountains analyzed in this study from the Northern Japanese Alps...... 161

Table 4-2. Climate data location information, coverage interval and parameters (present [x], not available [-]) for the radiosonde (IGRA), meteorological stations (JMA), and mountain station data (SUIMS/GUTFT)...... 162

Table 4-3. Linear regressions of the trends from 1910 to 2010 of climate parameters (temperature and precipitation) for each of the climate stations (Matsumoto, Takayama, Toyama, and Nagano)...... 163

Table 4-4. Adiabatic Temperature lapse rates (ºC 100 m-1) calculated from the mesoscale mountain stations and the radiosondes at Wajima and Tateno...... 164

Table 4-5. Differences between the mesoscale stations and the temperatures (ºC) calculated with the lapse rates applied to the meteorological stations (Matsumoto, Nagano, Takayama, and Toyama) and the radiosonde data calculated for 2600 m. Mesoscale station data is the mean precipitation and temperatures (± standard deviation) found at stations approximately 2600 m. Precipitation (mm) values have had Suzuki’s water balance equation applied to calculate the precipitation at 2600 m for each station. Precipitation data was not recorded for the radiosondes. Values under the mesoscale stations are the mean precipitation and temperature (standard deviation) for the months listed on the left. Values under the meteorological stations and radiosondes are the mean differences from the mesoscale station data, indicating if temperature and precipitation is similar to that of the mesoscale stations. * significant at p-value < 0.1...... 165

Table 4-6. Chronology statistics for Betula ermanii and Abies mariesii at the sites Chogatake (CHO), Tsubakuro (TSU), Sugorokudake (SUG), Norikura (NOW), and Kashimayari (KSA). Variance explained on the first principle component (VarPC1). ... 166

Table 4-7. Pearson’s correlation coefficients (significant are bold, p-value < 0.05) and response functions (*, p-value < 0.05) for the PC1 of each species and the four climate stations. Lower case month names are from the previous year and capitals are from the current year...... 167

Table 4-8. Pearson’s correlation coefficients (significant are bold, p-value < 0.05) and response functions (*, p-value < 0.05) for the PC1 of each species and minimum (min), mean, maximum(Max) temperatures as well as monthly total precipitation (mm) and the duration of sunshine (h) per month. Lower case month names are from the previous year and capitals are from the current year...... 168

Table 4-9. Pearson’s correlation coefficients (significant are bold, p-value < 0.05) and response functions (*, p-value < 0.05) for each Betula (A) and Abies (B) chronology

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and the four climate stations. Lower case month names are from the previous year and capitals are from the current year...... 169

Table 4-10. Pearson’s correlation coefficient (significant are bold, p-value < 0.05) for the PC1 of the entire Betula chronology (PC1 BE) and the PC1 of the reduced Betula chronology for cores with < five missing rings or were not truncated (PC1 BE <5MR) for the monthly climate parameters of temperature (mean), precipitation (sum) and PDSI(mean). Lower case month names are from the previous year and capitals are from the current year...... 172

Table 4-11. Linear regressions and their summary statistics between PC1 chronologies (Abies, Betula and reduced Betula [< 5 missing rings (MR) and no truncation]) and summer (JJA) climate parameters (precipitation [sum], temperature [mean], and PDSI [mean]). Summary statistics of climate parameter include parameter estimates, standard error and p-value. Summary statistics of the linear regression models include degrees of freedom (number of climate parameters: number of years analyzed), R2 and the model p-value...... 173

ACKNOWLEDGMENTS

I would like to thank those who have supported me and my research intellectually, physically, and financially over the duration of my dissertation. Particularly, I would like to thank my advisor, Dr. Alan Taylor, for letting me explore my own ideas and helping guide me though the Ph.D. process. Because of your support, I have gained exposure to a broader array of research than what I learned through my dissertation, while at the same time I had the freedom to pursue my own questions. This work would not have been possible without my collaborator Dr. Koichi

Takahashi at Shinshu University. There is too much to thank you for, from help with housing, research assistance, lab space, and permits to scientific conversations, language lessons, and being a friend.

My dissertation committee (Dr. Andrew Carleton, Dr. Margot Kaye and Dr. Erica

Smithwick) has been amazing through the process of my dissertation. Thank you for both the theoretical and practical conversations, and the encouragement to pursue my research goals. To those who persisted with me in Japan, collecting data rain or shine (but more rain than anything!), up and down the mountains and literally through the woods, I would like to thank you - Abby

Dolinger, Helena Kotala, Yosuke Hara, and Gina Radieve. Housing and logistics in Japan were provided by Shinshu University, Norikura Observatory Institute for Cosmic Ray Research, and

Norikura Kuraigahara Sanso.

I appreciate both Dr. Taylor and Dr. Takahashi for welcoming me into their labs and providing me with the space I needed to process and analyze my samples. In the lab I was assisted by a number of undergraduate research assistants: Steve Perkins, Grant Smith, Ryan

Gallagher, Helena Kotola, Abby Dolinger, Courtney Jackson, and Britt Eckerstrom. Thanks for the many hours you helped with the processing of hemispherical photographs, remote sensing imagery, and tree cores.

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My research was financially supported by funding from the Japanese Society for the

Promotion of Science Summer Program with the U.S. National Science Foundation East Asia and

Pacific Summer Institute (Grant No. 1209579), a National Science Foundation Doctoral

Dissertation Improvement Grant (Grant No. 1434242), a NASA Space Grant Fellowship from the

Pennsylvania Space Grant Consortium, The Society of Women Geographer’s National

Fellowship, a Student Research Grant from the Biogeography Specialty Group of the American

Association of Geography, and Academic Enrichment Funds from the Department of Geography at The Pennsylvania State University. Additional funding was provided to Dr. Takahashi to support my Japanese field assistant, Yosuke Hara, through the Japanese Society for the

Promotion of Science.

Lastly, I would like to thank those who have provided emotional support along the way. I owe a debt of gratitude to my fellow graduate students in the Department of Geography at Penn

State, especially those in the Vegetation Dynamics Lab. To my host mother, Sanae Takahashi, in

Kamakura, Japan, thank you for introducing me to Japanese culture, food and language. I am so happy you have taken up mountain hiking. Though everything Japan is often still confusing, I would never have made it so far without you. Thank you to my family for keeping in touch and encouraging me while I get lost in my thoughts and research. Finally, to David, I may not be as eloquent as you, but thank you for the support and encouragement over the past few years. Thank you for being my own personal barista and editor.

Here I find the true nature of the tree –

not in the bulk of its shape

but in the way its form alters my vision of the world.

– Stefanie Brook Trout, Prairie Gold: An Anthology of the American Heartland

Chapter 1

Introduction

Treelines are typically considered to be composed of gymnosperms (needle-leaf) instead of angiosperms (broad-leaf), but why? This understanding comes from the bulk of the research on treelines being conducted in the Northern Hemisphere, where the majority of the treelines are dominated by gymnosperms (Holtmeier and Broll 2010; Figure 1-1). Gymnosperm treelines are found in North America

(Pinus, Tsuga, Abies, Picea), European Alps (Pinus, Picea, Larix), Siberia (Larix, Pinus), Himalayas

(Abies, Juniperus) and in East Asia (Pinus, Picea, Larix, Abies). In the Southern Hemisphere, treeline genera are predominantly angiosperms, not gymnosperms (but see Veblen 1982; Figure 1-1). Angiosperm treelines are most notably found in the Andes (Nothofagus, Polylepis), New Zealand (Nothofagus),

Australia (Eucalyptus, Nothofagus) and East Africa (Erica, Hagenia). The commonality in genera between the Northern and Southern Hemispheres is due to both geologic and biogeographic history

(Holtmeier 2009).

Although, to my knowledge, there is only one gymnosperm treeline in the Southern Hemisphere

(Araucaria), there are relatively few angiosperm treelines when compared with the number of gymnosperm treelines in the Northern Hemisphere (Figure 1-1). These angiosperm treelines occur in the

Scandes of northern Europe (Betula), East Asia (Betula), the Caucasus Mountains (Betula), the Pyrenees

(Fagus), and in localized areas of North America (Betula, Populus). Furthermore, as a result of climate change, angiosperm tree species are invading into systems that were historically dominated by gymnosperms trees or angiosperm shrubs (Chapin and Starfield 1997; Pisarenko et al. 2001; Barret et al.

2011). The predominance of gymnosperm treelines in the Northern Hemisphere leads to the overarching question that drives my research; How do angiosperms outperform gymnosperms at high elevations to form treelines? I address this question in my dissertation by examining the climate-growth relationships

2 and regeneration niche of an angiosperm and a gymnosperm as well as the underlying edaphic properties across an elevation gradient.

Theoretical Framework

I approach this question through the theoretical framework of Bond’s “slow seedling” hypothesis

(1989). Bond (1989) developed the slow seedling hypothesis to explain the spatial partitioning of angiosperms and gymnosperms, in which gymnosperms have a lower sensitivity to pulses of resources

(i.e., light availability, nutrient deposition) than angiosperms. Having a greater sensitivity permits angiosperms to outcompete gymnosperms when additional resources (e.g., increases in light availability from forest gaps) become available in a forest (Bond 1989). According to this understanding, when gymnosperms and angiosperms are both present in the species pool, the distributions of gymnosperms are generally restricted to those areas where angiosperms are less tolerant and thus successful, such as areas with cold climates that lack nutrient rich or well drained soils.

Bond’s slow seedling hypothesis has been questioned as an oversimplification in determining the mechanisms that control angiosperm and gymnosperm distributions (Becker 2000; Lusk 2011; Brodribb et al. 2012). A number of studies have examined differences in the traits related to competition between angiosperms and gymnosperms. Maximum photosynthetic rates are generally higher for angiosperms

(Becker 2000; Lusk 2011). Leaf age influences the maximum photosynthetic rates of angiosperms and gymnosperms, and similar leaf lifespans may have comparable photosynthetic rates (Becker 2000) though some angiosperms nonetheless have greater rates for a given leaf lifespan (Lusk 2011). Temperate and treeline gymnosperms tend to have a lower risk of embolism from cavitation resulting from freeze-thaw events when compared with angiosperms, and this is due to the gymnosperms’ smaller diameter water conduits (tracheids in gymnosperms, vessels in angiosperms) (Sperry and Sullivan 1992; Field and

Brodribb 2001; Lusk 2011; Brodribb et al. 2012). While these smaller conduits help prevent freeze-thaw cavitation, they also limit the xylem’s conductivity of water that supports greater total leaf area and higher

3 whole plant-level photosynthetic rates (Brodribb et al. 2005). However, hydraulic conductivity at both the leaf and whole-plant level does not differ between gymnosperms and angiosperms based on architectural differences (Becker 2000). One of the life history traits that was not included in Bond’s hypothesis is species longevity, a trait in which variation can act as a mechanism that potentially leads to species coexistence (Lusk 2011). More generally, no single specific mechanism has yet been identified that enables prediction of whether a given environment should be expected to be occupied by angiosperm or gymnosperm-dominated forests (Brodribb et al. 2012).

In relation to climate and soil nutrients, Becker (2000) found no widespread evidence that gymnosperms are limited to nutrient poor environments, except in boreal and mountainous regions, the very places where treelines can form. If Bond’s slow seedling hypothesis and phytogeographic limitation hypotheses are correct, then gymnosperms should dominate both boreal and alpine treelines. Globally, conifers (i.e. not all clades of gymnosperms) frequently occur at high latitudes and elevations (Fragniere et al. 2015). As a result, most research in these regions has demonstrated that gymnosperms are typically dominant at high elevations and latitudes in cool climates, such as treeline (Holtmeier and Broll

2010).However, as stated above, there are in fact angiosperm treelines, but they are comparatively less studied (Figure 1-2). This dissertation is therefore an attempt to improve our limited understanding of angiosperm treelines.

In this dissertation, I examine Bond’s slow seedling hypothesis in a mixed alpine forest in central

Japan, where a deciduous angiosperm (Betula ermanii) treeline emerges from a gymnosperm dominated subalpine forest (Abies mariesii). The approaches I take in testing Bond’s hypothesis include investigations of the regeneration niche and niche overlap, the relationships between soil nutrients and species dominance, and the climate-growth responses of the dominant species on the landscape. To contextualize these three approaches to studying treelines, I will first provide a brief overview of the limitations that cause treelines and what is known about the alpine forests of central Japan.

Climatic Limitations

The study of why treelines occur has been of interest in western literature since Alexander von

Humboldt noted the occurrence of treeline and postulated that it was due to temperature limitation because treelines decrease in elevation with increased latitude (von Humboldt and Bonpland 1805). This astute observation has held true with the documentation of many more treelines today; the highest treelines are found in the tropics, and they decrease in elevation with increasing latitude (Körner 1998).

Additionally, continental treelines are higher than island treelines, in part due to having a greater land mass and thus greater continentality (Irl et al. 2016). Globally, low growing season temperatures (air [10

°C] and soil [8 °C]) limit tree establishment and growth at many treelines (Jobbágy and Jackson 2000;

Körner and Paulson 2004, Harsch et al. 2009; Randin et al. 2013).

In a global analysis, 52% of treelines are advancing (establishing upslope) from increasing temperatures due to climate change (Harsch et al. 2009). There are three main forms of treeline: diffuse

(gradual), abrupt, and krummholz (stunted) (Harsch and Bader 2011). Advancing treelines occur both for the diffuse treelines through an increase in mean summer temperatures and in krummholz by increasing winter temperatures (Harsch and Bader 2011). Cool summer temperatures reduce the photosynthetic rate, which leads to a reduction of stored carbohydrates which are used for growth, reproduction and maintenance (Hoch and Körner 2009). While cool summer temperatures limit tree development and growth, winter temperatures limit treelines through mechanical damage via wind abrasion and snow breakage (Hadley and Smith 1986; Holtmeier and Broll 2010). Abrupt treelines are believed to be primarily limited in distribution by seedling mortality (competition or exposure) and disturbances rather than by climate (Harsch and Bader 2011).

Landscape Interactions

The other 48% of treelines are not advancing (upslope establishment) and are either stable or retreating; these treelines are influenced more by biotic and non-climatic abiotic factors related to topography or soils (Harsch et al. 2009). In addition to climate, abiotic factors influence treeline distribution through geomorphology, disturbances (natural and anthropogenic), and differences in species’ life histories (Malanson et al. 2010). These factors are not as evident as climate as the primary driver of treeline elevation. In many instances, these influences interact to control the form (diffuse, abrupt, krummholz) and species composition of treeline.

Geomorphology

Treelines can occur below their climatic limit due to topography and substrate (Körner 2012).

One of the topographic controls is “summit syndrome”, the phenomenon of treeline position being depressed below the trees’ climatic potential due to shallow soils and high winds near the top of mountains (Körner 2012). Summit syndrome is not the only mechanism that naturally depresses treelines below their climatic potential; this lowering of treelines can also be topographically and substrate driven

(Körner 2012). The mass elevation effect also influences the elevation of treeline. In the central portion of a mountain range a treeline will be higher than those on either side due to air masses being lifted through orographic uplift and adiabatic cooling (Körner 2012).

On the macro- (slope, aspect, elevation) and micro-scales (curvature), there are topographically important variables that influence treeline distribution. In complex mountain systems, treeline can be depressed below its climatic potential due to steep slopes and cliffs that are unsuitable for colonization by trees (Macias-Fauria and Johnson 2013). In some glacially scoured regions, treelines form in ribbons along the deposits of glacial till (Butler et al. 2003). In other regions, the elevation of treeline can be numerous meters higher on a south facing slope than a north facing slope due to differences in solar

6 radiation thus temperature (Elliot and Kipfmuller 2010; Salzer et al. 2014), and this is especially true at high latitudes (Isaacs, unpublished data). In the Rocky Mountains, conifers (Picea engelmannii, Abies lasiocarpa, Pinus albicaulis, and Pinus contorta) establish on the leeward side of boulders due to the reduction of wind damage, increased soil depths created by sediment deposition, and increased temperatures resulting from the absorption of solar radiation by the boulder (Bekker 2005; Resler 2006).

In addition to topographic effects on treelines, soils can further influence forest vegetation at upper distributional limits. For example, nutrient limitation, especially from nitrogen (N), is known to limit net primary productivity (Vitousek and Howarth 1991; Chapin et al. 2002; LeBauer and Treseder

2008). Numerous studies have documented decreasing soil nitrogen from the interior of the forest to its limit at treeline (Masuzawa 1985; Davis et al. 1991; Malanson and Butler 1994; Sjögersten and Wookey

2005; Loomis et al. 2006; Sullivan et al. 2015), though there are exceptions where no significant change was documented (Hertel et al. 2008; Kammer et al. 2009). Alternatively, the treeline (or krummholz) can have greater soil nitrogen content (Hartley et al. 2012; Liptzin et al. 2013). These inconsistencies may be partly due to the many different sources of N on the landscape, including N derived from the bedrock

(Morford et al. 2011; Hahm et al. 2014), trees acting as a wind screen catching atmospheric N (Liptzin et al. 2009; Liptzin and Seastedt 2010), microbial decomposition under snow (Lipson et al. 1999; Grogan and Jonasson 2003; Schimel et al. 2004; Loomis et al. 2006; Liptzin et al. 2009), and localized self- reinforcement (positive feedbacks due to a combination of the buildup of leaf litter and self-replacement in gaps; [Phillips and Marion 2004]).

Disturbance

Disturbances can also affect treeline distributions and growth. The most common disturbances at treeline are from wind and snow. As mentioned above, with the summit syndrome, treeline can be depressed below its climatic potential due to desiccation and abrasion by wind (Körner 2012). High quantities of winter snow can have two opposing impacts on trees: damage verse protection. In regions

7 with heavy wet snow loads, trees can be damaged by a buildup of snow that snaps the branches (Hadley and Smith 1983; Jalkanen and Konopka 1998; Seki et al. 2005). In one study, snapping of fewer than 20 branches had little impact on the survival of Abies, while breakage of the main stem (> 5 cm diameter) resulted in 40% mortality within two years (Seki et al. 2005). Heavy snow cover can also protect trees by insulating them via a deep snow pack, which thereby protects the trees from wind abrasion and desiccation (Yoshino, 1973; Hadley and Smith 1983, 1986; Scott et al. 1993; Körner 1998; Cairns 2001;

Sturm et al. 2001; Yamazaki et al. 2003; Holtmeier and Broll 2010; Takahashi et al. 2011). Above the snow pack is the abrasion zone, which occurs primarily in the lowest 80 cm of branches exposed to air above the snowpack and is where most of the damage from wind and snow occurs (Scott et al. 1993).

Herbivory at treeline by either vertebrates or invertebrates can alter the distribution of treeline, depending on the type and intensity of pressure, by facilitating upward movement, lowering the treeline below its climate potential, or keeping the location of the treeline stationary over time (Cairns and Moen

2004). Cyclical outbreaks of autumnal moths (Epirrita autumnata) in Northern Sweden defoliate large swaths of Betula pubescens treeline (Young et al. 2014), and these outbreaks can cause the growth form of trees to change from monocormic (one main stem) to polycormic (many main stems; Lehtonen and

Heikkinen 1995) or even result in mortality (Bylund 1997). Beetle outbreaks (Dendroctonus ponderosae) alongside the pathogen white pine blister rust (Cronartium ribicola) in British Columbia have resulted in

21% mortality of the whitebark pine (Pinus albicaulis) in the forest and thus increased the risk of fire

(Campbell and Antos 2000). Browsing from caribou and reindeer in the Arctic has the potential to depress the treeline below its climatic potential (Oksanen et al. 1995; Moen and Danell 2003; Cairns et al. 2007).

Grazing by livestock (sheep, cows, guanaco) has shown to decrease the establishment rates of treeline species due to direct consumption, trampling, and soil compaction (Kozlowski 1999; Cuevas 2002; den

Herder et al. 2003).

Alpine forests of central Japan

Climate

Elucidating the factors controlling the treeline position and composition in central Japan is another focus of my research. As stated above, the dominant control of a majority of treelines is temperature. Central Japan has a temperate climate where the mean annual temperature has risen 2 °C over the past 100 years and annual total precipitation has decreased by roughly 100 mm (Figure 1-2).

Seasonally temperatures have also risen roughly 2 °C; however, precipitation has decreased by roughly 30 mm in June alone over that same time span. In addition to this temporal change in climate, there is a strong precipitation gradient from the west to the east side of the Japanese Alps. Temperatures on the west side are higher than those on the east, and the west side also receives greater precipitation (Figure 2-2;

Toyama is on the west side, Matsumoto on the east side). Furthermore, the seasonality of precipitation differs based on the side of the mountains and the timing of the summer and winter monsoons (Hirano and Matsumoto 2011). On the west side of the Japanese Alps, precipitation is high in both winter and summer, while on the east side, it is only high in the summer (Hirano and Matsumoto 2011).

Additionally, the west side receives approximately 50 mm more precipitation during the summer.

Mean annual temperatures are projected to increase by about 3 °C throughout Japan by 2100, with the largest increases occurring in the winter and spring (Japanese Meteorological Agency [JMA]

2014). Annual precipitation is projected to increase throughout Japan by approximately 100 mm over the next century as well; however, in central Japan, the projection does not show a significant change during this same time period (JMA 2014). On a seasonal basis, the increase in precipitation is expected primarily during winter (51 mm) and spring (56 mm) on the Pacific side of the Japanese Alps (JMA 2014). In

Chapter Four, I examine the climate-growth relationships of trees in the subalpine and at treeline to determine how these differences in climate affect the deciduous treeline in central Japan.

Species

The alpine forests of central Japan (2300-2700 m) are composed of three forest types (Figure 1-3) that form distinct bands, distinguished from one another based on elevation and species dominance

(Franklin et al. 1979; Yoshino 1978; Wardle 1979; Gansert 2004; Takahashi et al. 2012). The subalpine forest is the lowest of the three bands and is a mixed forest dominated by Abies mariesii Mast., (hereafter

Abies) with occasional individuals of A. veitchii Lindley, Picea jezoensis (Siebold and Zucc.) Carr., Pinus pumila (Pall.) Regel., (hereafter Pinus) and Betula ermanii Cham. (hereafter Betula, Franklin et al. 1979).

Emerging above the subalpine forest is a thin band of Betula composing an abrupt treeline (~2500 m), which adjoins a dense mat of P. pumila at the upper elevations (Franklin et al. 1979; Kajimoto 1992;

Gansert 2004; Takahashi et al. 2012).

Abies is shade-tolerant and reproduces through advanced regeneration (Franklin et al. 1979; Mori and Takeda 2004). Advanced regeneration is a form of the “storage effect”, in which species invest in their future with high rates of establishment on the forest floor under a dense canopy (Chesson 2000).

This trait for advanced regeneration enables species to remain on the landscape through periods of poor environmental conditions, so some individuals can take advantage of periods when conditions improve to grow and reach maturity (Chesson 2000; Mori and Takeda 2004).

Betula is shade-intolerant and there is little to no regeneration in the subalpine forests (Kohyama

1982; Kimura 1991; Yamamoto 1993; 1995; 1996). Betula individuals can establish on mineral soil created by uprooted gap-makers (Kohyama 1982; Kimura 1991; Yamamoto 1993; 1996). However, low

Betula establishment in some subalpine forests might be due to the rarity of uprooting (Yamamoto 1993) or the frequent mortality of Abies spp. from heart rot (Kanzaki 1984), where little mineral soil becomes exposed because they form standing snags. Abies spp. mortality produces logs for regeneration, which is an important substrate for Betula seedlings when mineral soil is absent (Hiura, Sano and Konno 1996).

Betula is generally found in small-scale continuously disturbed areas rather than large-scale disturbed stands (Kubota 1995). Additionally, in a 30 ha study area in the southern Japanese Alps 80% of the dead

Betula were uprooted (Kanzaki and Yoda 1986), thus there should be available microsites for Betula to establish in gap openings.

Pinus is also shade-intolerant and forms a dense mat from the Betula treeline up to approximately

2850 m or until the ridge lines (Kajimoto 1994; Takahashi and Yoshida 2009; Takahashi et al. 2012).

These Pinus mats are roughly 200 cm tall at their lowest elevation down to 10 cm tall at the upper ends of their distribution (Kajimoto 1993; Takahashi and Yoshida 2009). The height of the Pinus mat is correlated with the degree of protection from wind and other causes of damage by a thick layer of snow

(Kajimoto 1993; Takahashi and Yoshida 2009). Though technically not trees (see Holtmeier and Broll

2010), they are included in the analysis due to competition with Abies and Betula at the upper elevations.

Pinus has two mechanisms for reproducing: seeds and layering (Kajimoto 1993). Seeds are dispersed to open areas by the Spotted Nutcracker (Nucifraga caryocatactes spp. japonica (Hartert) L) however; layering is the dominant form of expansion. During the winter, the deep snow compresses the Pinus mat, thereby putting the branches in contact with the leaf litter. Over numerous years, adventitious roots form and take hold, locally re-rooting the Pinus (Kajimoto 1993).

For all species sapling density is highest in the bands in which trees are dominant; however,

Betula seedling, sapling and tree densities are relatively stable across the subalpine and Betula bands, with a few individuals in the Pinus mat (Takahashi et al. 2012). The stability of the density of Betula across all forest bands, and the separation of Abies and Pinus into bands in which they are dominant, suggests that there is both species coexistence as well as competitive exclusion in this system (Takahashi et al. 2012).

Disturbance

Regeneration in these forests appears to be driven by gap openings due to wind-disturbance, particularly from typhoons. Gaps due to typhoons have been both identified (Kanzaki 1984; Kanzaki and

Yoda 1986, Naka 1982, Altman et al. 2016) and not found (Watanabe et al. 1985 in Nakashizuka 1987,

Ariya et al. 2015). Most studies show that gaps form in years of severe typhoons (> 20 m/s) and cause similar rates of gap-phase dynamics to years without typhoons. Due to the approach of typhoons from the southeast, trees on the Japanese Sea side are larger and less influenced by wind than those on the Pacific

Ocean side (Cao and Ohkubo 1999).

The understory of many Japanese forests is dominated by Sasa spp., dwarf bamboo, which forms dense, low diversity understories (Hiura et al. 1996; Takahashi 1997; Takahashi et al. 2003; Nakashizuka

1987). Tree establishment in Sasa-dominated forest understories is lower than in other forests where Sasa spp. are absent. Pulses of tree regeneration occur every few decades after the Sasa blooms; Sasa is a monocarpic species, thus the culms (stems) die after blooming (Numata 1970, Nakashizuka 1987, Taylor et al. 2004). This culm mortality opens the forest floor for seedling establishment. In my study, however,

Sasa was present in only 6.9% of the sampling points.

Summary

In this dissertation, I test Bond’s slow seedling hypothesis in an alpine forest system in central

Japan. There, treeline is composed of Betula (angiosperm), and the subalpine forest and Pinus mat are composed largely of gymnosperms. The treeline is abrupt and has had no apparent upward movement over the past 50 years, indicating its long-term stability (Figure 1-4). Thus, it is an ecosystem in compositional equilibrium, which is coexistence for a period greater than is needed for species turnover

(see Veblen 1992). In this ecosystem where gymnosperms and angiosperms coexist, the dominance of each species is segregated along an elevation gradient. In this dissertation, I examine three of the factors that drive the delineation of treelines, soil nutrients, the regeneration niche, and climate, from the subalpine forest through the Betula treeline and into the Pinus mat.

In Chapter Two, I explicitly test Bond’s slow seedling hypothesis by examining the relationship between soil nutrients and tree importance values across the three forest bands using linear mixed-effect models for each species. In Chapter Three, I explore differences in the regeneration niche across ontogeny

(seedlings and trees) for Betula and Abies in the subalpine and Betula belts using a multivariate niche overlap approach. In Chapter Four, I examine the influence of climatic (temperature, precipitation and sun duration [hours of sun per day]) and disturbance on tree growth of dominant Betula and Abies. I end the dissertation (Chapter Five) with a brief synthesis of the meaning of this research in the broader context of

Japanese research and culture, I contrast this work with research outside Japan, and present some ideas for future investigations into the factors controlling the distributions of gymnosperms and angiosperms in the mixed alpine forests of central Japan.

References

Altman, J., P. Fibich, J. Leps, S. Uemura, T. Hara, and J. Doležal. 2016. Linking spatiotemporal

disturbance history with tree regeneration and diversity in an old-growth forest in northern Japan.

Perspectives in Plant Ecology, Evolution and Systematics 21: 1-13.

Ariya, U., K.-Y. Hamano, T. Makimoto, S. Kinoshita, Y. Akaji, Y. Miyazaki, M. Hirobe, and K.

Sakamoto. 2016. Temporal and spatial dynamics of an old-growth beech forest in western Japan.

Journal of Forest Research 21: 73-83.

Barret, K., McGuire, A., Hoy, E. and E. Kasischke. 2011. Potential shifts in dominant forest cover in

interior Alaska driven by variations in fire severity. Ecological Applications 21: 2380-2396.

Becker, P. 2000. Competition in the regeneration niche between conifers and angiosperms: Bond’s slow

seedling hypothesis. Functional Ecology 14: 401-412.

Bekker, M. 2005. Positive feedback between tree establishment and patterns of subalpine forest

advancement, Glacier National Park, Montana, U.S.A. Arctic, Antarctic, and Alpine Research 37: 97-

107.

Bond, W. J. 1989. The tortoise and the hare: Ecology of angiosperm dominance and gymnosperm

persistence. Biological Journal of the Linnaean Society 36: 227-249.

Brodribb, T., Holbrook, N. and N. Hill. 2005. Seedling growth in conifers and angiosperms: impacts of

contrasting xylem structure. Australian Journal of Botany 53: 749-755.

Brodribb, T., Pittermann, J. and D. Coomes. 2012. Elegance versus speed: examining the competition

between conifer and angiosperm trees. International Journal of Plant Science 173: 673-694.

Butler, D., Malanson, G., Bekker, M. and L. Resler. 2003. Lithologic, structural, and geomorphic controls

on ribbon forest patterns in a glaciated mountain environment. Geomorphology 55: 203-217.

Bylund, H. 1997. Stand age-structure influence in a low population peak of Epirrita autumnata in a

mountain birch forest. Ecography 20: 319-326.

Cairns, D. 2001. Patterns of winter desiccation in krummholz forms of Abies lasiocarpa at treeline sites in

Glacier National Park, Montana, USA. Geografiska Annaler 83A: 157-168.

Cairns, D. and J. Moen. 2004. Herbivory influences at treelines. Journal of Ecology 92: 1019-1024.

Cairns, D., Lafon, C., Moen, J. and A. Young. 2007. Influences of animal activity on treeline position and

pattern: implications for treeline responses to climate change. Physical Geography 28: 419-433.

Campbell, E. and J. Antos. 2000. Distribution and severity of white pine blister rust and mountain pine

beetle on whitebark pine in British Columbia. Canadian Journal of Forest Research 30: 1051-1059.

Cao, K-F. and T. Ohkubo. 1999. Suppression and release during canopy recruitment in Fagus crenata and

Acer mono in two old-growth beech forests in Japan. Plant Ecology 145: 281-290.

Chapin III, F. S. and A. Starfield. 1997. Time lags and novel ecosystems in response to transient climatic

change in arctic Alaska. Climate Change 35: 449-461.

Chapin, F. S. III., Matson, P., and H. Mooney. 2002. Terrestrial Decomposition. IN Principles of

Terrestrial Ecosystem Ecology. Springer-Verlag, New York. pp. 151-175.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecological

Systems 31: 343-366.

Cuevas, J. 2002. Episodic regeneration at the Nothofagus pumilio alpine tree line in Tierra del Fuego,

Chile. Journal of Ecology 90: 52–60.

Davis, J., Schober, A., Bahn, M. and B. Sveinbjörnsson. 1991. Soil carbon and nitrogen turnover at and

below the elevational treeline in northern Fennoscandia. Arctic and Alpine Research 23: 279-286. den Herder, M., Kytoviita, M. and P. Niemala. 2003. Growth of reindeer lichens and effects of reindeer

grazing on ground cover vegetation in a Scots pine forest and a subarctic heathland in Finnish

Lapland. Ecography 26: 3-12.

Elliot, G. and K. Kipfmuller. 2010. Multi-scale influences of slope aspect and spatial pattern on ecotonal

dynamics at upper treeline in the southern Rocky Mountains, U.S.A. Arctic, Antarctic, and Alpine

Research 42: 45-56.

Field, T. and T. Brodribb. 2001. Stem water transport and freeze-thaw xylem embolism in conifers and

angiosperms in a Tasmanian treeline heath. Oecologia 127: 314-320.

Fragniere, Y., Betrisey, S., Cardinaux, L., Stoffel, M. and G. Kozlowski. 2015. Fighting their last stand?

A global analysis of the distribution and conservation status of gymnosperms. Journal of

Biogeography 42: 809-820.

Franklin, F., Maeda, T., Ohsumi, Y., Matsui, M., Yagi, H. and G. Hawk. 1979. Subalpine coniferous

forests of central Honshu, Japan. Ecological Monographs 49: 311-334.

Gansert, D. 2004. Treelines of the Japanese Alps – altitudinal distribution and species composition under

contrasting winter climates. Flora 199: 143-156.

Grogan, P. and S. Jonasson. 2003. Controls on annual nitrogen cycling in the understory of a subarctic

birch forest. Ecology 84: 202-218.

Hadley, J. and W. Smith. 1986. Wind effects on needles of timberline conifers: seasonal influence on

mortality. Ecology 67: 12-19.

Hahm, W.J., Riebe, C., Kukens, C. and S. Araki. 2014. Bedrock composition regulates mountain

ecosystems and landscape evolution. Proceedings of the National Academy of Science 111: 3338-

3343.

Harsch, M., Hulme, P., McGlone, M. and R. Duncan. 2009. Are treelines advancing? A global meta-

analysis of treeline response to climate warming. Ecology Letters 12: 1040-1049.

Harsch, M. and M. Bader. 2011. Treeline form – a potential key to understanding treeline dynamics.

Global Ecology and Biogeography 20: 582-596.

Hartley, I. P., Garnett, M. H., Sommerkorn, M., Hopkins, D. W., Fletcher, B. J., Sloan, V. L., Phoenix, G.

K., and Wookey, P. A., 2012: A potential loss of carbon associated with greater plant growth in the

European Arctic. Nature Climate Change, 2: 875-879.

Hertel, D., Therburg, A. and R. Villalba. 2008. Above- and below-ground response by Northofagus

pumilio to climatic conditions at the transition from the steppe-forest boundary to the alpine treeline

in southern Patagonia, Argentina. Plant Ecology and Diversity 1: 21-33.

Hiura, T., Sano, J. and Y. Konno. 1996. Age structure and response to fine-scale disturbances of Abies

sachalinensis, Picea jezoensis, Picea glehnii, and Betula ermanii growing under the influence of a

dwarf bamboo understory in northern Japan. Canadian Journal of Forest Resource 26: 289-297.

Hirano, J. and J. Matsumoto. 2011. Secular and seasonal variations of winter monsoon weather patterns in

Japan since the early 20th century. International Journal of Climatology 31: 2330-2337.

Hoch, G. and C. Korner. 2009. Growth and carbon relations of tree line forming conifers at constant vs.

variable low temperatures. Journal of Ecology 97: 57-66.

Holtmeier, F. and G. Broll. 2005. Sensitivity and response of northern hemisphere altitudinal and polar

treelines to environmental change at landscape and local scales. Global Ecology and Biogeography

14: 395-410.

Holtmeier, F. and G. Broll. 2010. Wind as an ecological agent at treelines in North America, the Alps,

and the European subarctic. Physical Geography 31: 203-233.

Irl, S., Anthelme, F., Harter, D., Jentsch, A., Lotter, E., Steinbauer, M. and C. Beierkuhnlein. 2016.

Patterns of island treeline elevation – a global perspective. Ecography 39: 427-436.

Jalkanen, R. and B. Konopka. 1998. Snow-packing as a potential harmful factor on Picea abies, Pinus

sylvestris and Betula pubescens at high altitude in northern Finland. European Journal of Forest

Pathology 28: 373-382.

Japan Meteorological Agency, 2014: Global Warming Projection Vol.8,

http://ds.data.jma.go.jp/tcc/tcc/products/gwp/gwp8/index.html

Jobbágy, E. and R. Jackson. 2000. Global controls of forest line elevation in the northern and southern

hemispheres. Global Ecology and Biogeography 9: 253-268.

Kajimoto, T. 1992. Dynamics and dry matter production of belowground woody organs of Pinus pumila

trees growing on the Kiso Mountain range in central Japan. Ecological Research 7: 333-339.

Kajimoto, T. 1993. Shoot dynamics of Pinus pumila in relation to altitudinal and wind exposure gradients

on the Kiso mountain range, central Japan. Tree Physiology 13: 41-53.

Kajimoto, T. 1994. Aboveground net production and dry matter allocation of Pinus pumila forests in the

Kiso Mountain range, central Japan. Ecological Research 9: 193-204.

Kammer, A., Hagedorn, F., Shevechenko, I., Leifeld, J., Guggenberger, G., Goryacheva, T., Rigling, A

and P. Moiseev. 2009. Treeline shifts in the Ural Mountains affect soil organic matter dynamics.

Global Change Biology 15: 1570-1583.

Kanzaki, M. 1984. Regeneration in subalpine coniferous forests I. Mosaic structure and regeneration

process in Tsuga diversifolia forest. The Botanical Magazine, Tokyo 97: 297-311.

Kanzaki, M. and K. Yoda. 1986. Regeneration in subalpine coniferous forests II. Mortality and the pattern

of death of canopy trees. The Botanical Magazine, Tokyo 99: 37-51.

Kimura, W. 1991. Regeneration process on a subalpine mudflow. Ecological Research 6: 63-77.

Kohyama, T. 1982. Studies on the Abies population of Mt. Shimagare II. Reproductive and life history

traits. The Botanical Magazine, Tokyo 95: 167-181.

Kozlowski, T. 1999. Soil compaction and growth of woody plants. Scandinavian Journal of Forest

Research 14: 596–619.

Kubota, Y. 1995. Effects of disturbance and size structure on the regeneration process in a sub-boreal

coniferous forest, northern Japan. Ecological Research 10: 135-142.

Körner, C. 1998. A re-assessment of high elevation treeline positions and their explanation. Oecologia

115: 445-459.

Körner, C. and J. Paulsen. 2004. A world-wide study of high altitude treeline temperatures. Journal of

Biogeography 31: 713-732.

Körner, C. 2012. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits.

Springer, Basel.

Lehtonen, J. and R. Heikkinen. 1995. On the recovery of mountain birch after Epirrita damage in Finnish

Lapland, with a particular emphasis on reindeer grazing. Écoscience 2: 349-356.

LeBauer, D. and K. Treseder. 2008. Nitrogen limitation of net primary productivity in terrestrial

ecosystems is globally distributed. Ecology 89: 371-379.

Lehtonen, J. and R. Heikkinen. 1995. On the recovery of mountain birch after Epirrita damage in Finnish

Lapland, with a particular emphasis on reindeer grazing. Écoscience 2: 349-356.

Lipson, D., Schmidt, S. and R. Monson. 1999. Links between microbial population dynamics and

nitrogen availability in an alpine ecosystem. Ecology 80: 1623-1631.

Liptzin, D. and T. Seastedt. 2009. Patterns of snow, deposition, and soil nutrients at multiple spatial scales

at a Rocky Mountain tree line ecotone. Journal of Geophysical Research 114: G04002.

Liptzin, D. and T. Seastedt. 2010. Regional and local patterns of soil nutrients at Rocky Mountain

treelines. Geoderma 160: 208-217.

Liptzin, D., Sanford, R. and T. Seastedt. 2013. Spatial patterns of total and available N and P at alpine

treeline. Plant Soil 365: 127-140.

Loomis, P., Ruess, R., Sveinbjörnsson, B. and K. Kielland. 2006. Nitrogen cycling at treeline: Latitudinal

and elevational patterns across a boreal landscape. Écoscience 13: 544-556.

Lusk, C. 2011. Conifer-Angiosperm Interactions: Physiological Ecology and Life History. In Ecology of

tropical podocarps, Turner, B. and L. Cernusak (eds.). Smithsonian Contributions to Botany,

Smithsonian Institution Press, Washington, DC. Pp 157-164.

Macias-Fauria, M. and E. Johnson. 2013. Warming-induced upslope advance of subalpine forest is

severely limited by geomorphic processes. Proceedings of the National Academy of Science 110:

8117-8122.

Malanson, G. and D. Butler. 1994. Tree-tundra competitive hierarchies, soil fertility gradients, and

treeline elevation in Glacier National Park, Montana. Physical Geography 15: 166-180.

Malanson, G., Resler, L., Bader, M., Holtmeier, K-F., Weiss, D., Butler, D., Daniels, L. and D. Fagre.

