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PLANT SOIL FEEDBACKS WITH CHANGING VEGETATION STRUCTURE AND COMPOSITION IN A WARMING ARCTIC

By

JENNIE DEMARCO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2011

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© 2011 Jennie DeMarco

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To my parents, Tom and Janet, who taught me to work hard and be passionate about the work I do: two traits that are essential for getting through graduate school and

completing a dissertation. To my husband, Ben, for his unwavering patience, love, and support during this time and to my sons, Elias and Leonid, whose persistent curiosity

and questions about the world around them is a constant reminder to me of why science is so important and so much fun!

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ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Michelle Mack, for all of her intellectual

guidance during the construction, implementation, and completion of this dissertation

and to my committee members, Dr. Ted Schurr, Dr. Ramesh Reddy, and Dr. Max

Teplitski for their helpful comments. I would also like to thank Charmagne Wasykowski,

Grace Crummer, Julia Reiskind, Yi Wei Cheng, Anne Baker, Leslie Boby, Faye Belshe,

Hanna Lee, Caitlin Hicks, Mark Burton, the numerous volunteer pluckers who assisted

with the biomass harvest, and the many undergraduates at the University of Florida for

their assistance in sampling and processing the thousands of plant and soils needed to

complete this dissertation. I also thank Martin Lavoie and Grace Crummer for their

comments on earlier drafts of this manuscript. This research was supported by NSF

grants DEB-0516041, DEB-0516509 and the Arctic LTER (DEB-0423385).

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES ........................................................................................................ 10

ABSTRACT ................................................................................................................... 12

CHAPTER

1 INTRODUCTION .................................................................................................... 14

2 THE EFFECTS OF SNOW, SOIL MICROENVIRONMENT, AND SOIL ORGANIC MATTER QUALITY ON N AVAILABILITY IN THREE ALASKAN ARCTIC PLANT COMMUNITIES1 .......................................................................... 20

Background ............................................................................................................. 20 Methods .................................................................................................................. 24

Study Area ........................................................................................................ 24 Snow Manipulation ........................................................................................... 26

Characterizing ecosystem structure ................................................................. 28 Soil N dynamics ................................................................................................ 29

Statistical Analyses .......................................................................................... 32 Results .................................................................................................................... 33

Ecosystem structure across the shrub gradient ................................................ 33

Snow manipulation ........................................................................................... 34 Reciprocal soil core transplant experiment ....................................................... 36

Discussion .............................................................................................................. 36

Snow addition effects on N availability ............................................................. 36

Soil organic matter quality effects on N availability ........................................... 39 Soil microclimate versus soil organic matter quality effects on N availability .... 40 Implications for feedbacks to climate change ................................................... 42

3 CONTROLS OVER LITTER DECOMPOSITION IN THREE ARCTIC PLANT COMMUNITIES ...................................................................................................... 49

Background ............................................................................................................. 49 Methods .................................................................................................................. 54

Study Area ........................................................................................................ 54

Snow Manipulation ........................................................................................... 56 Common Substrate Experiment ....................................................................... 57

Common Environment Experiment ................................................................... 58 Calculations ...................................................................................................... 59 Initial Litter Quality ............................................................................................ 59

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Community Weighted Decomposition .............................................................. 60 Statistical Analysis ............................................................................................ 62

Results .................................................................................................................... 63

Effect of added snow on soil temperature ........................................................ 63 Common Substrate Experiment ....................................................................... 63 Common Environment Experiment ................................................................... 64

Deciduous shrub litter collected across the shrub gradient sites ................ 64 Initial litter quality and decay constants across species with and without

fertilization............................................................................................... 65 The relationship between initial litter quality and decay constants ............. 67

Community Weighted Decomposition .............................................................. 68 Discussion .............................................................................................................. 68

Microenvironment controls over litter decomposition ........................................ 68 Litter quality controls over litter decomposition ................................................. 72 Litter quality/quantity versus microclimate controls over litter decomposition ... 74

4 PLANT AND ECOSYSTEM RESPONSE TO LONG TERM EXPERIMENTAL WARMING AND NUTRIENT ADDITIONS IN ARCTIC SHRUB TUNDRA .............. 85

Background ............................................................................................................. 85 Methods .................................................................................................................. 88

Study Site and Treatments ............................................................................... 88 Environment ..................................................................................................... 89

Biomass ............................................................................................................ 90 Aboveground Net Primary Production .............................................................. 91

Species Diversity .............................................................................................. 92 Soil Properties .................................................................................................. 92 Carbon and Nitrogen Pools .............................................................................. 92

Statistical Analysis ............................................................................................ 93 Results .................................................................................................................... 93

Environmental Data .......................................................................................... 93

Biomass ............................................................................................................ 94

Aboveground Net Primary Production .............................................................. 94 Species Diversity .............................................................................................. 95 Soil Properties .................................................................................................. 95 Carbon and Nitrogen pools ............................................................................... 95 Allocation .......................................................................................................... 96

Discussion .............................................................................................................. 97 Controls over biomass and productivity ............................................................ 97 Changes in C and N pools ................................................................................ 99 Species diversity ............................................................................................ 100 Shifts in allocation .......................................................................................... 100

Changing C balance ....................................................................................... 101

5 CONCLUSION ...................................................................................................... 114

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APPENDIX

A Supplementary material for Chapter 2 .................................................................. 116

A.1 Additional methods: Characterizing ecosystem structure ............................... 116

B Supplementary material for Chapter 3 .................................................................. 119

LIST OF REFERENCES ............................................................................................. 131

BIOGRAPHICAL SKETCH .......................................................................................... 142

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LIST OF TABLES

Table page 2-1 Vegetation and soil characteristics for three arctic plant communities located

near Toolik Lake, AK .......................................................................................... 44

2-2 Soil characteristics from the top 10 cm of the organic soil at three sites near Toolik Lake, AK. ................................................................................................. 45