2010. Mountain Treelines: A roadmap for research orientation. Arctic, Antarctic and Alpine Research

43: 167-177.

Moen, J. and O. Danell. 2003. Reindeer in the Swedish mountains: an assessment of grazing impacts.

AMBIO 32: 397-402.

Masuzawa, T. 1985. Ecological studies on the timberline of Mt. Fuji I. Structure of plant community and

soil development on the timberline. Botanical Magazine of Tokyo 98: 15-28.

Moen, J. and Ö. Danell. 2003. Reindeer in the Swedish mountains: An assessment of grazing impacts.

AMBIO 32: 397-402.

Mori, A. and H. Takeda 2004. Effects of mixedwood canopies on conifer advance regeneration in a

subalpine old-growth forest in central Japan. Écoscience 11: 36-44.

Morford, S., Houlton, B. and R. Dahlgren. 2011. Increased forest ecosystem carbon and nitrogen storage

from nitrogen rich bedrock. Nature 477: 78-81.

Naka, K. 1982. Community dynamics of evergreen broadleaf forests in southwestern Japan. I. Wind

damaged trees and canopy gaps in an evergreen oak forest. The Botanical Magazine, Tokyo 95: 385-

399.

Nakashizuka, T. 1987. Regeneration dynamics of beech forests in Japan. Vegetatio 69: 169-175.

Numata, M. 1970. Conservation implications of bamboo flowering and death in Japan. Biological

Conservation 2: 227-229.

Oksanen, L., Moen, J. and T. Helle. 1995. Timberline patterns in northernmost Fennoscandia: relative

importance of climate and grazing. Acta Botanica Fennica 153: 93-105.

Phillips, J. and D. Marion. 2004. Pedological memory in forest soil development. Forest Ecology and

Management 188: 363-380.

Pisarenko, A., Strakhov, V., Paivinen, R., Kuusela, K., Dyakun, F. and V. Sdobnova. 2001. Development

of forest resources in the European part of the Russian Federation. European Forest Institute Research

Report 11. Brill, Leiden. p 57.

Randin, C., Paulsen, J., Vitasse, Y., Kollas, C., Wohlgemuth, T., Zimmermann, N. and C. Körner. 2013.

Do the elevational limits of deciduous tree species match their thermal latitudinal limits? Global

Ecology and Biogeography 22: 913-923.

Resler, L. 2006. Geomorphic controls of spatial pattern and process at alpine treeline. The Professional

Geographer 58: 124-138.

Salzer, M., Bunn, A., Grahm, N. and M. Hughes. 2014. Five millennia of paleotemperature from tree-

rings in the Great Basin, USA. Climate Dynamics 42: 1517-1526.

Schimel, J. Bilbrough, C. and J. Welker. 2004. Increased snow depth affects microbial activity and

nitrogen mineralization in two Arctic tundra communities. Soil Biology and Biochemistry 36: 217-

227.

Scott, P., Hansell, R. and W. Erickson. 1993. Influences of wind and snow on northern tree-line

environments at Churchill, Manitoba, Canada. Arctic 46: 316-323.

Seki, T., Kajimoto, T., Sugita, H., Daimaru, H., Ikeda, S. and T. Okamoto. 2005. Mechanical damage on

Abies mariesii trees buried below the snowpack. Arctic, Antarctic, and Alpine Research 37: 34-40.

Sjögersten, S. and P. Wookey. 2005. The role of soil organic matter quality and physical environment for

nitrogen mineralization at the forest-tundra ecotone in Fennoscandia. Arctic, Antarctic, and Alpine

Research 37: 118-126.

Sturm, M., McFadden, J., Liston, G., Chapin III, F.S., Racine, C. and J. Holmgren. 2001. Snow-shrub

interactions in Arctic tundra: A hypothesis with climatic implications. Journal of Climate 14: 336-

344.

Sullivan, P., Ellison, S., McNown, R., Brownlee, A. and B. Sveinbjörnsson. 2015. Evidence of soil

nutrient availability as the proximate constraint on growth of treeline trees in northwest Alaska.

Ecology 96: 716-727.

Takahashi, K. 1997. Regeneration and coexistence of two subalpine conifer species in relation to dwarf

bamboo in the understorey. Journal of Vegetation Science 8: 529-536.

Takahashi, K., Azuma, H. and K. Yasue. 2003. Effects of climate on the radial growth of tree species in

the upper and lower distribution limits of an altitudinal ecotone on Mount Norikura, central Japan.

Ecological Research 18: 549-558.

Takahashi. K. and S. Yoshida. 2009. How the scrub height of dwarf pine Pinus pumila decreases at the

treeline. Ecological Research 24: 847-854.

Takahashi, K., Okuhara, I., Tokumitsu, Y. and K. Yasue. 2011. Responses to climate by tree-ring widths

and maximum latewood densities of two Abies species at upper and lower altitudinal distribution

limits in central Japan. Trees 25: 745-753.

Takahashi, K., Hirosawa, T. and R. Morishima. 2012. How the timberline formed: altitudinal changes in

stand structure and dynamics around the timberline in central Japan. Annals of Botany 109: 1165-

1174.

Taylor, A., Jinyan, H. and Z. ShiQiang. 2004. Canopy tree development and undergrowth bamboo

dynamics in old-growth Abies-Betula forests in southwestern China: a 12-year study. Forest Ecology

and Management 200: 347-360.

Veblen, T. 1982. Regeneration patterns in Araucaria araucana forests in Chile. Journal of Biogeography

9: 11-28.

Veblen, T. 1992. Regeneration dynamics. In Plant Succession: Theory and prediction. Eds. Glenn-Lewin,

D., Peet, T. and T. Veblen. Chapman and Hall, London. Chapter 4.

Vitousek, P. and R. Howarth. 1991. Nitrogen limitation on land and in the sea: How can it occur?

Biogeochemistry 13: 87-115. von Humboldt, A., and Bonpland, A., 1805: Essai sur la Géographie des Plantes. Paris: Chez Levrault,

Schoell et Compagnie.

Watanabe, R., Nakashizuka, T., Honma, S., Hara, M. and S. Yoda. 1985. Studies on the Fagus crenata

forest in Kayanodaira II. Fallen trees by wind of the typhoon No. 10 in 1982. Bulletin of the Institute

of Nature Education. Shiga Heights, Shinshu University 22: 15-18. (in Japanese with an English

summary).

Wardle, P. 1977. Japanese timberlines and some geographic comparisons. Arctic and Alpine Research 9:

249-258.

Yamamoto, S-I. 1993. Gap characteristics and gap regeneration in a subalpine coniferous forest on Mt.

Ontake, central Honshu, Japan. Ecological Research 8: 277-285.

Yamamoto, S-I. 1995. Gap characteristics and gap regeneration in subalpine old-growth coniferous

forests, central Japan. Ecological Research 10: 31-39.

Yamamoto, S-I. 1996. Gap regeneration of major tree species in different forest types of Japan. Vegetatio,

127: 203-213.

Yamazaki, J., Ohashi, A., Hashimoto, Y., Negishi, E., Kumagai, S., Kubo, T., Oikawa, T., Maruta, E. and

Y. Kamimura. 2003. Effects of high light and low temperature during harsh winter on needle

photodamage of Abies mariesii growing at the forest limit on Mt. Norikura in Central Japan. Plant

Science 165: 257-264.

Yoshino, M. 1973. Wind-shaped trees in the subalpine zone in Japan. Arctic and Alpine Research 5:

A115-A126.

Yoshino, M. 1978. Altitudinal vegetation belts of Japan with special reference to climate conditions.

Arctic and Alpine Research 10: 449-456.

Young, A., Cairns, D., Lafon, C. and J. Moen. 2014. Geometrid moth outbreaks and their climatic

relations in northern Sweden. Arctic, Antarctic, and Alpine Research 46: 659-668.

Figure 1-2. Annual mean temperature (oC) and total annual precipitation (mm) in Matsumoto, Japan over the past century.

Figure 1-4. Repeat aerial photography from the 1960s (leaf-on) and 2000s (leaf-off) on Norikura at the upper boundary of the Betula belt (blue arrows) with the Pinus mat (yellow arrows).

Chapter 2

Influence of topography and edaphic properties on broadleaf-deciduous versus evergreen conifers in a high elevation forest

Introduction

The upper and lower boundaries in tree species’ distributions along gradients in elevation or latitude are typically attributed to limitation by temperature or moisture (Iverson and Prasad 2002;

Canham and Thomas 2010). However, additional factors such as soil nutrients (Sullivan et al. 2015) and interspecific competition (Takahashi et al. 2012) can also influence tree species’ distributions. Bond

(1989) developed the slow seedling hypothesis to explain the spatial partitioning of angiosperms and gymnosperms. The hypothesis states that the regeneration niche of gymnosperms makes them poor competitors because they are less sensitive to pulses of resources (e.g., light availability, nutrient deposition) than angiosperms (Bond 1989). Having a greater responsiveness permits angiosperms to outcompete gymnosperms when additional resources (e.g., increases in light availability from forest gaps) become available in a forest (Bond 1989). When in the same species pool as angiosperms, gymnosperms are relegated to nutrient poor, wet soils in areas with relatively cool climates. Thus, gymnosperms are typically dominant at high elevations and latitudes (Holtmeier and Broll 2010).

Alpine forests encompass the transition from subalpine forest through the timberline to the treeline (Smith, Johnson and Reinhardt 2007). Most research on alpine forests has been conducted on gymnosperms, specifically conifers (Figure 1-1). The preponderance of studies on conifers is partly due to their prevalence in the northern hemisphere where most research on alpine ecosystems has been conducted (Holtmeier and Broll 2010). Coniferous alpine forests are found in North America (Pinus,

Tsuga, Abies, Picea), Canadian Shield (Picea), European Alps (Pinus), Siberia (Pinus, Larix), Himalayas

(Abies, Juniperus) and in East Asia (Pinus, Picea, Larix, Abies). Angiosperm alpine forests exist predominantly in the southern hemisphere (Figure 1-1). They occur most notably in the Andes

(Nothofagus, Polylepis), New Zealand (Nothofagus), Australia (Eucalyptus) and East Africa (Erica,

Hagenia). In the northern hemisphere, angiosperm alpine forests have a limited distribution. They occur in the Scandes of northern Europe (Betula), East Asia (Betula), Caucasus Mountains (Betula), Pyrenees

(Fagus), and in localized areas of North America (Populus).

In central Japan, treeline occurs around 2500 m and is dominated by the angiosperm, Betula ermanii Cham. (hereafter Betula; [Gansert 2004; Takahashi et al. 2012]). The Betula treeline emerges above a mixed subalpine forest dominated by the gymnosperm Abies mariesii Mast. (hereafter Abies), where Betula along with the gymnosperms A. veitchii Lindley and Picea jezoensis (Siebold and Zucc.)

Carr. are a minority (Wardle 1977; Yoshido 1978; Takahashi et al. 2012). Above the Betula treeline is a dense mat of Pinus pumila (Pall.) Regel. (hereafter Pinus) which can extend up to 2850 m (Kajimoto

1994; Takahashi and Yoshida 2009; Takahashi et al. 2012). On its surface the occurrence of an angiosperm treeline above a gymnosperm dominated mixed subalpine forests is, counter to what one would expect based on Bond’s hypothesis, because elevation is negatively associated with both temperature and nutrient resources. This high elevation forest therefore provides an opportunity to explicitly test Bond’s slow seedling hypothesis for nutrient limitation.

Soil nutrients, especially nitrogen (N), are known to limit net primary productivity (Vitousek and

Howarth 1991; Chapin et al. 2002; LeBauer and Treseder 2008). Numerous studies have documented decreasing soil nitrogen content from the interior of the forest to its limit at treeline (Masuzawa 1985;

Davis et al. 1991; Malanson and Butler 1994; Sjögersten and Wookey 2005; Loomis et al. 2006; Sullivan et al. 2015), though there are exceptions where there is no significant change (Hertel et al. 2008; Kammer et al. 2009) or the treeline (or krummholz) has greater soil nitrogen content (Hartley et al. 2012; Liptzin et al. 2013). In addition to the mixed patterns in soil nitrogen content along forest-treeline gradients, there is also variation in soil nitrogen content beneath conifer (gymnosperm) and broad-leaf deciduous trees

(angiosperms). Soil nitrogen content beneath broad-leaf deciduous trees can be greater than that of soils under conifers in closed canopy forests (Osono and Takeda 2006; Xu and Shibata 2007; Calder et al.

2011), while the opposite pattern has also been demonstrated at forest edges with tundra (Masuzawa

1985; Stottlemyer 2001). This inconsistency may be partly due to the many different sources of N on the landscape, including N derived from the bedrock (Morford et al. 2011; Hahm et al. 2014), trees acting as a wind screen catching atmospheric N (Liptzin et al. 2009; Liptzin and Seastedt 2010), microbial decomposition under the snow (Lipson et al. 1999; Grogan and Jonasson 2003; Schimel et al. 2004;

Loomis et al. 2006; Liptzin et al. 2009), and localized self-reinforcement (positive feedback due to leaf litter and light conditions [ Phillips and Marion 2004]). Other causes of spatial variation could include differences in preference for various forms of N, effects of cation exchange capacity on nutrient retention, or nutrient use efficiency, but these factors are not addressed in this study.

Few studies have examined the edaphic (soil nutrient) conditions associated with the transition from the upper treeline into the alpine tundra in central Japan (Masuzawa 1985; Takahashi and Murayama

2014). Masuzawa (1985) examined the tundra to subalpine forest transition on Mt. Fuji; however, Mt.

Fuji does not have the Pinus mat that is common throughout the Japanese Alps (Wardle 1977). Takahashi and Murayama (2014) examined an elevation gradient from the Pinus mat up into the alpine tundra, along which they found a decrease in N. To my knowledge, no study has tested Bond’s hypothesis that gymnosperms will be restricted to nutrient poor habitats relative to angiosperms in an alpine environment.

In this study, I tested Bond’s slow seedling hypothesis along an elevation gradient in the Northern

Japanese Alps. Specifically, I focused on the role of topographic and edaphic conditions in segregating species along elevation gradients. Therefore, my first objective was to describe the changes in stand structure and composition of the dominant species (Abies, Betula and Pinus) as the system transitions from subalpine forest to treeline ecotone. My second objective was to determine the topographic and edaphic parameters influencing the distribution of each species along gradients in elevation. I expected the gymnosperms (Abies and Pinus) to dominate on nutrient poor locations and Betula on the nutrient rich locations.

Methods

Study Area Description

Mountains tall enough to host alpine forests run from the Japanese Sea near the city of Toyama to

Mt. Fuji near the shores of the Pacific Ocean (Figure 2-1). The northern portion of these mountains is known as the Northern Japanese Alps, which are almost entirely encompassed by the boundaries of

Chūbu-Sangaku National Park (CSNP). The Northern Japanese Alps contain all three alpine forest types described above. The understory of both subalpine and treeline forests is dominated by shrubs, primarily

Sorbus commixta Hedl. and ferns. The Japanese Alps are composed of granitic, metamorphic and other pre-Neogene bedrock, parts of which have been glacially carved (Sawagaki and Aoki 2011). Within

CSNP, the main geological formations between 2200 and 2650 m (alpine forest zone) are volcanic and granitic igneous rock, with outcrops of accretionary and sedimentary rock (Geological Survey of Japan).

Parent material is a key factor of soil nutrient composition which can support varying plant communities

(Anderson 1988). High elevation soils in Japan are categorized as podzols, which are leached of iron and have an eluvial horizon (Obara, 2011 or spodosols in the USDA Soil Taxonomy).

The mountainous topography of Japan creates a distinct east-west precipitation divide between the Sea of Japan (East Sea) and Pacific Ocean side of Japan (Figure 2-1 and 2-2, [Yoshino 1980; Ikeda et al. 2009]). On the Sea of Japan side (west), precipitation is highest in winter due to cool winds from the northwest picking up moisture over the Sea of Japan and depositing it on the western mountains near the

Sea of Japan (Wardle 1977; Ikeda et al. 2009). Most of this precipitation in the mountains falls as snow and can accumulate up to 5-10 m due to redistribution by the strong winds (Ikeda et al. 2009, Matsuoka

2013). On the Pacific side, a majority of the precipitation falls in summer due to storms moving north from the Pacific Ocean and South China Sea (Yoshino 1980). The Northern Japanese Alps form a topographic divide separating these two precipitation regimes.

Field Methods

I established five transects on the west-facing slopes from the mixed subalpine forest (hereafter

SA) through the Betula belt (hereafter BB) and into the Pinus mat (hereafter PM) throughout CSNP. To account for varying geologic formations, I distributed these transects among mountains with non-volcanic

(granite [n=1] and accretionary [n=1]) and volcanic (n = 3) bedrock. Transects were established on both volcanic and non-volcanic mountains in order to account for variation in chemical composition and soil textures among parent materials. The volcanic substrates have higher phosphorus and lower calcium content than non-volcanic (granitic and sedimentary) rocks (Anderson 1988). Furthermore, the rates of leaching and cation exchange differ depending on the substrate, nutrients and precipitation regime (Porder and Ramachandran 2012). Roughly 500 m below the treeline in Kamikochi (valley in the Northern

Japanese Alps) soil profiles show transition in sediments from near the top black carbonaceous mud which has fragments of plants from the surrounding forest (Kojima et al. 2015). Below the black mud are two layers one of gray mud which does not have plant fragments and a yellow-brown silt (Kojima et al.

2015). The silt has pebbles from both volcanic and sedimentary rocks in it, which are thought to have been deposited through aeolian deposition (Kojima et al. 2015).

Along each transect, I established sampling points every 10 m, beginning 120 m below the lower boundary of the BB and extending 120 m above the upper boundary of the BB. This allowed me to capture conditions encompassing the entire BB. Two of the transects did not reach 120 m above the BB due to the termination of the PM due to exposed rock or reaching the ridgeline. At each point topographic, understory community structure and light environment parameters were recorded. Topographic parameters included elevation (m), slope (o), aspect (o), and I recorded the latitude and longitude with a handheld GPS unit. I assessed canopy structure and edaphic parameters at every third point along each transect.

I recorded understory community structure with seedling and sapling density for each tree species and vegetative cover for other strata (forb, shrub and canopy). I used counts of short seedlings (0-50 cm

32 tall), tall seedlings (50-100 cm tall) within a circle of 15 m2 centered on each point and saplings (100-200 cm tall) within 30 m2. Seedling and sapling counts were extrapolated to per-hectare for comparison with tree density (see below). These densities cover the ontogenetic stages found in the forest understory and indicate whether replacement has been continuous as well as where regeneration occurs. I visually classified canopy, shrub, and forb cover within each circular plot 30 m2 into six categories: 1: 0-5%, 2: 5-

25%, 3: 25-50%, 4: 50-75%, 5: 75-95%, 6: 95-100%. I recorded the dominant shrubs (up to three species and including ferns) and the maximum shrub height (m). Measurements of litter depth (cm) were measured at the center of each point.

I quantified the light environment at each point with hemispherical photography using a fisheye lens at 170 cm above the ground. To constrain variability in light conditions and maximize contrast, I only recorded images in the mornings and evenings when the sun was below the horizon or during the day under cloudy conditions (Campbell and Norman 1998). I used Gap Light Analyzer (GLA) (Frazer,

Canham and Lertzman 1999) to calculate the Gap Light Index (GLI), which is based on the diffuse and direct light transmitted through the canopy (Canham 1988). Here, I restricted the statistical analysis of

GLI to the 9o radius directly above each point.

At every third point (i.e., every 30 m), I used point-center quarter (PCQ) methods (Cottam and

Curtis 1956) to quantify stand density, basal area and canopy composition. At each point, I measured the nearest tree in each quarter regardless of species. Trees are defined here as > 200 cm in height for Abies and Betula, while for Pinus, we counted individuals > 4 cm in basal diameter (BD) measured 20 cm above the ground surface as trees. If the nearest tree in a quarter was Pinus in either the SA or BB, I then additionally measured the nearest Abies, Betula or Picea individual. I recorded diameter at breast height

(DBH, cm) for Abies, Betula, and Picea, and BD (cm) for all species, because the low stature of Pinus, crown diameter (m), height (m) and distance from central point (m).

I collected soil samples (~300 g) from the center of every third point (i.e., every 30 m) to quantify the edaphic properties and nutrient content of the top 10 cm from beneath the litter layer. I collected these samples between July 30 and September 7 2013 and July 28th to August 25th 2014. Samples were air dried

33 in paper bags on the return to the lab (approx. 2-7 days post collection). I measured soil pH using a digital pH meter. I used a Merk reflectometer (RQflex plus 10, Merck Ltd, Germany) to determine soil (total

- + - + carbon, total nitrogen, NO3 , NH4 , K, P2O5). I report nitrate (NO3 ) and ammonium (NH4 ) as the

- + concentrations NO3 -N and NH4 -N, respectively.

Statistical Analysis

I calculated stand statistics to quantify density, basal area, and importance values of each species at each point along each transect (Cottam and Curtis 1956). I modified these standard statistics (Mitchell

2007) to account for each point along the transects as well as Pinus individuals in the SA and BB. Basal area was calculated based on the BD rather than DBH because few Pinus grow to breast height. I used each species’ importance value (IV) as the response variable in my analyses to model the limiting properties of treeline distribution.

Topographic variables were derived from both field measurements and an ASTER Global Digital

Elevation Model (GDEM) with a horizontal resolution of 30 meters (NASA LP DAAC, 2013). Elevation

(m), slope (o), aspect (northness and eastness), topographic position index (TPI) and topographic wetness index (TWI) were extracted from the GDEM. As indices the values of TPI and TWI each range from -1 to

+1, TPI indicates whether a 30 X 30 m raster cell is located on a ridge (+1), slope (0) or in a valley (-1);

[Jenness 2006]). TWI measures the concavity of a surface and whether it is poorly drained (-1; concave) or well drained (+1; convex [Jenness 2006]). The edaphic parameters included soil pH, total carbon (%),

- -1 + -1 total nitrogen (%), nitrate (NO3 -N mg L ), ammonium (NH4 -N mg L ), and phosphorus pentoxide

-1 (P2O5 mg L ).

Topographic and edaphic parameters were tested (Shapiro Wilk) for normality prior to statistical analysis because the statistical tests I am using assume normal distributions. Three variables (nitrate, ammonium and phosphorus pentoxide) did not have a normal distribution and were log transformed to achieve normality prior to statistical analysis. Significant differences in parameters between forest belts

34 and bedrock were tested with one-way and two-way ANOVAs (α=0.05) respectively, and post-hoc

Tukey’s HSD to determine significant differences between variables.

To test for the factors important in determining species dominance along an elevation gradient, I constructed global models (equation 1) for each species IV (Abies, Betula and Pinus) following Grueber et al. (2011). Global models include all parameters that are biologically relevant to the hypothesis, from the global model all possible submodels are created to determine which models are the best preforming

(Grueber et al. 2011). The best model has the lowest AIC (Akaike information criteria) and the top models are within 2 of the lowest AIC. My sample size (n=67) limited the number of parameters I could include in a given model without overfitting (Grueber et al. 2011), thus I used linear mixed-effect regressions to determine the variables limiting species IV using the package lme4 in R (Bates et al. 2013).

Mountain and bedrock were included as random effects. I assessed the influence of each of these random effects by their magnitude of the variance (Grueber et al. 2011).

Bond’s (1989) hypothesis that gymnosperms dominate on cool nutrient poor soils led to my prediction that gymnosperms would be dominant on the higher values of TPI, but this may be mediated by soil fertility. I included fixed terms in the global model based on the a priori hypothesis that forest position (higher elevation or more exposed, higher values of TPI) and nutrients limit the distribution of conifers in the forest. The global model included two topographic parameters (TPI and slope) and six of the nutrient related parameters (nitrate, ammonium, phosphorus pentoxide, pH, total nitrogen and total carbon) in the global model. I included the interactions between TPI and each of the nutrient variables to test my hypothesis that conifers would be restricted to the nutrient poor environments.

Interactions between edaphic parameters and TPI were assessed because TPI is a proxy variable for cool and wet environments. The global model for each species IV,

− + 푌푖푗 = 훽0푗 + 훽1푗TPI + 훽2푗NO3 − N + 훽3푗N퐻4 − N + 훽4푗P2O5 + 훽5푗pH + 훽6푗 Total N + 훽7푗 Total C

− + 훽8푗Slope + 훽9푗TPI ∗ NO3 − N + 훽10푗TPI ∗ P2O5 + 훽11푗TPI ∗ pH + 훽12푗TPI

∗ Total N + 훽13푗TPI ∗ Total C + 휇0푖 Mountain + 휇0푖 Bedrock + 푒푖푗

where Yij is the estimate for IV for each species, β0j is the random intercept for all 67 points (j),

β1j- β8j are the slopes for individual factors (TPI, nitrate, ammonium, phosphorus pentoxide, pH, total nitrogen, total carbon and slope), β8j- β13j are the slopes of the interactions of TPI and the edaphic variables (nitrate, ammonium, phosphorus pentoxide, pH, total nitrogen, total carbon). The influences of individual mountains and bedrock were included as random factors (μ). The term eij adds the residual error into the global model.

Parameters were standardized to have a mean of 0 and a standard deviation of 0.5 using the arm package in R (Gelman and Su 2013). Fixed effects were assessed after examining all possible submodels using the MuMin package in R (Bartón 2013). AICc was used rather than AIC to evaluate the submodels, to account for the small number of sampling points (n = 67) (Hurvich and Tsai 1989). Top models with an

AICc within two of the lowest AICc were averaged using MuMin to calculate the estimates and relative importance of each parameter. Results were plotted using the package ggplot2 in R (Wickerham 2009).

Results

Topographic Properties

The elevation of the alpine forests in the Japanese Alps varies by 100 meters depending on the bedrock (Table 2-1 and Figure 2-3). On the non-volcanic mountains, the BB is centered around 2584 m, while on the volcanic mountains it is centered at 2462 m. All transects I undertook were west facing, with limited variation in steepness across mountains (Table 2-1; two-way ANOVA). Although mean slopes did not significantly differ there was a smaller range of slopes on the volcanic than the non-volcanic mountains. Topographic Position Index (TPI) values were associated with both bedrock and forest belt but not their interaction (Table 2-1). High TPI values (near 1) indicate that the PM occurs almost exclusively on ridgelines rather than slopes or valleys, and this is particularly the case for on volcanic

36 mountains (Table 2-1). The range of TPI values for the volcanic mountains was smaller than that of the non-volcanic mountains (Table 2-1 and Figure 2-3).

Shrubs

Vegetation cover (forb, shrub and canopy) was primarily dependent upon the forest belt sampled, and shrub cover was also influenced by the bedrock (Table 2-2, two-way ANOVA). The SA and the BB both had 25-50% forb cover, while forb cover in the PM was between 5 and 25% (Table 2-2). Shrub cover was highest in the BB (25-50%) and lowest in the PM (5-25%), while the opposite was the case for canopy cover (25-50% and 50-75%, respectively [Table 2-2]).

S. commixta was dominant (35%) in all of the forest belts when the understory included shrubs

(Table 2-3). In both the SA (55%) and the BB (31%), there was an abundance of ferns, primarily bracken.

In the SA, the next most dominant shrub taxa were Vaccinium spp. (36%) and Acer spp. (22%); no other species occurs in at least 15% of the plots (Table 2-3). In the BB, there were four additional shrub taxa with > 15% occurrence: Acer spp., Alnus maximowiczii Pax., Rubus spp., and Prunus nipponica Matsum.

The shrub cover in the PM was very low; however, when shrubs did occur, they consisted primarily of S. commixta (26%) and Rhododendron spp. (45%, [Table 2-3]). In my study 90% of the Rhododendron spp. occurred in the PM, with the remainder in the adjacent BB.

The understory of many Japanese forests is dominated by Sasa spp. (Hiura et al. 1996; Takahashi

1997; Takahashi et al. 2003; Nakashizuka 1987), a dwarf bamboo, but Sasa was present in only 6.9% of the sampling points, regardless of the forest band (Table 2-3). Thus, the high elevation forests of central

Japan do not have the classical Japanese forest dynamics. Furthermore, I only noted Sasa spp. on the volcanic mountains (Table 2-3). The shrub Tripetaleia bracteata Maxim. was only represented on the non-volcanic mountains in this study, though it does exist on the volcanic mountains (A. Young, pers. obs.). S. commixta occurred at 50% of the points in the SA and BB on non-volcanic mountains but only at

20-30 % of the points on volcanic mountains. The woody shrubs A. maximowiczii and P. nipponica occurred in about 28% of the points on non-volcanic in the BB.

Light Environment

I took most of the hemispherical photographs when skies were cloudy (62%) or the sky was semi- cloudy or sun was blocked by mountains (17%; supplemental table 2-1). However, some pictures were taken when conditions were sunny (21%) due to the high frequency of rain events in the region

(supplemental table 2-1). The GLI at 170 cm was approximately 2-4 times greater in the PM than the BB or SA (Table 2-2; two-way ANOVA). The greater GLI in the PM was largely due to its lower mean canopy height (2.6 m) compared to the BB (8.5) or SA (11.0 m).

Soil Nutrients

Bedrock type, forest belt, and their interaction all influenced the depth of litter (Table 2-2). Litter depth in the PM (13.35 ± 0.69 cm) was more than double that of the BB (5.44 ± 0.53 cm) or SA (4.91 ±

0.52 cm). The mean litter depth in the BB and SA was 1.3 times deeper on volcanic than the non-volcanic mountains (Table 2-2). Deeper litter on the volcanic mountains may result from the higher net primary productivity due to the greater amounts of soil nitrate found on the volcanic mountains. Soils on both bedrock types and in all three belts are acidic (4.23 ± 0.04 pH). Soils on the non-volcanic mountains (4.10

± 0.07 pH) were more acidic than those on the volcanic mountains (4.32 ± 0.04 pH; two-way ANOVA).

Neither forest belt nor its interaction with bedrock influenced soil pH (Table 2-1; two-way ANOVA).

There were seven points that did not have soil data because those locations had little to no soil; all of these points occurred in the PM at the upper end of transects, from both of the non-volcanic and one of the volcanic mountains. .

The concentrations of nitrate and ammonium were higher on the volcanic (22.37 ± 3.68 mg L-1,

22.18 ± 2.39 mg L-1) than the non-volcanic mountains (7.81 ± 2.65 mg L-1, 11.83 ± 1.62 mg L-1). In addition, the concentration of nitrate was higher in the BB (28.28 ± 5.35 mg L-1) than the SA (11.93 ±

2.86 mg L-1) or PM (8.52 ± 3.62 mg L-1). The concentration of ammonium in the BB on the volcanic mountains was higher than the other two belts on the volcanic mountains and all of the belts on non- volcanic (Table 2-1). The interaction of forest belt and bedrock reveals that nitrate is significantly higher in the BB on volcanic mountains than any of the belts on the non-volcanic mountains or even the SA on volcanic mountains (Table 2-1, Figure 2-3). There were no differences in phosphate, total nitrogen, or total carbon between bedrock or forest belts (Table 2-1).

Canopy Structure

The height and basal diameter of the Abies declined as elevation increased from the SA to the BB

(60% and 38%, respectively), while Betula increased with elevation through these forest belts (23% and

16% respectively; Table 2-5). With increase in elevation from the BB to the PM, Pinus structure was relatively stable for these parameters (Table 2-5). Abies density decreased (-88%, SA to BB) and Pinus density increased (97%, BB to PM) with increasing elevation, while Betula density stayed relatively stable though decreasing across the elevation profile (-39% SA to BB, -10% BB to PM; Tables 2-4 and 2-

5). The stability of Betula structure was also evident in basal area (Table 2-5). The density of Abies was higher than Betula in the BB, but because the individuals were much smaller in diameter (Table 2-5); the basal area of Abies was smaller in the BB. Similar to the decrease of Abies tree density, there were fewer seedlings (-83%) and saplings (-55%) as elevation increased through the SA to the BB (Table 2-4). Betula seedling density was highest in the BB, though the density of saplings was higher in the SA. In the SA, there were also a few Picea trees, saplings and seedlings (Table 2-4). Pinus seedlings were most abundant in the SA, though saplings and trees increased 680% and 997% respectively, from the SA to the PM

(Table 2-4). In the SA, Pinus seedlings primarily established by seed, while those in the PM were established through vegetative layering.

Predictors of Importance Values

My models of the IV for each of the dominant species in the alpine forest confirmed Bond’s hypothesis that gymnosperm dominance occurred on nutrient poor soils, while angiosperms dominated on the nutrient rich soils. Neither of the random effects (individual mountain or bedrock) had much influence on the overall strength of the gymnosperm models (Table 2-6). Bedrock (volcanic or non-volcanic) explained 20% of the variance for the Betula IV model (Table 2-6). I averaged models within two AICc units of the best model (Table 2-7) to create the best model for each species (Table 2-8). Each species’ IV was influenced by different edaphic and topographic factors (Table 2-8). High levels of total carbon in the soil were indicative of high Abies IV (Table 2-8). Betula IV was highest with high concentrations of nitrate and on steep slopes (Table 2-8). The position on the landscape (TPI) was important for the two gymnosperm species; high Abies IV occurred when TPI was low and the opposite was true for Pinus

(Table 2-8). The Pinus model did not fully converge (Grueber et al. 2011) possibly due to the IV being either 0 or 300 in all but 15 of the plots. There was no soil under the Pinus at the upper elevation, thus there were fewer points dominated by Pinus. The confidence intervals were very large for the significant variables in the Pinus IV model, thus the results from the Pinus should be viewed with caution. Pinus IV were related to low levels of total nitrogen (0.43-0.67 mg L-1) as well as the interaction between TPI and total nitrogen (Table 2-8).

Discussion

In this study, I tested Bond’s hypothesis that gymnosperms should be restricted to nutrient poor habitats in cool environments relative to angiosperms on an alpine forest in central Japan. My results

40 indicate that the gymnosperms (Abies and Pinus) were dominant in areas with lower nitrate, ammonium and total nitrogen than the angiosperm (Betula) in the Northern Japanese Alps. However, the nutrient content of these alpine soils varied depending on the underlying bedrock and topography. The segregation of forest belts therefore is more complex than simply being determined by topography (elevation) and soil fertility. Below I discuss some of the challenges in testing Bond’s hypothesis in a field setting and the many inputs of nitrogen in the alpine forest system.

Canopy Structure

The pattern of tree height, diameter and density with increasing elevation of the three dominant species in this study (Table 2-5) have been well documented in central Japan and Hokkaido (Okitsu and

Ito 1984; Okitsu and Ito 1989; Hara et al. 1991; Mori and Takeda 2004; Takahashi 2003; Miyajima and

Takahashi 2007; Takahashi et al. 2012). The primary mechanism that defines the upper range limit of

Abies is mechanical damage due to snow and wind (Kajimoto et al. 2002; Sakai et al. 2003; Mori and

Mizumachi 2009; Takahashi et al. 2012; Takahashi 2014). The lower distribution limit of Pinus is due to shade-intolerance and competition with upright forest (Okitsu and Ito 1989; Takahashi 2003; Takahashi et al. 2012; Takahashi 2014). Betula, which is also a shade-intolerant species, dominates the transition zone between the Abies and Pinus. Betula spp. are typically classified as a pioneer species that establishes after a disturbance event (Ashburner and McAllister 2013). The primary disturbances that create small localized gaps in these alpine forests are due to wind damage, however, there are no apparent large scale gap openings in this study area in which Betula would be most likely to establish (Kohyama 1982;

Nakashizuka 1989; Kimura 1991; Yamamoto 1993; 1996). Regardless, Betula is regenerating on the landscape (Table 2-4), thus it may not be a pioneer species but a mid-successional species. An alternative explanation for the existence of the BB is that it has higher soil nutrients than the PM or the SA; that is, angiosperms are dominant on nutrient rich soils while gymnosperms are on the nutrient poor soils as stated in Bond’s slow seedling hypothesis (Bond 1989).

It has been proposed that nutrient loss in intermediate successional stages is lower than the losses in either late or early stages (Vitousek and Reiner 1975, Aber et al. 1989). However, research from the

Japanese Alps (Chapter 4 of this dissertation) showed that the Betula in the BB are on average 300 years old, approximately 100 years older than Abies in the SA. Because the highest concentrations of inorganic nitrogen were found in the BB, it does not seem that stand age is related to loss of nitrogen over time. In contrast with nitrogen, the size of soil carbon pools increases with tree age (Steltzer 2004). In this study, there was a slightly higher soil carbon content in the BB than the SA or PM (Table 2-1). All together, these results suggest that a simplistic linear pattern of succession is inadequate to explain the variation in nutrients among mountains and forest belts.

Bedrock-Nutrients

Differences in bedrock can be equally as important as altitude (temperature) in segregating species, but bedrocks that are chemically homogeneous can have many different vegetation communities

(Hahm et al. 2014). The weathering of rocks can contribute to the nitrogen pool of terrestrial ecosystems, but primarily only when the bedrock is nitrogen rich (Holloway and Dahlgren 2002, Morford et al. 2011).

Soil nitrogen can be 50% higher on nitrogen rich rocks (shale, slate, sandstone) than on nitrogen poor rocks (granite, basalt, gneiss; [Morford et al. 2011; Morford et al. 2016b]). Nitrogen derived from igneous and high grade metamorphic rock is considered a minor source of the total nitrogen pool (Morford et al.

2016a and Morford et al. 2016b). Furthermore, the carbon storage capacity is greater on nitrogen rich than nitrogen poor rocks (Morford et al. 2011).

The mountains in this study, had ammonium and nitrate concentrations that were significantly higher on volcanic than non-volcanic mountains (Table 1). Although the chemical composition of bedrock was not tested for nutrients in this study, the three volcanic mountains are much younger (mid-

Miocene to mid-Pleistocene) than the non-volcanic mountains (mid-Jurassic to late-Cretaceous)

(Geological Survey of Japan 2012). Older bedrock often has lower concentrations of nitrogen than

42 younger bedrock (Morford et al. 2016a). Furthermore, volcanism can lead to the expulsion of ammonium gasses and their subsequent deposition on surfaces, while other gasses of volcanic nitrogen need to be fixed by microorganisms in order to be accessible to vegetation (Holloway and Dahlgren 2002). Soils high in ammonium lead to acidic soils in the Klamath Mountains (Holloway and Dahlgren 2002). In this study, I found that the volcanic soils were higher in ammonium; however, the non-volcanic soils had a lower pH than the volcanic soils (Table 2-1). Soil pH at all sites indicated the soils were extremely acidic

(volcanic x̄ =4.32, non-volcanic x̄ =4.1; Broll et al. 2005).

Treeline Elevation

Differences in the elevation of treeline between the volcanic and non-volcanic mountains can be partially explained by summit syndrome and mass elevation effect (Körner 2012). Summit syndrome is the phenomenon of treeline position being depressed below the trees’ climatic potential due to shallow soils and high winds near the top of mountains (Körner 2012). In my study area, treelines on volcanic mountains were approximately 100 m below the treeline on non-volcanic mountains (Table 2-1), and the height of the nearest ridge directly upslope from the transects on the volcanic mountains was 100 m lower than on the non-volcanic mountains. It is therefore feasible that treelines on volcanic mountains is depressed due to the shallow soils and high winds near the ridgelines. However, summit syndrome is not the only mechanism that naturally depresses treelines below their climatic potential; this lowering of treelines can also be topographically and substrate driven (Körner 2012).

The mass elevation effect also influences the elevation of treeline. In the central portion of a mountain range a treeline will be higher than those on either side due to air masses being lifted through orographic uplift and adiabatic cooling (Körner 2012). Moist adiabatic lapse rates, like those found in this study, increase the temperatures at a slower rate than a dry adiabatic lapse rate due to latent heat being released during condensation. Due to this increased condensation and moist adiabatic lapse rate the atmosphere in central Japan would be classified as unstable. Through adiabatic cooling, air masses reach

43 their dew point temperature and condensation occurs, thereby enhances precipitation at the higher elevations (Barry 2008, pp 32-72).

Treeline elevation is lower on islands than it is on the mainland, in part due to decreased landmass and the influences of oceanic climate (Irl et al. 2016). The two non-volcanic mountains in this study are on the leeward side of two ridgelines over which air masses lift, leading to warmer temperatures at higher elevations that allow higher tree establishment (Figure 2-1). Although the impacts of summit syndrome and the mass elevation effect were not the focus of this study, there is the potential that both, along with edaphic variation and timing of precipitation due to monsoons, influence the treeline elevation on volcanic and non-volcanic mountains in the Northern Japanese Alps.

Nutrient Gradient

Lower nitrogen content occurs as elevation increases near treeline compared to the forest (Davis et al. 1991; Malanson and Butler 1994; Karlsson and Weih 2001; Sveinbjörnsson et al. 2002; Sjögersten and Wookey 2005; Kammer et al. 2009, Sullivan et al. 2015), though one study revealed the upper krummholz to have the highest concentrations of nitrogen (Liptzin et al. 2013). This general reduction in soil nutrients is thought to be partly due to the lower temperatures that reduce microbial activity at higher elevations (Karlsson and Weih 2001; Rustad et al. 2001; Loomis et al. 2006; Butler 2012; Sullivan et al.

2015). The pattern of decreasing ammonium from forest to treeline and up to the alpine tundra has been detected in a number of studies (Davis et al. 1991; Sjögersten and Wookey 2005), though similar spatial patterns in nitrate are rarely detected or the patterns can have an inverse relationship (Davis et al. 1991;

Grogan and Jonasson 2003; Hartley et al. 2012). Here, I found that nitrate and ammonium content were both highest in the BB; however, this was only true for the volcanic mountains (Table 2-1). One reason that I did not see the decrease in nitrogen with increased elevation is that the transition in other studies runs from the subalpine forest to tundra, while the upper elevation in this study was Pinus or exposed rock. I did not examine the tundra ecosystem of the Northern Japanese Alps in this study.

Compared with N content, previous research on the generality of the distribution of C content along the forest to tundra transition is inconclusive. Many studies have revealed a higher C content in the subalpine forest (Masuzawa 1985; Xu and Shibata 2007; Kammer et al. 2009; Liptzin and Seastedt 2010;

Hartley et al. 2012), particularly when there is a deciduous treeline above the subalpine forest (Masuzawa

1985; Xu Shibata 2007). However, soil carbon can be higher in the tundra (tussock and heath) than forest ecosystem (Wilmking et al. 2006; Hartley et al. 2012). In my study, similar to slightly higher values for percent total carbon (8.7-13.2) were found than reported elsewhere (Wilmking et al. 2006; Kammer et al.

2009; Liptzin and Seastedt 2010; Hartley et al. 2012). The differences in carbon and nitrogen content shown here along the elevation gradient and when separated by forest composition may be attributable to a number of factors, including differences in microbial activity, nutrient deposition, nitrogen fixation, and competition/facilitation.

Microbial Activity

On the mountains facing the Japanese Sea in central Japan, snow depths greater than 5 meters can accumulate prior to its redistribution by wind (Matsuoka 2013). Deep snowpack helps protect soils from deep freezing, thus it supports higher rates of microbial activity during the winter (Lipson et al. 1999;

Grogan and Jonasson 2003; Schimel et al. 2004; Loomis et al. 2006; Liptzin et al. 2009). Reduced snow cover could therefore reduce rates of decomposition (Wu et al. 2014). During the period of snow pack, up to 70% of the net N mineralization can occur through bacterial decomposition of organic litter (Loomis et al. 2006). N mineralization can accumulate upwards of 3 kg N ha-1 during the winter (Liptzin et al. 2009).

A majority of the microbial N mineralization under the snow is in the form of ammonium (Grogan and

Jonasson 2003).

The rates of decomposition depend on both the duration and depth of the snowpack (Liptzin et al.

2009). In the Rocky Mountains, decomposition increased 1% for every additional 10 cm of snow (Liptzin et al. 2009). When the snowpack melts in the spring, there is a pulse of available nitrogen into the system

45 for vegetation to take up, and this resource pool gradually decreases until it has been nearly entirely absorbed by the vegetation or leached out by rains (Grogan and Jonasson 2003; Schimel et al. 2004;

Loomis et al. 2006). Thus deep snowpack results in high nutrient content (Bowman 1992; Liptzin et al.

2013). Snow depths are greater near the forest edge than on the tundra due the redistribution of snow by the wind and the trees at the forest edge causing a wind break (Liptzin et al. 2009).

Differences in soil nutrient pools also occur between gymnosperms and angiosperms. A comparison between the gymnosperm, Picea glauca (Moench) Voss and the angiosperm shrub Betula nana L. in northwestern Alaska showed a difference in the size of the carbon pool (73 vs 61 g kg-1) but no difference in the nitrogen pool (4 vs 3 g kg-1) (Stottlemyer 2001). Soils beneath an Abies (gymnosperm) canopy had larger carbon and smaller nitrogen pools than Betula (angiosperm) in northern Japan (Xu and

Shibata 2007). So while carbon pools may be larger under gymnosperm canopies, the evidence for any similar difference in nitrogen pools is mixed. This lack of consistent patterns in nitrogen pools may be due to regional differences in the quantity or timing of precipitation, particularly as snow.

The rate of decomposition under the snow also depends on the quality and quantity of the litter

(Prescott et al. 2000; Grogan and Jonasson 2003). Betula produces a large quantity of annual leaf litter

229 g m-2 year-1, and the BB had the highest percentage of shrub cover that would add to the quantity of deciduous leaf litter (Tripathi et al. 2006). The litter depth in the BB was 5.4 ± 0.52 cm, which is comparable to litter from B. pubescens (4-8 cm) in the Scandes (Grogan and Jonasson 2003) and

Nothofagus pumilio (4.2-6 cm) in Patagonia (Hertel et al. 2008). The annual accumulation of Betula litter was higher than that of the conifers; however, overall litter depth was greatest in the PM (Table 2-2). In the Ural Mountains, the quantity of carbon in the forest and tundra ecosystems can also be similar due to lower rates of decomposition in the tundra (Kammer et al. 2009). Similarly, in Alaska decomposition at treeline was 30% lower than in the forest below (Loomis et al. 2006). Low rates of decomposition could help explain the greater litter depth in the PM.

The residence time of litter on the landscape differs between Betula and Abies. Tian and Takeda

(1998) found Betula litter lost 20.1% of its mass during the first winter and 9.4% during the second, while

Abies lost 15.4% the first winter and only 1.6% during the second winter. During the growing season mass loss was similar (~11.5%) for the two species (Tian and Takeda 1998). Due to Betula’s annual shedding of leaves into the system, there is a greater annual Betula leaf litter mass loss. Furthermore,

Betula litter had a higher nitrogen content (1.32%) than that of Abies (1.06%) in central Japan (Osono and

Takeda 2006), which may account for the higher mass loss in Betula than Abies litter. Similarly, in a study in Northern Japan the annual leaf litter accumulation was 229 g m-2 year-1 of which 3.3 g m-2 year-1, was nitrogen, resulting a in nitrogen content of 1.44% (Tripathi et al. 2006). Abies litter production is less than that of Betula, furthermore, it has a lower nitrogen content (1.06%, Osono and Takeda 2006). Thus the nitrogen return from Betula leaf litter is able to enrich the soils beneath a Betula canopy, which would further increase the total nitrogen content of these soils. One of the potential sources of higher nitrogen content in the Betula litter than the Abies litter is due to contributions of wood to the litter. Nitrogen content of wood, primarily from temperate forests, is higher for angiosperm wood than gymnosperms

(Harmon et al. 1986; Weedon et al. 2009). Angiosperm wood decomposes faster than gymnosperm wood, thus it is able to return additional nitrogen to the ecosystem (Weedon et al. 2009).

Nutrient Deposition

Snow is not only redistributed across the tundra, but it also acts as a matrix in which organic

2- (leaves, algae, bacteria) and inorganic (pollutants [sulfate (SO4 ) and nitrate], dust) substances adhere

(Liptzin and Seastedt 2010). In the Tateyama area in the northeastern portion of the Northern Japanese

Alps, dust from arid regions of western China is deposited onto the snow pack in the spring (Kohshima et al. 1994; Watanabe et al. 2010). In the Rocky Mountains, dust deposition during the winter accumulates much less in the alpine tundra (0.7g m-2) than the forest below (7.7 g m-2; Liptzin et al. 2009). The albedo of the snow decreases from spring to summer as organisms in the snow grow and plant matter decomposes, and the snow turns from white to brown (Kohshima et al. 1994). Nitrogen and dust deposition can increase the acidity of soils (Aber et al. 1989), though dust rich in calcium may neutralize

47 some of the acid (Liptzin and Seastedt 2010; Watanabe et al. 2010). The greater snow depths that likely develop under Betula individuals that make up the forest edge may thus not only increase decomposition, as discussed above, but also contain more organic and inorganic substances that enhance soil fertility.

Trees at the forest edge also receive greater loads of pollutants than the landscape as a whole relative to their height because the canopy there traps particles from the wind (Lovett and Kinsman 1990;

Liptzin and Seastedt 2010). In this study area, the highest elevation upright trees are the Betula (~8 m tall). Deposition of pollutants arrives in three forms: wet, dry, and cloud water, all of which occur in the

Japanese Alps (Uehara et al. 2015; Watanabe et al. 2010). Wet deposition of pollutants increases with elevation due to increased precipitation from orographic uplift (Lovett and Kinsman 1990). Wet deposition occurs as rain and snow, while dry deposition occurs as dust or in gas form. Cloud or fog containing pollutants can deposit them directly on the vegetation (Lovett and Kinsman 1990; Watanabe et al. 2010) and then potentially be directly absorbed into the plants (Carlo and Norris 2012). Because Pinus in the Northern Japanese Alps exists above the other two forest belts, it is more frequently located in fog or clouds, where it comes in contact with pollutants. In a Pinus stand on Tateyama, fog contributed twice as much ammonium and nitrate to the system than rain, and roughly 80% was deposited directly into the canopy (Uehara et al. 2015). Due to the deposition of nitrogen into the Pinus canopy, it is possible that my detection of low nitrogen in the PM soil may underestimate the actual values of nitrogen taken up by

Pinus.

Allelopathy and Nitrogen Fixers

Interactions between plant species are frequently limited to facilitation and direct competition, however, allelopathy (negative interaction between species due to chemical inhibition) can also occur.

Empetrum spp. and some Vaccinium spp. can limit the germination of conifer seedlings (Jaderlund et al.

1996; Dufour-Tremblay et al. 2012). Picea mariana (Mill.) B.S.P. seedlings are frequently outcompeted in Canadian tundra by B. glandulosa Michx., though laboratory experiments revealed that B. glandulosa

48 leachates can also limit P. mariana establishment (Dufour-Tremblay et al. 2012). To my knowledge no studies have found that Betula is influenced by allelopathy. In this study, Vaccinium spp. occurred in 32% of the SA points, so it appears unlikely that Abies recruitment was negatively impacted by Vaccinium spp.

Furthermore, there was no clear allelopathic effect of B. ermanii on A. mariesii or P. pumila because the species form mixed stands, though there may have bene a small effect enhancing the dominance of Betula at the BB.

Stands that include Alnus spp., which are nitrogen fixers, can have nitrogen enriched soils (Hart and Gunther 1989, Rhoades et al. 2001). Nitrogen fixation by Alnus spp. contributed 4.5 g N m-2 year-1 along the forest tundra ecotone in Alaska (Rhoades et al. 2001). A. maximowiczii occurred more frequently in the BB than the other forest belts, which could account for the relatively higher nitrate and ammonium. Alnus spp. added 1.5 g m-2 year-1 of nitrogen more than is being added by Betula annually

(see above), thus the combination of Betula and Alnus in the BB could add approximately 7.5 g m-2 year-1 a fairly substantial amount. Additionally, A. maximowiczii was more common on the non-volcanic

(16.7%) than the volcanic (5.6%) mountains (Table 2-3). The volcanic mountains, with less A. maximowiczii have significantly higher nitrogen content, thus the presence of A. maximowiczii alone does increase nitrogen content enough to explain the differences between geologic substrates. Thus, the presence of A. maximowiczii may not have significantly contributed to the nitrogen content of the soils in the Northern Japanese Alps. However, A. maximowiczii has been shown to increase the nitrogen content on Mt. Fuji (volcanic) in the deciduous treeline (Masuzawa 1985).

Conclusions

In this study, I assessed Bond’s hypothesis that gymnosperms are limited to nutrient poor and cool environments while angiosperms live in nutrient rich environments on an alpine forest in central

Japan. I found that Bond’s hypothesis is not universal across the Japanese Alps. Averaged across all mountains, ammonium, nitrate, and total carbon were highest in the BB, compared to the SA or PM, thus

49 supporting Bond’s hypothesis. However, the underlying bedrock played an important role. Volcanic soils had significantly higher concentrations of nitrate and ammonium than soils from non-volcanic mountains, and soils within the BB had the highest concentrations of these (forms of nitrogen). In total, his evidence suggests that lithology should be a key consideration when examining forest composition and species interactions.

References

Aber, J., Nadelhoffer, K., Steudler, P. and J. Melillo. 1989. Nitrogen saturation in northern forest

ecosystems. BioScience 39: 378-386.

Anderson, D. 1988. The effect of parent material and soil development on nutrient cycling in temperate

ecosystems. Biogeochemistry 5: 71-97.

Ashburner, K. and H. McAllister. 2013. The Genus Betula: A taxonomic revision of birches. Kew

Publishing. Royal Botanic Gardens, Kew.

Bartoń, K. 2009. MuMIn: multi-model inference. R package, version 1.15.6. Available at: http://r-forge.r-

project.org/projects/mumin/.

Bates, D., Maechler, M., Bolker, B. and S. Walker. 2015. Fitting linear mixed-effects models using lme4.

Journal of Statistical Software 67: 1-48.

Barry, R. 2008. Mountain Weather and Climate. Cambridge University Press, New York.

Bond, W. J. 1989. The tortoise and the hare: Ecology of angiosperm dominance and gymnosperm

persistence. Biological Journal of the Linnean Society 36: 227-249.

Bowman, W. 1992. Inputs and storage of nitrogen in winter snowpack in an alpine ecosystem. Arctic and

Alpine Research 24: 211-215.

Broll, G., Hiller, B., Bednorz, F., Muller, G. and T. Reineke. 2005. Guideline for describing soil profiles

in mountain ecosystems. In: Mountain Ecosystems: Studies in Treeline Ecology. Eds. Broll, G. and B.

Keplin. Springer – Verlag Berlin, Germany.

Butler, S., Melillo, J., Johnson, J., Mohan, J., Steudler, P., Lux, H., Burrows, E., Smith, R., Vario, C.,

Scott, L., Hill, T., Aponte, N. and F. Bowles. 2012. Soil warming alters nitrogen cycling in a New

England forest: implications for ecosystem function and structure. Oecologia 168: 819-828.

Cavard, X., Bergeron, Y., Chen, H., Paré, D., Laganière, J. and B. Brassard. 2011. Competition and

facilitation between tree species change with stand development. Oikos 120: 1683-1695.

Calder, W., Horn, K. and S. St. Clair. 2011. Conifer expansion reduces the competitive ability and

herbivore defense of aspen by modifying light environment and soil chemistry. Tree Physiology 31:

582-591.

Calder, W. and S. St. Clair. 2012. Facilitation drives mortality patterns along succession gradients of

aspen-conifer forests. Ecosphere 3: Article 57.

Campbell, G. and J. Norman. 1998. An Introduction to Environmental Biophysics-Second Edition.

Springer, New York, NY.

Canham, C. 1988. An Index for understory light levels in and around canopy gaps. Ecology 69: 1634-

1638.

Canham, C., Finzi, A., Pacala, S. and Burbank, D. 1994. Causes and consequences of resource

heterogeneity in forests: interspecific variation in light transmission by canopy trees. Canadian

Journal of Forest Research 24: 337-349.

Canham, C. and R.Q. Thomas. 2010. Frequency, not relative abundance, of temperate tree species varies

along climate gradients in eastern North America. Ecology 91: 3433-3440.

Carlo, T. and A. Norris. 2012. Direct nitrogen intake by petals. Oikos121: 1953-1958.

Chapin, F. S. III., Matson, P., and H. Mooney. 2002. Terrestrial Decomposition. In Principles of

Terrestrial Ecosystem Ecology. Springer-Verlag, New York. pp. 151-175.

Cottam and Curtis 1956. The use of distance measures in phytosociological sampling. Ecology 37: 451-

460.

Davis, J., Schober, A., Bahn, M. and B. Sveinbjörnsson. 1991. Soil carbon and nitrogen turnover at and

below the elevational treeline in northern Fennoscandia. Arctic and Alpine Research 23: 279-286.

Dufor-Tremblay, G., de Vriendt, L., Lévesque, E. and S. Bourdreau. 2012. The importance of ecological

constraints on the control of multi-species treeline dynamics in eastern Nunavik, Québec. American

Journal of Botany 99: 1638-1646.

Gelman, A., Su, Y.-S., Yajima, M., Hill, J., Pittau, M.G., Kerman, J. Zheng, T. and V. Dorie. 2015. arm:

data analysis using regression and multilevel⁄ hierarchical models. R package, version 1.8-6.

Available at: http://CRAN.R-project.org/package=arm.

Geological Survey of Japan, AIST (ed) 2012. Seamless digital geological map of Japan 1: 200,000. Jul 3,

2012 version. Research Information Database DB084, Geological Survey of Japan, National Institute

of Advanced Industrial Science and Technology. https://gbank.gsj.jp/seamless/maps.html?lang=en.

Grogan, P. and S. Jonasson. 2003. Controls on annual nitrogen cycling in the understory of a subarctic

birch forest. Ecology 84: 202-218.

Grueber, C., Nakagawa, S., Laws, R. and I. Jamieson. 2010. Multimodel inference in ecology and

evolution: challenges and solutions. Journal of Evolutionary Biology 24: 699-711.

Hahm, W.J., Riebe, C., Kukens, C. and S. Araki. 2014. Bedrock composition regulates mountain

ecosystems and landscape evolution. Proceedings of the National Academy of Science 111: 3338-

3343.

Hart, S. and A. Gunther. 1989. In situ estimates of annual net nitrogen mineralization and nitrification in a

subarctic watershed. Oecologia 80: 284-288.

Harmon, M., Franklin, J., Swanson, F., Sollins, P., Gregory, S., Lattin, J., Anderson, N., Cline, S.,

Aumen, N., Sedell, J., Lienkaemper, G., Cromack Jr. K. and K. Cummins. 1986. Ecology of Coarse

Woody Debris in Temperate Ecosystems. Advances in Ecological Research 15: 133-302.

Hartley, I. P., Garnett, M. H., Sommerkorn, M., Hopkins, D. W., Fletcher, B. J., Sloan, V. L., Phoenix, G.

K., and Wookey, P. A., 2012: A potential loss of carbon associated with greater plant growth in the

European Arctic. Nature Climate Change, 2: 875-879.

Hertel, D. and K. Wesche. 2008. Tropical moist Polylepis stands at the treeline in East Bolivia: the effect

of elevation on stand microclimate, above- and below-ground structure, and regeneration. Trees 22:

303-315.

Hertel, D., Therburg, A. and R. Villalba. 2008. Above- and below-ground response by Northofagus

pumilio to climatic conditions at the transition from the steppe-forest boundary to the alpine treeline

in southern Patagonia, Argentina. Plant Ecology and Diversity 1: 21-33.

Hiura, T., Sano, J. and Y. Konno. 1996. Age structure and response to fine-scale disturbances of Abies

sachalinensis, Picea jezoensis, Picea glehnii, and Betula ermanii growing under the influence of a

dwarf bamboo understory in northern Japan. Canadian Journal of Forest Resource 26: 289-297.

Holloway, J. and R. Dahlgren. 2002. Nitrogen in rock: Occurrences and biogeochemical implications.

Global Biogeochemical Cycles 16: 1118

Holtmeier, F. and G. Broll. 2010. Wind as an ecological agent at treelines in North America, the Alps,

and the European subarctic. Physical Geography 31: 203-233.

Hurvich, C. and C-L. Tsai. 1989. Regression and time series model selection in small samples.

Biometrika 76: 297-307.

Ikeda, S., Wakabayashi, R., Izumi, K. and K. Wawashima. 2009. Study of snow climate in the Japanese

Alps: Comparison to snow climate in North America. Cold Regions Science and Technology 59: 119-

125.

Irl, S., Anthelme, F., Harter, D., Jentsch, A., Lotter, E., Steinbauer, M. and C. Beierkuhnlein. 2016.

Patterns of island treeline elevation – a global perspective. Ecography 39: 427-436.

Iverson, L. and A. Prasad. 2002. Potential redistribution of tree species habitat under five climate change

scenarios in the eastern US. Forest Ecology and Management 155: 205-222.

Japan Meteorological Agency, 2014: Global Warming Projection Vol.8,

http://ds.data.jma.go.jp/tcc/tcc/products/gwp/gwp8/index.html

Jäderlund, A., Zackrisson, O. and M-C. Nilsson. 1996. Effects of Bilberry (Vaccinium myrtillus L.) litter

on seed germination and early seedling growth of four boreal tree species. Journal of Chemical

Ecology 22: 973-986.

Jenness, J. 2006. Topographic Position Index extension for ArcView 3.x, v. 1.3a. Jenness Enterprises.

Available at: http://www.jennessent.com/arcview/tpi.htm

Kammer, A., Hagedorn, F., Shevechenko, I., Leifeld, J., Guggenberger, G., Goryacheva, T., Rigling, A

and P. Moiseev. 2009. Treeline shifts in the Ural Mountains affect soil organic matter dynamics.

Global Change Biology 15: 1570-1583.

Kajimoto, T. 1994. Aboveground net production and dry matter allocation of Pinus pumila forests in the

Kiso Mountain range, central Japan. Ecological Research 9: 193-204.

Kajimoto, T., Seki, T., Ideda, S., Daimaru, H., Okamoto, T. and H. Onodera. 2002. Effects of snowfall

fluctuation on tree growth and establishment of subalpine Abies mariesii near upper forest-limit of

Mt. Yumori, northern Japan. Arctic, Antarctic, and Alpine Research 2: 191-200.

Kammer, A., Hagedorn, F., Shevchenko, I., Leifeld, J., Guggenberger, G., Goryacheva, T., Rigling, A.

and P. Moiseev. 2009. Treeline shifts in the Ural Mountains affect soil organic matter dynamics.

Global Change Biology 15: 1570-1583.

Karlsson, P. and M. Weih. 2001. Soil temperatures near the distribution limit of the mountain birch

(Betula pubescens spp. czerepanovii): Implications for seedling nitrogen economy and survival.

Arctic, Antarctic, and Alpine Research 33: 88-92.

Kohshima, S., Yoshimura, Y., Seko, K. and T. Ohata. 1994. Albedo reduction by biotic impurities on a

perennial snow patch in the Japan Alps. Snow and Ice Covers. IAHS 223: 323-330.

Kojima, S., Hasegawa, Y., Iwamoto, N., Komura, K. and H. Nagata. 2015. Geomorphological and

geological characteristics of deep-seated gravitational slope deformation in the Kamikochi area,

central Japan. 10th Asian Regional Conference of the International Association of Engineering

Geology and the Environment.

Körner, C. 2012. Alpine Treelines: Functional ecology of the global high elevation tree limits. Springer,

Basel.

LeBauer, D. and K. Treseder. 2008. Nitrogen limitation of net primary productivity in terrestrial

ecosystems is globally distributed. Ecology 89: 371-379.

Lipson, D., Schmidt, S. and R. Monson. 1999. Links between microbial population dynamics and

nitrogen availability in an alpine ecosystem. Ecology 80: 1623-1631.

Liptzin, D. and T. Seastedt. 2009. Patterns of snow, deposition, and soil nutrients at multiple spatial scales

at a Rocky Mountain tree line ecotone. Journal of Geophysical Research 114: G04002.

Liptzin, D. and T. Seastedt. 2010. Regional and local patterns of soil nutrients at Rocky Mountain

treelines. Geoderma 160: 208-217.

Liptzin, D., Sanford, R. and T. Seastedt. 2013. Spatial patterns of total and available N and P at alpine

treeline. Plant Soil 365: 127-140.

Loomis, P., Ruess, R., Sveinbjörnsson, B. and K. Kielland. 2006. Nitrogen cycling at treeline: Latitudinal

and elevational patterns across a boreal landscape. Écoscience 13: 544-556.

Lovett, G. and J. Kinsman. 1990. Atmospheric pollutant deposition to high-elevation ecosystems.

Atmospheric Environment 24A: 2767-2786.

Malanson, G. and D. Butler. 1994. Tree-tundra competitive hierarchies, soil fertility gradients, and

treeline elevation in Glacier National Park, Montana. Physical Geography 15: 166-180.

Masuzawa, T. 1985. Ecological studies on the timberline of Mt. Fuji I. Structure of plant community and

soil development on the timberline. Botanical Magazine of Tokyo 98: 15-28.

Matsuoka, N. 2013. Cold region geomorphology in Japan. Geographical Review of Japan Series B 86:

22-32.

Mitchell, K. 2007. Quantitative Analysis by the point-centered quarter method. Geneva: Department of

Mathematics and Computer Science, Hobart and William Smith Colleges.

Miyajima, Y. and K. Takahashi. 2007. Changes with altitude of the stand structure of temperate forests on

Mount Norikura, central Japan. Journal of Forest Research 12: 187-192.

Mori, A. and H. Takeda. 2004. Effects of mixedwood canopies on conifer advance regeneration in a

subalpine old-growth forest in central Japan. Écoscience 11: 36-44

Mori, A. and E. Mizumachi. 2009. Within-crown structural variability of dwarfed mature Abies mariesii

in snowy subalpine parkland in central Japan Journal of Forest Research 14: 155-166.

Morford, S., Houlton, B. and R. Dahlgren. 2011. Increased forest ecosystem carbon and nitrogen storage

from nitrogen rich bedrock. Nature 477: 78-81.

Morford, S., Houlton, B. and R. Dahlgren. 2016a. Direct quantification of long-term rock nitrogen inputs

to temperate forest ecosystems. Ecology 97: 54-64

Morford, S., Houlton, B. and R. Dahlgren. 2016b. Geochemical and tectonic uplift controls on rock

nitrogen inputs across terrestrial ecosystems. Global Biogeochemical Cycles 30: 333-349.

Nakashizuka, T. 1987. Regeneration dynamics of beech forests in Japan. Vegetatio 69: 169-175.

NASA LP DAAC, 2013, ASTER Level 1. NASA EOSDIS Land Processes DAAC, USGS Earth

Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota

(https://lpdaac.usgs.gov), accessed May 15, 2012.

Obara, H., Maejima, Y., Kohyama, K., Ohkura, T. and Y. Takata. 2015. Outline of the Comprehensive

Soil Classification system of Japan - First Approximation. Japan Agricultural Research Quarterly 49:

217-226.

Osono, T. and H. Takeda. 2006. Fungal decomposition of Abies needle and Betula leaf litter. Mycologia

98: 172-179.

Okitsu, S. and K. Ito. 1984. Vegetation dynamics of the Siberian dwarf pine (Pinus pumila Regel) in the

Taisetsu mountain range, Hokkaido, Japan. Vegetatio 58: 105-113.

Okitsu, S. and K. Ito. 1989. Conditions for the development of the Pinus pumila zone of Hokkaido,

northern Japan. Vegetatio 84: 127-132.

Phillips, J. and D. Marion. 2004. Pedological memory in forest soil development. Forest Ecology and

Management 188: 363-380.

Porder, S. and S. Ramachandran. 2012. The phosphorus concentration of common rocks – a potential

driver of ecosystem P status. Plant and Soil 367: 1-2.

Prescott, C., Zabek, L., Staley, C. and R. Kabzems. 2000. Decomposition of broadleaf and needle litter in

forests of British Columbia: influences of litter type, forest type, and litter mixtures. Canadian Journal

of Forest Research 30: 1742-1750.

Rhoades, C., Oskarsson, H., Binkley, D. and B. Stottlemyer. 2001. Alder (Alnus crispa) effects on soils in

ecosystems of the Agashashok River valley, northwest Alaska. Écoscience 8: 89-95.

Rustad, L., Campbell, J., Marion, G., Norby, R., Mitchell, M., Hartley, A., Cornelissen, J., Gurevitch, J.

and GCTE-NEWS. (2001). A meta-analysis of the response of soil respiration, net nitrogen

mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 126:

543-562.

Sawagaki, T. and T. Aoki. 2011. Late Quaternary Glaciations in Japan. Developments in Quaternary

Science. 15: 1013-1021.

Schimel, J. Bilbrough, C. and J. Welker. 2004. Increased snow depth affects microbial activity and

nitrogen mineralization in two Arctic tundra communities. Soil Biology and Biochemistry 36: 217-

227.

Sjögersten, S. and P. Wookey. 2004. Decomposition of mountain birch leaf litter at the forest-tundra

ecotone in the Fennoscandian mountains in relation to climate and soil conditions. Plant and Soil 262:

215-227.

Sjögersten, S. and P. Wookey. 2005. The role of soil organic matter quality and physical environment for

nitrogen mineralization at the forest-tundra ecotone in Fennoscandia. Arctic, Antarctic, and Alpine

Research 37: 118-126.

Smith, W., Johnson, D. and K. Reinhardt. 2007. Alpine Forest. In The Encyclopedia of Ecology. Elsevier,

NY.

Steltzer, H. 2004. Soil carbon sequestration with forest expansion in an arctic forest-tundra landscape.

Canadian Journal of Forest Research 34: 1538-1542.

Stottlemyer, R. 2001. Biogeochemistry of a treeline watershed, Northwestern Alaska. Journal of

Environmental Quality 30: 1900-1998.

Sveinbjörnsson, B. 2000. North American and European treelines: External forces and internal processes

controlling position. AMBIO 29: 388-395.

Sveinbjörnsson, B., Hofgaard, A. and A. Lloyd. 2002. Natural causes of the tundra-taiga boundary.

AMBIO 12: 23-29.

Sullivan, P., Ellison, S., McNown, R., Brownleee, A. and B. Sveinbjörnsson. 2015. Evidence of soil

nutrient availability as the proximate constraint on growth of treeline trees in northwest Alaska.

Ecology 96: 716-727.

Takahashi, K. 1997. Regeneration and coexistence of two subalpine conifer species in relation to dwarf

bamboo in the understory. Journal of Vegetation Science 8: 529-536.

Takahashi, K. 2003. Effects of climate conditions on shoot elongation of alpine dwarf pine (Pinus pumila)

at its upper and lower altitudinal limits in central Japan. Arctic, Antarctic, and Alpine Research 35: 1-

Takahashi, K., Uemura, S., Suzuki, J-I. and T. Hara. 2003. Effects of understory dwarf bamboo on soil

water and the growth of overstory trees in a dense secondary Betula ermanii forest, northern Japan.

Ecological Research 18: 767-774.

Takahashi, K. and S. Yoshida. 2009. How the scrub height of dwarf pine Pinus pumila decreases at the

treeline. Ecological Research 24: 847-854.

Takahashi, K., Hirosawa, T. and R. Morishima. 2012. How the timberline formed: altitudinal changes in

stand structure and dynamics around the timberline in central Japan. Annals of Botany 109: 1165-

1174.

Takahashi, K. 2014. Effects of wind and thermal conditions on timberline formation in central Japan: a

lattice model. Ecological Research 29: 121-131.

Takahashi, K. and Y. Murayama. 2014. Effects of topographic and edaphic conditions on alpine plant

species distribution along a slope gradient on Mount Norikura, central Japan. Ecological Research 29:

823-833.

Tian, X-J. and H. Takeda. 1998. Decomposition process of leaf litter in a coniferous forest. In

Environmental Forest Science 54: 223-230.

Tripathi, S., Sumida, A., Shibata, H., Ono, K., Uemura, S., Kodama, Y. and T. Hara. 2006. Leaf litterfall

and decomposition of different above- and belowground parts of birch (Betula ermanii) trees and

dwarf bamboo (Sasa kurilensis) shrubs in a young secondary forest in Northern Japan. Biology and

Fertility of Soils 43: 237-246.

Uehara, Y., Kume, A., Chiwa, M., Honoki, H., Zhang, J. and K. Watanabe. 2015. Atmospheric deposition

and interactions with Pinus pumila Regal canopy on Mount Tateyama in the Northern Japanese Alps.

Arctic, Antarctic and Alpine Research 47: 389-399. van Breemen, N., Finzi, A. and C. Canham. 1997. Canopy tree – soil interactions within temperate

forests: effects of soil elemental composition and texture on species distributions. Canadian Journal of

Forest Research 27: 1110-1116.

Vitousek, P. and W. Reiner. 1975. Ecosystem succession and nutrient retention: a hypothesis. BioScience

25: 376-381.

Vitousek, P. and R. Howarth. 1991. Nitrogen limitation on land and in the sea: How can it occur?

Biogeochemistry 13: 87-115.

Watanabe, K., Honoki, H., Iwai, A., Tomatsu, A., Norikake, K., Miyashita, N., Yamada, K., Yamada, H.

Kawamura, H. and K. Aoki. 2010. Chemical characteristics of fog water at Mt. Tateyama, near the

coast of the Japan Sea in central Japan. Water Air Soil Pollution 211: 379-393.

Wardle, P. 1977. Japanese timberlines and some geographic comparisons. Arctic and Alpine Research 9:

249-258.

Wickham, H. 2009. ggplot: Elegant graphics for data analysis. Springer-Verlag New York. URL

http://cran.r-project.org/package=ggplot2

Wilmking, M., Harden, J. and K. Tape. 2006. Effect of tree line advance on carbon storage in NW

Alaska. Journal of Geophysical Research 11: G02023

Wu, Q., Wu, F., Tan, B., Yang, W., Ni, X. and Y. Yang. 2015. Carbon, Nitrogen, and Phosphorus stocks

in soil organic layer as affected by forest gaps in the alpine forest of the eastern Tibet Plateau.

Russian Journal of Ecology 46: 246-251.

Yoshino, M. 1978. Altitudinal vegetation belts of Japan with special reference to climatic conditions.

Arctic and Alpine Research 10: 449-456.

Yoshino, M. 1980. The climate regions of Japan. ERDKUNDE 34: 81-87.

Xu, X. and H. Shibata. 2007. Landscape patterns of overstory litterfall and related nutrient fluxes in a

cool-temperate forest watershed in northern Hokkaido, Japan. Journal of Forest Research 18: 249-

254.

Weedon, J., Cornwell, W., Cornelissen, J., Zanne, A., Wirth, C. and D. Coomes. 2009. Global meta-

analysis of wood decomposition rates: a role for trait variation among tree species? Ecology Letters

12: 45-56.

Figure 2-3. Box-whisker plots of the topographic (elevation, slope (%), and Topographic Position Index -1 -1 -1 [TPI]) and edaphic (nitrate [NO3-N; mg L ], ammonium [NH4-N; mg L ], phosphate [P2O2; mg L ], pH, total carbon [%] and total nitrogen [%]) parameters used in the linear mixed effects models. Data from transect points is divided based on bedrock (non-volcanic or volcanic) and forest belt (subalpine [SA; purple], Betula belt [BB; blue] and Pinus mat [PM; red].

Table 2-1. Topographic (Topographic Wetness Index [TWI], Topographic Position Index [TPI], slope (°) and elevation (m)) and edaphic parameters (pH, ammonium (mg L-1), nitrate (mg L-1), total carbon (%), total nitrogen (%), and phosphorus pentoxide (mg L-1)) of bedrock (non- volcanic and volcanic) in forest belts (Subalpine, Betula belt, Pinus mat) taken every 30m along transects. The number of points per forest belt by bedrock is denoted in parentheses next to the forest belt names. Values are the means (± standard error). Significant pairwise differences are indicated by letters within a row. (two-way ANOVA [df = 5,61], Tukey’s HSD, α =0.05).

Non-Volcanic Volcanic p-value Subalpine (9) Betula belt (9) Pinus mat (8) Subalpine (13) Betula belt (15) Pinus Mat (13) Bedrock Belt Bedrock:Belt a a a a a a pH 4.06 (0.07) 4.00 (0.08) 4.26 (0.20) 4.24 (0.09) 4.35 (0.09) 4.35 (0.088) 0.012 0.363 0.421 b b b b a ab Ammonium 14.22 (3.36) 6.95 (1.32) 14.65 (3.23) 15.96 (2.49) 30.10 (4.97) 19.26 (3.23) 0.001 0.266 0.01 b b b b a b Nitrate 8.29 (4.32) 11.90 (6.25) 2.66 (0.83) 14.45 (3.78) 38.10 (6.58) 12.13 (5.68) 0.002 0.001 0.162 a a a a a a Total Carbon 10.26 (1.69) 10.95 (1.48) 9.00 (2.87) 11.94 (2.35) 13.21 (2.30) 8.76 (1.33) 0.474 0.251 0.839 a a a a a a Total Nitrogen 0.75 (0.08) 0.65 (0.06) 0.67 (0.12) 0.80 (0.15) 0.83 (0.12) 0.43 (0.06) 0.998 0.046 0.2 a abc ab abc bc c TWI 3.02 (0.41) 1.95 (0.36) 2.54 (0.51) 2.01 (0.28) 1.65 (0.21) 1.06 (0.18) <0.001 0.032 0.167 b ab ab b b a TPI -0.16 (0.10) 0.02 (0.12) 0.03 (0.09) 0.01 (0.05) -0.02 (0.04) 0.29 (0.04) 0.028 0.002 0.095 a a a a a a Slope 24.83 (2.88) 30.70 (4.07) 24.19 (5.13) 28.17 (1.29) 28.91 (1.75) 28.31 (1.66) 0.407 0.462 0.481 a a a a a a Phosphate pentoxide 5.70 (3.17) 1.49 (0.60) 8.06 (2.82) 7.54 (3.24) 7.46 (3.03) 6.69 (3.04) 0.38 0.782 0.507 b a a d c b Elevation 2539 (9.4) 2584 (7.5) 2612 (11.4) 2409 (5.6) 2462 (6.6) 2521 (0.7) <0.001 <0.001 0.067

Table 2-2. Vegetation cover (forb, shrub and canopy cover; [% cover classified into 6 categories (0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75%, 5: 75- 95%, 6: 95-100%)]), light environment (Gap Light Index [GLI]) and litter depth (cm) of the bedrock (non-volcanic and volcanic) and forest belt (Subalpine, Betula belt, Pinus mat) taken every 10m along transects. The number of points per forest belt by bedrock is denoted in parentheses next to the forest belt names. Values are the means (± standard error). Significant pairwise differences are indicated by letters within a row. (two- way ANOVA [df =5,61], Tukey’s HSD, α =0.05).