3-1 Initial mass remaining (IMR), initial carbon remaining (ICR), initial N remaining (INR), and decay constants for the common substrate, Betula papyrifera var. neoalaskan. ................................................................................ 76

3-2 Two-way ANOVA results comparing k, INR, ICR, and the proportion of ICR:INR after three years of incubation across all sites and treatments. ............ 76

3-3 Initial litter quality of senesced leaves of Betula nana collected in both unfertilized plots and plots that had been fertilized for five, 14, and 20 years ..... 77

4-1 Soil properties measured across all four treatments. Different letters indicate significance across treatments. ........................................................................ 110

4-2 Vascular plant species rank by treatment based on aboveground biomass ..... 111

4-3 Aboveground biomass (g/m2) for the most abundant species and functional groups .............................................................................................................. 112

4-4 Three-way ANOVA results comparing aboveground biomass among treatments......................................................................................................... 113

4-5 Two-way ANOVA results comparing N or C pool across treatments (C, NP, T, NP + T) within the same component ............................................................ 113

4-6 Two-way ANOVA results comparing biomass, C, or N allocation across treatments (C, NP, T, NP + T) within the same plant part ................................. 113

A-1 Belowground biomass at each site ................................................................... 117

A-2 K2SO4 extractable soil nutrients down to 10 cm within the organic layer. ......... 118

B-1 Soil temperature at 5 cm during the growing season (62 days) and winter (272 days) for the three year period our litter decomposition bags were incubated .......................................................................................................... 125

B-2 Three-way repeated measures ANOVA comparing differences in soil temperature between vegetation type, treatment, and year. ............................ 126

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B-3 Initial litter quality for senesced leaves and stems of Betula nana and Salix pulchra collected in each of our three plant communities and incubated at a common site ..................................................................................................... 127

B-4 Results from two-way ANOVA’s comparing leaves and stems of Betula nana and Salix pulchra collected in each of our three plant communities and incubated at a common site. ............................................................................. 128

B-5 Initial litter quality for senesced leaves of seven species of vascular plants and three moss species collected from control and fertilized plots in a moist acidic tundra community and incubated in a common site ............................... 130

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LIST OF FIGURES

Figure page 1-1 Potential pathways by which shrubs enhance their dominance through a

positive-feedback mechanism where shrubs alter the biophysical and biogeochemical environment resulting in increased nutrient availability. ............ 19

2-1 Soil temperature and snow depth across three sites near Toolik Lake, AK. a) Weekly mean, maximum, and minimum air temperature at 5 m. b) Weekly mean (± SE) soil temperature measured at 5 cm depth within the organic layer under ambient and snow addition treatments at each site (n = 3-4). c) Mean (± SE) ambient and manipulated snow depth during the winter of 2005-2006 and 2006-2007. ......................................................................................... 46

2-2 Mean (± SE) net N-mineralization in intact, resin capped organic soil cores incubated in the control and snow addition treatments at each of the three sites during the summer (mid June-Sept 2006; 74 days) and winter (Sept 2006-June 2007; 280 days).. .............................................................................. 47

2-3 Mean (± SE) net N-mineralization in intact, resin capped organic soil cores incubated in the control treatment or reciprocally transplanted and incubated in one of the other sites ...................................................................................... 48

3-1 Initial mass, C, and N remaining of the common substrate (Betula papyrifera var. neoalaskana) ............................................................................................... 78

3-2 Decay constants, initial C and N remaining, and the proportion of initial C:N remaining for the common substrate .................................................................. 79

3-3 Percent initial leaf litter N collected from plants that were unfertilized and fertilized with 10 g N/m2/yr for 5 years ................................................................ 80

3-4 Leaf litter decay constants (k) vs. leaf percent C, cell solubles, and cellulose for 11 vascular plants species ............................................................................ 81

3-5 (Left) Percent initial leaf litter lignin of Betula nana collected from four sites that have been fertilized for zero, five, 14, or 20 years. (Right). Decay constant, k, of Betula nana leaves ...................................................................... 82

3-6 Comparison of the influence of litter quality/quantity and litter quality/quantity with microenvironment in mean community weighted litter decay constant (k, 1/yr) across three arctic plant communities. ....................................................... 83

3-7 Soil % N for each site vs. decay constant (k), initial C remaining, initial N remaining, and the proportion of initial C to N remaining of the common substrate ............................................................................................................. 84

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4-1 Aboveground biomass (g/m2) from shrub tundra harvested in the eighteenth year of treatment (C = control, NP = Nitrogen and phosphorus additions, T = warming manipulation, and NP + T = Nitrogen and phosphorus additions plus warming manipulation) ..................................................................................... 103

4-2 Aboveground vascular net primary production (ANPP) across treatments and separated by plant parts ................................................................................... 104

4-3 Vascular plant biomass dominance-diversity curves sampled 18 years after initiation of treatments. ..................................................................................... 104

4-4 Total ecosystem C and N pools separated by above and belowground for each treatment. ............................................................................................... 105

4-5 Ecosystem C and N pools after 18 years of experimental manipulation of nutrients and temperature ................................................................................ 106

4-6 Proportional allocation of vascular plant biomass, C, and N to different plant parts across each treatment ............................................................................. 107

4-7 Carbon to nitrogen ratios of plant tissues across treatments. .......................... 108

4-8 Percent biomass, C, and N allocation among leaves (■), aboveground stems (□), and belowground stems (■) within each functional group across treatments......................................................................................................... 109

A-1 Annual ambient snow depth for each plant community. .................................... 117

B-1 Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species .................................................................................. 119

B-2 Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species .................................................................................. 120

B-3 Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species .................................................................................. 121

B-4 Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species .................................................................................. 122

B-5 Leaf litter decay constants (k) vs. leaf percent C, cell soluble, and cellulose for 11 vascular plant species ............................................................................ 123

B-6 Stem litter decay constants (k) vs. percent cellulose for four deciduous shrub species incubated for three years in a common garden ................................... 124

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

PLANT SOIL FEEDBACKS WITH CHANGING VEGETATION STRUCTURE AND

COMPOSITION IN A WARMING ARCTIC

By

Jennie DeMarco

December 2011

Chair: Alice Harmon Major: Botany

Climate warming in the arctic may shift vegetation from graminoids to deciduous

shrub dominance, potentially altering the structure and function of the ecosystem

through influences on the abiotic and biotic controls over carbon (C) and nitrogen (N)

cycling. Shrubs may influence the soil microclimate and litter inputs to the soil, altering

the rate at which nutrients are cycled back to soil and thus become available to plants.

In the arctic tundra near Toolik Lake, Alaska, we experimentally manipulated snow

depth across three arctic plant communities that varied in their initial shrub abundance

to test whether the snow that accumulates around arctic deciduous shrubs alters the

soil microclimate enough to increase soil N availability and nutrient turnover.

Specifically, we tested whether the addition of snow provides a more favorable

microclimate for N mineralization and litter decomposition. In addition, we investigated

the influence of soil organic matter (SOM) and litter quality on N availability and nutrient

turnover. We found that winter snow addition increased soil N availability in the summer

only through increased rates of N mineralization but had no effect on litter

decomposition rates. In addition, SOM quality was greatest in the plant community with

the highest abundance of shrubs resulting in faster turnover and greater N availability.

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In contrast, litter decomposition rates were slower in shrub dominant communities

resulting in slower nutrient turnover and higher retention of N on the litter. We conclude,

that on a short time scale shrub interactions with snow increase N availability, at least in

the summer, at a time when plants are more active. In addition, our study suggests

that a transition to a shrubbier arctic could lead to retention of N in the litter layer and an

increase in N availability in the soil potentially leading to a positive feedback to

increased shrub growth.

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CHAPTER 1 INTRODUCTION

The global climate is warming with the arctic region warming at a faster rate

(Overpeck et al. 1997, Serreze and Francis 2006, Kaufman et al. 2009). Warmer

temperatures in the arctic may alter ecosystem structure and function, and lead to a

positive feedback to global and regional climate change. Arctic systems are nitrogen

(N) limited due to slow decomposition rates driven by cold temperatures. Manipulation

experiments within this region have shown that increased temperature stimulates

decomposition which can result in more nutrients available for plants (Chapin et al.

1995, Hobbie 1996, Aerts et al. 2006a). Fertilization experiments have shown that

arctic plants respond positively to added (N) however, plant species respond differently

with the deciduous shrub Betula nana having a more positive response (Chapin and

Shaver 1996). Thus an increase in temperature can drive decomposition of soil organic

matter (SOM), increasing nutrient availability leading to a shift in plant species

composition that is dominated more with deciduous shrubs. Warming can also lead to

increase in shrubs indirectly by causing thawing of permanently frozen soil (Schuur et

al. 2007).

There is evidence that shrubs are expanding into the arctic region. Current and

historical photos of Northern Alaska show that deciduous shrubs (Betula, Salix and

Alder) have increased in abundance and/or size over the last 50 years (Sturm et al.

2001b, Tape et al. 2006). Satellite data have recorded an increase in photosynthetic

activity of terrestrial vegetation in northern latitudes over the past two decades and

some of this enhanced productivity has been attributed to an increase in growth of

shrubs (Jia and Epstein 2003, Stowe et al. 2004, Jia et al. 2009, Forbes et al. 2010).

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Pollen records indicate that past warming has led to movement of trees and shrubs

north (Payette et al. 1989, Oswald et al. 1999).

What are the implications of increased shrubs in the arctic? Changes in species

composition to more shrub dominance can have dramatic affects on how the arctic will

influence the global carbon (C) cycle. In the past, the arctic region has been a sink for

C because litter inputs have exceeded decomposition rates. Cold temperatures limit

decomposition of SOM resulting in a buildup of organic C that can be greater than

12,000 years old (Ping et al. 1997). However, with a shift to more shrub dominated

tundra, it is unclear whether the arctic will continue to serve as a sink for C and may

even switch to a source of C to the atmosphere. An increase in shrubs could lead to an

increase in plant productivity and biomass resulting in more uptake of atmospheric CO2

and the storage of more C aboveground in woody tissue or belowground in rhizomes

(Shaver and Chapin 1991), roots, and ectomycorrhizal fungi (Clemmensen et al. 2006)

resulting in a negative feedback to climate change. In addition, more woody stems

associated with shrubs could mean more C stored in the soil because stems

decompose slowly (Hobbie 1996). However, shrub lands have a lower albedo than

tundra which can lead to an increase in absorbed solar radiation during the snow-free

period, resulting in a positive feedback to climate change (Chapin et al. 2005). In

addition, it has also been shown that deciduous shrubs in fertilized tundra cycled C and

N faster than control tundra resulting in a net loss of deep soil C over a 20 year period

(Mack et al. 2004). Thus, there is a potential for a positive feedback to warming with

increased shrub cover. Since the arctic stores 20-30 percent of the total amount of

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terrestrial soil-bound C (McGuire et al. 2009) it is important to better understand the

mechanisms behind these potential feedbacks to climate change.

The mechanisms that drive shrub growth and expansion are not well understood.

The snow-shrub hypothesis suggests that shrubs enhance their dominance through a

positive-feedback mechanism where shrubs increase winter soil temperatures and

enhance decomposition by trapping snow and thus increase mineralization and nutrient

availability (Sturm et al. 2001a) (Fig. 1-1). Because, shrubs respond positively to

increased nutrient availability (Chapin and Shaver 1996), increased shrub growth and

expansion may create a positive feedback. The degree of the positive feedback is

increased with warming because warmer temperatures accelerate decomposition

(Hobbie 1996) resulting in more nutrients available leading to further shrub growth.