Non-Volcanic Volcanic p-value Subalpine (27) Betula belt (28) Pinus mat (11) Subalpine (38) Betula belt (39) Pinus Mat (31) Geology Belt Bedrock:Belt b b a b b a Litter Depth 3.90 (0.44) 4.55 (0.83) 15.27 (0.91) 5.62 (0.82) 6.08 (0.68) 12.68 (0.86) 0.009 <0.001 0.045 b b a b b a GLI 15.99 (5.47) 26.20 (4.11) 68.05 (12.53) 21.98 (3.59) 24.96 (4.23) 55.47 (7.29) 0.507 <0.001 0.343 a ab b ab a b Forb Cover 3.85 (0.27) 3.50 (0.25) 2.45 (0.34) 3.23 (0.19) 3.67 (0.26) 2.65 (0.26) 0.257 0.001 0.217 ab a bc bc a c Shrub Cover 3.11 (0.26) 3.86 (0.27) 2.36 (0.52) 2.57 (0.20) 3.62 (0.30) 1.83 (0.17) 0.013 <0.001 0.813 abc bc a ab c a Canopy Cover 3.96 (0.24) 3.53 (0.22) 5.09 (0.25) 4.18 (0.16) 3.4 (0.26) 4.90 (0.13) 0.443 <0.001 0.623

Non-Volcanic Volcanic Total Subalpine (27) Betula belt (28) Pinus mat (11) Subalpine (38) Betula belt (39) Pinus mat (31) Non-Volcanic Volcanic Lonicera spp. 3.7 14.3 0 5.3 2.6 3.2 7.6 3.7 Sorbus spp. 55.6 53.6 27.3 23.7 28.2 25.8 50 25.9 Acer spp. 11.1 7.1 0 28.9 41.0 0 7.6 25.0 Vaccinium spp. 33.3 3.6 9.1 31.6 17.9 16.1 16.7 22.2 Alnus maximowiczii 11.1 28.6 0 2.6 12.8 0 16.7 5.6 Prunus nipponica 0 28.6 9.1 5.3 17.9 0 13.6 8.3 Rubus spp. 3.7 25.0 0 2.6 15.4 9.7 12.1 9.3 Rhododendron spp. 0 0 54.5 0 5.1 41.9 9.1 13.9 Sasa 0 0 0 7.9 17.9 6.5 0 11.1 Tripetaleia bracteata 7.4 0 0 0 0 0 3.0 0 Euonymus tricarpus 3.7 0 0 18.4 2.6 0 1.5 7.4 Fern spp. 63.0 57.1 0 50.0 12.8 3.2 50.0 23.1

Subalpine Betula belt Pinus mat x σ N x σ N x σ N Abies Short Seed 30,112 50,071 60 5,089 5,934 27 1,362 912 14 Tall Seed 3,328 2,371 58 1,283 1,137 29 2,271 1,466 11 Sapling 3,513 2,880 54 1,581 1,620 25 - - - Tree 1,388 - 25 166 - 17 83 - 1 Betula Short Seed 8,852 14,907 41 23,463 40,884 32 19,442 29,343 7 Tall Seed 997 743 6 831 498 4 399 133 5 Sapling 1,661 2,309 7 1,208 792 11 - - - Tree 369 - 15 224 - 21 249 - 2 Picea Short Seed 1,403 1,197 30 877 310 3 - - - Tall Seed 475 164 7 332 0 3 - - - Sapling 1,477 3,009 9 465 163 5 480 228 9 Tree 18 - 1 4 - 1 - - - Pinus Short Seed 1,972 0 1 1,644 986 2 332 0 1 Tall Seed 1,661 0 1 - - - 332 0 1 Sapling 941 446 6 831 166 4 17,064 19,804 11 Tree 18 - 1 157 - 13 6,992 - 21

Table 2-5. Canopy forest structure for each forest belt and the five species. Root crown diameter (cm) and tree height (m) were measured for each individual tree and averaged (standard error). Species density (trees –ha), basal area (m2 –ha) and importance values (0-300) were calculated for each species in each forest belt. The number of trees used for the calculations per species and forest belt are in parentheses after the importance values. Abies Abies Betula Picea Pinus Root Crown Diameter (cm) mariesii veitchii ermanii jezoensis pumila Total Subalpine 20.9 (1.3) 26.8 (3.7) 41.3 (0) 6.4 (0) 22.2 (1.3) Betula belt 12.6 (1.9) 18.6 (8.4) 34.8 (3.1) 16.8 (0) 7 (0.3) 20.0 (1.7) Pinus mat 27.8 (0) 27.2 (6.2) 6.2 (0.2) 7.2 (0.6) Tree Height (m) Subalpine 8.1 (0.6) 6.4 (0.6) 8.5 (0) 0.4 (0) 7.7 (0.5) Betula belt 3.2 (0.3) 5.0 (1.4) 7.7 (0.5) 3.18 (0) 1.3 (0.1) 4.5 (0.3) Pinus mat 1.2 (0) 8.0 (1.4) 1.3 (0.1) 1.6 (0.2) Density (trees -ha) Subalpine 1387.7 368.9 17.6 17.6 1781.7 Betula belt 165.9 17.9 224.2 4.5 157.0 569.6 Pinus mat 83.2 249.7 6992.1 7325.0 Basal Area (m2 -ha) Subalpine 62.1 29.1 2.4 0.1 93.6 Betula belt 3.1 0.9 29.6 0.1 0.6 34.4 Pinus mat 5.1 16.8 22.8 44.7 Importance Value Subalpine 203.4 (79) 87.4 (21) 5.9 (1) 3.4 (1) Betula belt 69.1 (37) 11.2 (4) 163.7 (50) 2.9 (1) 53.1 (35) Pinus mat 16.6 (1) 49.5 (3) 233.9 (76)

Abies IV Betula IV Pinus IV Mountain 0 (0) 0 (0) 163.5 (2%) Geology 0 (0) 1318 (20.4%) 0 (0%) Residual 8801 (100) 5128 (79.5%) 8047.1 (98%)

AIC 834.70 803 829.9 AICc 848.9 817.2 844.1 R2m 0.27 0.40 0.48 R2c 0.27 0.52 0.49

AIC Model df AICc ∆ AIC weight IV.AM 4/7 6 822.82 0 0.18 1/4/7 7 822.84 0.02 0.17 3/4/7 7 823.4 0.58 0.13 4 5 824.38 1.56 0.08 1/6/4/7 8 824.44 1.62 0.08 1/3/4/7 8 824.55 1.73 0.07 1/8/4/7 8 824.56 1.74 0.07 1/4/7/9 8 824.57 1.75 0.07 4/5/7/10 8 824.58 1.76 0.07 3/4/7/9 8 824.8 1.98 0.07

IV.BE 2/8 6 792.6 0 0.7 2/5/8 7 794.33 1.73 0.3

IV.PP 3/8/5/7/10 9 824.95 0 0.18 3/8/5/7/11/10 10 825.08 0.12 0.16 3/5/7/10 8 825.89 0.93 0.11 3/5/7/9/10 9 826.29 1.33 0.09 1/3/8/5/7/9/10 11 826.42 1.46 0.08 1/3/8/5/7/10 10 826.46 1.5 0.08 1/3/5/7/10 9 826.51 1.55 0.08 1/3/5/7/9/10 10 826.58 1.63 0.08 3/6/8/5/7/10 10 826.76 1.81 0.07 3/8/4/5/7/9/10 11 826.94 1.98 0.06 + - Model terms are coded as: 1, NH4 -N; 2, NO3 -N; 3, P2O5; 4, total carbon; 5, total nitrogen; 6, pH; 7, TPI; 8, slope (degree); 9, TPI:total carbon; 10, TPI:total nitrogen; 11, TPI:P2O5

Table 2-8. Averaged top models and their summary results (standardized coefficients, unconditional standard error, 95% confidence interval and relative importance) for the importance value (IV) of each species (Abies mariesii [AM], Betula ermanii [BE], and Pinus pumila [PP]). Each importance value comprises 67 points. 95% CI Standardized Unconditional Relative Predictor coefficient SE 2.50% 97.50% Importance IV.AM Intercept 113.05 13.62 85.82 140.29 *** Total Carbon 76.20 29.76 16.85 135.56 1 * TPI -49.97 28.24 -104.50 -4.22 0.92 + NH4 -N -21.38 29.22 -99.16 8.91 0.47

P2O5 -10.78 23.89 -101.47 21.83 0.27 pH 2.03 9.95 -24.95 76.80 0.14 Slope (degree) -1.79 9.36 -75.02 26.40 0.08 TPI : Total Carbon 7.50 27.16 -51.81 159.97 0.07 Total Nitrogen 0.16 9.44 -67.67 72.06 0.07 TPI : Total Nitrogen 7.65 31.48 -11.51 221.41 0.07

IV.BE Intercept 92.16 24.82 42.53 141.79 *** - NO3 -N 86.25 22.00 42.28 130.22 1 *** Slope (degree) 61.04 20.67 19.74 102.33 1 ** Total Nitrogen 6.05 15.03 -22.87 63.72 0.3

IV.PP Intercept 78.17 17.58 42.98 113.36 ***

P2O5 74.70 29.95 14.79 134.61 1 * Slope (degree) -38.43 37.30 -119.38 -0.30 1 Total Nitrogen -133.42 32.53 -198.49 -68.35 1 *** TPI 64.07 25.75 12.54 115.61 1 * TPI : Total Nitrogen -237.66 76.22 -389.42 -85.89 0.64 **

TPI : P2O5 50.83 68.34 -19.67 230.96 0.48 + NH4 -N 12.99 25.02 -18.31 98.26 0.32 pH -1.87 9.72 -78.81 26.03 0.07 Total Carbon -2.18 11.80 -99.75 32.63 0.06 p-value < 0.001***, 0.01**, 0.05*

Supplemental Table 2-S1. Count and percentages of the sky conditions (Sunny, Cloudy, Semi Cloudy/ Indirect) when hemispherical photographs were taken at each point along each transects (Chogatake, Kashimayari, Norikura, Sugorokudake, and Tsubakuro) and by geologic substrate (volcanic and non- volcanic. Sky Conditions (Count) Sky Conditions (%) Semi Cloudy/ Semi Cloudy/ Transects Sunny Cloudy Indirect Total Sunny Cloudy Indirect Chogatake 14 18 4 SA 8 5 2 15 53.33 33.33 13.33 B 5 9 2 16 31.25 56.25 12.50 P 1 4 5 20.00 80.00 0.00 Kashimayari 1 35 1 SA 1 10 1 12 8.33 83.33 8.33 B 0 18 0 18 0.00 100.00 0.00 P 0 7 0 7 0.00 100.00 0.00 Norikura 8 9 11 SA 2 4 7 13 15.38 30.77 53.85 B 3 2 0 5 60.00 40.00 0.00 P 3 3 4 10 30.00 30.00 40.00 Sugorokudake 14 20 9 SA 7 2 4 13 53.85 15.38 30.77 B 3 11 2 16 18.75 68.75 12.50 P 4 7 3 14 28.57 50.00 21.43 Tsubakuro 2 25 3 30 SA 2 7 3 12 16.67 58.33 25.00 B 0 12 0 12 0.00 100.00 0.00 P 0 6 0 6 0.00 100.00 0.00 Total 39 107 28 SA 20 28 17 65 30.77 43.08 26.15 B 11 52 4 67 16.42 77.61 5.97 P 8 27 7 42 19.05 64.29 16.67

Geology Volcanic SA 10 16 12 38 26.32 42.11 31.58 B 6 31 2 39 15.38 79.49 5.13 P 7 17 7 31 22.58 54.84 22.58 Non-Volcanic SA 10 12 5 27 37.04 44.44 18.52 B 5 21 2 28 17.86 75.00 7.14 P 1 10 0 11 9.09 90.91 0.00

Chapter 3

Influence of Forest Type and Ontogeny in Determining the Regeneration Niche in High Elevation Forests

Introduction

Mechanisms of coexistence in forest systems occur at different temporal and spatial scales depending on competitive ability. Simple models of competition have shown that a superior competitor will dominate when resources (e.g., light, soil nutrients, moisture, etc.) are stable, while the inferior competitor can dominate when resources are variable (Chesson 2000; Tillman 2004). Life history strategies influence how species take advantage of the timing and quantity of, and variation and trade-offs in species’ traits can lead to coexistence (Veblen 1986; Veblen 1992; Suzuki and Tsukahara 1987;

Ameztegui and Coll 2011). The dominant life history strategies are based on species’ abilities to tolerate shade, and shade tolerance is a continuum that depends on many traits. Variation of life history strategies has been found among plants and can be reduced down to traits of leaves (maximum photosynthetic rate, size, longevity), seeds (mass, chemical composition), wood (density, anatomical structure), and whole- plants (tree height, crown diameter) (Wright et al. 2004; Poorter et al. 2006; Shipley et al. 2006; Chave et al. 2009). The use of traits, rather than simply comparing species or individuals, allows for contrasts across widely disparate subpopulations. For example, subpopulations that differ in multiple ways, such as different species at different ontogenetic stages, can still be compared along shared trait axes. Traits therefore provide a unified framework for analysis.

Trade-offs between traits have been identified which help explain the differences in species between vertical growth and light compensation point (Lusk et al. 2015), leaf size and wood density

(Wright et al. 2007), annual growth rates and wood density (Chave et al. 2009). There are trade-offs in

74 traits between shade-tolerant and shade-intolerant species, for example in a temperate forest in New

Zealand shade-intolerant (angiosperms) species have greater vertical growth than shade-tolerant

(gymnosperm) species (Lusk et al. 2015). However, greater vertical growth comes at cost of a higher light compensation point (amount of light necessary for photosynthesis to maintain the current structure; Lusk et al. 2015). In a global analysis of wood functional traits, higher values of wood density were correlated with slower decay and decreased mortality (Chave et al. 2009). Higher mortality rates of trees with lower wood density may result from less structural support during storms. However, a trade-off exists because by investing in greater wood density, trees have a lower annual growth rate (Chave et al. 2009), which could keep an individual in the understory longer, reducing its overall growth potential. Investment in stem structure through wood density is also a trade-off with leaf size, possibly due plant hydraulics

(Wright et al. 2007).

Variations in species’ life history strategies help maintain their local coexistence. For example, one of the reasons Picea spp. can coexist with Abies spp. is their longevity, even though the stem density of Abies spp. is higher and they are more shade-tolerance (Veblen 1986; Taylor et al. 2006; Nishimura et al. 2010). The dominant species in the canopy (Picea spp.) that are less shade-intolerant and have smaller populations of seedlings and saplings in the understory, while the high density species (Abies spp.) are more shade-tolerant and regenerate through advanced regeneration (Franklin et al. 1979; Mori and

Komiyama 2008).

Advanced regeneration is a form of the “storage effect”, in which species invest in their future with high rates of establishment on the forest floor under a dense canopy (Chesson 2000). This trait for advanced regeneration enables species to remain on the landscape through periods of poor environmental conditions, so some individuals can take advantage of periods when conditions improve to grow and reach maturity (Chesson 2000; Mori and Komiyama 2008). Shade-tolerant species can tolerate low light conditions and survive in the understory for many years, while shade-intolerant species need gap openings or to have rapid vertical growth to emerge into the canopy (Kohyama 1980; Givnish 1988;

Yamamoto 1996; Ameztegui and Coll 2011). Knowledge of the traits associated with species’ particular

75 life history strategies, including the regeneration niche, is therefore important for understanding species coexistence and spatial separation.

The general concept of the regeneration niche is one of four plant niches outlined by Grubb

(1977) that describes the requirements for species’ replacement from one generation to the next. Several processes contribute to the regeneration niche: seed production, dispersal, germination, seedling establishment and growth (Grubb 1977). The growth of established seedlings to maturity links the regeneration niche to the habitat niche (Grubb 1977) across ontogeny, the continuum encompassing different life stages (seedling, sapling, and tree; [Grubb 1977; Cavender-Bares and Bazzaz 2000; Lusk and Warton 2007; Boydon et al. 2009; Spasojevic et al. 2014]).

Life history strategies are often considered static in landscape models of species (i.e. Landis-ii

[Scheller and Mladenoff 2004]), primarily because models often ignore how variation in traits across ontogeny, or the above mentioned life stages, could contribute to species’ success (King 1996; Lusk

2004). Each life stage for a species has a characteristic response to biotic and abiotic filters (Quero et al.

2008). Seedling and sapling density can be more strongly influenced by soil moisture and nutrient availability than for mature trees (Schurman and Baltzer 2012). Mature individuals represent the surviving legacy of seedling and sapling distributions, but they do not necessarily represent the complete regeneration niche of those earlier life stages (Schurman and Baltzer 2012). Dimensions of canopy architecture that influence an individual’s capacity to capture and use light for growth can also differ across ontogeny and may depend on whether individuals are exposed to full or partial light (Kohyama

1987; King 1996). Changes in life history traits such as architecture can shift an individual from being considered shade-tolerant to shade-intolerant across ontogeny, and this phenomenon is referred to as a rank reversal (Boyden et al. 2009). For example, in Chile shade-intolerant species had a higher leaf area to plant biomass ratio than shade-tolerant species when they were under five cm tall, indicating the shade- intolerant species invested more into leaf structure at an early growth stage. However, as the seedlings grew above five centimeters shade-tolerant species invested more into leaves than total plant biomass, showing a reversal in trends based on ontogeny (Lusk 2004). Examining life history traits and

76 environmental attributes across species’ ontogeny may therefore reveal important mechanisms for species coexistence that are not captured by examining only one life stage (Lusk 2004; Poorter 2007; Quero et al.

2008; Boyden et al. 2009; Barabas et al. 2014; Lasky et al. 2015).

In the alpine forest of Japan, it is unclear whether the coexistence of B. ermanii and A. mariesii, the primary competitors in these alpine forests, is shaped by the regeneration niche, the habitat niche, or a combination of the two. In this study, I investigate differences in the regeneration niche across ontogeny and forest bands (dominance) in an alpine forest in central Japan using a niche overlap approach (Geange et al. 2011). The alpine forests of Japan have been studied by many researchers (Franklin et al. 1979;

Mori et al. 2004), but the roles of the regeneration niche and shifts in traits across ontogeny have not previously been examined, particularly across the distinctly different forest bands. Because of the differences in life history strategies between species, I hypothesize that there will be less niche overlap between species than within a species across forest belts and ontogenetic stages. For trait values should vary more between intraspecific species than intraspecific variation (McGill et al. 2006). Furthermore, the least overlap will occur between forest belts and ontogeny. For example, seedlings of species X in forest

A will show the least overlap with trees of species Y in forest B.

Methods

Study Area

The alpine forest of central Japan (2300-2700 m) is composed of three forest types that form distinct bands, distinguished from one another based on elevation and species dominance (Franklin et al.

1979; Yoshino 1978; Wardle 1979; Gansert 2004; Takahashi et al. 2012). The subalpine forest is the lowest of the three bands and is a mixed forest dominated by Abies mariesii Mast., with occasional individuals of A. veitchii Lindley, Picea jezoensis (Siebold and Zucc.) Carr., Pinus pumila (Pall.) Regel., and Betula ermanii Cham. (Franklin et al. 1979). Above the subalpine forest is a thin band of B. ermanii

77 composing an abrupt treeline (~2500 m), which adjoins a dense mat of P. pumila at the upper elevations

(Franklin et al. 1979; Kajimoto 1992; Gansert 2004; Takahashi et al. 2012). The P. pumila mat is protected in the winter from desiccation by a thick layer of snow (Körner 2003).

In this system, B. ermanii and P. jezoensis are both long-lived and shade-intolerance, while A. mariesii is similar to other Abies spp. in their shade-tolerance and strategy of advanced regeneration

(Takahashi and Obata 2014). Sapling density is highest in the band in which the trees are dominant; however, B. ermanii seedling, sapling and tree densities are relatively stable across the subalpine and

Betula band with few individual in the pine mat (see This dissertation, Chapter 2; Takahashi et al. 2012).

The stability of B. ermanii density across all forest bands, and the separation of A. mariesii and P. pumila into bands in which they are dominant, suggests that there is both species coexistence as well as competitive exclusion in this system (Takahashi et al. 2012). Examining the regeneration niche across ontogeny in these segregated forest bands will provide insights into species coexistence and competitive exclusion in the alpine forest of central Japan.

Field Methods

The Northern Japanese Alps comprise the northern mountainous region of central Japan and are protected within Chūbu-Sangaku National Park (Figure 3-1). To quantify regeneration and habitat niches, transects were established from the subalpine forest (hereafter, SA) through the Betula Belt (hereafter,

BB) into the pine mat (hereafter, PM) on five mountains during the summers of 2013 and 2014 (Table 3-

1). Mountains were chosen based on the presence of all three belts, a west facing slope, not too steep to access (< 40o), and distance to campgrounds (< 3 km).

Each transect ran through the entire width of the BB and down 120 m into the SA. The length of the BB ranged from 70-170 m. Seedlings (< 0.5 m tall) and trees (> 2 m tall) were sampled along each transect to examine differences in the regeneration niche across ontogeny. Seedlings were sampled every

10 m and trees every 30 m. The number of sampling points on each transect ranged from 21-27 for

78 seedlings and 8-10 for trees. Measurements of whole plant traits and environmental attributes were recorded for both seedlings and trees along each transect, some parameters were measured at an individual (tree or seedlings) location while others were recorded at the sampling point (Table 3-2).

Seedling Measurements

At each point, plant traits and environmental attributes for the nearest seedling of each species (A. veitchii, A. mariesii, B. ermanii, P. pumila, P. jezoensis) within a five meter radius were recorded. Plant traits recorded were height to the top of the crown (HT), height to crown base (HB), basal diameter (BD), crown diameter (CD, orthogonal measurements), and distance to nearest conspecific tree (m). The age dependency of canopy measurements was reduced by converting them into crown shape ratios (crown- height ratio [HT-HB/HT], crown-diameter ratio [CD/BD]) to provide structural investment information on relative life history strategy (shade-tolerant vs. shade-intolerant) (Table 3-2). The use of crown-height ratio and crown-diameter ratio instead of measurement values permits the comparison of crown shape across ontogenetic stages. Distance to nearest conspecific reflects intraspecific competition. In addition to measuring seedlings, an unoccupied location (15 cm radius) was sampled at each point, at an azimuth farthest from the occupied seedling locations and one meter from the point. The unoccupied locations were used to characterize the proportion of different types of substrate on the forest floor for comparison as a random variable.

Environmental attributes were measured within a circular plot (15 cm radius) around each seedling. Measurements included were relative light environment, microtopography (concave, convex), litter depth (cm), and visual classification of ground, shrub and tree cover using the following 6 categories: 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75%, 5: 75-95%, 6: 95-100% (Table 3-2). Light environment above each seedling was measured using hemispherical photography. Photographs were taken with a fisheye lens directly above each seedling in the morning or evening when the sun was below the horizon or under cloudy conditions, to constrain light conditions and maximize contrast (Campbell

79 and Norman 1998). Photographs were processed using Gap Light Analyzer (GLA) (Frazer, Canham and

Lertzman 1999) and Gap light Index (GLI) was calculated for each photograph using the diffuse and direct light transmitted through the canopy (Equation 1; Canham 1988).

퐺퐿퐼 = [(푇푑푖푓푓푢푠푒푃푑푖푓푓푢푠푒) + (푇푏푒푎푚푃푏푒푎푚)] × 100 Equation 1

Microtopography (concave, convex) influences soil moisture conditions by the collection or shedding of water. Soil moisture influence seedling establishment, thus microtopography was recorded as concave or convex for each individual as a proxy for soil moisture potential. The substrate for each seedling was recorded as bare soil/ litter (S.L), rock, coarse woody debris (CWD), or live wood ([LIVE] if growing epiphytically). Litter depth (cm) and establishment height above ground level (m) was measured at each seedling.

Tree/ Canopy Measurements

Trees were sampled every 30 m along the transects, where each tree sampling point overlapped every third seedling point. The point-centered quarter (PCQ) method was used to measure forest structure

(density, frequency, importance value). Identification of species, distance and diameter of each tree species was measured in each quadrat. Species traits of each tree were measured in the same manner as for seedlings (i.e. height, basal diameter, canopy diameter, substrate, etc.). Canopy, ground, and shrubs cover were recorded, as above; within a 30 m2 circle around each transect point. Slope was recorded at each transect point. Bark thickness (cm) and wood density (cm3) were measured for trees from increment cores. One increment core was taken from each tree across the dominant slope at 20 cm above ground level.

Niche Overlap

Niche overlap (NO) analysis was used to evaluate the differences in the regeneration niche between species, ontogenetic stages and forest belts. Niche overlap analysis provides a measure of species similarity and differences in niche space (species traits [i.e., crown shape ratios] and environmental attributes [e.g., substrate, slope, light]). Hutchinson (1957) first described the multivariate space in which environmental (abiotic) factors and competition (biotic factors) limit occupation for a species and define differences in niche among co-occurring species. Explicit consideration of both species traits and environmental attributes within a multivariate space permits identification of niche differences that promote coexistence (Kraft et al. 2014).

Traditional multivariate analysis of species-attribute relationships uses two matrices: (1) a main matrix of species traits; and (2) a corresponding matrix of environmental data (McCune and Grace 2002).

NO does not use a main matrix, rather a matrix of species traits and environmental attributes and a group identifier (e.g., species) for each individual (i.e., tree and seedling). In NO, species traits and environmental attributes are incorporated as binary, count, categorical, continuous, measurement (positive continuous data and log transformed), ratio, proportion and electivity (habitat) data (sensu Geange et al.

2011; Table 3-2). Electivity data are similar to a categorical variable, except that they include the potential habitat in addition to the occupied habitat using Manly’s alpha (see Geange et al. 2011).

Potential habitat (Manly’s alpha) is roughly the proportion of a species in a specific habitat out of the availability of that habitat. Available habitats were based on the unoccupied seedling data points. Before analysis the data were transformed (mixture models [binary and count data] and kernel density estimation

[all other data types]) to a scale of 0 to 1, which permits incorporation of discrete and continuous data into the NO equation (Equation 2; Geange et al. 2011). Each transformed species trait and environmental variable is an axis (t) in the model which incorporates all traits and parameters (T). To identify NO between species, values are inserted into the NO equation as combinations of iterative pairs of species (i, j).

1 푁푂 = ∑푇 푁푂 Equation 2 푖,푗 푇 푡=1 푖,푗,푡

An output distance matrix of NO is composed of 1-NOi,j for each species pair. NOi,j is the sum of

NO for a pair of species for each species trait and environmental condition. Complete overlap between two species occurs when NOi,j equals 1, while complete separation equals 0. The NO distance matrix was visually displayed using non-metric multidimensional scaling (nMDS) with the vegan package in R version 3.1.2 (R Development Core Team, 2010). A test statistic compares 푁푂푖,푗 for each axis with a

푁푂푖,푗 of pseudo-values to determine if the distribution of values is statistically different from random.

Pseudo-value of NO were generated by shuffling species labels 1,000 times so that all species are randomly distributed in niche space. The Holm-Bonferroni method was used for post-hoc corrections for multiple comparisons (Holm 1979). NO analysis was conducted in R version 3.1.2 (R Development Core

Team, 2010) using code from Geange et al. (2011).

Species Overlap

NO was computed as the average NO for all individuals regardless of ontogenetic stage from all forest belts. I treat the measurements taken for trees and seedlings as the same even though some of the parameters (as above) were sampled at different resolutions (i.e. litter depth is measured at each seedling while only at the central point for trees). Due to a small sample size of three of the species (see Results), analysis was conducted on A. veitchii and B.ermanii.

Subpopulation Overlap

Regeneration niche differences for species based on ontogenetic stage and forest belt were analyzed by splitting the species dataset into ontogenetic stages (Tree [T] and seedling [S]) and forest belts (SA and BB). To incorporate ontogenetic stage and forest belt, subpopulation identifiers were

82 assigned to represent species, forest belt, and ontogenetic stage (i.e., BE.SA.T is B. ermanii, Subalpine

Forest, Tree).

The NO distances of subpopulations were graphically displayed in nMDS space. Significant NO axes were identified by examining the output matrices of each variable for subpopulations to determine if they explained the distribution of species, ontogenetic stage or forest belts in niche space (Figure 3-2A).

To identify the contribution of specific parameters to NO, ANOVA with Tukey’s HSD post-hoc analysis was run to identify significant differences among parameters and were visualized using box-whisker and bar graphs (Table 3-8, Figure 3-3 and 3-4).

Results

A total of 200 seedlings and 229 trees were sampled along transects on the five mountains. A. veitchii (Trees, n=4), P. jezoensis (Trees, n=2), and P. pumila (Seedlings, n=2) were eliminated from analysis due to a low sample size (Table 3-3). The dominant trees in the SA were A. mariesii while in the

BB they were B. ermanii (Table 3-4). More than half (~60%) of the seedlings were found in the SA belt

(Table 3-4) and they were mainly A. mariesii (41%) and B. ermanii (34% [Table 3-3]). The niche overlap analysis was performed on A. mariesii and B. ermanii (hereafter, Abies and Betula) on seedlings and trees

(Table 3-4).

Seedlings varied in size from 7.8 ± 1.3 cm (± standard error of the mean) tall for Betula to 20.7 ±

1.4 cm for Abies). The average basal diameter was 0.34 ± 0.03 cm (Betula 0.14 ± 0.08, Abies 0.51 ±

0.04). Although the diameter of the two species was relatively similar, the crown diameter was not. Betula had a much higher diameter to crown-diameter ratio (Betula 0.59 ± 0.06, Abies 0.35 ± 0.04 (Figure 3-3) despite Abies having a much deeper crown (Betula 0.34 ± 0.04, Abies 0.69 ± 0.07; [Figure 3-3]).

These trends were reversed in the trees. Betula were taller and larger in diameter than Abies.

Height averaged 734 ± 39.2 cm for Betula and 652.6 ± 45.1 cm for Abies, while diameter averaged 32.3 ±

2.4 cm for Betula and 18.2 ± 1.1 cm for Abies. Crown-diameter ratio values were much lower for trees of

83 both species compared to seedlings (Figure 3-3). However, the crown-height ratio (crown depth) for trees was similar to that of Abies seedlings (Figure 3-3).

Species Overlap

The NO between Abies and Betula for the full dataset was significantly different (NO 0.78[σ =

0.100], p <0.001). Crown depth and distance to conspecific had the lowest NO values (0.669 and 0.638, respectively). Furthermore, all but two parameters (curvature and shrub cover) were significantly different between species (Table 3-5).

Subpopulation Overlap

The NO values for the subpopulations (species, forest belt and ontogenetic stage) also showed that slope curvature was not important in NO (α=0.05); all other parameters had at least one subpopulation with a significant difference in NO (Table 3-6). The smallest NO was between species, ontogenetic stage, and forest belts (i.e., AM.SA.T vs BE.B.S; Table 3-6). NO was highest between trees

(0.72), regardless of species or forest belt and lowest between trees and seedlings (0.61).

The nMDS graphically showed a separation between forest belts and ontogenetic stages demonstrating that both factors were important in determining the regeneration niche (Figure 3-2A). Axis one predominantly separated the ontogenetic stages, with trees on the right and seedlings on the left

(Figure 3-2B). Axis two separated the forest belts with BB on the bottom and SA on top (Figure 3-2C).

Within the separation of subpopulations by ontogenetic stage and forest belt, there was a subdivision by species along axis one. Subpopulations including Betula were on the left of those including Abies for the same ontogenetic stage and forest belt (Figure 3-2A and D). The distribution of subpopulations by forest belt and ontogenetic stage in the nMDS indicates that there were some underlying factors separating

Betula and Abies unrelated to forest belt or ontogenetic stage. Betula covered a larger area within the

84 nMDS space than Abies, though the area they occupied overlaps (Figure 3-2D). Although forest belt and ontogenetic stage both occupied smaller areas, they also did not overlap one another (Figure 3-2B and C).

Separation of ontogenetic stages (axis one) was associated with differences in crown-diameter ratio, litter depth, and forb cover (Table 3-8 and Figures 3-3). Conversely, separation among the forest belts (axis two) was primarily dependent on percent slope, distance to conspecific, and canopy cover

(Table 3-8 and Figures 3-3). Differences amongst species were based on the crown-height ratio and establishment height above ground level. Trees had a deeper litter depth than seedlings, and trees in the

BB have a deeper litter depth than in the SA (Figure 3-3).

Abies and Betula significantly differed in all parameters except slope. Furthermore, slope was the only parameter in the subpopulation analysis that is not significantly different among the subpopulations in the two-way ANOVA and post-hoc HSD analysis (Table 3-8). However, the niche overlaps significantly differed among the three subpopulations with the shallowest slope (BE.SA.S, AM.SA.T, and

AM.SA.B) with the trees from the BB. Slopes ranged from 28.4-34.14 degrees with a mean of 30.24 degrees.

Four parameters were excluded from the NO analysis due to being measured only for seedlings

(GLI) or trees (wood density, bark thickness and tree age). Though these four parameters were not included in the NO analysis they are important in understanding species life history and the regeneration niche. Differences between forest belts and species for each of these four parameters were detected using

ANOVA and post-hoc Tukey HSD analysis. Overall, the GLI was higher for Betula than for Abies

(Figure 3-5A). In the subalpine forest, GLI was significantly higher for Betula (Table 3-8 and Figure 3-

5A). Betula had a higher wood density than Abies (Figure 3-5B). In the SA, Betula wood density was significantly higher than Abies. Bark was thinner for the Abies than Betula, especially for the Abies in the

BB (Figure 3-5C). Abies was younger than Betula, and the Abies in the BB were the youngest (푥̅=1937).

Abies in the SA were similar in age to the Betula in both the SA and BB (Figure 3-5D).

Discussion

In this study, I have demonstrated that the ecological factors that may reduce niche overlap appeared to differ between subpopulations of Betula and Abies, and as a result, these species’ niche requirements underwent a rank reversal across ontogeny. The main factor separating subpopulations was not species, as was originally hypothesized, but ontogenetic stage (axis 1) and forest belt (axis 2, [Figure

3-2A]). There was no overlap between seedlings and trees (Figure 3-2B), suggesting that there is a distinct separation between the two ontogenetic stages. There was also no overlap among the forest belts

(Figure 3-2C), which showed a distinct separation between the BB and SA. The overall NO values were high (0.5-0.7) for the overlaps for both species and subpopulations; nonetheless, there were important differences between species or subpopulations (Tables 3-5 and 3-6).

Previous studies have found that even small differences in NO can result in significantly differentiated regeneration niches and species coexistence (Hutchinson 1957; May and MacArthur 1972).

Low NO is indicative of low competition between species (Pianka 1974). The inclusion of both species traits and environmental attributes allows for the identification of differences in the biotic and abiotic components of the niche that can lead to species coexistence (Kraft et al. 2014). Here, ontogenetic stages did not appear to compete with one another, as indicated by the average low NO value; however, the tree subpopulations were likely in competition with each other regardless of species or forest belt (Table 3-6).

The highest NO values occurred amongst subpopulations within the same species and among forest bands for the same ontogenetic stage, while the lowest NO values occurred for subpopulations across species, forest belt and ontogenetic stage (Table 3-6).

Ontogenetic Stage

The lack of overlap between subpopulations based on ontogenetic stages was strongly associated with three parameters measured at the individual-level: crown-diameter ratio, forb cover and litter depth.

Naturally, there were distinct differences in size between seedlings (< 50 cm tall) and trees (> 200 cm tall). Crown-diameter ratio is an expression of shade-tolerance and represents the relative investments into structural support (stem diameter) versus photosynthetic area (canopy diameter; [Givnish 1988; King

1996; Ameztegui and Coll 2011; Poorter et al. 2012]). A large crown-diameter ratio occurs in an individual that has invested more carbon towards canopy diameter than stem diameter; this growth habit is common in understory tree species (King 1996). A shade-tolerant species will have a higher crown- diameter ratio than a shade-intolerant species, for a shade-intolerant species will invest relatively more carbon in vertical growth (Kohyama 1980; Givnish 1988; King 1996; Ameztegui and Coll 2011).

In Oregon, a study of the relationships between crown width and tree height in angiosperms and gymnosperms demonstrated that gymnosperms had wider crowns at lower values of height (King

1991).However, the crown diameter values of most species were similar at tree heights between 15 and 25 m (King 1991). King (1991) also found that shade-tolerant conifers had thicker stems than shade-tolerant angiosperms for a given height, and the crowns of gymnosperms were twice as wide. The values of the ratio between crown width and stem diameter reported in King (1991) are similar to those found in this study (Betula seedlings [50-70], Betula trees [23-26], Abies seedlings [30-50], Abies trees [17-21], Table

3-8).Additionally, angiosperm saplings tended toward higher crown-diameter ratios (46-98) than did gymnosperm saplings (44-67), though these ratios decreased as crown width increased (King 1991).

These results corroborate my own that demonstrate that the higher crown width to stem diameter ratios were high in Betula seedlings but much lower in mature Betula trees.

On the forest floor, light is a limiting resource, and to capture more light, seedlings of both species had higher crown-diameter ratios than the trees (Figure 3-3). Seedlings of both species were less shade-tolerant than trees, with Betula seedlings being the most shade-intolerant (Figure 3-3). The large crown-diameter ratio of Betula may result from a reduction in self-shading because understory trees can have large crown areas (Takahashi and Obata 2014) or the prioritization of vertical growth over stem diameter growth (Canham 1988). In northern Japan, light-saturated photosynthetic rates of Betula in open and closed forests were higher than those of Abies sachalinensis F. Schmidt throughout the growing

87 season, indicating that Betula is less shade tolerant (Bontempo e Silva et al. 2016). In the spring, prior to

Betula leafing out, A. sachalinensis photosynthetic rates were at their lowest due to over exposure of solar radiation and low temperatures (Bontempo e Silva et al. 2016). Additionally, herbivory or disturbances like ice storms could potentially have altered the shape of the crown diameters by reducing photosynthetic material, but there was no noticeable evidence (e.g. standing snags, forest gaps) of either form of damage in this study (See Chapter four).

Betula and Abies trees did not have a significantly different crown-diameter ratio from one another in this study, suggesting that a convergence of shade-tolerance (crown-diameter ratio) occurs across ontogeny, where both species have approximately equal investment in structural support and photosynthetic area as they grow into mature trees (Figure 3-2B and 3-3). The convergence in shade- tolerance was evident by the smaller area occupied in nMDS space compared to the area covered by seedlings (Figure 3-2B). The consideration of ontogeny is thus important when attempting to distinguish species by shade-tolerance.

Litter depth was greater under trees than seedlings (Figure 3-3), due in part to the trees modifying their environment through leaf litter accumulation (Facelli and Carson 1991). The occurrence of ontogenetic difference in litter depth in this study was opposite to that found in the forests of the Amazon basin, where litter depth was greater under saplings than seedlings (Jurinitz et al. 2013). However, seedlings in that study were younger and still had cotyledons and saplings were the same size as the seedlings in this study (< 50 cm tall). Litter depth has commonly been shown to influence seedling establishment and survival both positively and negatively, depending on factors such as microtopography, wind redistribution, and even stemflow from nearby trees (Sayer 2006). However, the survival rate of

Betula seedlings was not influenced by litter depth (Akasaka and Tsuyuzaki 2005). Deeper litter generally correlates with greater soil moisture and rates of decomposition in a wide range of forests, as both changes likely result from the insulating properties of a deeper litter layer (Sayer 2006). Betula rooting depth is usually shallow, allowing seedlings to tap into the nutrients and moisture near the surface, while

Abies roots tend to grow deeper (Narukawa and Yamamoto 2003; Doi et al. 2008; Ashburn and

MacAllister 2013).

Forest Belt

The separation of forest belts (BB and SA) was dominated by three parameters: canopy cover, distance to conspecific and slope. The amount of surrounding canopy cover was high for all individuals; nonetheless, it is significantly higher for Abies trees in the SA than the Betula (seedlings and trees) in the

BB. In part canopy cover is related to the differences in crown geometry and shade-tolerance among species (Canham et al. 1994). Canopies of shade-intolerant species (Betula; Koike 1988) let in more light than canopies of shade-tolerant species (Abies) (Canham et al. 1994).

The distance to the nearest conspecific (a proxy for density) for Abies was greater within the BB than the SA. Shorter distances to conspecifics indicate a dense forest stand and a reduction in light reaching the forest floor. Abies seedlings were more than twice as far from a conspecific when they are in

BB (4.0 m) than in the SA (1.4 m), though there was no difference in trees. Betula (seedlings and trees) had a similar (3.4 m) distance from each other in both the SA and in the BB, which roughly corresponds to the radius of Betula tree crowns. There is increased transmission of light through the canopy at the junctures between tree canopies (Canham et al. 1994). The uniformity in distance to conspecific of both the Betula seedlings and trees suggests that light availability is particularly important for establishment.

Slope is another important variable influencing the separation of the two forest belts. The average slope in the Northern Japanese Alps is 35º, this angle has been found across bedrock types and is thought to be the angle of repose for the clastic materials from the Japanese Alps (Katsube and Oguchi 1999).

Though the mean values were not significantly different from one another, the range of slope values was larger in the SA (20-38º) than in the BB (32-38º; [Figure 3-3]). Also, the steeper slopes of the BB are indicative of the importance of the light environment because shade-intolerant species like Betula can

89 lean downward into the slope where the tree canopy from below is lower, thus reaching the canopy faster than they would by growing straight up (Umeki 1995; Matsuzaki et al. 2006).

Species

There were clear differences among subpopulations based on ontogenetic stages and forest belts, however, species differences were less distinctive due to their overlap (Figure 3-2D). As with ontogenetic stages, the two main factors associated with the separation of the two species were functional traits: crown-diameter ratio and crown-height ratio. Similar to crown-diameter ratio, a higher crown-height ratio value (deeper crown) is indicative of a shade-tolerant species (King 1996; Poorter et al. 2006). Deeper crowns allow for more light interception; however, there is a trade-off in maintenance costs (Canham

1988). Shaded branches invest less in growth than sun exposed branches, though shaded branches of an understory tree can invest more than shaded branches of a canopy tree (Sprugel 2002). Low crown-height ratio values are suggestive of shade-intolerant species that prioritize vertical growth rather than a full canopy (Canham 1988). Shaded branches of canopy trees grow less than the sun-exposed branches, and they are frequently shed in order to redirect photosynthates to more productive branches (Sprugel 2002).

In this study, the mean crown-height ratio in mature trees was approximately 1.5 - 2 times greater in Abies than for Betula. These apparently shade-tolerant Abies trees reproduce through advanced regeneration, where cohorts of seedlings establish throughout the understory and maintain themselves, even under low light conditions, but with very low growth rates. When a canopy gap appears, only then do they invest in upward growth (Yamamoto 1993). As Abies seedlings wait for gaps to form, they put on and maintain branches that extend to the forest floor (Takahashi and Obata 2014). Similarly, mature Abies trees in this study did not prune off many of their lower branches, but maintained them, even under a full canopy. For instance, the average height of the lowest branch was 2 m for Abies and 3 m for Betula in this study). By contrast, Betula seedlings had a high crown-diameter ratio; however, the depth of the crown was minimal,

90 extending only 1-2 cm below the top in most individuals. They may thus have been investing in upward growth and horizontal branching to capture light, a strategy typical of shade-intolerant species.