Although it has been well document that snow around shrubs is deeper than non-

shrubby areas and deeper snow results in warmer soil temperatures, it has not been

directly tested that soil under snow trapped by shrubs mineralizes more N than non-

shrubby systems. It is also not clear whether shrubs utilize the N that has been

mineralized over the winter.

Alternatively, shrubs may also have effects on N release that are independent of

their effects on winter soil temperatures and may differ in the direction of their effects.

In moist acidic tundra the deciduous shrub, Betula nana, allocates 79 percent of its total

biomass to new and old stems (Shaver et al. 2001) which decompose three times

slower than their leaves and one to eight times slower than leaves and stems from

graminoids and evergreen shrubs found within the Alaskan arctic tundra (Hobbie 1996).

Thus a species composition shift to more deciduous shrub dominance may alter nutrient

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turnover through biotic controls by producing larger quantities of litter that is of lower

quality (Hobbie, 1992; Buckeridge et al., 2010) possibly slowing nutrient turnover in litter

resulting in a decrease in plant-available N and a negative plant-soil feedback that does

not promote further shrub expansion (Fig. 1-1).

These two potential mechanisms lead to the question of whether changes in

microclimate or changes in the chemical composition (quality) and the amount (quantity)

of litter will have a stronger control on litter decomposition and nutrient release. In

arctic and boreal systems, differences in litter quality among species and SOM quality

across different vegetation types can have a larger effect on N mineralization than

differences in temperature (Flanagan and Van Cleve, 1983; Giblin et al., 1991;

Nadelhoffer et al., 1991; Hobbie, 1996). This pattern has also been seen in alpine

tundra of the South Western French Alps, where Baptist (2010) found that species

specific differences in litter quality had a stronger control over decomposition rates than

differences in timing of snowmelt which is associated with differences in snow depth

and soil temperature. Although there is much evidence to suggest that shrubs can

influence their environment to alter key biogeochemical processes that control plant

nutrient supply, there have been no published studies to date that have directly tested

the effect of added snow (at the depth that would be trapped by shrubs) or increased

shrub cover on N turnover in soil or litter.

The goal of the study for the second and third chapters of this dissertation were to

understand the relative importance of mechanisms through which arctic deciduous

shrubs affect N dynamics by investigating how differences in soil microclimate, litter

quality, and nutrient availability influence net N-mineralization and litter decomposition-

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two of the main pathways by which organic N is made available for plant uptake. This

study used experimental manipulations of snow across three different arctic plant

communities to directly test these potential mechanisms.

In addition to understanding mechanisms that drive shrub expansion, I was also

interested in understanding how climate driven changes in environment such as nutrient

availability and warming would influence ecosystem structure and function in currently

established riparian shrub tundra. There are few, if any, studies that directly test shrub

tundra responses to warming and added nutrients in Alaskan shrub tundra even though

shrub dominated communities make up 22% of the land cover in the Arctic tundra

ecosystems of Alaska and 36.5% of the non-glaciated Pan-Arctic tundra biome (Walker

et al. 2005). Those studies that have tested the response of shrub tundra to warming

and nutrient additions have been concentrated in sub-arctic systems in Northern

Sweden where the dominate shrubs are evergreen and are compositionally more similar

to heath tundra communities in Alaska than riparian shrub communities (Parsons et al.

1994, Michelsen et al. 1996, Molau and Alatalo 1998). In contrast, shrub tundra

communities in Northern Alaska are dominated by deciduous shrubs, whose functional

traits may allow them to respond more rapidly to environmental change compared to

evergreen shrubs. Understanding how Alaskan shrub tundra communities will respond

to environmental change is becoming even more critical as these communities are

currently expanding (Tape et al. 2006, Forbes et al. 2010) and are expected to continue

to increase with future warming (Walker et al. 2006).

The objective of the fourth chapter of this dissertation was to understand controls

over plant productivity and C and N storage in shrub tundra ecosystems in order to

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make inferences about how these systems will respond to environmental change. To

investigate whether plant productivity is limited by temperature, nutrients, or an

interaction between the two we examined the plant and ecosystem response from the

longest running (18 years) nutrient and warming experiment in Alaskan arctic riparian

shrub tundra ecosystems. In addition, we tested whether these environmental changes

altered total ecosystem N and C storage.

Figure 1-1. Potential pathways by which shrubs enhance their dominance through a positive-feedback mechanism where shrubs alter the biophysical and biogeochemical environment resulting in increased nutrient availability.

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CHAPTER 2 THE EFFECTS OF SNOW, SOIL MICROENVIRONMENT, AND SOIL ORGANIC MATTER QUALITY ON N AVAILABILITY IN THREE ALASKAN ARCTIC PLANT

COMMUNITIES1

Background

Arctic air temperatures in the last decade have been the warmest on record and

are expected to continue to rise at a faster rate than the rest of the world (Overpeck et

al. 1997, ACIA 2004, Serreze and Francis 2006). Warmer temperatures can have

profound effects on biogeochemical processes important for ecosystem function,

potentially leading to changes in species composition and ecosystem structure (Chapin

et al. 1995, Walker et al. 2006). Warmer air temperatures can cause an increase in

deciduous shrub growth (Bret-Harte et al. 2001) both directly and indirectly through

stimulation of SOM decomposition and mineralization of organically bound nitrogen (N),

resulting in more plant available N (Nadelhoffer et al. 1991, Hobbie and Chapin 1996).

Arctic plant species respond differently to N fertilization: the deciduous shrub Betula

nana responded more strongly over a five to 20 year time-scale than graminoids such

as Eriophorum vaginatum, the tussock forming sedge typic of upland tundra

ecosystems throughout the arctic (Chapin et al. 1995, Chapin and Shaver 1996, Shaver

et al. 2000, Mack et al. 2004). In addition, warming can also cause thawing of

permanently frozen soil, leading to changes in topography that may promote the

establishment of deciduous shrubs (Schuur et al. 2007).

1Reprinted with permission from DeMarco, J., M. C. Mack, and M. S. Bret-Harte. 2011. The effects of snow, soil microenvironment, and soil organic matter quality on N availability in three Alaskan Arcitic plant communities. Ecosystems 14:804-817.

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Changes in vegetation composition from graminoid dominance to increasing shrub

dominance within the arctic may already be underway (Payette et al. 1989, Myneni et al.

1997, Oswald et al. 1999, Sturm et al. 2001b, Zhou et al. 2001, Stowe et al. 2004) and it

has been hypothesized (Sturm et al. 2001a) that these changes are due to increased

shrub growth in response to increased soil temperatures, which enhance N

mineralization.

Changes in ecosystem structure and function could result in a positive feedback

that accelerates the transition to shrub tundra. Plant species traits can influence nutrient

cycling by altering some of the major controls over soil N transformations: chemical

composition of SOM and soil microclimate (Chen and Stark 2000, Mack and D'Antonio

2003). Species specific differences in the quality and quantity of plant litter inputs to the

soil can influence SOM quality (Pastor and Post 1986, Hobbie 1992, Wedin and Pastor

1993). Plant species can alter the soil microclimate by changes in canopy architecture,

rooting depth, and litter depth deposition (Van Cleve et al. 1983, Matson and Boone

1984, Burke 1989, Wedin and Pastor 1993, Seastedt and Adams 2001).

Arctic shrubs may influence nutrient cycling by altering abiotic and/or biotic

controls over soil N transformations. The snow-shrub hypothesis suggests that shrubs

can alter the abiotic soil environment via their interactions with snow (Sturm et al.

2001a). Tundra areas with taller and more abundant shrubs accumulated greater snow

depth due to greater retention of snow fall (e.g. less snow lost to wind events) and

trapping of wind distributed snow than tundra areas with less shrubs (Sturm et al.

2001a, Pomeroy et al. 2006). Snow acts as an insulator that can increase soil

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temperature (Brooks et al. 1996, 1998, Grogan and Jonasson 2003, Schimel et al.

2004, Wahren et al. 2005) and the availability of water to soil microorganisms (Coxson

and Parkins 1987, Romanovsky and Osterkamp 2000, Mikan et al. 2002) and therefore,

potentially regulate the rate at which microbes can mineralize N over the winter.

Indeed, winter CO2 emissions and N mineralization have been found to be higher in

soils that had deeper snow cover (Brooks et al. 1996, Schimel et al. 2004).

Furthermore, increasing evidence suggests that microbial activity and mineralization

over the winter can provide available nutrients during spring thaw that may influence soil

and plant communities through the growing season (Grogan and Jonasson 2003,

Buckeridge and Grogan 2008). Therefore, snow accumulation by shrubs could

indirectly influence N availability by maintaining warmer soil temperatures in winter,

allowing soil microbes to stay active longer to mineralize organic substrates and thus

potentially supply more N to shrub growth.

Snow manipulation studies in the arctic that have increased snow depth 5.5 times

ambient show an increase in N mineralization (Schimel et al. 2004, Borner et al. 2008);

however, it has not been directly tested whether the moderate amount of snow (< 1 m)

that is trapped by deciduous shrubs is enough to result in an increase in available N.

Alternatively, shrubs may enhance N availability independent of their effects on winter

soil temperatures by altering the quantity and quality of litter and SOM substrates for

microbial decomposition and nutrient release (Hobbie 1992, Buckeridge et al. 2010).

Furthermore, we know little about the relative importance of snow-mediated effects

versus SOM quality mediated effects on soil nutrient dynamics in arctic shrub tundra

systems. In arctic tussock tundra and boreal systems, differences in litter quality among

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species and SOM quality across different vegetation types can have a larger effect on N

mineralization than differences in temperature (Flanagan and Van Cleve 1983, Giblin et

al. 1991, Nadelhoffer et al. 1991, Hobbie 1996). Abiotic influences through changes in

the soil microenvironment or biotic influences through changes in the quality and

quantity of litter inputs may both be important in influencing N mineralization rates;

however, the magnitude and time scale of their influence may differ. Changes in

microenvironment may occur over relatively short time scales, within a few years, while

changes in litter quality inputs and SOM quality would occur over a relatively longer time

scale, decades to centuries, as it would take more time for litter inputs to accumulate

and be incorporated into the SOM (Shaver et al. 2000). Understanding the time scale

and relative magnitude of these mechanisms is crucial for understanding how a

shrubbier arctic may influence N dynamics and feedback to the global C cycle

The goal of my study was to understand the relative importance of mechanisms

through which arctic deciduous shrubs could affect N dynamics under realistic levels of

snow addition by investigating how differences in soil microclimate and SOM quality

influence N availability across three different arctic plant communities. My intent was to

isolate the effects of snow from the effects of shrubs on N mineralization. My objectives

were three fold: (1) to test whether realistic levels of snow accumulation maintain winter

soil temperatures high enough to stimulate microbial activity and increase N availability

(2) to compare the relative effects of snow versus shrubs on N availability via effects on

the main drivers of N mineralization: SOM quality versus microclimate, and (3) to better

understand how shrubs and snow addition influence N availability over winter and

summer seasons. I hypothesized that 1) the addition of snow would slow temperature

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decline in the winter and lead to greater N availability, 2) SOM quality will have a greater

effect on N availability than changes in soil microclimate, and 3) the microclimate effects

of shrubs on N-mineralization would be more pronounced in the winter than in the

summer.