A pair of environmental attributes also may have influenced the partial separation of Abies and

Betula: litter depth and the height of the raised surface on which the individual established. I expected to find greater leaf litter in the BB than the SA because broadleaf-deciduous species have a higher rate of litter accumulation than evergreen species (Chapin et al. 2002), and the BB had a higher percentage shrub cover than in the SA. On the nearby mountain of Ontake, decomposition of Betula litter occurred at a slightly higher rate during the first winter (20.1% by mass) than Abies litter (15.4%); however, in the second year Betula lost 9.4% while Abies only lost 1.6% of its litter mass (Tian and Takeda 1998). Thus, litter depth was deeper near Abies due to lower litter decomposition compared with Betula, which likely accounts for the higher carbon content found in soils beneath an Abies canopy than Betula canopy in northern Japan (Xu and Shibata 2007).

There have been numerous studies in Japan on the associations between seedling establishment and substrate (Doi et al. 2008; Sugita and Tani 2001; Narukawa and Yamamoto 2002, Mori et al. 2004;

Mizumachi 2005). These studies all demonstrated that Picea and Tsuga are frequently found on elevated surfaces (rocks and CWD), while Abies can be found on most surfaces. The decay status of logs is generally associated with the success of establishment for conifers, due to the nutrient and moisture resources that the logs provide (Harmon and Franklin 1989; Doi et al. 2008). Furthermore, seedlings of shade-intolerant species establish more successfully on surfaces raised off the ground (rocks, CWD) in order to have greater access to light (Taylor et al. 2004; Iijima et al. 2007; Akasaka and Tsuyuzaki 2005).

Here, I found that, Betula seedlings and trees established on taller raised surfaces than those of Abies

(Figure 3-4). Furthermore, these seedlings occurred on higher surfaces than did the Betula trees. The time since establishment may in part explain why I found fewer trees on elevated surfaces than seedlings. For instance, if Betula individuals established on logs, the logs may have rotted away over the years, but few stilted Betula were present. Betula individuals (seedlings and trees) were more frequently found on elevated surfaces (CWD, live wood, or rocks) than were those of Abies (Figure 3-4).

Ontogenetic specific parameters

Classifying Betula as a shade-intolerant species across ontology is further supported by the four parameters that I measured for only one of the ontogenetic stages (GLI, wood density, bark thickness and age). Betula seedlings established where GLI was high (Figure 3-5A). There were no obvious gaps due to storm events (e.g., typhoons). Gaps formation in years of severe typhoons (winds > 20 m/s) is similar to years without typhoons (Kanzaki and Yoda 1986; Yamamoto 1995). Nonetheless, Betula seedlings occurred in areas with greater access to light than those of Abies in this study. Shade-tolerant species have lower light compensation points, thus Abies existing in lower light conditions supports its classification as a shade-tolerant species (Lusk 2002). Angiosperms are less shade-tolerant and have a higher light compensation point than gymnosperms (podocarps) in a warm-temperate rain forest in New Zealand

(Lusk et al. 2015). As discussed above, the BB as a whole had a lower canopy cover, and more light penetrated down to the forest floor. This may partially explain why there was no difference between the

GLI for Abies and Betula in the BB. In the SA, there was a large difference in GLI between the two species.

Wood density in this study was higher for Betula than for Abies, which is indicative of a greater investment of carbon into structure to prevent damage (Kitajima 1994; Chave et al. 2009) and is associated with a lower mortality rate (Lasky et al. 2015; Chave et al. 2009; Kraft et al. 2010; Adler et al.

2014; Woodall et al. 2015). Dense wood is also associated with large diameter stems that can support large diameter crowns (Poorter et al. 2012). Though I did not analyze mortality rates here, Betula individuals were generally older than those of Abies (Figure 3-5D). However, the mortality of trees was low, as there were no observed standing dead trees in any of the study areas. In this study, Betula had both greater wood density and a larger crown-diameter ratio than Abies (Table 3-8), suggesting that

Betula supports its larger crowns through dense wood rather than secondary growth. Additionally, this greater wood density may contribute to the greater longevity of Betula compared with Abies (see Chapter

Four).

The youngest Abies were found in the BB and were notably younger than the Betula or the Abies trees in the SA (Figure 3-5D). Shade-tolerant species typically have the higher wood density (Wright et al. 2010; Kunstler et al. 2016), and bark thickness is associated with a tree’s resistance to stressors, such as insects, fire and pathogens (Paine et al. 2010). The pattern here was inverse to that of tree age, as the thinnest bark occurred in the Abies individuals from the BB. There was a positive though not statistically significant trend between bark thickness and age, suggesting that there may have been more contributing to the reduced bark thickness of Abies trees than age alone.

Conclusion

In addition to the interspecific variation, the intraspecific variation in niche space suggests that differences in niches across ontogeny may have contributed to the coexistence of Betula and Abies

(Figure 3-2D). Seedlings of Betula regardless of forest belt were closer together than Betula adults (Table

3-7). Abies had the opposite pattern, where trees were closer to one another than to the seedlings (Table 3-

7). As mentioned above, Abies frequently regenerates through advanced regeneration. The convergence of

Abies trees from disparate seedlings suggests that Abies seedlings may not be selective in their regeneration niche, but rather that Abies trees have only one life history strategy for survival. Conversely, the divergence of the Betula trees from a limited seedling regeneration niche (Figure 3-2D), may have been due to limited availability of mineral soil for Betula seedlings (Yamamoto 1993). However, through ontogeny, they appear to have been able to adapt to the forest conditions (by bending towards light, having a wide or deep crown, or modifying soil nutrients). In other mixed forests in Northern Japan, a reversal of species’ competitive superiority across ontogeny helped promote species coexistence to a greater extent than did differences in the regeneration niche (Nishimura et al. 2010). Previous research has stressed the importance of studying the regeneration niche and species’ requirements across ontogeny to determine whether there are trade-offs or periods of growth when one ontogenetic stage was more important (Lusk 2004; Poorter 2007; Quero et al. 2008; Boyden et al. 2009; Spasojevic et al. 2014;

Sendall et al. 2015; Lasky et al. 2015). Here, I have demonstrated that there was a rank reversal (Boyden et al. 2009) from seedlings to trees in their niche requirements, and, critically, that the important factors associated with separating subpopulations appeared to differ between Betula and Abies.

References

Akasaka, M. and S. Tsuyuzaki. 2005. Tree seedling performance in microhabitats along an elevational

gradient on Mount Koma, Japan. Journal of Vegetation Science 16: 647-654.

Ameztegui, A. and L. Coll. 2011. Tree dynamics and co-existence in the montane-sub-alpine ecotone: the

role of different light-induced strategies. Journal of Vegetation Science 22: 1049-1061.

Bond, W. 1989. The tortoise and the hare: Ecology of angiosperm dominance and gymnosperm

persistence. Biological Journal of the Linnean Society 36: 227-249.

Bontempo e Silva, E., Ono, K., Sumida, A., Uemura, S. and T. Hara. 2016. Contrasting traits, contrasting

environments, and considerations on population dynamics under a changing climate: an

ecophysiological field study of two co-dominant tree species. Plant Species Biology 31: 38-49.

Boyden, S., Reich, P. Puettmann, K. and T. Baker. 2009. Effects of density and ontogeny on size and

growth ranks of three competing tree species. Journal of Ecology 97: 277-288.

Campbell, G. and J. Norman. 1998. An Introduction to Environmental Biophysics-Second Edition.

Springer, New York, NY.

Canham, C. 1988. An Index for understory light levels in and around canopy gaps. Ecology 69: 1634-

1638.

Canham, C., Finzi, A., Pacala, S. and D. Burbank. 1994. Causes and consequences of resource

heterogeneity in forests: interspecific variation in light transmission by canopy trees. Canadian

Journal of Forest Research 24: 337-349.

Canham, C. and R. Thomas. 2010. Frequency, not relative abundance, of temperate tree species varies

along climate gradients in eastern North America. Ecology 91: 3433-3440.

Chaves, J., Coomes, D., Jansen, S., Lewis, S., Swenson, N. and A. Zanne. 2009. Towards a worldwide

wood economics spectrum. Ecology Letters 12: 351-366.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecological

Systems 31: 343-366.

Doi, Y., Mori, A. and H. Takeda. 2008. Conifer establishment and root architectural responses to forest

floor heterogeneity in an old-growth subalpine forest in central Japan. Forest Ecology and

Management 255: 1472-1478.

Facelli, J. and W. Carson. Heterogeneity of plant litter accumulation in successional communities.

Bulletin of the Torrey Botanical Club 118: 62-66.

Franklin, F., Maeda, T., Ohsumi, Y., Matsui, M., Yagi, H. and G. Hawk. 1979. Subalpine coniferous

forests of central Honshu, Japan. Ecological Monographs 49: 311-334.

Frazer, G., Canham, C. and K. Lertzman. 1999 Gap Light Analyzer (GLA), Version 2.0: Imaging

software to extract canopy structure and gap light transmission indices from true-colour fisheye

photographs, user’s manual and program documentation. Burnaby, British Columbia and

Millbrook, New York, Simon Fraser University and the Institute of Ecosystem Studies.

Gansert, D. 2004. Treelines of the Japanese Alps – altitudinal distribution and species composition under

contrasting winter climates. Flora 199: 143-156.

Geange, S., Pledger, S., Burns, K. and J. Shima. 2011. A unified analysis of niche overlap incorporating

data of different types. Methods in Ecology and Evolution 2: 175-184.

Grubb, P. 1977. The maintenance of species-richness in plant communities: the importance of the

regeneration niche. Biological Review 52: 107-145.

Harmon, M. and J. Franklin. 1989. Tree seedlings on logs in Picea-Tsuga Forests of Oregon and

Washington. Ecology 70: 48-59.

Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics

6: 65-70.

Hutchinson, G. 1957. Concluding remarks. Cold Spring Harbor Symposium for Quantitative Biology. 22:

415-427.

Iijima, H. Masato, Shibuya, M. and H. Saito. 2007. Effects of surface and light conditions of fallen logs

on the emergence and survival of coniferous seedlings and saplings. Journal of Forest Research

12: 262-269.

Jurinitz, C., de Oliveira, A. and E. Bruna. 2015. Abiotic and Biotic Influences on Early-Stage Survival in

Two Shade-Tolerant Tree Species in Brazil’s Atlantic Forest. Biotropica 45: 728-736.

Kajimoto, T. 1992. Dynamics and dry matter production of belowground woody organs of Pinus pumila

trees growing on the Kiso Mountain range in central Japan. Ecological Research 7: 333-339.

Kanzaki, M. and K. Yoda. 1986. Regeneration in subalpine coniferous forests II. Mortality and the pattern

of death of canopy trees. The Botanical Magazine, Tokyo 99: 37-51.

Katsube, K. and T. Oguchi. 1999. Altitudinal changes in slope angle and profile curvature in the Japan

Alps: A hypothesis regarding a characteristic slope angle. Geographical Review of Japan 72: 63-

72.

King, D. 1991. Tree allometry, leaf size and adult tree size in old-growth forests of western Oregon. Tree

Physiology 9: 369-381.

King, D. 1996. Allometry and life history of tropical trees. Journal of Tropical Ecology 12: 25-44.

Kohyama, T. 1980. Growth pattern of Abies mariesii saplings under conditions of open-growth and

suppression. The Botanical Magazine, Tokyo 93: 13-24.

Kohyama, T. 1987. Significance of architecture and allometry in saplings. Functional Ecology 1: 399-

404.

Koike, T. 1988. Leaf structure and photosynthetic performance as related to the forest succession of

deciduous broad-leaved trees. Plant Species Biology 3: 77-87.

Kraft, N., Metz, M., Condit, R. and J. Chave. 2010. The relationship between wood density and mortality

in a global tropical forest data set. New Phytologist 188: 1124-1136.

Kunstler, G., Falster, D., Coomes, D., Hui, F., Kooyman, R., Laughlin, D., Poorter, L., Vanderwel, M.,

Vieilledent, G., Wright, S. J., Aiba, M., Baraloto, C., Caspersen, J., Cornelissen, J. H., Gourlet-

Fleury, S., Hanewinkel, M., Herault, B., Kattge, J., Kurokawa, H., Onoda, Y., Penuelas, J.,

Poorter, H., Uriarte, M., Richardson, S., Ruiz-Benito, P., Sun, I-F., Stahl, G., Swenson, N.,

Thompson, J., Westerlund, B., Wirth, C., Zavala, M., Zeng, H., Zimmerman, J., Zimmermann, N.

and M. Westoby. 2016. Plant functional traits have globally consistent effects on competition.

Nature 529: 204-207.

Lasky, J., Bachelot, B., Muscarella, R., Schwartz, N., Forero-Montaña, J., Nytch, C., Swenson, N.,

Thompson, J., Zimmerman, J. and M. Uriarte. 2015. Ontogenetic shifts in trait-mediated

mechanisms of plant community assembly. Ecology 96: 2157-2169.

Lusk, C. 2002. Leaf area accumulation helps juvenile evergreen trees tolerate shade in temperate

rainforest. Oecologia 132: 188-196.

Lusk, C. 2004. Leaf area and growth of juvenile temperate evergreens in low light: species of contrasting

shade tolerance change rank during ontogeny. Functional Ecology 18: 820-828.

Lusk, C., Jorgensen, M. and P. Bellingham. 2015. A conifer-angiosperm divergence in the growth vs.

shade tolerance trade-off underlies the dynamics of a New Zealand warm-temperature rain forest.

Journal of Ecology 103: 479-488.

McCune, B. and J. Grace. 2002. Analysis of Ecological Communities. MjM Software, Gleneden Beach,

Oregon, USA. 304pp.

McGill, B., Enquist, B., Weiher, E. and M. Westoby. 2006. Rebuilding community ecology from

functional traits. Trends in Ecology and Evolution 21: 178-185.

Matsuzaki, J., Masumori, M. and T. Tange. 2006. Stem phototropism of trees: a possible significant factor

in determining stem inclination on forest slopes. Annals of Botany 98: 573-581.

May, R. and R. MacArthur. 1972. Niche overlap as a function of environmental variability. Proceedings

of the National Academy of Science 69: 1109-1113.

Mori, A., Mizumachi, E., Osono, T., and Y. Doi. 2004. Substrate-associated seedling recruitment and

establishment of major conifer species in an old-growth subalpine forest in central Japan. Forest

Ecology and Management 196: 287-297.

Mori, A. and A. Komiyama. 2008. Differential survival among life stages contributes to co-dominance of

Abies mariesii and Abies veitchii in a sub-alpine old-growth forest. Journal of Vegetation Science 19:

239-244.

Narukawa, Y. and S. Yamamoto. 2003. Development of conifer seedlings roots on soil and fallen logs in

boreal and subalpine coniferous forests in Japan. Forest Ecology and Management 175: 131-139.

Nishimura, N., Kato, K., Sumida, A., Ono, K., Tanouchi, H., Iida, S., Hoshino, D., Yamamoto, S-I. and T.

Hara. 2010. Effects of life history strategies and tree competition on species coexistence in a sub-

boreal coniferous forest of Japan. Plant Ecology 206: 29-40.

Pianka, E. 1974. Niche overlap and diffuse competition. Proceedings of the National Academy of Science

71: 2141-2145.

Paine, C., Stahl, C., Courtois, E., Patino, S., Sarmiento, C. and C. Baraloto. 2010. Functional explanations

for variation in bark thickness in tropical rain forest trees. Functional Ecology 24: 1202-1210.

Poorter, L., Bongers, L. and F. Bongers. 2006. Architecture of 54 moist-forest tree species: traits, trade-

offs, and functional groups. Ecology 87: 1289-1301.

Poorter, L. 2007. Are species adapted to their regeneration niche, adult niche, or both? The American

Naturalist 169: 433-442.

Poorter, L., Lianes, E., Moreno-de las Heras, M. and M. Zavala. 2012. Architecture canopy tree species in

relation to wood density, shade tolerance and climate. Plant Ecology 213: 707-722.

Quero, J. Gomez-Aparicio, L., Zamora, R. and F. Maestre. 2008. Shifts in the regeneration niche of an

endangered tree (Acer opalus ssp. granatense) during ontogeny: Using an ecological concept for

application. Basic and Applied Ecology 9:635-644.

Sayer, E. 2006. Using experimental manipulation to assess the roles of leaf litter in the functioning of

forest ecosystems. Biological Review 80:1-31.

Schurman, J. and J. Baltzer. 2012. Environmental correlates of tree species distributions vary among age

classes in a northern temperate forest. Plant Ecology 213:1621-1632.

Sendall, K., Lusk, C., and P. Reich. 2015. Becoming less tolerant with age: sugar maple, shade, and

ontogeny. Oecologia 179:1011-1021.

Shipley, B., Lechowicz, M., Wright, I. and P. Reich. 2006. Fundamental trade-offs generating the world

wide leaf economics spectrum. Ecology 87: 535-541.

Spasojevic, M., Yablon, E., Oberle, B. and J. Myers. 2014. Ontogenetic trait variation influences tree

community assembly across environmental gradients. Ecosphere 5: Article 129.

Sprugel, D. 2002. When branch autonomy fails: Milton’s Law of resource availability and allocation.

Tree Physiology 22: 1119-1124.

Taylor, A., Jinyan, H. and Z. ShiQiang. 2004. Canopy tree development and undergrowth bamboo

dynamics in old-growth Abies-Betula forests in southwestern China: a 12-year study. Forest

Ecology and Management 200: 347-360.

Takahashi, K., Hirosawa, T. and R. Morishima. 2012. How the timberline formed: altitudinal changes in

stand structure and dynamics around the timberline in central Japan. Annals of Botany 109:1165-

74.

Takahashi, K. and Y. Obata. 2014. Growth, allometry and shade tolerance of understory saplings of four

subalpine conifers in central Japan. Journal of Plant Research 127: 329-338.

Tian, X. and H. Takeda. 1998. Decomposition process of leaf litter in a coniferous forest. Environmental

Forest Science 54:223-230.

Umeki, K. 1995. A comparison of crown asymmetry between Picea abies and Betula maximowicziana.

Canadian Journal of Forest Research 25: 1876-1880.

Veblen, T. 1986. Treefalls and the coexistence of conifers in subalpine forests of the central Rockies.

Ecology 67: 644-649.

Veblen, T. 1992. Regeneration dynamics. In Plant Succession: Theory and prediction. Eds. Glenn-Lewin,

D., Peet, T. and T. Veblen. Chapman and Hall, London. Chapter 4.

Woodall, C., Russell, M., Walters, B., D’Amato, A., Zhu, K. and S. Saatchi. 2015. Forest production

dynamics along a wood density spectrum in eastern US forests. Trees 29: 299-310.

Wright, I., Reich, P., Westoby, M., Ackerly, D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T.,

Cornelissen, J., Diemer, M., Flexas, J., Garnier, E., Groom, P., Gulias, J., Hikosaka, K., Lamont,

B., Lee, T., Lee, W., Lusk, C., Midgley, J., Navas, M-L., Niinemets, U., Oleksyn, J., Osada, N.,

100

Poorter, H., Poot, P., Prior, L., Pyankov, V., Roumet, C., Thomas, S., Tjoelker, M., Veneklaas, E.

and R. Villar. 2004. The worldwide leaf economics spectrum. Nature 428: 821-827.

Wright, I., Ackerly, D., Bongers, F., Harms, K., Ibarra-Manriquez., Martinez-Ramos, M., Mazer, S.,

Muller-Landau, H., Paz, H., Pitman, N., Poorter, L., Silman, M., Vriesendorp, C., Webb, C.,

Westoby, M. and S. Wright. 2007. Relationships among ecologically important dimensions of

plant trait variation in seven neotropical forests. Annals of Botany 99: 1003-1015.

Yamamoto, S-I. 1993. Gap characteristics and gap regeneration in a subalpine coniferous forest on Mt.

Ontake, central Honshu, Japan. Ecological Research 8: 277-285.

Yoshino, M. 1978. Altitudinal vegetation belts of Japan with special reference to climate conditions.

Arctic and Alpine Research 10: 449-456.

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Figure 3-1. Study sites (red dots) are located within Chubu-Sangaku National Park (black polygon) which encompasses most of the Northern Japanese Alps.

102

103

Figure 3-3. Variables that explain axis one and two for the Forest Belts in the non-metric multidimensional scaling (nMDS) plot. Blues is Abies and green is Betula, shades run from Subalpine (SA [darkest]) to Betula belt (BB [lightest]). Axis one is explained by dominant canopy, slope (o), and crown-height ratio. Axis two is explained by crown-height ratio; as well as crown ratio, litter depth, and raised. Samples sizes ranged from 22- 78 individuals (BE.BB.T [50], BE.SA.T [20], BE.BB.S [29], BE.SA.S [41], AM.BB.T [36], AM.SA.T [78], AM.BB.S [22], AM.SA.S [59]).

104

Figure 3-4. Establishment substrates (soil/litter = red, rock = purple, live wood = yellow and course woody debris = green) for each of the species, forest belt and ontogenetic stages. Samples sizes ranged from 22-78 individuals (BE.BB.T [50], BE.SA.T [20], BE.BB.S [29], BE.SA.S [41], AM.BB.T [36], AM.SA.T [78], AM.BB.S [22], AM.SA.S [59]).

105

Figure 3-5. Variables for seedlings (Gap Light Index [A]) or trees (Wood Density [B], Bark Thickness [C], and AGE [D]) that were not included into NO analysis. Blues is Abies and green is Betula, shades run from BB (dark) to SA (light). Samples sizes ranged from 22-78 individuals (BE.BB.T [50], BE.SA.T [20], BE.BB.S [29], BE.SA.S [41], AM.BB.T [36], AM.SA.T [78], AM.BB.S [22], AM.SA.S [59]).

106

Table 3-1. Study area location information (Latitude and Longitude) and topographical description (elevation [m], aspect [o], and slope [o] for the sites in the Northern Japanese Alps.

Site ID Latitude (oN) Longitude (oE) Elevation (m) Aspect (o) Slope (o) Chogatake CHO 36.29 137.72 2580 264 26 Kashimayari KSM 36.61 137.74 2416 245 37 Norikura West NOW 36.16 137.54 2452 266 25 Sugorokudake SUG 36.36 137.60 2440 280 30 Tsubakuro TSU 36.40 137.71 2570 292 33

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Variable Variable Type Data Type Seedling Tree Substrate resource Rock CWD Soil/Litter Live I I Slope continuous slope degree P P Crown-height Ratio proportion (Total Height - Base Height) / Total Height (cm) I I Crown-diameter Ratio proportion Canopy Diameter / Root Crown Diameter (cm) I I Litter Depth measurement Depth (cm) I P Raised measurement Establishment height above ground (cm) I I Distance to Conspecific measurement Distance to conspecific (m) I I Curvature binary Convex Concave I P Forb Cover categorical 0-5 5-25 25-50 50-75 75-95 95-100 I P Shrub Cover categorical 0-5 5-25 25-50 50-75 75-95 95-100 I P Canopy Cover categorical 0-5 5-25 25-50 50-75 75-95 95-100 I P Gap Light Index (10cm) measurement Gap Light Index calculated from Hemispherical Photo, 10 cm I - Bark Thickness measurement Width (cm) - I Wood Density measurement Density (g/cm3) - I

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Table 3-3. Count of measured seedlings (< 50 cm tall) and trees (> 200 cm tall) for each of the five species found at the study sites.

Seedlings Trees Abies mariesii 81 116 Abies veitchii 15 4 Betula ermanii 70 71 Picea jezoensis 31 2 Pinus pumila 2 36

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Subalpine Betula belt Seedlings Trees Seedlings Trees Abies mariesii 59 78 22 36 Betula ermanii 41 20 29 50

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Table 3-5. Niche overlap (NO) between Abies and Betula with the full dataset statistically significant (p- values < 0.05) overlaps are noted with a *.

NO Substrate 0.714* Slope 0.858* Crown-Height Ratio 0.642* Crown Ratio 0.812* Litter Depth 0.752* Raised 0.740* Distance to Conspecific 0.653* Curvature 0.996 Forb Cover 0.783* Shrub Cover 0.854 Canopy Cover 0.787* Overall 0.778 * significant (p < 0.05)

AM.BB.S AM.BB.T AM.SA.S AM.SA.T BE.BB.S BE.BB.T BE.SA.S BE.SA.T AM.BB.S AM.BB.T 0.643 AM.SA.S 0.734 0.665 AM.SA.T 0.583 0.787 0.707 BE.BB.S 0.736 0.578 0.639 0.541 BE.BB.T 0.671 0.760 0.638 0.689 0.640 BE.SA.S 0.714 0.544 0.702 0.555 0.770 0.623 BE.SA.T 0.587 0.682 0.673 0.773 0.548 0.682 0.594

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Table 3-7. Unit less distances in ordination space (non-metric multidimensional scaling [nMDS]) between the subpopulations (Abies [AM] and Betula [BE] across two forest belts: Betula belt [BB] and subalpine forest [SA] and two ontological states: Seedlings [S] and Trees [T]). . AM.BB.S AM.BB.T AM.SA.S AM.SA.T BE.BB.S BE.BB.T BE.SA.S BE.SA.T AM.BB.S AM.BB.T 0.265 AM.SA.S 0.204 0.192 AM.SA.T 0.342 0.106 0.201 BE.BB.S 0.153 0.383 0.244 0.434 BE.BB.T 0.172 0.093 0.149 0.179 0.296 BE.SA.S 0.189 0.359 0.189 0.389 0.089 0.283 BE.SA.T 0.336 0.167 0.152 0.091 0.396 0.203 0.337

Table 3-8. Significance between subpopulations (Abies [AM] and Betula [BE] across two forest belts: Betula belt [BB] and subalpine forest [SA] and two ontological states: Seedlings [S] and Trees [T]) and the environmental attributes. Environmental attributes included were topographic (slope; degrees), structural (crown-height ratio and crown-diameter ratio), establishment site (litter depth [cm], raised surface [cm], distance to conspecific [m]), vegetation cover (forb, shrub and canopy cover; [% cover classified into 6 categories [0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75%, 5: 75-95%, 6: 95-100%]), light environment (Gap Light Index [GLI]), and wood properties (Density [cm3], bark thickness [cm], and tree age [years]. Values are the means (± standard error). Significant difference is indicated by difference in letters within a row. (ANOVA [df = 7,327; Slope- Canopy Cover], ANOVA [df = 3,147; GLI], ANOVA [df = 3, 175; Wood Density], ANOVA [df = 3,155; Bark thickness], and ANOVA [df = 3,162; Age], Tukey’s HSD, α =0.05).

Seedlings Trees p-value AM.BB.S BE.BB.S AM.SA.S BE.SA.S AM.BB.T BE.BB.T AM.SA.T BE.SA.T Slope 0.144 34.13 (1.44) a 32.34 (1.27) a 28.85 (1.49) a 28.39 (1.53) a 32.72 (0.91) a 30.46 (0.89) a 29.15 (1.39) a 30.00 (2.55) a Crown-height ratio <0.001 0.87 (0.06) a 0.30 (0.06) c 0.64 (0.03) ab 0.35 (0.03) c 0.79 (0.03) a 0.63 (0.03) ab 0.67 (0.02) ab 0.56 (0.05) b Crown-diameter ratio <0.001 49.2 (0.9) abc 50.7 (0.8) ab 29.9 (0. 2) bcd 70.4 (1.55) a 21.4 (0.1) cd 23.2 (0.1) cd 17.5 (0.1) d 26.3 (0.3) bcd Litter <0.001 5.16 (0.84) bcd 3.00 (0.47) c 5.08 (0.59) bc 2.52 0.41) cd 10.3 (1.29) a 8.02 (1.12) bc 6.44 (0.42) bc 6.63 (0.61) abc Raised <0.001 0.11 (0.03) bc 0.29 (0.06) ab 0.20 (0.05) abc 0.36 (0.6) a 0.06 (0.02) c 0.12 (0.03) bc 0.07 (0.02) c 0.27 (0.12) abc Distance to conspecific <0.001 3.98 (1.14) a 3.28 (0.51) ab 1.41 (0.08) b 3.17 (0.39) ab 2.24 (0.25) ab 3.97 (0.67) a 1.75 (0.11) b 2.28 (0.39) ab Forb Cover <0.001 3.00 (0.34) ab 4.31 (0.35) a 2.75 (0.24) b 3.93 (0.32) a 3.56 (0.23) ab 4.12 (0.21) a 3.5 (0.16) ab 3.8 (0.40) ab Shrub Cover <0.001 2.36 (0.34) bcd 2.79 (0.34) bc 1.69 (0.16) d 1.93 (0.23) cd 3.36 (0.27) ab 4.1 (0.20) a 2.78 (0.14) bc 2.65 (0.22) bcd Canopy Cover <0.001 3.64 (0.39) abc 3.21 (0.30) abc 4.49 (0.17) a 3.8 (0.25) bc 4.17 (0.17) ab 3.2 (0.22) abc 4.03 (0.13) ab 4.00 (0.28) c GLI (at 10cm) 0.006 10.02 (2.96) ab 20.21 (3.32) a 9.31 (1.98) b 20.48 (4.00) ab - - - - Wood Density <0.001 - - - - 0.56 (0.02) ab 0.62 (0.01) a 0.54 (0.01) b 0.62 (0.01) a Bark Thickness 0.015 - - - - 0.37 (0.04) ab 0.56 (0.05) a 0.52 (0.03) a 0.58 (0.05) a Age <0.001 - - - - 1937.92 (7.58) a 1874.74 (13.04) b 1886.41 (7.56) b 1870.00 (21.08) b

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Chapter 4

Climate-growth Relationships in two forest species at their upper elevation limits in the Japanese Alps

Introduction

Climatic limits on the distribution of temperate tree species at both regional and landscape scales are typically attributed to threshold values in temperature or precipitation (Iverson and Prasad 2002;

Canham and Thomas 2010). Although maximum temperatures limit the ranges for many tree species at both high latitudes and high elevations (Jobbágy and Jackson 2000; Körner and Paulsen 2004; Harsch et al. 2009; Randin et al. 2013), minimum temperatures can also be important in limiting species distributions along elevation gradients. Low temperatures can limit the upward migration of trees along elevation gradients (Moen et al. 2004; Bell et al. 2014; Greenwood and Jump 2014).

Globally, low growing season temperatures limit tree establishment and growth at many treelines

(Jobbágy and Jackson 2000; Körner and Paulson 2004, Harsch et al. 2009; Randin et al. 2013). Treeline is the ecotone between the closed subalpine forest and tundra where trees are generally greater than 3 m tall

(Holtmeier et al. 2003, Holtmeier and Broll 2005). Some treelines (Körner 1998; Oberhuber 2004; Harsch et al. 2009; Elliot 2012; Hagedorn et al. 2014), and species near their range limits (Pederson et al. 2004), are limited by winter temperatures. However, not all species at treeline are limited by temperatures

(summer or winter). In Central and South America, drought limits tree growth at treeline (Biondi 2001;

Daniels and Veblen 2004), and tree growth at treelines in the Himalayas is limited by moisture stress in the months prior to the summer monsoon (Dawadi et al. 2013; Liang et al. 2014). Generally, growing season precipitation is positively associated with radial growth in treeline trees (Linderholm et al. 2003;

Young et al. 2011); however, a negative impact of growing season precipitation on radial growth has been documented in trees at treeline sites in both the western USA and in east Asia (Eckstein et al. 1991;

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Peterson and Peterson 2001; Takahashi et al. 2003; Nakatsuka et al. 2004; Takahashi et al. 2005; Dawadi et al. 2013; Liang et al. 2014; Hagedorn et al. 2014).

In Northeast Asia, coniferous and broad-leaved deciduous trees often have similar growth responses to temperature (Table 4-S1). Studies on conifer species generally show a positive radial growth response to growing season and late fall temperatures with no consistent growth relationships for precipitation (Table 2-41., [Kobayashi et al. 1995; Yasue et al. 1996; Yasue et al. 1997; Fujiwara et al.

1999; Davi et al. 2002; Takahashi et al. 2011; Tei et al. 2015]). Broad-leaved deciduous species also show a positive growth response to growing season temperature and have a consistent positive growth response to precipitation (Table 4-S1, [Yasue et al. 1996; D’Arrigo et al. 1997; Takahashi et al. 2003; Takahashi et al. 2005; Doležal et al. 2010; Wang et al. 2013]). However, where conifer (Abies spp.) and deciduous

(Betula spp.) species occur near their lower thermal limit, there is often a negative response to both winter and summer precipitation (Takahashi et al. 2003, 2005; Liang et al. 2014; Dawadi et al. 2013).

The alpine treeline in Northeast Asia is dominated by B. ermanii, where it forms a belt above the

A. mariesii dominated subalpine forest (Wardle 1977; Franklin 1979; Fujimoto and Miyakawa 1991). In the Northern Hemisphere there are several subalpine forests composed of the genus Betula (Körner 2013;

Ashburner and McAllister 2013). Betula commonly emerges above subalpine forests of mixed broad- leaved deciduous and coniferous composition to dominate at treeline (Scandinavia [Kullman 1993]; northeastern USA [Cogbill and White 1991], Himalayas [ Dawadi et al. 2013; Liang et al. 2014],

Caucasus [Akhalkatsi et al. 2006; Hughes et al. 2009], Ural Mountains [Kapralov et al. 2006] Northeast

Asia [Wardle 1977; Ohsawa 1990; Gansert 2002]).

Dendroclimatic analyses in all of these systems indicate that growth of Betula spp. at treeline has been associated at least in part with summer temperatures (Norway [Kirchhefer 1996]; Sweden [Eckstein et al. 1991; Young et al. 2011]; Kamchatka [Doležal et al. 2010], China [Wang et al. 2013], and Japan

[Takahashi et al. 2005]). The subalpine forests from which Betula spp. treelines emerge are typically dominated by conifers from the genera Abies, Picea, or Pinus. Growth of these subalpine conifers is most

116 responsive to summer temperatures (Eckstein et al. 1991; Kobayashi et al. 1995; Linderholm et al. 2003;

Takahashi et al. 2011; Young et al. 2011; Gaire et al. 2014; Tei et al. 2015).

Longevity in Betula species is quite variable. Various species can live for around 100-150 years in Scandinavia (B. pubescens Ehrh. [Levanič and Eggertsson 2008; Young et al. 2011]), roughly 380 years in the northeastern North America (B. alleghaniensis Britt. [Vasiliauskas 1995], B. lenta L.

[Pederson et al. 2013]), 150 years in Labrador (B. papyrifera Marshall [Kershaw and Laroque 2012]), 450 years in the Himalayas (B. utilis D.Don [Dawadi et al. 2013; Liang et al. 2014]) and 170-250 years in

Northeast Asia (B. ermanii Cham. [Takahashi et al. 2005; Wang et al. 2013]). Betula species’ longevity

(100-450 years) thus falls within the range of broad-leaved deciduous species in the mesic temperate forests of the northeastern North America, which vary from 100 to 650 years old, species with high maximum ages were found in remote regions (Di Filippo et al. 2015). Longevity of broad-leaved deciduous species at treeline ranges from 100-450 years (Takahashi et al. 2005; Levanič and Eggertsson

2008; Young et al. 2011; Dawadi et al. 2013; Liang et al. 2014), within the range of ages for broad-leaved deciduous species. Conifers at treeline in arid regions have the potential to be more than 1,000 years old

(Graumlich 1993; Bunn et al. 2005; Salzer et al. 2014; Zhang et al. 2015),. In high latitude and in mesic high elevation forests, conifer longevity is similar to that of broad-leaved deciduous species and range from 150-500 (Yasue et al. 1997; Biondi 2001; Davi et al. 2002; Bigler and Veblen 2009; Liang et al.

2010; Körner 2012).

Because Betula spp. at treeline often respond to summer temperatures, as do the conifers that comprise the subalpine forest below the treeline, it remains unclear whether climate is a primary driver maintaining the B. ermanii treeline. Therefore, the first objective of this study was to assess differences in climate-growth relationships using a dendrochronological approach between B. ermanii and A. mariesii within a mountain in which both species are experiencing approximately the same climate. My second objective was to identify whether climate-growth relationships are spatially variable among mountains and climate stations on the Northern Japanese Alps have a regional climate-growth response shared among both species.

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Study Area

Tree growth climate relationships were studied in the high elevation forests of Chūbu-Sangaku

National Park in the Northern Japanese Alps, central Japan (Figure 2-1). I established five study sites on west facing slopes in high elevation forests throughout the park, including Chogatake (CHO),

Kashimayari (KSM), Norikura (NOW), Sugorokudake (SUG), and Tsubakuro (TSU) (Table 4-1). The composition of these alpine forests is similar along the elevation gradient, but dominance changes markedly with elevation (Figure 2-2, Takahashi et al. 2012). The mixed subalpine forests are dominated by conifers, while forests at treeline are dominated by B. ermanii (hereafter Betula) with only a scattering of Abies mariesii (hereafter Abies). The subalpine forest is dominated by Abies but also includes a mixture of A. veitchii Lindley, Picea jezoensis (Siebold and Zucc.) Carr. and B. ermanii (Figure 2-2).

Shrubs and ferns (Pteridium spp.) dominate the understory of both forest types with Sorbus commixta

Hedl., Acer spp. and Vaccinium spp. being most abundant. The understory at the upper edge of the Betula belt is composed of scrub pine (Pinus pumila (Pall) Regel).

Chūbu-Sangaku National Park was established in 1946 and there were no documented disturbances at the sites but evidence of past logging was present in SUG in the form of rotten Betula cut stumps (pers. obs.). Natural disturbances in the Northern Japanese Alps include typhoons (Yamamoto

1995), landslides, (Matsuoka and Sakai 1999; Kajimoto et al. 2004; Kariya et al. 2007), and volcanic activity (Powers 1916). Gap openings in Japan are typically attributed to typhoons (Ida 2000), however, in the subalpine forests the gap openings due to typhoon and classic gap-phase dynamics are roughly the same average gap size 30-100m2 (Kanzaki and Yoda 1986, Komiyama et al. 1981, Kubota et al. 1994,

Yamamoto 1995).

The climate of central Japan is highly seasonal, involving six defined seasons that revolve around the duration of the spring (Baiu) and fall (Akisame) rains (Inoue and Matsumoto 2003). During the rainy seasons, the proportion of sunshine declines markedly. The annual average rate (% of total hours) of sunshine is around 45%. During Baiu this value is approximately 30%, then sunshine peaks at around

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52% in midsummer, and subsequently drops below 40% through the Akisame period (Inoue and

Matsumoto 2003). The mountainous topography of Japan creates a distinct east-west precipitation divide between the Sea of Japan (East Sea) and Pacific Ocean side of Japan (Fig. 1, [Yoshino 1980; Ikeda et al.

2009]). On the Sea of Japan side (west) precipitation is highest during the winter months due to cool winds from the northwest picking up moisture over the Sea of Japan and depositing it in the western mountains near the Sea of Japan (Wardle 1977; Ikeda et al. 2009). Most of this precipitation in the mountains falls as snow and can accumulate up to 5-10 m (Ikeda et al. 2009). On the Pacific side, a majority of the precipitation falls during the summer months due to tropical storms moving northward from the Pacific Ocean and South China Sea (Yoshino 1980). The Northern Japanese Alps form the divide separating these two precipitation regimes.

Methods

To identify climate-tree growth relationships, I used climate data from four meteorological stations (Matsumoto, Nagano, Toyama and Takayama) surrounding the Northern Japanese Alps (Table 4-

2, Figure 4-1 and 4-3). Monthly temperature and precipitation from 1899 to present were available for three stations, and from 1939 to present for the other station (Table 4-2, Japanese Meteorological Agency, http://www.data.jma.go.jp/). Mean total annual precipitation from Toyama, the most westerly station, was approximately 2.2, 2.4 and 1.3 fold higher than at Matsumoto, Nagano and Takayama, respectively.

Temperatures were coldest in January/February and warmest in July/August at all four stations (Figure 4-

3). Mean annual temperatures at each station have increased (p < 0.05) by 1-2 ºC over the past 100 years

(Table 4-3). Precipitation during the same time period has declined by 50-100 mm at three of the sites

(Matsumoto, Takayama and Nagano) and increased by about 50 mm at Toyama, but none significantly

(p> 0.05). All precipitation was recorded as mm of water, and precipitation in winter that falls as snow was expressed as snow water equivalent by the Japanese Meteorological Agency. The Palmer Drought

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Severity Index (PDSI) was calculated from an assumed available water capacity (10 cm), latitude (36º N), temperature and precipitation using the package PDSI in the program R (Zang 2013).

Tree growth can have greater sensitivity to minimum or maximum temperatures than to mean temperature (Oberhuber 2004, Way and Oren 2010). Consequently, I determined the association between monthly mean daily minimum and maximum temperatures as well as monthly total duration of sunshine at the Matsumoto station and radial growth. I included sunshine duration because it has been suggested to be more reliable than temperature or precipitation for detecting seasonal changes in Japan (Inoue and

Matsumoto 2003).

Lapse Rates of Temperature

The meteorological stations are in close proximity to the study sites (20-80 km); however, they are at lower elevations (60-600 m a.s.l.) than the high elevation forests (2300-2700 m a.s.l.).

Consequently, I used standard temperature lapse rates to estimate temperature conditions at the high elevation forest sites. Regional and seasonal specific lapse rate values are available for the east slope of

Norikura (Sato 2011). These lapse rates were verified using eight mountain meteorological stations

(1450-3080m, Institute of Mountain Science, Shinshu University and Takayama Flux Tower Site, Gifu

University) situated in Chūbu-Sangaku National Park, albeit with a short record (2006-2014) (Table 4-2).

The average temperature lapse rate calculated between the mountain stations and the meteorological stations was 0.65 ºC 100 m-1 (i.e., close to the moist adiabatic lapse rate), with seasonal variation ranging between 0.53 ºC 100 m-1 and 0.71 ºC 100 m-1 (Table 4-4). I also used synoptic-scale radiosonde data

(1961-present) to validate lapse rates for the Northern Japanese Alps region. Radiosondes on weather balloons are used to record pressure, temperature, and relative humidity as they ascend. Radiosonde lapse rates were lower (0.45-0.65 ºC 100 m-1) than those calculated between the meteorological and mountain stations (Table 4-4). The lower radiosonde lapse rates may reflect the influence of the free atmosphere rather than surface heat flux as for the station data.

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Moist adiabatic lapse rates, like those found in this study, increase the temperatures at a slower rate than a dry adiabatic lapse rate due to latent heat being released during condensation, thus temperatures at higher elevations will be warmer than if the air was dry. Temperatures from the meteorological stations with the lapse rates extrapolated from 600 m to 2600 m were not significantly different for a majority of the months and mountain stations (Table 4-5). Thus the lapse rate calculated by

Sato (2011) for Norikura and the global average lapse rate (0.65 ºC 100 m-1) are each similar to those estimated here. Furthermore, when lapse rates are extrapolated to 2600 m, the summer temperatures (July and August) at treeline are above the 10 °C threshold for treeline development globally (Table 4-5,

[Körner and Paulsen 2004]).

There are no standard precipitation rates as there are for temperature. However, rates of warm and cool season precipitation on the east facing slopes of Norikura in the Northern Japanese Alps was estimated based on water balance drainage (Suzuki et al. 2008). Using linear regression models on the four meteorological stations’ data, I found no significant difference between the modeled precipitation and values recorded at the mountain stations for the Matsumoto and Nagano stations, while August and

October were significantly different for the Takayama and Toyama stations (Table 4-5). Because the standard moist adiabatic lapse rate for temperature and the linear regressions for precipitation were not appreciably different from those determined for in the mountain stations, we used the meteorological station data to derive the climate-growth relationships (Table 4-5). Temperature and precipitation calculated for the mountains was used the climate-growth relationships.

Field Sampling

To identify climate-growth relationships, I collected increment cores from the dominant trees in the Betula belt and in the subalpine forest at each mountain during the summers (July-September) of 2013

(Chogatake [CHO], Sugorokudake [SUG], Tsubakuro [TSU]) and 2014 (Kashimayari [KSM], Norikura

[NOW]). I cored dominant trees with no apparent damage in the both forest belts, including 20 trees each

121 of Betula (DBH > 30 cm) and Abies (DBH > 25 cm) in the subalpine forest, and 20 trees of Betula (DBH

> 30 cm) in the Betula belt. I took two cores from each tree at 1.37 m above ground level, parallel to the slope contour to minimize compression (small rings) or reaction wood (large rings). Mean heights of sampled Betula and Abies individuals were 9.3 ± 0.2 m (standard error of the mean) and 13.6 ± 0.3 m, respectively, and mean crown diameters for the trees were 9.4 ± 0.3 m and 4.8 ± 0.1 m, respectively.

Chronology Development

Cores were mounted and sanded to a high polish using progressively finer grits of sand paper

(120-1000). Growth rings were then visually cross-dated using standard techniques (Stokes and Smiley

1967). Ring widths were then measured with a Velmex measuring stage (±0.001 mm, Velmex Inc.

Bloomfield, NY, USA) using J2X (VoorTech Consulting, Holderness, NH, USA). Visual cross-dating was then statistically verified using COFECHA (Holmes 1986). Cores with segments that contained 10-

20 missing rings in a 30 - 50 year period, or that had a poor correlation to other cores in COFECHA, were either truncated or removed from consideration of use in the ring width chronology. I standardized cross- dated ring-width measurements with age-dependent splines to create standardized and residual chronologies using ARSTAN47 (Cook et al. 2012). Age-dependent splines are smoothing splines with time-varying flexibility (Melvin et al. 2007). The splines used here begin with a 20 year window and increased by one year throughout the length of the core. The signal strength of each chronology was verified using rbar, inter-series correlation, and expressed population signal (EPS).

Principal component analysis (PCA) was used to identify the common radial growth signal for each species across all sites for a common time series using the ARSTAN chronologies and the stats package of R 3.1.2 (R Core Team 2014). The variance found between series for each species was separated onto principal components (PCs), those of which containing eigenvectors greater than one are considered to have the most common variance (Guiot 1990).

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Climate-Growth Relationships

I identified the common growth responses of the Abies and Betula PCs to monthly climatic variables (mean temperature, total precipitation, and PDSI) using correlation and boot strapped response function analysis with the treeclim package 1.0.16 in R 3.1.2 (R Core Team 2014; Zang and Biondi

2015). The interval for the climate-radial growth analysis extends from April of the prior growing season until October of the current growing season (18 months). Inclusion of the prior growing season permits detection of lagged growth responses related to resource acquisition in the previous year (carbon, nutrients). I used linear regressions to determine the influence of temperature, precipitation and PDSI on growth in both Abies (PC1) and Betula (PC1) trees (Table 4-11).

In addition to the climate-growth analysis on the Abies and Betula PCs, I analyzed climate- growth relationships for the standardized mountain chronologies of each species, to examine the spatial response of climate-growth relationships across the precipitation gradient (Figure 3; graphs were generated in R 3.1.2 using the reshape2, grid, gridExtra and ggplot2 packages (Wickham 2007, 2009; R

Core Team 2014; Baptisete 2016).

Growth Variation

Suppression and release events as well as missing rings were found in all sample sites. Missing rings were identified during the cross-dating process. Suppressions and releases of growth were detected using the Nowacki and Abrams (1997) method for detecting releases with the TRADER program in R

(Altman et al. 2014). To detect suppression events, the ring width measurements were multiplied by -1.

Suppression events were underdiagnosed during the 20th century because some cores were truncated as a result of both missing rings and severe suppression. I thus analyzed how climate-growth relationships were influenced by these missing rings or truncations due to extensive periods of suppression for Betula.

To do so, I used cores that had fewer than five consecutive rings missing and were complete (i.e. not

123 truncated) to create a new Betula chronology. I then used the same correlation and response function analyses and linear regression methods as were used to detect the climate-growth response of cores without large numbers of continuous missing rings.

Results

Chronology Comparison

Chronologies were successfully developed for three Betula and five Abies sites (Table 4-6 and

Figure 4-4). CHO-BE was a combination of the Betula belt samples and the subalpine Betula samples, while the other Betula chronologies were only the Betula belt samples. The oldest Betula (1631) sample was 150 years older than the oldest Abies individual (1781, [Figure 4-4]). The mean ring width for Abies was 0.715 mm (σ = 0.254) and was 0.692 mm (σ = 0.229) for Betula [Table 4-6]). Rbar values ranged from 0.468-0.486 for Betula and 0.356-0.441 for Abies [Table 4-6]. The inter-series correlations between sites were higher for Betula (0.582-0.617) than for Abies (0.450-0.539 [Table 4-6]). Correlation of ring widths for Betula between sites was lowest for the period of 1900-1950 (0.39); during this period SUG-

BE was not significantly correlated with the other two Betula chronologies. All other time periods had significant correlation within each species; correlations between the two species were not significant.

Chronologies with EPS values > 0.85 are typically considered as having a reliable strength of signal (Wigley et al. 1985), although this is an arbitrary threshold. Instead of solely using EPS values >

0.85 as a cut-off of signal strength, I used the combined criteria that chronologies needed to maintain both stable (did not drop drastically) EPS values and a sample depth > 5 trees as a cut-off (Allen et al. 2015).

With these criteria, the Betula chronologies were stable back to between 1735 and 1765, and the five

Abies chronologies were stable between 1820 and 1865.

The principal component analysis for both species resulted in only the first eigenvector having a value above one (i.e., statistically significant). The first principal component (PC1) for each species was

124 used in the climate-growth analysis. The PC1 for Betula explains 70.0% of the variance among sites across the common period of 1765-2013, and PC1 for Abies explained 64.14% across the common period of 1865-2013 (Figure 4-5). Betula and Abies were both negatively loaded on to their respective PC1s

(Table 4-6). The variance explained by each chronology on the two PC1s ranged from 39 - 49% for Abies and 55 - 59% for Betula (Table 4-6).

Climate-Growth Relationships

The PC1 loadings for each species were multiplied by -1 so the climate-growth relationships would have the same sign as the individual chronologies of each species. Temperature-growth relationships for the PCs of both species were positive (p < 0.05) for the current summer growing season

(July/August) at all climate stations (Table 4-7 and Figure 4-10). Additionally, Abies had a strong positive growth response to fall temperatures (November/December) and a negative response to the previous growing season (July). Betula growth was negatively associated with early spring temperature

(February/March). This negative spring temperature response for Betula was most evident for the

Matsumoto and Takayama stations (Table 4-7).

Abies growth had few significant associations with precipitation. Two of the climate stations

(Toyama and Nagano) showed a significant growth response to both prior (positive) and current

(negative) May precipitation (Table 4-7). Abies growth was also positively influenced by February precipitation at these two climate stations. On the other hand, Betula growth had a negative response to the current growing season precipitation (June-September), though the month of significance varied

(Table 4-7 and Figure 4-10). Betula growth was also negatively correlated with winter precipitation from

Takayama and Matsumoto (Table 4-7). Additionally, the growth response to PDSI varied between the two species. Betula growth had no consistent or clear association with PDSI, while Abies growth correlated positively with PDSI values during the previous summer, fall, and winter (Figure 4-10).

125

Due to the importance of summer (JJA) temperature and precipitation for Betula, linear regressions were constructed for both Betula and Abies using summer temperature (mean), precipitation

(sum), and PDSI (mean) as predictor variables (Table 4-11). The regression was consistent with the correlations and response functions for Betula in that this species had a significant positive association with summer temperatures. By contrast, the regression for Abies did not corroborate the correlation and response functions because the overall Abies model was not statistically significant.

Betula growth was positively influenced by minimum and maximum July temperatures from

Matsumoto (Table 4-8). In addition to July, growth increased with high maximum temperatures from

June to August. Growth was negatively influenced by the mean and maximum winter temperatures

(February and March [Table 4-8]). Growth of Betula was positively correlated with the duration of sunshine (h), particularly in July when precipitation was negatively correlated with Betula growth (Table

4-8).

Abies growth responses to minimum and maximum temperatures were similar to monthly patterns for mean temperatures (Table 4-8). During the current growing season only mean July temperature influenced Abies growth. The duration of July sunshine (h) had an influence on Abies growth similar to temperature, with increased growth associated with lower sunshine.

Annual growth of individual chronologies for both species showed similar trends to the PCs for the meteorological stations (Table 4-9A and B). Betula chronologies had a positive growth response to

July temperatures and were significant (p < 0.05) in all but two combinations (TSU-BE/Matsumoto and

TSU-BE/Toyama). The trend of negative growth responses to March temperatures was also seen in the individual analysis of mountains, though only significant for CHO-BE at all stations and SUG-BE at

Takayama. Precipitation was more variable among chronologies though there was still a trend of negative response to the current growing season (Table 4-9A and B).

All Abies chronologies had a positive growth response for November/December temperature for all climate stations (Table 4-9B). A negative growth response to the previous growing season’s temperature was characteristic for all but the TSU-AM chronology for at least two climate stations. The

126 positive growth response during the current growing season was only present in three of the chronologies

(KSA-AM, NOW-AM, and SUG-AM), and primarily for the Takayama climate station (Table 4-9B). All chronologies had a positive growth relationship with February precipitation at the Toyama climate station.

Other consistent growth responses were found for three of the chronologies (CHO-AM, KSM-AM, and

SUG-AM) with Matsumoto station in the previous May (Table 4-9B).

Growth Variation

In the species-level analysis in which individual trees were aggregated across all mountains, there were no suppression or release events that occurred in more than 20% of the cores for either species when sample size was 10 or more individuals (Figure 4-7). The mountain-level analysis revealed that suppression and release events occurred in at least 20% of the Betula cores (Figure 4-7 and4-8). A comparison of the periods of suppression and release among mountains indicated Betula trees frequently experienced these periods across multiple mountains. Of note were the 1820s, as there was suppression of growth evident across all mountains, and in the 1960s, there was a release event that occurred on all mountains (Figure 4-7 and 4-8). Where Abies and Betula chronologies co-occurred, there were no clear shared patterns in either suppressions or releases (Figure 4-8). In Betula, there were releases in multiple chronologies after the occurrences of missing rings (Figure 4-8).

Abies have few missing rings (7, 0.029%), while Betula had comparatively more missing rings

(242, 1.1% [Table 4-6]). Due to severe suppression during the 20th century, where 10-20 years were missing in a 30-50 year period, there sections were removed. These portions that were removed due to severe suppression and missing rings are not included in the tally of missing rings. Missing rings from

1911 and 1923 were from CHO-BE, while those missing from the 1960s and late 1980s are primarily from SUG-BE, and those from TSU-BE were more evenly dispersed across time (Figure 4-9A). The seven missing rings from Abies came from three of the five sites and were distributed over the past 200 years. There were no age related patterns with the Betula missing rings, however the cores that had

127 portions removed were on average older than the complete cores (cores without a portion removed;

Figure 4-9B and C). Most cores (63%) had missing rings, regardless of whether they were complete

(64%) or had portions removed (60%). Of the cores that had missing rings, 88% of those that had portions removed have two or fewer missing rings, while only 47% of the complete cores had two or fewer missing rings. Thus, cores that did not have a portion removed, have a higher number of missing rings

(Figure 4-9B and C).

The missing rings, the portions of cores removed and the cores that were not cross-dated were all inherently not expressed in the climate-growth relationships described above because they represent absent data rather than zeros. In an attempt to detect the influence of the presence of missing Betula rings on the climate-growth relationships, chronologies were constructed for only the complete cores (i.e. not truncated) and those with fewer than five consecutive missing rings. These reduced chronologies had higher interseries correlations than the chronologies that included the missing rings (CHO-BE 0.609

[+4.6%], TSU-BE 0.666 [+11.3%], SUG-BE 0.637 [+3.2%]). The PC1 of the reduced chronology had similar loadings for each chronology, than the chronologies that included the missing rings (CHO-BE

57.6% [-1.4%], TSU-BE 60.6% [+2.5%], SUG-BE 54.8% [-1.4%]). The climate-growth relationships for the reduced PC1chronology revealed no differences in terms of the monthly climate parameters significantly correlated with growth. However, the correlation coefficients of these same significant months were slightly higher in the reduced chronology (Table 4-10). Linear regressions between the reduced and complete Betula chronology were similar to one another, however, the R2 of the overall model for the reduced chronology was higher than the overall model for the complete chronology (Table

4-11). This indicates that growth of trees that had more than five consecutive missing rings or were truncated due to suppression were slightly more sensitive to the climate parameters.

128

Discussion

Temperature-Growth Relationships

Trees growing at the colder limits of their ranges, whether considered by latitude or elevation, are thought to be primarily constrained by growing season temperature (Körner and Paulsen 2004), though some treelines are limited by winter temperatures (Harsch et al. 2009). Here, I found that Abies and

Betula at their elevation limits in the Northern Japanese Alps responded to both temperature and precipitation at different times of the year (see Table 4-9), indicating that the limits to growth in these species was not simply related to growing season temperature (1901-2011). Nonetheless, both Abies and

Betula had the typical positive growth response of treeline species, especially those growing in diffuse treelines, to summer temperature (Harsch and Bader 2011). The distributions of Abies become diffuse at their upper elevations, while those of Betula are abrupt. Abrupt treelines may be driven more by seedling mortality and disturbances than climate (Harsch and Bader 2011). The rate of seedling mortality is beyond the scope of this paper, however, the occurrence of disturbances can be detected through the occurrence of missing rings (see discussion below).

Warm-season temperatures are associated with positive growth (correlation coefficients around

0.2-0.35) at treeline on the eastern slopes of Norikura in the Northern Japanese Alps in A. mariesii, but not A. veitchii or P. jezoensis ssp. hondoensis (Fujiwara et al. 1999; Takahashi et al. 2011). Both A. mariesii and P. jezoensis ssp. hondoensis on Norikura show positive growth responses to temperature in

November and December (Fujiwara et al. 1999; Takahashi et al. 2011). I found similar climate-growth responses in this study, including on the western slope of Norikura (Table 4-9B and Figure 7). Positive responses to cool season temperatures have also been found in Chamaecyparis obtusa (Siebold and

Zucc.) Endl. (Yonenobu and Eckstein 2006) and Cryptomeria japonica (L.f.) D.Don (Kojo 1987) in Japan as well as A. spectabilis (D. Don) Spach in the Himalayas (Gaire et al. 2014; Chhetri and Cairns 2016).

However, winter temperatures can limit tree growth (Pederson et al. 2004; Carrer et al. 2007; Harsch et al.

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2009). The positive response of Abies to warm winter temperatures resulted from the continuation of photosynthesis and storage of carbohydrates until the ground freezes (Sakai and Larcher 1987; Givnish

2002). These carbohydrates are often used in forming the ring in the following year (Sakai and Larcher

1987; Givnish 2002). In contrast, Betula leaves senesce in September to October, precluding winter season growth (Watanabe et al. 2013). At this point in the year, Betula individuals are less competitive to

Abies for many resources and not just due to Abies still having the ability to photosynthesize (Schulze et al. 1977; Givnish 2002). In boreal forests, the investment in root structure as a proportion of net primary productivity was 19% for deciduous (Larix spp., Betula spp. and Populus spp.) forests compared to 36% found in evergreen forests (Gower et al. 2001). With the larger root structure, ability to photosynthesize until the ground freezes, and the added nutrients deposited from senesced leaves, Abies should have the competitive advantage in the fall to winter (Givnish 2002). This may account for the positive growth relationship between warm winter temperatures (November and December) and Abies ring growth shown here.

Negative growth responses to July temperatures in the previous growing season, such as those shown here for Abies, have been attributed to summer temperature induced drought stress (Peterson and

Peterson 2001; Girardin and Tardif 2005). High summer precipitation in the Northern Japanese Alps likely precludes the occurrence of drought, though these negative responses could be due to heat or sun stress. Picea needles may have temperatures 2-12 ºC greater than the ambient air temperature during the summer depending on form (upright or krummholz; [Hadley and Smith 1986]), and needle temperatures may be up to 17 ºC higher during the winter when the surrounding air temperature is below 0 ºC

(Strimbeck et al. 1993). Furthermore, reduced rates of photosynthesis in Picea needles can result from photoinhibition during the winter in northern Japan (Kayama et al. 2009). It is likely that similar increases in temperature occurred for Abies needles in my study area. High winter temperatures at the needle level are induced by high light absorption, with light coming from both above and reflected off of snow. In many species, high light absorption leads to photodamage of the needles, however, A. mariesii is able to acclimate to the strong light environment during winter (Yamazaki et al. 2003, Yamazaki et al. 2007).

130

Likely as a result of similar acclimation, did not see a strong negative growth response in the Abies to sun duration during the winter. In P. pumila, there can be reduced photosynthetic potential in response to water stress during the growing season (Nagano et al. 2013). High temperatures at treeline correlate with solar radiation, and excess solar radiation can cause dieback in trees at treelines (Harsch and Bader 2011).

In this study, Abies growth response paralleled the growth responses found in other treeline sites in Japan, though not those throughout Northeast Asia, while Betula exhibited widespread positive growth responses to growing season temperatures in this study, like they do throughout Japan and Northeast Asia

(Table 4-S1, 4-7, 4-9B and Figure 4-10). Summer temperature growth responses are common in Betula spp. treelines in Fennoscandia (Eckstien et al. 1991; Levanič and Eggertsson 2008) and Nepal (Dawadi et al. 2014; Liang et al. 2014). The generally positive growth response to high temperatures is during the short photosynthetically active period between leaf budburst in late June until leaf senescence in early

October (Gansert et al. 1999).

During the late winter and early spring, prior to the photosynthetically active period, Betula displayed negative growth responses to high temperatures (Table 4-7 and 4-9B). Betula buds begin to swell in the beginning of May and leaves near treeline flush in mid- to late June on Mt. Fuji (Gansert et al. 1999). In Scandinavia, increased temperatures during the early spring can reduce the thermal time needed for budburst in B. pubescens (Heide 1993). Negative responses to warm March temperatures may therefore have been due to accelerating leaf bud phenology, making buds and young leaves more susceptible to frost events in the late spring and early summer (Inouye 2000). During the bud swell stage in May, a nine day period of low temperatures (~-3 oC) can delay the onset of budburst by up to four weeks (Gansert et al. 1999). In addition to the risk of frost injury on buds from accelerated phenology, there is a risk of xylem vessel cavitation that would reduce photosynthetic area of the canopy (Sperry et al. 1994). The sensitivity of Betula to seasonal variation in warming, including the susceptibility to frost, may therefore be of importance to the upper elevation distribution of Betula in the mountains.

Annual temperatures throughout Japan are projected to increase by about 3 ºC by the end of the

21st century, with the largest increases expected to occur in the winter and spring (Japanese

131

Meteorological Agency (JMA) 2014). The increase in temperatures, particularly summer warming, should have a positive effect on tree growth near treeline if we only look at the temperature climate-growth relationships. However, the potential impacts of the winter and spring temperature increases will likely have opposing impacts on Abies and Betula. Increases in winter temperature will likely improve growth conditions for Abies, while increases in spring temperature could have detrimental impacts to Betula development. Increased of spring temperatures may accelerate Betula leaf phenology, putting young buds and leaves at greater risk of frost damage (Inouye 2000).

Precipitation-Growth Relationships

The influence of precipitation on tree growth was less consistent between the sites and the four meteorological stations than that of temperature (Table 4-7). High winter (February) precipitation was important for growth of Abies. The mountain climate data and estimates from lapse rates applied to the lower elevation meteorological station data both indicate that February temperatures at the sites are approximately -11 ºC (Table 4-5), thus precipitation in February would have fallen as snow. The climate data indicated that precipitation was the highest at Toyama during February (Figure 4-3), as was the correlation between Abies growth and February precipitation (Table 4-7 and 4-9B). This shows a clear spatial pattern in the responses of trees to the local climate during winter, where tree growth in the more maritime climates had a positive relationship to increased precipitation. Lower amounts of precipitation during February did not, however, negatively influence tree growth in more continental climates. Similar positive climate-growth relationships to winter precipitation have been found in other conifer species

(Laroque and Smith 1999; Oberhuber 2004; Gaire et al. 2014). Increased winter precipitation in the Ural

Mountains has also been associated with higher shrub and tree abundance due to the insulating effect of snow on soil temperature resulting in higher rates of nutrient mobilization (Hagedorn et al. 2014).

Large quantities of winter snow can have two opposing impacts on trees: damage and protection.

The former is common in regions where there is a heavy wet snow load that can build up and snap the

132 branches (Hadley and Smith 1983; Jalkanen and Konopka 1998; Seki et al. 2005). Snapping of fewer than

20 branches had little impact on the survival of Abies in Northern Honshu Japan, while breakage of the main stem (> 5 cm diameter) resulted in 40% mortality within two years (Seki et al. 2005). Large quantities of snow can also protect trees by insulating them via a deep snow pack that reduces damage from wind abrasion and desiccation (Yoshino, 1973; Hadley and Smith 1983, 1986; Scott et al. 1993;

Körner 1998; Sturm et al. 2001; Yamazaki et al. 2003; Cairns 2003; Holtmeier and Broll 2010; Takahashi et al. 2011). The abrasion zone occurs primarily in the lowest 80 cm of branches exposed to air above the snowpack (Scott et al. 1993).

Previous and current May precipitation at Toyama resulted in negative growth responses in Abies

(Table 4-7 and 4-9). Precipitation in May is likely still in the form of snow (Table 4-6), which can negatively impact growth by delaying the onset of the growing season. Budburst is not until the end of

June for Abies mariesii (Fujiwara et al. 1999), though snow in May could damage buds through abrasion when they are more sensitive (Seki et al. 2004).

The negative growth responses shown in this study therefore suggest responses to precipitation in

Betula differ from those of Abies. In Northeast Asia, Betula growth can be positively associated with precipitation (Takahashi et al. 2003; Doležal et al. 2010; Takahashi et al. 2011; Wang et al. 2011).

However, individuals at treeline in Japan and Himalayas had negative responses to precipitation during the growing season (Takahashi et al. 2011; Dawadi et al. 2013). Similar to the Himalayan birch (B. utilis), treeline individuals of B. ermanii are not only limited by temperature, but also by the precipitation regime. Growth in Himalayan birch has a stronger positive relationship with pre-monsoon precipitation than with summer temperature (Liang et al. 2014). This sensitivity to pre-monsoon precipitation indicates that the timing of precipitation is more important for growth and development than either the total amount of precipitation or growing season temperature for B. utilis (Liang et al. 2014). My results for Betula in the Northern Japanese Alps showed a different relationship between growth and precipitation. Here, growth was not positively correlated with large quantities of summer precipitation, but it was associated with high summer solar radiation (Table 4-8). This pattern has been demonstrated previously for Betula at

133 treeline of positive correlations with solar radiation and negative ones with precipitation (Takahashi et al.

2005). The growing season is relatively short for the latitude of the region (late June to early October

[Gansert 1999]). While the proportion of sunshine hours was greater during the summer than during preceding and subsequent the rainy seasons, the quantity of sunshine during this key period of time likely directly affected growth (Table 4-8).

Betula growth responded negatively to precipitation during the winter (January) and spring

(April) in this study (Table 4-7, 4-8, 4-9A and Figure 4-10). Precipitation during both the winter and spring would have fallen as snow in the mountains (Table 4-5). The mechanism causing these negative growth responses to winter and spring precipitation is less straightforward than the decline in sunshine associated with summer precipitation. Greater snowfall in both January and April may have negatively impacted tree growth by delaying the onset of the growing season. However, if increased winter precipitation delayed the onset of the growing season, similar relationships for February and March might be expected but were not evident here. It is also unlikely that Betula individuals were physically damaged due to snow load, for their stems are highly flexible, making them more resilient to snow load than coniferous species (Jalkanen and Konopka 1998).

Annual precipitation in Japan is projected to increase (100 mm) over the next century, and although the projections for central Japan show a similar annual increase, these regional projections are not statistically significant (JMA 2014). Nonetheless, significant seasonal increases in precipitation are expected during the winter (51 mm) and spring (56 mm) on the Pacific side of the Japanese Alps, and these two seasonal increases make up nearly the entire projected annual increase in this region (JMA

2014). Due to the simultaneous increase in projected temperatures, the models suggest that overall snow depth is expected to decrease and the snowy period in Japan will likely shorten. However, at the high elevations of the Northern Japanese Alps, even with these increased temperatures, precipitation should still fall largely as snow between November and April because temperatures will likely remain below freezing (Table 4-5). Thus, it is possible that the duration of snow cover might increase in the Northern

Japanese Alps on the Pacific side. Increased winter precipitation (snow) will seemingly improve the

134 growing conditions of Abies while negatively impacting that of Betula. Currently, both species have had negative responses to spring precipitation that most likely falls as snow. Future increases in spring precipitation in the high elevation forests could fall as snow, rain, or even ice. It is unknown how Abies and Betula will respond to these changes in type of precipitation during the dehardening period at the onset of the growing season.

PDSI-Growth Relationships

In addition to the meteorological data (Figure 4-3), the monthly moist adiabatic lapse rate indicated that the Japanese Alps received large quantities of moisture year-round (Table 4-4). Growth limitations due to drought are typically found in arid, low elevation forests (Allen et al. 2010; Clark et al.

2016). Water stress or drought in mesic treeline systems have each been found in monsoonal regions where low precipitation during the pre-monsoon season can limit tree growth (Dawadi et al. 2013; Liang et al. 2014). In the mesic deciduous temperature forests of eastern North America, drought was shown to be the main determinant of tree growth (Martin-Benito and Pederson 2015). In this study, the influence of drought on tree growth was not consistent between the two species. Betula trees showed little response to

PDSI, suggesting that growth in these individuals was not limited by precipitation (Figure 4-10). By contrast, annual growth in Abies individuals was sensitive to drought conditions in the previous year.

When temperatures were high and precipitation was low, Abies had reduced growth (Figure 4-10).

Drought sensitivity at high elevations may result from temperatures increasing at greater rates at high elevations and latitudes than low elevation and latitudes (Jump et al. 2009). In addition to the upper elevation of Abies individuals being limited by mechanical damage from wind and snow, the upper range limits may also be due to drought constraining growth.

Mean annual temperature and total annual precipitation throughout Japan are projected to increase by about 3 ºC and 100 mm, respectively, by the end of the 21st century (JMA 2014). However, as mentioned above, this projected increase in precipitation is not statistically significant for central Japan

135

(JMA 2014). Water stress or drought in central Japan may therefore become more common. Here, I have shown that there is the potential for high temperatures and low precipitation during the growing season and fall season to reduce tree growth in the following year for Abies (Figure 4-10). With increasing temperatures and stable precipitation in central Japan, it is possible that moisture stress will become more strongly limit Abies growth in the coming decades.

Growth Variation

A global study on chronologies with missing tree-rings demonstrated that the majority occurred in the southwestern USA, and of these, the majority were attributed to drought rather than unfavorable temperatures (St. George et al. 2013). The authors found that coniferous genera (Pinus spp., Pseutotsuga spp., Larix spp., and Picea spp.) comprised the bulk of the missing rings, while the broad-leaved genus

Quercus held the fifth most missing rings (St. George et al. 2013). The only Betula record included in their analysis was from Sweden, in which B. pubescens had no missing rings (St. George et al. 2013).

However, Betula are prone to having missing rings, particularly in species from Arctic and alpine ecosystems, that are often attributed to either unfavorable temperatures or drought (B. nana L. [Wilmking et al. 2012; Hollesen et al. 2015], B. utilis [Liang et al. 2014], B. papyrifera Marshell [Girardin and Tardif

2005], B. pubescens [Eckstein et al. 1991; Levanič and Eggertsson 2008], and B. ermanii [Takaoka

1993]). Abies spp. were also included in the global analysis of missing rings (147 chronologies of the

2,359 total chronologies); however, they had < 0.01% missing rings and were therefore not discussed in the paper (St. George et al. 2013). However, missing rings in Abies spp. have elsewhere been attributed to suppression (A. balsamea (L.) Mill. [Parent et al. 2002]), insect outbreaks (A. alba Mill. [Camarero et al.

2002] and A. balsamea [Krause and Morin 1995]) and drought (A. concolor (Gord. and Glend.) Lindl. Ex

Hildebr. [Meko et al. 2013] and A. lasiocarpa (Hooker) Nuttall [Bigler et al. 2007]).

Similar to other species of Betula in Arctic and alpine regions, missing rings were common in many of the individuals (63%) investigated here. Missing rings in Betula have been attributed to climatic

136 variables such as low summer temperatures (Eckstien et al. 1991) and summer drought (Girardin and

Tardif 2005; Liang et al. 2014). The years with missing rings found here were inconsistent across all mountains, indicating that they were unlikely to have been created by synchronous large-scale climatic processes (Figure 4-9A). Furthermore, there were no significant correlations between the climate parameters and the occurrence of missing rings. Continuous missing outer rings have been attributed to four variables: cambial age, stem length, stem proportion/length below peat surface, and defoliation by herbivores (Wilmking et al. 2012). In Hokkaido, continuous missing outer rings were detected in 40% of the Betula individuals, primarily subdominant trees (Takaoka 1993). In the present study, there were no clear age related patterns of missing rings; missing rings occurred periodically, and there were periods of continuously missing rings (Figure 4-9B and C).

Disturbances, primarily insect outbreaks are known to cause missing rings in Betula (Eckstein et al. 1991; Kirchhefer 1996; Levanič and Eggertsson 2008; Wilmking et al. 2012; Young et al. 2014;

Young et al. 2016). Insect outbreaks have been documented in Japan at lower elevations, with a trend of fewer insect outbreaks as elevation increases (Kamata 2002). The decline in insect outbreaks with elevation could explain the absence of any evidence of outbreaks in the high elevation forests of the

Japanese Alps. Another mechanism may be the timing (growing season vs winter) and intensity of regional volcanic eruptions may be an important driver of reduced growth via cooling, thus reducing growth in Betula individuals in the Northern Japanese Alps (A. Young, unpublished data). Potential mechanisms could include direct defoliation, reductions in solar radiation, or changes in soil chemistry. In

Kamchatka, conspecific Betula trees had similar growth responses to nearby volcanic eruptions, though not all eruptions were detectable in the growth rings (Doležal et al. 2010). One of the largest eruptions in

Kamchatka took place ~50 km from the studied trees in March 1956, though there was no growth reduction in response to this eruption (Doležal et al. 2010). Further research is thus needed to disentangle how growth is affected by volcanic activity compared with other disturbances such as typhoons and ice storms.

137

Conclusion

The interactive effects of temperature and precipitation on tree growth in the Northern Japanese

Alps suggest this system can be a bellwether to other treeline systems in which broad-leaved deciduous trees are becoming more common. Betula growth appears likely to increase as a result of increasing summer temperatures. However, increasing spring temperatures and precipitation may negatively impact

Betula growth and whether the positive impacts will outweigh the negative ones is as yet unknown. There is greater uncertainty for growth in Abies, for it showed mixed responses to summer temperatures; however, increasing winter temperatures and precipitation appear likely to lead to increased growth. The mechanism behind the missing rings found in this study needs to be further investigated, in this analysis, they were due to neither climate nor known insect outbreaks, though potentially resulted of from regional volcanic activity.

138

References

Allen, K., Nichols, S., Evans, R., Cook, E., Allie, S., Carson, G., Ling, F. and P. Baker. 2015. Preliminary

December-January inflow and streamflow reconstructions from tree rings for western Tasmania,

southeastern Australia. Water Resources Research 51: 5487-5503.

Altman, J., Fibich. P., Doležal. J. and T. Aakala. 2014. TRADER: a package for Tree Ring Analysis of

Disturbance Events in R. Dendrochonologia 32: 107-112, http://cran.r-

project.org/package=TRADER.

Akhalkastsi, M., Abdaladze, O., Nakhutsrishvili, G. and W. Smith.2006. Facilitation of seedling

microsites by Rhododendron caucasicum extends the Betula litwinowii alpine treeline, Caucasus

Mountains, Republic of Georgia. Arctic, Antarctic, and Alpine Research 38: 481-488.

Ashburner, K. and H. McAllister. 2013. The Genus Betula: A taxonomic revision of birches. Kew

Publishing. Royal Botanic Gardens, Kew.

Baptiste, A. 2015. gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.2.1.

http://cran.r-project.org/package=gridExtra

Bell, D., Bradford, J. and W. Lauenroth. 2014. Mountain landscapes offer few opportunities for high-

elevation tree species migration. Global Change Biology 20: 1441-1451.

Bigler, C., Gavin, D., Gunning, C. and T. Veblen. 2007. Drought induces lagged tree mortality in a

subalpine forest in the Rocky Mountains. Oikos 116: 1983-1994.

Bigler, C. and T. Veblen. 2009. Increased early growth rates decrease longevities of conifers in subalpine

forests. Oikos 118: 1130-1138.

Biondi, F. 2001. A 400-year tree-ring chronology from the Tropical treeline of North America. AMBIO

30: 162-166.

Bunn, A., Graumlich, L., and D. Urban. 2004. Trends in twentieth-century tree growth at high elevations

in the Sierra Nevada and White Mountains, USA. The Holocene 15: 481-488.

139

Cairns, D. 2001. Patterns of winter desiccation in krummholz forms of Abies lasiocarpa at treeline sites in

Glacier National Park, Montana, USA. Geografiska Annaler 83A: 157-168.

Canham, C. and R.Q. Thomas. 2010. Frequency, not relative abundance, of temperate tree species varies

along climate gradients in eastern North America. Ecology 91: 3433-3440.

Camarero, J., Martín Bernal, E. and E. Gil-Pelegrín. 2002. A dendroecological study of a Epinotia

subsequana Hw. (Lepidoptera: Tortricidae) attack on a fir (Abies alba Mill.) forest in the Aragon

Pyrenees (Diazas, Torla). Investigación Agraria: Sistemas y Recursos Forestale 11: 395-407. (ESP).

Carrer, M., Nola, P., Eduardo, J., Motta, R. and C. Urbinati. 2007. Regional variability of climate-growth

relationships in Pinus cembra high elevation forests in the Alps. Journal of Ecology 95: 1072-1083.

Chhetri, P. and D. Cairns. 2016. Dendroclimatic response of Abies spectabilis at treeline ecotone of Barun

Valley, eastern Nepal Himalaya. Journal of Forestry Research 27: 1163-1170.

Clark, J., Iverson, L., Woodall, C., Allen, C., Bell, D., Gragg, D., D’Amato, A., David, F., Hersh, M.,

Ibanez, I., Jackson, S., Matthews, S., Pederson, N., Peters, M., Schwartz, M., Waring, K. and N.

Zimmermann. 2016. The impacts of increasing drought on forest dynamics, structure, and

biodiversity in the United States. Global Change Biology 22: 2329-2352.

Cogbill, C. and P. White. 1991. The latitude-elevation relationship for spruce-fir forest and treeline along

the Appalachian mountain chain. Vegetatio 94: 153-175.

Cook, E., Krusic, P., Holmes, R. and K., Peters. 2012. Program ARSTAN version 47. Available from

http://www.ldeo.columbia.edu/tree-ring-labratory [accessed April 10, 2016].

D’Arrigo, R., Yamaguchi, D., Wiles, G., Jacoby, G., Osawa, A. and D. Lawrence. 1997. A Kashiwa oak

(Quercus dentata) tree-ring width chronology from northern costal Hokkaido, Japan. Canadian

Journal of Forest Research 27: 613-617.

Daniels, L. and T. Veblen. 2004. Spatiotemporal influences of climate on altitudinal treeline in northern

Patagonia. Ecology 85: 1284-1296.

140

Davi, N. D’Arrigo, R., Jacoby, G., Buckley, B. and O. Kobayashi. 2002. Warm-season annual to decadal

temperature variability from Hokkaido, Japan, inferred from maximum latewood density (AD 1557-

1990) and ring width data (AD 1532-1990). Climatic Change 52: 201-217.

Dawadi, B., Liang, E., Tian, L., Devkota, L.P. and T. Yao. 2013. Pre-monsoon precipitation signal in tree

rings of timberline Betula utilis in the central Himalayas. Quaternary International 283: 72-77.

Di Filippo, A., Pederson, N., Baliva, M., Dinella, A., Kitamura, K., Knapp, H., Schirone, B. and G.

Piovesan. 2015. The longevity of broadleaf deciduous trees in Northern Hemisphere temperate

forests: insights from tree-ring series. Frontiers in Ecology and Evolution 3: 46.

Doležal, J., Ishii, H., Kyncl, T., Takahashi, K., Vetrova, V., Homma, K., Sumida, A. and T. Hara. 2010.

Climatic factors affecting radial growth of Betula ermanii and Betula platypylla in Kamchatka.

Canadian Journal of Forest Research 40: 273-285.

Eckstien. D., Hoogesteger, J. and R. Holmes. 1991. Insect-related differences in growth of birch and pine

at northern treeline in Swedish Lapland. Holarctic Ecology 14: 18-23.

Elliott, G. 2012. Extrinsic regime shifts drive abrupt changes in regeneration dynamics at upper treeline in

the Rocky Mountains, USA. Ecology 93: 1614-1625.

Franklin, J., Haeda, T., Ohsumi, Y., Matsui, M., Yagi, H. and G. Hawk. 1979. Subalpine coniferous

forests of central Honshu, Japan. Ecological Monographs 49: 311-334.

Fujimoto, S. and M. Miyakawa. 1991. Growth characteristics of Betula ermanii in particular reference to

response patterns at timber limes. Journal of the Faculty of Agriculture, Hokkaido University 65: 219-

228.

Fujiwara, T., Okada, N. and K. Yamashita. 1999. Comparison of growth response of Abies and Picea

species to climate in Mt. Norikura, central Japan. Journal of Wood Science 45: 92-97.