To test my hypotheses, I measured SOM quality and N availability across three

plant communities that represented natural variation across the landscape in shrub

abundance. Here I consider SOM quality as an integrative variable that includes both

the soil organic matter and the soil microbial community. As an index of N availability, I

measured net N mineralization using in situ intact soil cores (DiStefano and Gholz

1986). Incubating cores in situ gives us an estimate of N-mineralization under natural

field moisture and temperature conditions. To test the effects of snow depth on N

availability, I directly manipulated snow depth, via snow fences, at each of the three

sites. To compare the influence of site differences in microenvironment and shrub

abundance on the controls over N mineralization, I conducted a reciprocal soil core

transplant of intact soil cores between the three sites. This experimental design

enables us to determine the relative long term effect of SOM quality (including the soil

microbial community) versus the short term effect of microclimate on N mineralization.

To understand the importance of seasonality on N availability, I measured N-

mineralization in both the summer and winter, in the snow fence treatments and in the

reciprocally transplanted soil cores.

Methods

Study Area

All sites are located near Toolik Field Station (Lat. 68°38’N, Long. 149°38’W,

elevation 760m) and the Arctic Long Term Ecological Research (LTER) program sites

25

in the foothills region of the North Slope of Brooks Range, Alaska, USA. This area was

glaciated during the late Pleistocene and includes large areas of Itkillik I (deglaciated ca.

60 000 yr) and Itkillik II (deglaciated about 10 000 yr) glacial drifts (Hamilton 1986). The

entire foothills region of the Brooks Range is treeless and underlain by continuous

permafrost, 250-300 m thick (Osterkamp and Payne 1981). Mean annual air

temperature is around -10°C, with monthly mean summer temperatures from 7-12 °C.

Annual precipitation is 318 mm, with 43% falling in the winter as snow

(http://ecosystems.mbl.edu/ARC). Average snow depth is 50 cm, although snow

distribution is variable due to redistribution by wind. Snow-melt usually occurs in May.

In the fall of 2005, three sites were selected that varied primarily in deciduous

shrub abundance, hereafter referred to as low, medium and high shrub sites. Sites

were chosen to have similar state factors (climate, relief, parent material, and time) but

varied in the abundance of deciduous shrubs (Jenny 1994). The sites represent a

natural gradient of increasing shrub abundance because the same species of deciduous

shrubs (Betula nana, and Salix pulchra) are found at all three sites (except S.

richardsonii, which is found only at the medium shrub site); however, their percent cover

increases from 15 to 94 % (Table 1). My sites are within 1 km of each other, and have

similar parent material, and time since last glaciation (Itkillik I, deglaciated ca. 60 000

yr), and regional climate, although microclimates vary across sites due to differences in

slope and aspect. The low shrub site is located on top of gently rolling hills, while the

medium and high shrub sites are located in depressions along water tracks of

ephemeral streams fed by spring snowmelt. Elevation changes from about 764 m at the

26

low shrub site to 741 m at the medium and high shrubs sites. My selection of sites is

useful to aid in understanding how a shrubbier arctic may influence nutrient cycling.

My low shrub abundance site is located in moist acidic tussock tundra where the

vegetation consists of approximately equal biomass of graminoids (Eriophorum

vaginatum and Carex bigelowii), dwarf deciduous shrubs (B. nana, Vaccinium

uliginosum, and S. pulchra), evergreen shrubs (Ledum palustre ssp. decumbens and V.

vitis-idea), and mosses (Hylocomium splendens, Aulacomnium turgidum, Dicranum

spp., and Sphagnum spp.) (Shaver and Chapin 1991). In Alaskan upland tundra,

tussock tundra is the most abundant vegetation type. In my medium shrub abundance

site, vegetation consists of graminoids (primarily C. bigelowii), deciduous shrubs (B.

nana, V. uliginosum, S. pulchra and S. richardsonii), and mosses (H. splendens and

Dicranum spp.). My high shrub abundance site has predominantly deciduous shrubs

(B. nana, S. pulchra, and some Potentilla fruticosa) with some evergreen or wintergreen

shrubs (V. vitis-idaea and Linnaea borealis), forbs (Polygonum bistorta, Petasites

frigidus, Stellaria longipes, Valeriana capitata, and Artemisia alaskana), graminoids

(Poa arctica, C. bigelowii, and Calamagrostis canadensis), and mosses (Sphagnum

spp. and H. splendens).

Snow Manipulation

To determine the influence of increased snow depth on N availability, snow fences

(1.5 m high and 62 m long) were set up in the fall of 2005 at the low and medium sites

to manipulate snow depth. For the high site, the patchy nature of the shrub stands

made it necessary to set up two separate snow fences (1.5 m high and 32 m long) in

patches with similar shrub composition and density. My purpose for adding snow was

to simulate the amount of snow that might be trapped by deciduous shrubs; therefore,

27

the height of the snow fences was selected to match the maximum shrub height within

the region. Winds are predominantly from the south in the winter, so the fences were

oriented E-W, and snow drifts accumulated on the northern side of the fences. Two

treatments (control=ambient snow and drift=manipulated snow) were set up at each

site. The drift plots were set up 4 m from the fence on the northern side of the fence,

because this was the zone of maximum snow accumulation. At the low and medium

sites, the control plots were set up on the southern (non-drift) side of the fence, 10 m

from the fence at the low site, and 7 m from the fence at the medium site. These buffer

zones were left to prevent the control from being exposed to snow trapped by the fence.

At the high shrub sites, control plots were located in line with one of the fences,

beginning 5 m from its end, and in 3 discontinuous blocks of tall shrubs to the south

(control side) of the fence, beginning approximately 15 m from the fence. This

arrangement was chosen because the cover of tall shrubs was discontinuous on the

southern (control) side of the fences. For all sites, plots on the drift side of the fences

were located in the zone of maximum snow accumulation, which was relatively uniform.

Within each treatment, 18 2 by 10 m plots, with 1 m buffer strips between, were

established. For this study, six plots per treatment (n=6) were randomly assigned to

measure N mineralization and nitrification. Remaining plots were used for additional

experiments.

Maximum snow depth was measured every meter in April of 2006 and every 2 m

in 2007 along two 12-m transects running parallel to the snow fences at the control and

drift plots for each site. Values from the two transects were then averaged. In 2007,

28

snow depth was not measured on the control side for one of the snow fences from the

high site. Only values for the vicinity of the experimental plots are presented here.

Annual depth of soil thaw was measured in 2005, using a metal probe, at every

meter along a 60-m transect within the control and snow addition plots at the low and

medium shrub sites (n=60). For the high shrub site, thaw depth was measured every

meter along two separate 30-m transects that were along the two 30-m long snow

fences located at both the control and snow addition plots. Soil temperature, at 5 cm

within the organic layer, was measured continuously (1-3 h intervals) from June 2006-

June 2007 in each study plot (n= 3-4) by using Ibutton temperature data loggers

(IButtonLink, LLC, East Troy, WI). Weekly mean soil temperatures were calculated

from mid-June 2006 through mid-May 2007 for all treatments and sites. Annual soil

temperatures were calculated for each site and treatment and included 336 days of

measurements.

Characterizing ecosystem structure

Live aboveground biomass in each site was determined by harvesting all plant

species within 12 10 by 40 cm quadrants at the low shrub site. For both the medium

and high shrub sites, the understory was removed from six similar 10 by 40 cm

quadrants nested within a 50 by 50 cm quadrant, from which the overstory was

removed (See Appendix). From the harvested species, we separated out height

responsive deciduous shrubs from those shrubs that do not have the physiological

ability to substantially increase their height. It is these tall shrubs that have the potential

to “trap” snow. In this ecosystem type Betula nana, Salix pulchra, S. glauca, and S.

richardsonii are the only species that have the capacity to grow tall. Only total biomass

and deciduous tall shrub biomass are presented here.

29

To estimate belowground biomass, rhizome biomass was determined by

harvesting the same 10 x 40 cm quadrants used to determine aboveground biomass

according to methods described in (Bret-Harte et al. 2008); while root biomass within

the organic layer of each plot was measured in 5 by 5 cm soil monoliths that extended

down to the surface of the mineral layer (see Appendix). All biomass was dried at 60 C

for a minimum of 48 hours before weighing and is expressed on an area basis. Soil

bulk density was determined using a separate set of soil monoliths sampled in July of

2007. Soil pH was measured on mineral soils, collected in June and July of 2007, by

mixing a 1 to 2 slurry of soil and DI water and measuring within 30 minutes using a pH

meter (Thermo Orion 250A+, Orion Research, Inc, Boston, Massachusetts, USA).

Soil N dynamics

I used the in situ soil incubation method (DiStefano and Gholz 1986, Hart and

Firestone 1989) to assess (1) site differences in net N mineralization and nitrification, (2)

snow effects on net N mineralization and nitrification, and (3) SOM versus microclimate

effects on net N mineralization and nitrification. At each site (Low, Medium, and High)

and within each treatment (Ambient and Snow addition) at each plot (n = 6), five soil

cores were removed from the top 10 cm of the organic layer in either June or

September of 2006. Soils were sampled with a 5 cm diameter metal corer; new frost

boils and Eriophorum vaginatum tussocks were avoided. One core (initial) was

removed from the ground, chilled, and processed (within 48 h of sampling) for pools of

inorganic N (N-NH4+ and N-NO3

-), dissolved organic N (DON), chloroform-fumigated

microbial biomass N (MB-N), bulk soil percent C and N, and soil moisture. The other

four cores (final) were placed in a 12 cm long and 5 cm diameter PVC tube and capped

30

at the top with one resin bag, and at the bottom with two resin bags. Resin bags were

made of nylon that was soaked in 1.2M HCl for 2 h before filling with ion exchange

resins. Bags contained 17 g fresh weight (49.4% moisture, 8.28 g oven-dry equivalent)

of mixed-bed ion exchange resins (IONAC® NM-60 H+/OH- form, type I beads 16-50

mesh; J. T. Baker, Phillipsburg, New Jersey, USA). One core was then returned to its

original hole in the control plot, one core was transplanted to a randomly assigned snow

addition plot within the same site for incubation, while the other two remaining cores

were each transplanted to a randomly assigned control plot from one of the other two

sites for incubation. By doing this, I held the substrate constant but altered the

environment of the incubation. All final cores were incubated for 74 days in the summer

(mid June 2006-Sept 2006) and 280 days in the winter (Sept 2006-mid June 2007) to

look at seasonal affects on N availability. At the end of the incubation periods, soil

cores were removed and the soil was processed (within 48 hours of sampling) for pools

of N-NH4+ and N-NO3

-, DON, MB-N, bulk soil percent C and N, and soil moisture in

exactly the same way as the initial core. Resin bags were removed from cores and

frozen until processing (see below). Net N mineralization was calculated as the

difference between the DIN (NH4+ and NO3

-) in the initial soil core and the DIN in the

final soil core plus the DIN accumulated on the middle resin bag. Net N nitrification was

calculated as the difference between the nitrate in the initial soil core and the nitrate in

the final soil core plus the nitrate accumulated on the middle resin bag. The percent of

mineralized N that was nitrified was calculated by dividing the N nitrified by the amount

of N that was mineralized and multiplying by 100. Nutrient pool sizes and annual net N

31

mineralization were calculated using the soil bulk density obtained from soil harvest

conducted in 2007.

Prior to analysis, soils were homogenized by hand and the >2 mm diameter

fraction (e.g., roots, rhizomes, course woody debris, and rocks) was removed. Soil

water content was calculated by subtracting the dry weight of the soil (60 °C for 48 h)

from the wet weight of the soil and then dividing by the dry weight of the soil. To

determine bulk soil percent C and N, a subsample of <2mm soil fraction was dried at

60º C for 48 h, ground to a fine powder on a Wiley-mill with a #40 mesh screen, and

then analyzed using an ECS 4010 elemental analyzer (Costech Analytical, Valencia,

California, USA).