Gaire, N., Koirala, M., Bhuju, D. and H. Borgaonkar. 2014. Treeline dynamics with climate change at the

central Nepal Himalaya. Climate of the Past 10: 1277-1290.

141

Gansert, D., Backes, K. and Y. Kakubari. 1999. Altitudinal and seasonal variation of frost resistance of

Fagus crenata and Betula ermanii along the Pacific slope of Mt. Fuji, Japan. Journal of Ecology 87:

383-390.

Gansert, D. 2002. Betula ermanii, a dominant subalpine and subarctic treeline tree species in Japan:

ecological traits of deciduous tree life in winter. Arctic, Antarctic, and Alpine Research 34: 57-64.

Girardin, M-P. and J. Tardif. 2005. Sensitivity of tree growth to the atmospheric vertical profile in the

Boreal Plains of Manitoba, Canada. Canadian Journal of Forest Research 35: 48-64.

Givnish, T. 2002. Adaptive significance of evergreen vs deciduous leaves: Solving the triple paradox.

Silva Fennica 36: 703-743.

Gower, S., Krankina, O., Olson, R., Apps, M., Linder, S. and C. Wang. 2001. Net primary production and

carbon allocation patterns of boreal forest ecosystems. Ecological Applications 11: 1395-1411.

Graumlich, L. 1993. A 1000-year record of temperature and precipitation in the Sierra Nevada.

Quaternary Research 39: 249-255.

Greenwood, S. and A. Jump. 2014. Consequences of treeline shifts for the diversity and function of high

altitude ecosystems. Arctic, Antarctic, and Alpine Research 46: 829-840.

Guiot, J. 1990. Methods of calibration. In: Methods of dendrochronology: applications in the

environmental sciences. Cook, E. and L. Kairiukstis (eds). Kluwer Academic Publisher, Boston. pp

165-178.

Hadley, J. and W. Smith. 1983. Influence of wind exposure on needle desiccation and mortality for

timberline conifers in Wyoming, USA. Arctic and Alpine Research 15: 127-135.

Hadley, J. and W. Smith. 1986. Wind effects on needles of timberline conifers: seasonal influence on

mortality. Ecology 67: 12-19.

Hagedorn, F., Shiyatov, S., Mazepa, V., Devi, N., Grigor’ev, A., Bartysh, A., Fomin, V., Kapralov, D.,

Terent’ev, M., Bugman, H., Rigling, A. and P. Moiseev. 2014. Treeline advances along the Ural

Mountain range – driven by improved winter conditions? Global Change Biology 20: 3530-3543.

142

Hao, Z., Zhao, S., Tao, D. and M. Wang. 1993. Species diversity of higher plants in lime Korean pine

forest on northern slope of Changbai Mountain. Chinese Journal of Ecology 12: 1-5.

Harsch, M., Hulme, P., McGlone, M. and R. Duncan. 2009. Are treelines advancing? A global meta-

analysis of treeline response to climate warming. Ecology Letters 12: 1040-1049.

Harsch, M. and M. Bader. 2011. Treeline form – a potential key to understanding treeline dynamics.

Global Ecology and Biogeography 20: 582-596.

Heide, O. 1993. Daylength and thermal time responses of bud burst during dormancy release in some

northern deciduous trees. Physiological Plantarum 88: 531-540.

Hollesen, J., Buchwal, A., Rachlewicz, G., Hansen, B., Hansen, M., Stecher, O. and B. Elberling.2015.

Winter warming as an important co-driver for Betula nana growth in western Greenland during the

past century. Global Change Biology 21: 2410-2423.

Holmes, R. 1986. User’s manual for program COFECHA. In: Holmes, R.L., Adams, R.K., Fritts, H.C.

(Eds.), Tree-ring Chronologies of Western North America: California, Eastern Oregon, and Northern

Great Basin. Laboratory of Tree-Ring Research, University of Arizona, Tucson, pp. 41–49.

Holtmeier, F., Broll, G., Muterthies, A. and K. Anschlag. 2003. Regeneration of trees in the treeline

ecotone: northern Finnish Lapland. FENNIA 181: 103-128.

Holtmeier, F. and G. Broll. 2005. Sensitivity and response of northern hemisphere altitudinal and polar

treelines to environmental change at landscape and local scales. Global Ecology and Biogeography

14: 395-410.

Holtmeier, F. and G. Broll. 2010. Wind as an ecological agent at treelines in North America, the Alps,

and the European subarctic. Physical Geography 31: 203-233.

Hughes, N., Johnson, D., Akhalkatsi, M. and O. Abdaladze. 2009. Characterizing Betula litwinowii

seedling microsites at the alpine-treeline ecotone, Central Greater Caucasus Mountains, Georgia.

Arctic, Antarctic, and Alpine Research 41: 112-118.

Ida, H. 2000. Treefall gap disturbance in an old-growth beech forest in southwestern Japan by a

catastrophic typhoon. Journal of Vegetation Science 11: 825-832.

143

Ikeda, S., Wakabayashi, R., Izumi, K. and K. Kawashima. 2009. Study of snow climate in the Japanese

Alps: comparison to snow climate in North America. Cold Regions Science and Technology 59: 119-

125.

Inoue, T. and Matsumoto. 2003. Seasonal and secular variations of sunshine duration and natural seasons

in Japan. International Journal of Climatology 23: 1219-1234.

Inouye, D. 2008. Effects of climate change on phenology, frost damage, and floral abundance of montane

wildflowers. Ecology 89: 353-362.

Iverson, L. and A. Prasad. 2002. Potential redistribution of tree species habitat under five climate change

scenarios in the eastern US. Forest Ecology and Management 155:205-222.

Jalkanen, R. and B. Konopka. 1998. Snow-packing as a potential harmful factor on Picea abies, Pinus

sylvestris and Betula pubescens at high altitude in northern Finland. European Journal of Forest

Pathology 28: 373-382.

Japan Meteorological Agency, 2014: Global Warming Projection Vol.8,

http://ds.data.jma.go.jp/tcc/tcc/products/gwp/gwp8/index.html

Jobbágy, E. and R. Jackson. 2000. Global controls of forest line elevation in the northern and southern

hemispheres. Global Ecology and Biogeography 9: 253-268.

Jump, A., Mátyás, C. and J. Peñuelas. 2009. The altitude-for-latitude disparity in the range retractions of

woody species. Trends in Ecology and Evolution 24: 694-701.

Kajimoto, T., H. Dalmaru, T. Okamoto, T. Otani, and H. Onodera. 2004. Effects of snow avalanche

disturbance on regeneration of subalpine Abies mariesii forest, northern Japan. Arctic, Antarctic,

and Alpine Research 36:436-445.

Kanzaki, M. and K. Yoda. 1986. Regeneration in subalpine coniferous forests II. Mortality and the pattern

of death of canopy trees. The Botanical Magazine, Tokyo 99: 37-51.

Kamata, N. 2002. Outbreaks of forest defoliating insects in Japan, 1950-2000. Bulletin of Entomological

Research 92: 109-118.

144

Kapralov, D., Shiyatov, S., Moiseev, P. and V. Fomin. 2006. Changes in the composition, structure, and

altitudinal distribution of low forests at the upper limit of their growth in the Northern Ural

Mountains. Russian Journal of Ecology 37: 367-372.

Kariya, Y., Sato, G., Mokudai, K., Komori, J., Ishii, M., Nishii, R., Miyazawa, Y. and N. Tsumura. 2007.

Rockfall hazard in the Daisekkei Valley, the northern Japanese Alps, on 11 August 2005.

Landslides 4:91-94.

Kayama, M., Makoto, K., Nomura, M., Sasa, K. and T. Koike. 2009. Growth characteristics of Sakhalin

spruce (Picea glehnii) planted on the northern Japanese hillsides exposed to strong winds. Trees

23: 145-157.

Kershaw, G. and C. Laroque. 2012. The dendroclimatic potential of white birch. The Northeastern

Geographer 4: 28-38.

Kirchhefer, A. 1996. A dendrochronological study on the effects of climate, site, and insect outbreaks on

the growth of Betula pubescens coll. in northern Norway. Palaeoclimate Research 20: 93-106.

Kobayashi, O., Funada, R., Yasue, K. and J. Ohtani. 1998. Evaluation of the effects of climatic and

nonclimatic factors on the radial growth of Yezo spruce (Picea jezoensis Carr) by

dendrochronological methods. Annals of Forest Science 55:277-286.

Kojo, Y. 1987. A dendrochronological study of Cryptomeria japonica in Japan. Tree-Ring Bulletin 47: 1-

21.

Krause, C. and H. Morin. 1995. Changes in radial increment in stems and roots of balsam fir [Abies

balsamea (L.) Mill.] after defoliation spruce budworm. The Forestry Chronicle 71:747-754.

Kubota, Y. and T. Hara. 1996. Recruitment processes and species coexistence in a sub-boreal forest in

Northern Japan. Annals of Botany 78:741-748.

Kullman, L. 1993. Tree limit dynamics of Betula pubescencs ssp. tortuosa in relation to climate

variability: evidence from central Sweden. Journal of Vegetation Science 4:765-772.

Körner, C. 1998. A re-assessment of high elevation treeline positions and their explanation. Oecologia

115: 445-459.

145

Körner, C. and J. Paulsen. 2004. A world-wide study of high altitude treeline temperatures. Journal of

Biogeography 31: 713-732.

Körner, C. 2013. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits.

Springer, Basel.

Laroque, C. and D. Smith. 1999. Tree-ring analysis of yellow-cedar (Chamaecyparis nootkatensis) on

Vancouver Island, British Columbia. Canadian Journal of Forest Research 29: 115-123.

Levanič, T. and O. Eggertsson. 2008. Climatic effects on birch (Betula pubescens Ehrh.) growth in

Fnjoskadalur valley, northern Iceland. Dendrochronologia 25: 135-143.

Liang, E., Dawadi, B., Pederson, N. and D. Eckstein. 2014. Is the growth of birch at the upper timberline

in the Himalayas limited by moisture or by temperature? Ecology 95:2453-2465.

Linderholm, H., Solberg, B. and M. Lindbolm. 2003. Tree-ring records from central Fennoscandia: the

relationship between tree growth and climate along a west-east transect. The Holocene 13: 887-895.

Martin-Benito, D. and N. Pederson. 2015. Convergence in drought stress, but a divergence of climatic

drivers across a latitudinal gradient in a temperate broadleaf forest. Journal of Biogeography 42:

925-937.

Matsuoka, N. and H. Sakai. 1999. Rockfall activity from an alpine cliff during thawing periods.

Geomorphology 28:309-328.

Melvin, T., Briffa, K., Nicolussi, K. and M. Grabner. 2007. Time-varying-response smoothing.

Dendrochronologia 25: 65-69.

Meko, D., Touchan, R., Villanueva Díaz, J., Griffin, D., Woodhouse, C., Castro, C., Carillo, C. and S.

Leavitt. 2013. Sierra San Pedro Mártir, Baja California cool-season precipitation reconstructed from

earlywood width of Abies concolor tree rings. Journal of Geophysical Research 118: 1660-1673.

Moen, J., Aune, K., Edenius, L. and A. Angerbjörn. 2004. Potential effects of climate change on treeline

position in the Swedish mountains. Ecology and Society 9:16.

Mori, A. and H. Takeda. 2003. Architecture and neighbourhood competition of understorey saplings in a

subalpine forest in central Japan. Écoscience 10: 217-224.

146

Nagano, S., Nakano, T., Hikosaka, K. and E. Maruta. 2013. Pinus pumila photosynthesis is suppressed by

water stress in a wind-exposed mountain site. Arctic, Antarctic, and Alpine Research 45:229-237.

Nakatsuka, T., Ohnishi, K., Hara, T., Sumida, A., Mitsuishi, D., Kurita, N. and S. Uemura. 2004. Oxygen

and carbon isotopic ratios of tree-ring cellulose in a conifer-hardwood mixed forest in northern Japan.

Geochemical Journal 38:77-88.

Nowacki, G. and M. Abrams. 1997. Radial-growth averaging criteria for reconstructing disturbance

histories from presettlement-origin oaks. Ecological Monographs 67: 225-249.

Oberhuber, W. 2004. Influence of climate on radial growth of Pinus cembra within the alpine timberline

ecotone. Tree Physiology 24:291-301.

Ohsawa, M. 1990. An Interpretation of Latitudinal Patterns of Forest Limits in South and East Asian

Mountains. Journal of Ecology 78: 326-339.

Parent, S., Morin, H. and C. Messier. 2002. Missing growth rings at the trunk base in suppressed balsam

fir saplings. Canadian Journal of Forest Research 32:1776-1783.

Pederson, N., Cook, E., Jacoby, G., Peteet, D. and K. Griffin. 2004. The influence of winter temperatures

on the annual radial growth of six northern range margin tree species. Dendrochronologia 22: 7-29.

Pederson, N., Bell, A., Cook, E., Lall, U., Devineni, N., Seager, R., Eggleston, K. and K. Vranes. 2013. Is

an Epic Pluvial Masking the Water Insecurity of the Greater New York City Region? Journal of

Climate 26: 1339-1354.

Peterson, D. and D. Peterson. 2001. Mountain hemlock and growth responds to climatic variability at

annual and decadal time scales. Ecology 82: 3330-3345.

Powers, S. 1916. The eruption of Yake-dake, Japan, in 1915. Geographical Review 1: 359-362.

Randin, C., Paulsen, J., Vitasse, Y., Kollas, C., Wohlgemuth, T., Zimmermann, N. and C. Körner. 2013.

Do the elevational limits of deciduous tree species match their thermal latitudinal limits? Global

Ecology and Biogeography 22: 913-923.

R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

147

Scott, P., Hansell, R. and W. Erickson. 1993. Influences of wind and snow on northern tree-line

environments at Churchill, Manitoba, Canada. Arctic 46: 316-323.

Sakai, A. and W. Larcher. 1987. Frost survival in plants. Springer-Verlag, Berlin.

Salzer, M., Bunn, A., Grahm, N. and M. Hughes. 2014. Five millennia of paleotemperature from tree-

rings in the Great Basin, USA. Climate Dynamics 42: 1517-1526.

Sato, N. 2011: Lapse rate on the east slope of Mt. Norikura. MS. Thesis. Shinshu University, Matsumoto

(in Japanese with English summary).

Seki, T., Kajimoto, T., Sugita, H., Daimaru, H., Ikeda, S. and T. Okamoto. 2005. Mechanical damage on

Abies mariesii trees buried below the snowpack. Arctic, Antarctic, and Alpine Research 37: 34-

40.

Schulze, E.-D., Fuchs, M. and M. I. Fuchs. 1977. Spatial distribution of photosynthetic capacity and

performance in a mountain spruce forest of northern Germany. Oecologia 30: 239-148.

Sperry, J., Nichols, K., Sullivan, J. and S. Eastlack. 1994. Xylem embolism in ring-porous, diffuse-

porous, and coniferous trees of Northern Utah and interior Alaska. Ecology 75:1736-1752.

St. George, S., Ault, T. and M. Torbenson. 2013. The rarity of absent growth rings in Northern

Hemisphere forests outside the American Southwest. Geophysical Research Letters 40: 3727-3731.

Strimbeck, G., Johnson, A. and D. Vann. 1993. Midwinter needle temperature and winter injury of

montane red spruce. Tree Physiology 13:131-144.

Stokes, M. and T. Smiley. 1967. An Introduction to tree-ring dating. The University of Arizona Press,

Tucson.

Sturm, M., McFadden, J., Liston, G., Chapin III, F.S., Racine, C. and J. Holmgren. 2001. Snow-shrub

interactions in Arctic tundra: A hypothesis with climatic implications. Journal of Climate 14: 336-

344.

Suzuki, K., Kuramoto, T., Tanaka, M. and S-K. Shah. 2008. Water balance and mass balance in a

mountainous river basin, Northern Japanese Alps. Bulletin of Glaciological Research 26: 1-8.

148

Takahashi, K., Azuma, H. and K. Yasue. 2003. Effects of climate on the radial growth of tree species in

the upper and lower distribution limits of an altitudinal ecotone on Mount Norikura, central Japan.

Ecological Research 18: 549-558.

Takahashi, K., Tokumitsu, Y. and K. Yasue. 2005. Climatic factors affecting the tree-ring width of Betula

ermanii at the timberline on Mount Norikura, central Japan. Ecological Research 20: 445-451.

Takahashi, K., Okuhara, I., Tokumitsu, Y. and K. Yasue. 2011. Responses to climate by tree-ring widths

and maximum latewood densities of two Abies species at upper and lower altitudinal distribution

limits in central Japan. Trees 25: 745-753.

Takaoka, S. 1993. The effect of missing rings on stand-age estimation of even-aged forests in northern

Hokkaido, Japan. Ecological Research 8: 341-347.

Tei, S., Yonenobu, H., Suzuki, S., Ohyama, M., Gotanda, K., Nakagawa, T. and A. Sugimoto. 2015.

Reconstructed July temperatures since AD 1800, based on a tree-ring chronology network in the

Northwest Pacific region, and implied large-scale atmospheric-oceanic interactions. Palaeogeography,

Palaeoclimatology, Palaeoecology 435: 203-209.

Vasiliauskas, S. 1995. Interpretation of age-structure gaps in hemlock (Tsuga canadensis) populations of

Algonquin Park. PhD Thesis, Department of Geography, Queen’s University. 170pp.

Wang, X., Zhao, X. and L. Gao. 2013. Climatic response of Betula ermanii along an altitudinal gradient

in the northern slope of Changbai Mountain, China. Dendrobiology 70: 99-107.

Wardle, P. 1977. Japanese timberlines and some geographic comparisons. Arctic and Alpine Research 9:

249-258.

Watanabe, T., Fukuzawa, K. and H. Shibata. 2013. Temporal changes in litterfall, litter decompositions

and their chemical composition in Sasa dwarf bamboo in a natural forest ecosystem of northern

Japan. Journal of Forest Research 18: 129-138.

Way, D. and R. Oren. 2010. Differential responses to changes in growth temperature between trees from

different functional groups and biomes: a review and synthesis of data. Tree Physiology 30: 669-688.

149

Wickham, H. 2007. Reshaping Data with the reshape Package. Journal of Statistical Software 21:1-20.

URL http://cran.r-project.org/package=reshape2

Wickham, H. 2009. ggplot: Elegant graphics for data analysis. Springer-Verlag New York. URL

http://cran.r-project.org/package=ggplot2

Wigley, T., Jones, P. and K. Briffa. 1987. Cross-dating methods in dendrochronology. Journal of

Archaeological Science 14:51-64.

Wilmking, M., Hallinger, M., Van Bogaert, R., Kyncl, T., Babsst, F., Hahne, W., Juday, G., de Luis, M.,

Novak, K. and V. Völlm. 2012. Continuously missing outer rings in woody plants at their

distributional margins. Dendrochronologia 30: 213-222.

Yamamoto, S-I. 1995. Gap characteristics and gap regeneration in subalpine old-growth coniferous

forests, central Japan. Ecological Research 10:31-39.

Yamazaki,J., Ohashi, A., Hashimoto, Y., Negishi, E., Kumagai, S., Kubo, T., Oikawa, T., Maruta, E. and

Y. Kamimura. 2003. Effects of high light and low temperature during harsh winter on needle

photodamage of Abies mariesii growing at the forest limit on Mt. Norikura in Central Japan. Plant

Science 165: 257-264.

Yasue, K., Funada, R., Kondo, T., Kobayashi, O. and K. Fukazawa. 1996. The effect of climatic factors

on the radial growth of Japanese ash in northern Hokkaido, Japan. Canadian Journal of Forest

Research 26: 2052-2055.

Yasue, K., Funada, R., Fukazawa, K. and J. Ohtani. 1997. Tree-ring width and maximum density of Picea

glehnii as indicators of climatic changes in northern Hokkaido, Japan. Canadian Journal of Forest

Research 27:1962-1970.

Yonenobu, H. and D. Eckstein. 2006. Reconstruction of early spring temperature for central Japan from

the tree-ring widths of Hinoki cypress and its verification by other proxy records. Geophysical

Research Letters 33: L10701

Yoshino, M. 1973. Wind-shaped trees in the subalpine zone in Japan. Arctic and Alpine Research 5:

A115-A126.

150

Yoshino, M. 1980. The climate regions of Japan. Erdkunde 34: 81-87.

Young, A., Cairns, D., Lafon, C. and J. Moen. 2011. Dendroclimatic relationships and possible

implications for mountain birch and Scots pine at treeline in northern Sweden through the 21st

century. Canadian Journal of Forest Research 41:450-459.

Young, A., Cairns, D., Lafon, C. and J. Moen. 2014. Geometrid moth outbreaks and their climatic

relations in northern Sweden. Arctic, Antarctic, and Alpine Research 46:659-668.

Young, A., Watts, D., Taylor, A. and E. Post. 2016. Species and site differences influence climate-shrub

growth responses in West Greenland. Dendrochronologia 37:69-78.

Zang, C. 2014. scPDSI Calculation. R package version 3.3.1. https://github.com/cszang/pdsi

Zang, C. and F. Biondi. 2015. treeclim: an R package for the numerical calibration of proxy-climate

relationships. Ecography, 38: 431-436. http://cran.r-project.org/package=treeclim

Zhang, H., Shao, X. and Y. Shang. 2015. Which climatic factors limit radial growth of Wilian juniper at

the upper treeline on the northeastern Tibetan Plateau? Journal of Geographic Science 25: 1173-1182.

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152

A B

Figure 4-2. Density (tree/ha) of species in the Betula belt and Subalpine Forest (A) and the Absolute cover (m2/ha) of species in the two belts (B).

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Figure 4-3. Monthly total precipitation (mm) and mean temperature (oC) for each of the four meteorological stations surrounding the Northern Japanese Alps, displayed as a hythergraph.

154

155

5.0

2.5 BETULA

0.0

-2.5

-5.0

5.0

2.5

ABIES Principal Component Score Principal Component 0.0

-2.5

-5.0 1750 1800 1850 1900 1950 2000 year

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Figure 4-7. Percent of Betula (A) and Abies (B) cores expressing a release (positive) or suppression (negative) in growth from 1700-2011.

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159

160

161

Site ID Latitude (ºN) Longitude (ºE) Elevation (m) Aspect (º) Slope (º) Chogatake CHO 36.29 137.72 2580 264 26 Kashimayari KSM 36.61 137.74 2416 245 37 Norikura West NOW 36.16 137.54 2452 266 25 Sugorokudake SUG 36.36 137.60 2440 280 30 Tsubakuro TSU 36.40 137.71 2570 292 33

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Station Elevation Longitude Latitude Interval Temperature Precipitation Source Wajima 14 136° 54' 00" 37° 22' 48" 1963-2013 x - IGRA City Tateno 31 140° 07' 48" 36° 02' 60" 1963-2013 x - IGRA City Toyama 62 137° 12' 06" 36° 42' 30" 1939-2013 x x JMI City Nagano 415 138° 11' 30" 36° 39' 42" 1889-2013 x x JMI City Takayama 565 137° 15' 12" 36° 09' 18" 1899-2013 x x JMI City Matsumoto 613 137° 58' 12" 36° 14' 48" 1899-2013 x x JMI City Asahi 650 137° 58′ 41" 36° 15′ 05" 2008-2010 x x SUIMS City Takayama Flux 1420 137° 25' 20" 36° 08' 32" 2007-2013 x x GUTFT Forest Norikura 1450 137° 37′ 47" 36° 07′ 20" 2006-2010, 2013 x x SUIMS Forest Kamikochi 1530 137° 40′ 07" 36° 15′ 12" 2009-2013 x x SUIMS Forest Nisho 2355 137° 37′ 02" 36° 15′ 55" 2009-2013 x x SUIMS Forest Tsubakuro 2710 137° 42′ 55" 36° 23′ 57" 2009-2013 x x SUIMS Ridge Norikura 2798 137° 33′ 27" 36° 07′ 14" 2009-2013 x x SUIMS Ridge Yaridake 3070 137° 38′ 44" 36° 20′ 25" 2009-2013 x x SUIMS Ridge

IGRA (Integrated Global Radiosonde Archive) SUIMS (Shinshu University, Institute of Mountain Science) GUTFT (Gifu University, Takayama Flux Tower Site)

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Table 4-3. Linear regressions of the trends from 1910 to 2010 of climate parameters (temperature and precipitation) for each of the climate stations (Matsumoto, Takayama, Toyama, and Nagano).

Station slope intercept R2 p-values df

Matsumoto 0.0192 -26.49 0.66 <0.001 115

Takayama 0.0137 -16.54 0.42 <0.001 113 Toyama 0.0218 -29.45 0.48 <0.001 74

Annual

Temperature Nagano 0.0116 -11.27 0.40 <0.001 124

Matsumoto -0.9016 2824.41 0.02 0.098 115

Takayama -0.8990 3535.87 0.01 0.263 113 Toyama 0.4938 1353.74 0.01 0.786 74

Annual

Precipitation Nagano -0.5413 2030.88 0.02 0.146 124

164

Table 4-4. Adiabatic Temperature lapse rates (ºC 100 m-1) calculated from the mesoscale mountain stations and the radiosondes at Wajima and Tateno.

Mesoscale Radiosonde Stations Wajima Tateno Jan 0.76 0.65 0.51 Feb 0.63 0.56 0.46 Mar 0.73 0.59 0.51 Apr 0.70 0.59 0.55 May 0.70 0.56 0.55 Jun 0.65 0.55 0.53 Jul 0.63 0.57 0.54 Aug 0.60 0.61 0.57 Sep 0.55 0.58 0.52 Oct 0.57 0.6 0.5 Nov 0.59 0.6 0.47 Dec 0.70 0.62 0.45

165

Mesoscale Stations Matsumoto Nagano Takayama Toyama Wajima Tateno

June 247.8 (45.8) -18.96 -40.30 13.49 41.37 - - July 359.8 (130.8) -85.11 -91.41 111.77 76.88 - - Aug 162.5 (65.2) 69.77 90.48 174.90 * 168.60 * - - Sep 211.3 (99.6) 50.54 24.24 101.10 129.84 - - Precipitation Oct 138.9 (65.2) 141.07 75.67 75.67 * 265.42 * - - Jan -14.8 (1.4) -1.00 -2.59 * -2.33 * -1.89 -1.42 2.23 Feb -11.6 (1.6) 0.12 -1.43 -1.43 -0.90 -1.79 1.27 Mar -10.1 (1.8) 0.24 -1.59 -1.44 -1.64 -0.69 2.16

Apr -4.4 (0.4) 0.49 -1.09 -0.92 -1.68 * -1.52 0.50

May 1.5 (0.9) 0.59 -0.78 -0.54 -2.09 * -0.87 0.01 Jun 6.9 (0.8) 0.56 -0.47 -0.16 -1.81 * -2.21 * -1.73 * Jul 10.9 (0.6) 0.73 -0.51 -0.18 -1.25 -2.37 * -1.73 *

Temperature Aug 12.5 (1.0) 0.94 0.15 0.07 -0.26 -2.73 * -2.44 * Sep 8.7 (0.8) 1.30 0.88 0.55 0.66 -1.79 -0.58 Oct 2.1 (0.9) 0.75 0.28 0.09 0.93 -1.92 * 0.53 Nov -4.7 (1.6) 0.65 -0.38 -0.48 0.89 -1.36 1.63 Dec -11.0 (0.8) -0.33 -1.37 -1.55 -0.90 -0.36 3.38 *

166

Site Sampled Used # cores Correlation MS rbar EPS 1st year last year > 5 cores EPS drop-off missing rings VarPC1

CHO 40 28 55 0.582 0.362 0.468 0.936 1718 2011 1723 1735 58 -58.4%

TSU 20 17 33 0.598 0.359 0.473 0.898 1711 2011 1744 1765 37 -59.1%

SUG 20 19 38 0.617 0.416 0.486 0.91 1631 2013 1742 1700 147 -55.6% Betula CHO 20 14 27 0.504 0.205 0.38 0.852 1789 2012 1851 1860 1 -42.3% TSU 20 19 37 0.539 0.211 0.417 0.898 1818 2012 1853 1865 3 -46.8% SUG 20 16 29 0.505 0.229 0.407 0.872 1783 2013 1827 1835 3 -45.9%

NOW 20 19 38 0.515 0.223 0.441 0.915 1816 2013 1838 1850 0 -39.5%

KSM 20 20 40 0.450 0.209 0.356 0.904 1781 2013 1781 1820 1 -48.5% Abies

167

BETULA ABIES Month MAT TAK TOY NAG MAT TAK TOY NAG apr -0.02 -0.07 0.02 -0.11 0.00 0.05 0.08 -0.01 may 0.10 0.08 0.06 0.06 0.03 0.08 0.09 0.02

jun 0.04 -0.02 0.09 -0.01 -0.07 -0.04 -0.05 -0.06

jul -0.13 -0.12 -0.07 -0.14 -0.27* -0.25 -0.17 -0.25* aug -0.01 0.03 0.06 -0.03 -0.13 -0.12 -0.20 -0.16

sep 0.05 0.09 0.14 0.04 0.02 0.00 0.04 0.09 Previous oct -0.14 -0.13* -0.03 -0.16 0.05 0.09 0.14 0.09 nov 0.03 0.05 0.14 0.02 0.31* 0.30* 0.38* 0.31* dec -0.02 -0.03 -0.06 -0.13 0.33* 0.37* 0.34* 0.35* JAN -0.13 -0.07 -0.10 -0.10 0.13 0.19 0.09 0.14 FEB -0.16* -0.12 -0.17 -0.13 -0.03 0.06 -0.01 0.02

Temperature MAR -0.21 -0.23 -0.15 -0.20 0.01 0.06 0.07 0.00

APR 0.02 0.00 0.06 -0.03 0.15 0.19 0.14 0.10 MAY -0.04 -0.10 -0.16 -0.14 -0.07 -0.08 -0.08 -0.08 JUN 0.18 0.14 0.16 0.20* -0.06 -0.08 -0.11 -0.11 Current JUL 0.28 0.29* 0.29 0.30* 0.15 0.23 0.15 0.12 AUG 0.15 0.13 0.31 0.22 0.16 0.15 0.09 0.15 SEP -0.09 -0.07 0.05 -0.10 0.04 0.03 0.20 0.04 OCT -0.01 -0.03 0.03 -0.02 -0.03 -0.01 0.02 -0.06 apr -0.08 -0.03 -0.16 -0.07 0.12 0.06 -0.10 0.19 may 0.08 0.05 0.06 0.04 0.20 0.18 0.21 0.13

jun -0.01 0.05 0.04 0.01 0.16 0.07 0.08 0.10

jul 0.09 0.09 0.10 0.10 0.13 0.20 0.06 0.03 aug -0.11 -0.10 -0.04 -0.11 0.05 0.12 0.19 0.01

sep 0.10 0.02 -0.01 0.05 0.06 -0.01 -0.02 -0.07 Previous oct 0.05 0.03 -0.08 0.01 -0.05 -0.09 -0.16 -0.04 nov -0.09 -0.04 -0.03 -0.05 -0.05 -0.01 -0.12 -0.06 dec 0.07 0.11 0.01 0.17 0.15 -0.08 -0.10 -0.02 JAN -0.18 -0.18 -0.09 -0.05 -0.02 -0.12 0.06 -0.06 FEB -0.01 0.08 0.02 0.08 0.08 0.12 0.36* 0.19

Precipitation MAR -0.02 0.00 0.04 -0.07 0.06 -0.05 0.13 0.01

APR -0.15 -0.19 -0.31 -0.17 0.03 -0.05 -0.16 -0.04 MAY -0.05 0.00 0.00 0.03 -0.06 -0.16 -0.19 -0.03 JUN -0.25 -0.09 0.10 -0.15 0.04 -0.01 0.12 0.03 Current JUL -0.31 -0.33* -0.07 -0.14 -0.14 -0.18 -0.18 -0.08 AUG -0.24 -0.16 -0.20 -0.24 -0.12 0.02 -0.01 -0.10 SEP -0.15 -0.21* -0.23* -0.12 -0.01 -0.10* 0.03 -0.02 OCT 0.08 0.04 0.13 0.04 -0.09 -0.16 -0.01 -0.08

168

Month Min Mean Max Precip Sun apr 0.06 0.00 -0.04 0.12 -0.11 may 0.11 0.03 -0.05 0.20 -0.15

jun -0.07 -0.07 -0.09 0.16 0.02

jul -0.24* -0.27* -0.28* 0.13 -0.25* aug -0.08 -0.13 -0.18 0.05 -0.14

sep 0.00 0.02 0.02 0.06 0.06 Previous oct 0.02 0.05 0.15 -0.05 0.03 nov 0.26* 0.31* 0.33* -0.05 -0.11

dec 0.32* 0.33* 0.29* 0.15 -0.11 JAN 0.13 0.13 0.12 -0.02 -0.11 Abies FEB -0.03 -0.03 -0.01 0.08 -0.01 MAR 0.03 0.01 -0.02 0.06 -0.18

APR 0.14 0.15 0.13 0.03 -0.03 MAY -0.08 -0.07 -0.04 -0.06 0.03 JUN -0.11 -0.06 -0.02 0.04 0.05 Current JUL 0.11 0.15 0.16 -0.14 0.09 AUG 0.16 0.16 0.16 -0.12 0.04 SEP 0.02 0.04 0.05 -0.01 -0.02 OCT 0.05 -0.03 -0.07 -0.09 -0.13 apr -0.04 -0.02 -0.04 -0.08 -0.08 may 0.08 0.10 0.09 0.08 0.09

jun 0.00 0.04 0.01 -0.01 -0.01

jul -0.16 -0.13 -0.11 0.09 -0.12 aug -0.05 -0.01 0.00 -0.11 -0.01

sep 0.04 0.05 0.05 0.10 -0.06 Previous oct -0.18 -0.14 0.01 0.05 0.12 nov 0.00 0.03 0.09 -0.09 0.01 dec -0.01 -0.02 -0.04 0.07 -0.13 JAN -0.12 -0.13 -0.10 -0.18 0.00

Betula FEB -0.16 -0.16* -0.12 -0.01 0.03 MAR -0.17 -0.21 -0.19* -0.02 0.00

APR -0.05 0.02 0.07 -0.15 0.06 MAY -0.05 -0.04 -0.01 -0.05 0.07 JUN 0.11 0.18 0.21 -0.25 0.07 Current JUL 0.16* 0.28 0.33* -0.31 0.32* AUG 0.05 0.15 0.22 -0.24 0.17 SEP -0.15 -0.09 -0.02 -0.15 -0.01 OCT 0.00 -0.01 0.00 0.08 -0.05

169

Table 4-9. Pearson’s correlation coefficients (significant are bold, p-value < 0.05) and response functions (*, p-value < 0.05) for each Betula (A) and Abies (B) chronology and the four climate stations. Lower case month names are from the previous year and capitals are from the current year.

A CHO-Betula SUG-Betula TSU-Betula MONTH MAT TAK TOY NAG MAT TAK TOY NAG MAT TAK TOY NAG apr -0.04 0.03 0.02 -0.05 -0.06 -0.11 0.03 -0.12 -0.07 -0.08 -0.04 -0.10 may 0.04 0.08 0.08 0.03 0.11 0.10 0.10 0.09 0.04 0.01 0.05 0.02

jun 0.02 0.07 0.07 0.03 0.09 0.02 0.07 0.06 -0.09 -0.10 -0.01 -0.09

jul -0.14 -0.09 -0.08 -0.14 -0.05 -0.03 0.09 -0.06 -0.17 -0.17 -0.15 -0.15 aug 0.01 0.09 0.09 0.02 -0.07 -0.04 -0.05 -0.10 -0.01 0.02 0.14 0.00

sep 0.00 0.09 0.09 0.00 0.11 0.14 0.16 0.12 -0.02 -0.01 0.08 -0.03 Previous

oct -0.15 -0.08 -0.09 -0.14 -0.05 -0.01 0.11 -0.03 -0.20 -0.19* -0.11 -0.21 nov 0.02 0.04 0.04 0.02 0.03 0.08 0.12 0.07 0.00 0.02 0.05 -0.03 dec -0.11 0.00 -0.01 -0.11 -0.02 0.00 -0.03 -0.07 -0.03 -0.04 0.01 -0.12 JAN -0.12 -0.10 -0.09 -0.11 -0.04 -0.01 -0.02 -0.04 -0.11 -0.03 -0.02 -0.08 FEB -0.12 -0.13 -0.13 -0.12 -0.07 -0.10 -0.09 -0.09 -0.13 -0.05 -0.09 -0.11

MAR -0.23 -0.21* -0.22 -0.22 -0.10 -0.19 -0.04 -0.13 -0.16 -0.12 -0.11 -0.13 Temperature

APR 0.00 0.04 0.04 0.00 0.02 -0.03 0.05 -0.04 -0.01 -0.01 0.08 -0.02 MAY -0.14 -0.10 -0.10 -0.14 0.00 -0.05 -0.09 -0.07 -0.05 -0.08 -0.12 -0.11 JUN 0.17 0.13 0.13 0.16 0.23 0.18 0.17 0.23* 0.03 0.02 0.05 0.07 Current JUL 0.23 0.24 0.24 0.23 0.26 0.29* 0.33 0.27* 0.15 0.18 0.18 0.19* AUG 0.21 0.17 0.16 0.21 0.11 0.07 0.16 0.12 0.09 0.08 0.24 0.15 SEP -0.04 0.02 0.02 -0.04 -0.05 -0.05 0.04 -0.07 -0.15 -0.14 -0.05 -0.14 OCT -0.03 -0.02 -0.02 -0.03 0.04 0.04 0.09 0.03 -0.07 -0.07 -0.05 -0.06 apr -0.05 0.05 0.05 -0.05 -0.05 -0.07 -0.12 -0.06 -0.13 -0.05 -0.15 -0.07 may -0.01 0.05 0.04 -0.02 0.14 0.05 0.01 0.07 0.05 0.04 0.03 0.06

jun -0.05 -0.06 -0.05 -0.05 0.02 0.09 -0.01 0.03 0.04 0.09 0.15 0.06

jul 0.17 0.14 0.13 0.17 -0.03 -0.05 -0.08 -0.05 0.13 0.12 0.13 0.12 aug -0.20* -0.06 -0.05 -0.20* -0.07 -0.03 0.06 -0.07 -0.07 -0.15 -0.15 0.01

sep 0.10 -0.01 -0.02 0.10 0.06 0.05 -0.03 -0.01 0.07 0.00 -0.03 0.05 Previous

oct 0.00 0.09 0.09 0.00 0.06 0.01 -0.08 0.03 0.00 -0.01 -0.05 0.00 nov -0.07 -0.05 -0.05 -0.07 -0.09 0.00 -0.07 -0.07 -0.08 -0.03 0.02 0.02 dec 0.13 0.10 0.10 0.13 -0.08 0.00 -0.01 0.05 0.18 0.15 0.00 0.24* JAN -0.11 -0.23* -0.23* -0.11 -0.13 -0.05 -0.02 0.10 -0.08 -0.14 -0.14 -0.08 FEB 0.02 0.05 0.05 0.02 -0.02 0.08 0.00 0.11 0.03 0.04 -0.08 0.04

MAR -0.14 -0.06 -0.06 -0.13 -0.09 0.02 0.04 -0.04 0.07 0.04 0.09 0.01 Precipitation

APR -0.22 -0.15 -0.14 -0.22 -0.07 -0.12 -0.16 -0.12 -0.08 -0.17 -0.27 -0.06 MAY -0.01 0.01 0.00 0.00 -0.04 -0.07 -0.05 -0.01 -0.02 0.05 0.04 0.07 JUN -0.17 -0.09 -0.09 -0.17 -0.16 -0.05 0.04 -0.09 -0.15 -0.07 0.15 -0.10 Current JUL -0.12 -0.25 -0.25 -0.13 -0.23 -0.34* -0.15 -0.10 -0.22 -0.19 -0.03 -0.09 AUG -0.24 -0.20 -0.19 -0.24* -0.15 -0.04 -0.04 -0.18 -0.13 -0.14 -0.27 -0.14 SEP -0.04 -0.16 -0.16 -0.03 -0.08 -0.11 -0.22 -0.09 -0.15 -0.24* -0.21* -0.15 OCT 0.01 0.07 0.07 0.01 0.16 0.10 0.12 0.13 -0.04 -0.09 0.01 -0.05

170

B CHO-Abies KSM-Abies NOW-Abies SUG-Abies TSU-Abies MONTH MAT TAK TOY NAG MAT TAK TOY NAG MAT TAK TOY NAG MAT TAK TOY NAG MAT TAK TOY NAG apr -0.05 0.00 0.01 -0.06 -0.06 -0.01 0.07 -0.05 0.00 0.06 0.04 0.04 0.06 0.06 0.05 0.02 0.07 0.11 0.10 0.05 may -0.01 0.05 0.02 0.03 -0.09 0.00 0.09 -0.05 0.06 0.11 0.06 0.08 0.08 0.11 0.08 0.06 0.07 0.08 0.09 0.05

jun 0.05 0.05 0.09 0.06 -0.14 -0.11 -0.01 -0.11 -0.12 -0.06* -0.09 -0.10 -0.06 -0.01 -0.03 -0.05 0.00 -0.01 0.02 0.01

us jul -0.25 -0.26 -0.10 -0.21 -0.28* -0.24 -0.22 -0.30* -0.15 -0.15* -0.03 -0.13 -0.23 -0.22* -0.15 -0.18* -0.16 -0.13 -0.07 -0.12 aug -0.20 -0.19 -0.27 -0.24* -0.15 -0.11 -0.14 -0.15 -0.06 -0.07 -0.12 -0.09 -0.05 -0.05 -0.14 -0.04 -0.09 -0.10 -0.18 -0.09

sep -0.03 -0.06 -0.08 0.02 -0.03 -0.01 0.02 0.02 0.04 0.03 0.11 0.15 0.07 0.05 0.13* 0.17 0.00 -0.03 0.03 0.08 Previo oct 0.07 0.09 0.17 0.14 -0.02 0.02 0.15 0.04 0.12 0.15 0.15 0.15 0.04 0.08 0.02 0.05 0.02 0.03 0.12 0.07 nov 0.25 0.28* 0.39* 0.30* 0.27* 0.24* 0.38* 0.28* 0.25* 0.24* 0.24 0.23 0.20* 0.23* 0.22 0.21 0.29* 0.25* 0.37* 0.28* dec 0.29* 0.31* 0.24 0.30* 0.24* 0.33* 0.34 0.30* 0.26 0.32* 0.26 0.32* 0.22 0.24 0.16 0.21* 0.30* 0.30* 0.33* 0.31* JAN 0.12 0.15 0.10 0.12 0.12 0.14 0.12 0.16 0.08 0.17 0.10 0.10 0.08 0.15 -0.08 0.08 0.12 0.17 0.11 0.11 FEB 0.01 0.07 0.00 0.04 0.05 0.13 0.12 0.13 -0.05 0.04 0.01 0.01 -0.09 0.01 -0.15 -0.05 -0.02 0.00 -0.03 -0.04

MAR 0.00 0.06 0.05 0.03 0.05 0.11 0.17 0.07 0.03 0.08 0.08 0.05 0.02 0.04 0.01 -0.01 -0.08 -0.03 0.01 -0.06

Temperature APR 0.04 0.07 0.01 0.01 0.10 0.17 0.14 0.09 0.12 0.14 0.09 0.10 0.15 0.19 0.11 0.10 0.17 0.17 0.17 0.13 MAY -0.16 -0.20* -0.24 -0.14 -0.09 -0.09 0.01 -0.09 0.03 0.06 0.02 0.04 -0.02 -0.02 -0.14 -0.03 -0.04 -0.08 -0.06 -0.06 JUN -0.04 -0.08 -0.03 -0.10 0.00 -0.04 0.03 -0.04 -0.05 -0.04 -0.11 -0.07 -0.03 0.02 -0.11 -0.04 -0.16 -0.18 -0.19 -0.17 Current JUL 0.15 0.18 0.13 0.08 0.14 0.22 0.16 0.12 0.22 0.27 0.30 0.20 0.10 0.17 0.12 0.12 0.02 0.08 0.02 0.01 AUG 0.11 0.08 0.07 0.08 0.09 0.12 0.11 0.11 0.21 0.18 0.14 0.16 0.17 0.16 0.09 0.20 0.10 0.06 0.05 0.08 SEP -0.05 -0.05 0.10 -0.06 0.08 0.09 0.26 0.12 0.09 0.09 0.23 0.13 0.04 0.01 0.16 0.07 -0.01 -0.04 0.13 0.00 OCT -0.07 -0.06 0.00 -0.08 0.01 0.02 0.12 0.01 0.10 0.13 0.14 0.10 -0.09 -0.06 -0.07 -0.09 -0.07 -0.06 0.03 -0.11 apr 0.10 0.02 -0.12 0.12 0.06 0.02 -0.11 0.18 0.09 0.00 -0.01 0.08 0.11 0.09 -0.14* 0.20 0.17 0.12 -0.01 0.15 may 0.18 0.15 0.13 0.10 0.23 0.16 0.21 0.12 0.14 0.07 0.18 0.11 0.19 0.22 0.20 0.16 0.05 0.10 0.17 0.08

jun 0.14 0.12 0.00 0.12 0.04 -0.05 0.04 0.01 0.16 0.09 -0.01 0.09 0.20 0.07 0.11 0.09 0.13 0.08 0.10 0.10

us jul 0.20 0.23 0.06 0.08 0.17 0.27* 0.21 0.07 0.06 0.14 -0.16 -0.01 0.04 0.08 0.09 0.03 0.07 0.10 0.00 -0.03 aug 0.01 0.06 0.13 0.03 0.03 0.11 0.07 -0.01 0.06 0.09 0.21 0.05 0.06 0.06 0.17 0.00 0.04 0.18 0.19 0.00

sep -0.09 -0.13 -0.15 -0.15 0.17 0.07 -0.01 0.03 0.02 -0.01 0.03 -0.10 0.08 0.03 0.10 -0.01 0.02 -0.03 -0.05 -0.08 Previo oct 0.05 0.01 -0.15 0.00 -0.06 -0.14 -0.20 -0.06 -0.11 -0.13* -0.22 -0.07 -0.02 -0.03 -0.05 -0.04 -0.03 -0.05 -0.06 -0.03 nov 0.06 0.06 -0.11 0.01 -0.03 0.04 -0.07 -0.01 -0.05 -0.03 -0.11 -0.03 -0.11 -0.10 -0.10 -0.10 -0.06 -0.01 -0.11 -0.06 dec 0.17 0.09 0.00 0.12 0.24* -0.01 -0.10 0.04 0.11 -0.09 -0.15 -0.07 0.01 -0.20 -0.15 -0.12 0.08 -0.07 -0.04 -0.07 JAN -0.06 -0.16 0.03 -0.10 -0.08 -0.09 0.03 -0.13 0.21* 0.08 0.17 0.10 -0.07 -0.15 0.07 -0.04 -0.06 -0.16 -0.01 -0.05 FEB 0.09 0.11 0.42* 0.22 0.12 0.15 0.30 0.19 0.02 0.05 0.15 0.13 0.06 0.04 0.24 0.05 0.06 0.10 0.38* 0.15

MAR 0.09 0.01 0.19 0.07 0.11 0.01 0.14 0.06 0.01 -0.11 0.07 0.01 0.01 -0.08 0.06 -0.04 0.02 -0.07 0.12 -0.02

Precipitation APR 0.03 0.00 -0.08 -0.02 0.00 -0.08 -0.19 -0.03 -0.01 -0.12 -0.09 -0.10 -0.06 -0.12 -0.31* -0.08 0.16* 0.11* -0.02 0.05 MAY 0.00 -0.09 -0.13 0.02 -0.05 -0.15* -0.16 -0.08 -0.05 -0.16 -0.20 -0.03 -0.09 -0.13 -0.18 -0.06 -0.04 -0.10 -0.16 -0.03 JUN -0.01 -0.02 0.00 -0.02 -0.05 -0.06 0.12 -0.01 0.03 -0.02 0.03 -0.02 0.06 0.01 0.16 0.07 0.08 0.03 0.11 0.06 Current JUL -0.02 -0.10 -0.14 0.01 -0.11 -0.10 -0.18 -0.10 -0.26* -0.29* -0.38* -0.15 -0.18 -0.28* -0.16 -0.11 -0.02 0.01 0.07 0.04 AUG -0.11 -0.01 -0.11 -0.10 -0.13 0.02 -0.01 -0.07 -0.11 -0.01 0.04 -0.09 -0.08 0.04 -0.07 -0.09 -0.08 0.05 0.07 -0.01 SEP -0.04 -0.10* -0.10 -0.06 0.02 -0.07 -0.03 0.01 -0.08 -0.11 0.06 -0.05 -0.05 -0.13 0.00 -0.02 0.07 0.01 0.08 0.02 OCT -0.01 -0.05 -0.01 0.02 -0.09 -0.16 -0.02 -0.09 -0.12 -0.17 -0.07 -0.09 -0.03 -0.09 0.13 -0.02 -0.11 -0.13 -0.03 -0.08

171

Table 4-S1. Significant correlation coefficients (p-values < 0.05) and response functions (p-value < 0.05) between northeast Asian tree-ring chronologies and temperature and precipitation records.

172

Temperature Precipitation PDSI PC1 PC1 BE PC1 BE PC1 PC1 BE Month BE < 5MR PC1 BE < 5MR BE < 5MR apr -0.028 -0.005 -0.081 -0.048 0.121 0.105 may 0.107 0.136 0.073 0.084 0.051 0.034

jun 0.041 0.045 -0.015 0.004 0.061 0.062 jul -0.123 -0.109 0.092 0.099 0.072 0.076 aug -0.008 -0.011 -0.104 -0.094 -0.019 0.002

sep 0.05 0.05 0.092 0.065 0.02 0.04 Previous oct -0.14 -0.129 0.055 0.053 0.009 0.039 nov 0.03 0.049 -0.083 -0.054 0.062 0.087 dec -0.017 -0.007 0.084 0.073 -0.005 0.024 JAN -0.133 -0.101 -0.185 -0.212 -0.01 -0.01 FEB -0.166 -0.099 -0.009 0.047 0.112 0.126 MAR -0.216 -0.208 -0.02 0.004 -0.022 -0.007

APR 0.013 0.027 -0.151 -0.149 -0.024 -0.004 MAY -0.033 -0.036 -0.046 -0.049 -0.121 -0.114 JUN 0.188 0.249 -0.255 -0.274 -0.091 -0.06

Current JUL 0.28 0.319 -0.311 -0.306 -0.064 -0.031 AUG 0.151 0.247 -0.24 -0.281 -0.159 -0.152 SEP -0.086 -0.045 -0.151 -0.165 -0.177 -0.177 OCT 0.001 0.039 0.083 0.085 -0.308 -0.34

173

model Abies Estimate Standard Error p-value df R2 p-value Intercept -2.773 5.176 0.593 3:108 0.026 0.4129 Precipitation (sum) 0.04 0.043 0.353 Temperature (mean) -0.001 0.001 0.288 PDSI (mean) 0.158 0.222 0.477

Betula Intercept -1.495 3.666 0,684 3:108 0.199 <0.001 Precipitation (sum) 0.002 0.031 0.96 Temperature (mean) -0.005 0.001 <0.001 PDSI (mean) 0.149 0.157 0.345

Betula < 5MR, truncated Intercept -4.586 3.703 0.218 3:104 0.255 <0.001 Precipitation (sum) 0.022 0.029 0.455 Temperature (mean) -0.005 0.001 <0.001 PDSI (mean) 0.293 0.159 0.069

Chapter 5

Synthesis

Testing Bond’s Hypothesis

This research tested Bond’s (1989) slow seedling hypothesis in the Northern Japanese Alps by examining the role of soil nutrients on species tree density (Chapter 2), regeneration niche (Chapter 3) and climate- growth relationships (Chapter 4). Overall, I found that Bond’s hypothesis is not universal across the

Japanese Alps. As a particular example of this pattern, soil nitrogen was highest in the Betula Belt on volcanic mountains, as would be expected by the hypothesis, but soil nitrogen was similar across belts on non-volcanic mountains. This mixed evidence suggests that lithology plays an important role and should be considered when examining forest composition and species interactions.

When I examined the regeneration niche, I found evidence in support of Bond’s hypothesis in that

Betula, though limited in their regeneration niche, has a broad habitat niche compared to Abies, which has a broad regeneration niche but a narrow habitat niche. The broad regeneration niche of Abies is likely due to the large investment in seedlings through advanced regeneration; however, many of these individuals do not successfully mature into the forest canopy. Those that do make it to the canopy are similar in structure and habitat. Betula has a narrow regeneration niche limited by suitable sites with low litter depth on raised surfaces in high light, through ontogeny the habitat niche expands.

Lastly, while examining growth and climate-growth response of canopy trees, I detected disturbance events through missing and white rings in the Betula trees (Figure 4-4 and Figure 4-9).

However, Abies are complacent to these disturbance events, which is suggestive that Bond’s hypothesis is correct and that the gymnosperms have a lower responsiveness than the angiosperms (Figure 4-4).

175 Analysis on the suppression and releases of the two species in respect to the disturbance is needed to determine if the species respond similarly or if Abies has a lower responsiveness to disturbances. There were no fires or insect outbreaks that I am aware of in the Japanese Alps, however, there are periodic volcanic eruptions. Additionally, investigations into the occurrence and intensity of volcanic eruptions in the Japanese Alps are needed to determine if they are the cause of the missing and white rings found in

Betula or if these are due to a currently undocumented disturbance. Betula responds positively to summer temperatures and negatively to summer precipitation, while Abies has positive growth in the first year after warm and wet winters. At each species upper limit, the maximum age of Betula is approximately

100 years greater than that of Abies.

An explanation for the Betula ermanii treeline

It has long since been known that Abies is limited at higher elevations due to mechanical damage from snow and wind during the winter months resulting in a decrease in Abies density with increasing elevation (Figure 5-1; Kajimoto et al. 2002; Takahashi et al. 2012). Pinus is limited at its upper elevation due to the even greater exposure to wind and extreme temperatures, while at its lower elevation limit,

Pinus is protected from wind and other damage during winter because of a thick layer of snow (Kajimoto

1993). Pinus is tallest at its lower elevations or in protected sites in which redistributed snow accumulates

(Kajimoto 1993). The lower elevation limit of Pinus occurs as a result of competition with taller trees.

Without this competition, Pinus would likely extend much farther down the mountains (Kajimoto 1993).

Abies is shade-tolerant and Pinus is shade-intolerant; furthermore, the short stature of Pinus is genetic and does not allow it to compete for light with the tall trees (Takahashi et al. 2012). At this junction between the occurrences of Abies and Pinus is the Betula belt. Betula, like Pinus, is shade-intolerant; however, because it can grow as an upright tree, it can outcompete Pinus for light. Betula is not mechanically

176 damaged by winter winds and snow like Abies, but individuals will instead swing and bend like Robert

Frost’s Birches (1916) during the winter.

Increased snow depth helps protect Pinus from winter winds and fluctuations in temperatures

(Kajimoto 1993; Takahashi et al. 2012), and snow can weigh down braches of Abies causing them to break (Kajimoto et al. 2002; Takahashi et al. 2012). However, the question remains: what does snow do for Betula? Except for the most extreme situations, snow loads do not break Betula branches, not only because the branches lack leaves, but also in part due to the greater wood density of Betula branches compared with those of Abies (Table 3-9; Figure 3-5). Although snow can be quite deep, it is never deep enough to ‘protect’ Betula from winter winds. Snow may be beneficial to the maintenance of the Betula belt, but not in the way snow benefits Pinus survival through the winter. One hypothesis that stems from this research is that nitrogen may be higher in the Betula belt due to increased snow accumulation. Deeper snow pack increases soil microbial activity, thereby resulting in increased levels of inorganic nitrogen

(Lipson et al. 1999; Grogan and Jonasson 2003). Nevertheless, there is no direct evidence for this in the

Japanese Alps, so this hypothesis will need to be tested in future work. On a steep mountainside, Betula trees are the highest upright trees and act as a wind break, capturing snow while it is being redistributed by wind. Furthermore, this snow acts like a matrix for atmospheric deposition of nitrogen (ammonium and nitrate) and dust, possibly increasing the nitrogen content of soils in the Betula belt.

Although the above discussion accounts for the structure of the trees, it does not account for regeneration. Abies reproduces through advanced regeneration; this species has seedlings through all three forest belts, though seedlings are most common in the subalpine forest (Table 2-4). Betula seedlings are thought to require large gaps and mineral soil or elevated surfaces (logs and rocks) for establishment

(Kohyama 1984; Yamamoto 1993; Hiura, Sano, and Konno 1996; Yamamoto 1996). These conditions are not common at treeline in the Northern Japanese Alps, so Betula seedlings are few and far between.

Furthermore, in both the subalpine forest and Betula belt, Betula seedlings and trees were fairly uniformly distributed with similar distance (~3m) from their nearest conspecific, indicating that successful Betula

177 recruitment is light limiting (Table 3-9; Figure 3-3). Pinus has two mechanisms for recruitment: seeds and layering (Kajimoto 1993). I only found two Pinus seedlings that clearly derived from seeds (i.e., they were not connected to an adult) (Table 3-3). All were on elevated surfaces, presumably where a cone was consumed or left by a Spotted Nutcracker (Nucifraga caryocatactes spp. japonica (Hartert) L), fulfilling a role similar to Clark’s Nutcracker in western North America. One of the main mechanisms believed to control abrupt treelines is seedling mortality and competition (Harsch and Bader 2011). The abrupt treeline of the Japanese Alps is an additional example of where upward development of the Betula treeline is limited by competition with the Pinus.

The existence of the Betula treeline could also be that Betula trees are just old relics of a past disturbance. However, there would likely then be absolutely no regeneration and one observes multiple age classes in the forest stands. The establishment of the Betula belt could thus be due to these individuals being older than the Abies or the Pinus; however, the maintenance of the Betula belt may be possible due to the hypothesis above.

The competition with the Pinus, the mechanical damage of the Abies, longevity of Betula, and the increased nitrogen at the Betula belt, are all factors that have up to this point allowed Betula to persist in an environment where it is theoretically unexpected. Additional studies and experimental tests are needed to determine if this combination of competition, stress, nutrient deposition and longevity is the cause of the Betula belt at high elevations in central Japan.

Future Directions

In this dissertation, I demonstrated the importance of lithology and edaphic parameters influencing the location of the Betula belt in Chapter 2 using linear mixed effect models. Additional sampling to increase the replication from the volcanic and non-volcanic mountains and expanding onto the other dominant substrates in the Japanese Alps, would help paint a broader picture of the influence of

178 lithology on vegetation structure. Furthermore, sampling of additional slope-aspects of these mountains, such as the east-facing aspects, would greatly benefit a more expansive analysis. Snow accumulation is typically greater and lasts longer into the growing season on the eastside of the mountains due to their being leeward of the winds, resulting in a decrease of wind speed and deposition of wind dispersed snow.

The east-facing aspects, however, are steeper than the west-facing ones which may make conducting field surveys difficult.

In addition to the missing rings discussed in Chapter 4, there are distinct white rings that occur uniformly within a given mountain and frequently are followed by one or two missing rings. As discussed above, a more thorough investigation of volcanic and other disturbance processes is needed given that the treelines of the Japanese Alps share few types of disturbance with many other treelines. Additionally, I am interested in improving our understanding of the wood properties of these rings. The white rings are highly reminiscent of rings that form in direct response to insect outbreaks (Young et al. 2014; Young et al. 2016). They are white because there is a decreased amount of lignin in the ring due to the defoliation event (Hogg et al. 2002). These rings could potentially be tested for differences in wood density. Typical wood density analysis with scanners is limited to conifer species due to the contrast between the early and late wood. However, recent developments with the use of blue light intensity (Campbell et al. 2007) have made it feasible to employ a scanner to assess the density of latewood and correlate the variation in these measurements to climatic events. Even more recently, there has been an effort to examine the density of earlywood using blue light intensity (Björklund et al. 2014). I hypothesize that this same technique can be used in diffuse porous species to investigate changes in wood density. I have already had some initial conversations with Dr. Greg Wiles of Wooster College about testing these techniques with diffuse porous species.

Incorporating my results from this dissertation, I would like to continue to study the deciduous treelines of the Northern Hemisphere. Additionally, I would like to visit the different broad-leaved deciduous forests of the Northern and Southern Hemisphere to determine what underlying site-level

179 properties they share, as well as whether there are sets of traits in the species that develop at treelines that make them unique. For instance, in a preliminary literature review, the various species of Betula, Populus, and Nothofagus all are diffuse porous (Sperry et al. 1994; Dettmann et al. 2013). These small vessels may help prevent cavitation in winter, for smaller diameter vessels allow trees to more easily control the supply of water to meet the demand of vapor pressure. Furthermore, megafossil wood from deciduous treelines (Betula pubescens) above the current treeline has been found in Europe (Sweden [Kullman

2013] and Spain [Rubiales and Génova 2015]). The question nevertheless remains: does the megafossil wood share wood properties with living trees at the current treeline? Lastly, there is little known about the

Betula litwinowii treeline from the Caucasus Mountains, due in part to the cutting of these trees for fuel and browsing of the subsequent regrowth by cattle (exceptions: Akatov 2009; Hughes et al. 2009). I have begun discussing with a potential collaborator (Dr. Dario Martin-Benito) the possibility of visiting the region to do an extensive search for mature stands of B. litwinowii on which to conduct dendrochronological analysis.

Importance of this research in Japan

Chubu-Sangaku National Park (CSNP) is one of the biggest (174,323 ha) and most mountainous

(11 mountains > 3,000 m) of the Japanese National Parks. According to the Natural Parks Law (1957), parks are meant to “preserve beautiful scenic areas and their ecosystems and to contribute to the health, recreation and culture of citizens by promoting their utilization” (Ministry of the Environment 2016).

Furthermore, the slogan of CSNP is “Outstanding mountain landscape that represents Japan – breathtaking mountains, beautiful valleys and rock ptarmigans” (Ministry of the Environment 2016). The importance of mountains to the cultural identity of the Japanese people is apparent in that in 2014, a federal holiday was established to celebrate mountains annually on August 11th,and this year (2016) is the first that it will be enacted.

180 In addition to being beautiful natural and cultural landmarks, the parks represent features of Japan that were established to be ‘economic growth poles’ by enhancing the local economy through sightseeing

(Jones 2009; Jones et al. 2009). CSNP is easily accessed by train, bus, or car from the metropolitan regions of Tokyo, Kyoto, and Osaka. For that reason, there have been roughly 10 million visitors to the park annually since the mid-1970s (Jones et al. 2009). Kamikochi, the Yosemite Valley of Japan, is the jewel of CSNP, and it alone receives over 2 million visitors annually (Jones 2009). In a recent study,

85.7% of the visitors to Kamikochi were sightseers who spent fewer than 6 hours in the park, while the remaining 14% were mountain climbers and day hikers (Jones 2009). Two reasons these numbers are so low are that mountain climbers typically like to avoid the crowds and there are better trailheads and hikes outside of Kamikochi (personal observation).

Not only are the parks heavily visited, but trips to the mountains create a boost to the local economy through expenditures on travel, lodging, souvenirs and equipment. In a recent assessment, over

$8 billion will be spent in association to this year’s Mountain Day which corresponds with Obon (a week- long religious holiday to honor forbears that many people take off for summer vacation; [Yui and Urabe,

2016]). Thus, the preservation of National Parks like CSNP results in not only cultural investments but economic ones as well.

Although Mountain Day is expected to draw people into the mountains this summer, in the autumn, people flock to the mountains for the fall foliage. Colors begin to change in late September in the mountains as temperatures begin to drop, prior to leaf fall (Watanabe et al. 2013). Viewing fall foliage is a common tourist activity in the northeastern United States, but Americans do not have a word for it. In

Japanese, there are two words koyo (general fall foliage colors) and momiji (similar to koyo but specific to maple). According to the Japanese National Tourism Organization, viewing koyo is the top activity in autumn. It is as important as viewing cherry blossoms in the spring. Every autumn, tourists flock to the mountains for koyo. CSNP is renowned for its colors: green, yellow, orange and red. The greens come from the Abies mariesii and Pinus pumila, yellow from the Betula ermanii and the orange and reds are

181 from the Sorbus commixta. This forest community that is so well known for its natural beauty during koyo is the forest community studied here.

Furthering the knowledge base on how this forest interacts with climate, disturbance and competition within the forest will help in managing the forest and preserving its natural beauty. The preservation of this forest community is also in line with the revised National Park Law of 2003, in which the preservation of the natural ecosystem is now one of the National Park system’s mandates.

Additionally, one of three key species of study is P. pumila, which has been granted the status of a

National Monument. Since 1919, there has been a law for the ‘Preservation of Historical Sites, Places of

Scenic Beauty, and National Monuments’; thus, furthering the knowledge base on P. pumila is itself an act of compliance with the preservation law.

Understanding how and why the Betula belt exists is important not just for managing the ecosystem, but also for its beauty during koyo. The results from Chapter 4 on the climate-growth response of Betula and Abies indicate that with future warming, especially warming in winter, Betula’s dominance at treeline may be in jeopardy. Higher winter temperatures will likely increase Abies’s growth and possibly limit the amount of mechanical damage during the winter. There are already some Abies trees in the Betula belt and Pinus mat (Table 3-5); however, they frequently have mechanical damage (tops are sheared off at snow height, browning and/ or mortality of needles). If Abies is able to establish in the

Betula belt and Pinus mat, the Betula belt may be at risk. The only other alternative for the trees will be migrating upslope as well.

Broad-leaf deciduous treeline

This research has implications that extend beyond the cultural and economic impacts in Japan discussed above. Currently, treelines composed of Betula spp. occur not just in Japan but also in

Scandinavia, the Caucasus Mountains, China and Nepal. Betula pubescens, the current treeline species in

182 the Scandes, extended south to the mountains of Spain in the late Holocene (2000-3000 years BP) and partially composed the treeline there at that time (Rubiales and Génova 2015). In Alaska, broad-leaved deciduous trees are expanding into tundra and coniferous forest ecosystems that have not had deciduous trees since the early Holocene (Lloyd et al. 2006). Fluctuations in vegetation composition over geological time that typify these ongoing shifts have occurred in unison with changes in the climate (Lloyd et al.

2006, Barrett et al. 2011).

Shifts in the relative dominance of either angiosperms or gymnosperms in the alpine environment can affect important ecological processes like decomposition and community assembly in the forest understory. These can result from changes in, for example, local albedo, the seasonality of carbon cycling, or the susceptibility of forests to disturbance. Tree establishment and growth are important indicators of an ecosystem’s response to increasing temperatures. It is therefore imperative that scientists evaluate those processes and conditions that promote both angiosperm and gymnosperm forests in alpine environments. This knowledge can provide important information to help improve models designed to predict vegetation change in these alpine environments as a result of a warmer climate, thereby allowing managers and foresters to plan over longer time scales.

Anthropogenic influences on the landscape can be important direct and indirect drivers of compositional change. Direct drivers include large scale logging, such as that in the Ural Mountains where Betula spp. and Populus spp. compose less than 30% of the pre-logged forest and dominate the composition of the regrowth (Pisarenko et al. 2001). Models of increased temperatures resulting from climate change have demonstrated that the potential for an increase in the abundance of broad-leaved deciduous trees can occur when mean summer temperatures are 10-15 °C, while constraint in the coniferous forests may result from an abrupt temperature threshold at around 12-13 °C (Chapin and

Starfield 1997). Furthermore, there was a lag in the response to increased warming over a period of 150-

200 years in the Alaskan Arctic where tundra transformed into a forest (> 5 trees per hectare) (Lloyd

2005, Chapin and Starfield 1997).

183 Indirect drivers of shifts in vegetation include increased frequency and intensity of insect outbreaks and fires in the boreal to arctic forests (Barrett et al. 2011). Regions that are disturbed, especially by fire, are frequently filled in by early successional such as Betula and Populus. By 2100, models predict that there will be a 1-15% increase in the land cover of deciduous forests due to large fires

(Barrett et al. 2011). In the boreal forests of Alaska, large fires increase the albedo and decrease the sensible heat flux of the surface compared to unburned forests (Randerson et al. 2006). Furthermore, an increase in deciduous trees is expected to further decrease the sensible heat flux, in part due to their having a higher evapotranspiration rate than conifers and thus a greater latent heat flux (Eugster et al.

2000). However, these values are typically reported for just the summer leaf-on period; when the entire growing season of the conifers is compared to deciduous trees, the evapotranspiration rates are similar

(Liu et al. 2005). Although the rates of evapotranspiration over the year may even out between deciduous and coniferous trees, there is a large difference in the quantity of water that deciduous trees pull from deeper in the soil profile. In a recent study, deciduous trees used 21-26% of snowmelt water in soils compared to only 0.62-0.64% in conifers (Young-Robertson et al. 2016). This water was then transpired into the atmosphere as water vapor, which is an important greenhouse gas, and the increase of water- vapor due to a transition of the tundra to broad-leaf deciduous trees will increase Artic warming 1.5 times more than due to changes in surface albedo (Swann et al. 2010). There is thus a complex relationship between the increase of deciduous trees and regional climate.

The B. ermanii treeline of Japan contrasts with the deciduous forest of Alaska in a number of ways. There is currently little evidence of large-scale disturbances in the alpine forests of central Japan

(Nakamura 1985; Kaazaki and Yoda 1986; Nakashizuka et al. 1993). Due to the lack of disturbances, land cover is not converting from tundra or coniferous forests into deciduous forests. The Japanese Alps are wet with year-round precipitation (Figure 2-2), and while in the interior of Alaska an increase in water vapor has the potential to increase temperatures, it is not currently known whether water vapor from B. ermanii contributes to regional warming in Japan. The differences in disturbances and climate between

184 these boreal and alpine forests imply that not all deciduous treelines are controlled by the same interactions or mechanisms. The results of this dissertation lead me to the opinion that the impact of small scale disturbances through mechanical damage on the A. mariesii, competition with the P. pumila, and the potential for increased microbial decomposition under snow in the Betula belt have helped maintain B. ermanii’s prominence at treeline in the Northern Japanese Alps (Figure 5-1), though this latter mechanism of the effect of snow remains untested and warrants further exploration.

185 References

Akatov, P. 2009. Changes in the upper limits of tree species distribution in the Western Caucasus (Belaya

River Basin) related to recent climate warming. Russian Journal of Ecology 40: 33-38.

Bond, W. J. 1989. The tortoise and the hare: Ecology of angiosperm dominance and gymnosperm

persistence. Biological Journal of the Linnean Society 36:227-249.

Barrett, K., McGuire, A., Hoy, E. and E. Kasischke. 2011. Potential shifts in dominant forest cover in

interior Alaska driven by variation in fire severity. Ecological Applications 21: 2380-2396.

Björklund, J., Cunnarson, B., Seftigen, K., Esper, J. and H. Linderholm. 2014. Blue intensity and density

from northern Fennoscandian tree rings, exploring the potential to improve summer temperature

reconstructions with earlywood information. Climate of the Past 10: 877-885.

Campbell, R., McCarroll, D., Loader, N., Grudd, H., Robertson, I. and R. Jalkanen. 2007. Blue intensity

in Pinus sylvestris tree-rings: developing a new palaeoclimate proxy. The Holocene 17: 821-828.

Chapin III, F. S. and A. Starfield. 1997. Time lags and novel ecosystems in response to transient climatic

change in Arctic Alaska. Climate Change 35: 449-461.

Dettmann, S., Perez, C. and F. Thomas. 2013. Xylem anatomy and calculated hydraulic conductance of

four Nothofagus species with contrasting distribution in South-Central Chile. Trees 27: 685-696.

Eugster, W., Rouse, W., Pielke, R., McFadden, J., Baldocchi, D., Kittel, T., Chapin III, F.S., Liston, G.,

Vidale, P.L., Vaganov, E. and S. Chambers. 2000. Land-atmosphere energy exchange in Arctic

tundra and boreal forest: available data and feedbacks to climate. Global Change Biology 6: 84-115.

Frost, R. 1916. Mountain Interval. Henery Holt and Company, New York, NY.

Grogan, P. and S. Jonasson. 2003. Controls on annual nitrogen cycling in the understory of a subarctic

birch forest. Ecology 84: 202-218.

Harsch, M. and M. Bader. 2011. Treeline form – a potential key to understanding treeline dynamics.

Global Ecology and Biogeography 20: 582-596.

186 Hiura, T. and Sano, J. and Y. Konno. 1996. Age structure and response to fine-scale disturbances of Abies

sachalinensis, Picea jezoensis, Picea glehnii, and Betula ermanii growing under the influence of a

dwarf bamboo understory in northern Japan. Canadian Journal of Forest Research 26: 289-297.

Hogg, E., Hart, M. and V. Lieffers. 2002. White tree rings formed in trembling aspen saplings following

experimental defoliation. Canadian Journal of Forest Research 32: 1929-1934.

Hughes, N., Johnson, D., Akhalkatsi, M. and O. Abdaladze. 2009. Characterizing Betula litwinowii

seedling microsites at the alpine-treeline ecotone, central greater Caucasus Mountains, Georgia.

Arctic, Antarctic, and Alpine Research 41: 112-118.

Ministry of the Environment, https://www.env.go.jp/en/nature/nps/park/index.html Accessed August 9,

2016

Jones, T. 2009. Nature-based tourism in a Japanese national park – a case study of Kamikochi. Bulletin of

Tokyo University, Forestry 121: 87-116.

Jones, T., Nagata, S., Nakajima, M. and K. Masuyama. 2009. Prefectural branding in Japan - Tourism,

National Parks and the Shinshu Brand. Place Branding and Public Diplomacy 5: 192-201

Kajimoto, T. 1993. Shoot dynamics of Pinus pumila in relation to altitudinal and wind exposure gradients

on the Kiso mountain range, central Japan. Tree Physiology 13: 41-53.

Kajimoto, T., Seki, T., Ikeda, S., Daimaru, H., Okamoto, T. and H. Onodera. 2002. Effects of snowfall

fluctuation on tree growth and establishment of subalpine Abies mariesii near upper forest-limit of

Mt. Yumori, Northern Japan. Arctic, Antarctic, and Alpine Research 34: 191-200.

Kanzaki, M. and K. Yoda. 1986. Regeneration in subalpine coniferous forests II. Mortality and the pattern

of death of canopy trees. The Botanical Magazine, Tokyo 99: 37-51.

Kohyama, T. 1984. Regeneration and coexistence of two Abies species dominating subalpine forests in

Central Japan. Oecologia 62: 156-161.

Kullman, L. 2013. Ecological tree line history and palaeoclimate – review of megafossil evidence from

the Swedish Scandes. Boreas 42: 555-567.

187 Lipson, D., Schmidt, S. and R. Monson. 1999. Links between microbial population dynamics and

nitrogen availability in an alpine ecosystem. Ecology 80: 1623-1631.

Liu, H., Randerson, H., Lindors, J. and F.S. Chapin III. 2005. Changes in the surface energy budget after

fire in boreal ecosystems of interior Alaska: An annual perspective. Journal of Geophysical Research

110: D13101.

Lloyd, A. 2005. Ecological histories from Alaskan tree lines provide insight into future change. Ecology

86: 1687-1695.

Lloyd, A. H., M. E. Edwards, B. P. Finney, J. A. Lynch, V. Barber, and N. H. Bigelow. 2006. Holocene

development of the Alaskan boreal forest. Pages 62–78 in F. S. Chapin III, M. W. Oswood, K. Van

Cleve, L. A. Viereck, D. L. Verbyla, editors. Alaska’s changing boreal forest. Oxford University

Press, New York, New York, USA.

Nakashizuka, T., Iida, S., Suzuki, W. and T. Tainmoto. 1993. Seed dispersal and vegetation development

on a debris avalanche on the Ontake Volcano, central Japan. Journal of Vegetation Science 4: 537-

542.

Nakamura, T. 1985. Forest succession in the subalpine region of Mt. Fuji, Japan. Vegetatio 64: 15-27.

Pisarenko, A., Strakhov, V., Paivinen, R., Kuusela, K., Dyakun, F. and V. Sdobnova. 2001. Development

of forest resources in the European part of the Russian Federation. European Forest Institute Research

Report 11. Brill, Leiden. p 57.

Randerson, J., Liu, H., Flanner, M., Chambers, S., Jin, Y., Hess, P., Pfister, G., Mack, M., Treseder, K.,

Welp, L., Chapin III, F.S., Harden, J., Goulden, M., Lyons, E., Neff, J., Schuur, E. and C. Zender.

2006. The impact of boreal forest fire on climate warming. Science 314: 1130-1132.

Rubiales, J. and M. Génova. 2015. Late Holocene pinewoods persistence in the Gredos Mountains

(central Spain) inferred from extensive megafossil evidence. Quaternary Research 84: 12-20.

Sperry, J., Nichols, K., Sullivan, J. and S. Eastlack. 1994. Xylem embolism in ring-porous, diffuse-

porous, and coniferous trees of northern Utah and interior Alaska. Ecology 75: 1736-1752.

188 Swann, A., Fung, I., Levis, S., Bonan, G. and S. Doney. 2010. Changes in Arctic vegetation amplify high-

latitude warming through the greenhouse effect. Proceedings of the National Academy of Science

107: 1295-1300.

Takahashi, K., Hirosawa, T. and R. Morishima. 2012. How the timberline formed: altitudinal changes in

stand structure and dynamics around the timberline in central Japan. Annals of Botany 109: 1165-

1174.

Watanabe, T., Fukuzawa, K. and H. Shibata. 2013. Temporal changes in litterfall, litter decompositions

and their chemical composition in Sasa dwarf bamboo in a natural forest ecosystem of northern

Japan. Journal of Forest Research 18: 129-138.

Yamamoto, S-I. 1993. Gap characteristics and gap regeneration in a subalpine coniferous forest on Mt.

Ontake, central Honshu, Japan. Ecological Research 8: 277-285.

Yamamoto, S-I. 1996. Gap regeneration of major tree species in different forest types of Japan. Vegetatio

127: 203-213.

Young, A., Cairns, D., Lafon, C. and J. Moen. 2014. Geometrid moth outbreaks and their climatic

relations in Northern Sweden. Arctic, Antarctic, and Alpine Research 46: 659-668.

Young, A., Watts, D., Taylor, A. and E. Post. 2016. Species and site differences influence climate-shrub

growth responses in West Greenland. Dendrochronologia 37: 69-78.

Young-Robertson, J., Bolton, W., Bhatt, U., Cristóbal, J. and R. Thoman. 2016. Deciduous trees are a

large and overlooked sink for snowmelt water in the boreal forest. Scientific Reports 6: 29504.

Yui, M. and E. Urabe. 2016. August 8. “Scaling Mt. Fuji seen fueling $8 billion Japan holiday boost.”

Bloomberg. Retrieved from http://www.bloomberg.com.

189

190 VITA

Amanda Beatrice Young

EDUCATION

The Pennsylvania State University 2016 Ph.D. Geography Texas A&M University 2008 M.S. Geography Gustavus Adolphus College 2003 B.S. Biology; Scandinavian Studies

PUBLICATIONS Young, A., Watts, D., and A. Taylor and E. Post. 2016. Species and site differences influence on climate- shrub growth responses in West Greenland. Dendrochronologia 37:69-78. Young, A., Cairns, D., Lafon, C. and J. Moen. 2014. Geometrid moth outbreaks and their climatic relations in Northern Sweden. Artic, Antarctic, and Alpine Research 46:659-668. Maxwell, R.S., Taylor, A., Skinner, C., Safford, H. Isaacs, R., Airey, C. and A. Young. 2014. Landscape scale modeling of reference period forest conditions and fire behavior on heavily-logged lands. Ecosphere 5:32. Cairns, D., Lafon, C., Mouton, M., Stuteville, R., Young, A. and J. Moen. 2012. Comparing two methods for ageing trees with suppressed, diffuse-porous rings (Betula pubescens ssp. czerepanovii). Dendrochronologia 30:252-256. Young, A., Cairns, D., Lafon, C. and J. Moen. 2011. Dendroclimatic relationships and possible implications for mountain birch and Scots pine at treeline in northern Sweden through the 21st century. Canadian Journal of Forest Research 41: 450-459. Quann, S., Young, A., Laroque, C., Falcon-Lang, H. and M. Gibling. 2010. Dendrochronological dating of coal mine workings at the Joggins Fossil Cliffs, Nova Scotia, Canada. Atlantic Geology 46:185- 194. Cairns, D., Lafon, C., Moen, J. and A. Young. 2007. Influences of animal activity on treeline position and pattern: implications for treeline responses to climate change. Physical Geography 28:419-433.

SELECT GRANTS AND AWARDS 2016 Best Student Oral Presentation, Ameridendro 2016, Mendoza, Argentina 2015 Geography Alumni Scholars Award, Penn State Department of Geography 2014 Doctoral Dissertation Research Improvement Grant, National Science Foundation 2013 National Fellowship, Society of Women Geographers 2014 Biogeography Specialty Group PhD Grant, American Association of Geographers 2012 Graduate Research Fellowship, NASA Pennsylvania Space Grant Consortium 2012 East Asia and Pacific Summer Institute Fellowship, National Science Foundation

PROFESSIONAL ASSOCIATIONS 2014-2016 Ecological Society of America 2013-2016 Society of Women Geographers 2007-2016 American Association of Geographers 2008-2016 Tree-Ring Society