Pools of dissolved inorganic N (N-NH4+ and N-NO3

-) were measured by extracting

10 g of fresh soil with 50 ml of 0.5 M K2SO4. The soil slurry was agitated on a shaker

table for 2 h, allowed to sit overnight in a cooler, and then vacuum filtered through a

Whatman GF/A filter. Filtrate was frozen until analyzed colorimetrically, on segmented

flow autoanalyzer (Astoria analyzer, Astoria-Pacific, Inc, Clackamas, Oregon, USA).

Dissolved organic N (DON) was measured on a subsample of the K2SO4 extract

that was digested with a persulfate oxidation digestion procedure (Sollins et al. 1999)

prior to colorimetric analysis. Because this digestion procedure converts all forms of N

to NO3-, DON was calculated by subtracting the DIN measured previously from the total

N that was determined in the digestion procedure.

Microbial biomass N was determined using the chloroform fumigation method.

Ten grams of fresh soil was incubated with 100 ml of pentene stabilized chloroform in a

glass dessicator for 24 h. Post incubation, soils were extracted with 0.5 M K2SO4

32

exactly the same way as for DIN. Fumigated extracts were digested using the same

persulfate oxidation procedure used for DON analysis. Nitrate was then measured

colorimetrically. Chloroform labile-N was calculated by subtracting the DON and DIN

concentrations from the initial, un-fumigated sample from the total N that was extracted

from the post-fumigated sample.

After incubation, resin bags were rinsed with deionized water to remove soil and

then extracted with 50 ml of 2N KCl. Resins and KCl were agitated for 1 h and then

filtered through a pre-leached Whatman #1 filter. Filtrate was immediately frozen. At

time of analysis, extracts were thawed and measured for N-NH4+ and N-NO3

-

colorimetrically.

Statistical Analyses

Properties of ecosystem structure across sites were analyzed using a One-way

Analysis of Variance (ANOVA) with site as the main effect (JMP 7.0, 2007, SAS).

These include aboveground and belowground biomass, soil bulk density, soil percent N

and C, C:N ratio, soil C and N stocks, and soil pH. The F-statistic, degrees of freedom,

and p-values are reported. Potassium sulfate extractable soil nutrients (NH4+, NO3

-,

DON, and MB-N) and seasonal net N-mineralization were analyzed using a Two-way

ANOVA with site and season as the main effects and site by season as the interaction

term. Differences in snow depth, soil temperature, soil moisture, and net N-

mineralization across sites and between treatments were analyzed using a Two-way

ANOVA with site and treatment (ambient and snow addition) as the main effects and

site by treatment as the interaction term. Soil organic matter quality effects on net N-

mineralization were analyzed using a two-way ANOVA with soil origin and incubation

location as the main effects and soil origin by incubation location as the interaction term.

33

Tukey’s multiple comparisons test was used when significance was obtained from

ANOVAs. Data were tested for normality (Shapiro-Wilks), and ln-transformed when

necessary to meet the assumptions of ANOVA. Data sets of ammonium and nitrate

pools were not normally distributed, even when ln-transformed, and were analyzed

using Kruskal Wallis tests. Separate comparisons were made across sites within the

same season and across seasons within each site.

Results

Ecosystem structure across the shrub gradient

Aboveground biomass was almost twice as large in the high shrub site than in the

medium or the low shrub sites (One-way ANOVA, Table 2-1, F2,21 = 19.39, P <0.0001),

primarily due the greater biomass of deciduous shrubs (Table 2-1, F2,21 = 63.68, P

<0.0001). Total belowground biomass was also greatest in the high shrub site (F2,21 =

3.75, P = 0.04). Rhizomes made up the largest portion of the belowground biomass,

with the high site having the greatest rhizome biomass (F2,21 = 5.80, P <0.01). Root

biomass did not differ among sites and most belowground biomass was found in the

organic layer (Appendix A-1).

The medium and high shrub sites had SOM C and N stocks that were 2-3 times

greater than stocks of the SOM C and N in the low shrub site (Table 2-2). This

difference was primarily driven by differences in soil bulk density among the sites, since

concentrations of SOM C (Table 2-2) and soil depth (Table 2-1) did not differ among

sites. The C:N ratio was twice as high (41± 3) at the low shrub site than at the medium

(22 ± 1) and high (18 ± 1) shrub sites. Mineral soil pH was 0.5 units more acidic at the

low shrub site than at the medium and high shrub sites (Table 1, F2,27 = 7.07, P <0.01).

34

There was both a site difference (Appendix A-2; F2,30 = 50.82, P <0.0001) and a

seasonal difference (F1,30 = 18.79, P <0.001) in the ammonium (NH4+) concentrations.

The medium and high shrub sites had 8-19 times respectively higher soil (NH4+)

concentrations than the low shrub site in June and 4-10 times higher concentrations

than the low shrub site in September leading to a 14-28 fold difference in standing NH4+

pools in June and a 7-14 fold difference in standing NH4+ pools in September (Appendix

A-2, χ22 = 13.05, P <0.01; χ2

2 = 12.29, P <0.01). Dissolved organic nitrogen (DON) and

microbial biomass nitrogen (MB-N) concentrations followed a similar pattern as NH4+

concentrations with both a site (F2,30, = 8.67, P <0.003; F2,30 = 5.38, P <0.01) and a

season difference (F1,30 = 5.42, P <0.01; F1,30 = 7.11, P <0.01), and site by season

interaction (Appendix A-2, F2,30 = 4.49, P = 0.02; F2,30 = 3.62, P = 0.04). Nitrate (NO3-)

concentrations were low and did not differ among sites or between sampling dates.

The high shrub site mineralized more N in both the summer and the winter than

the low and medium shrub sites (Two-way ANOVA, site: F2,28 = 24.85, P <0.0001; Table

2-1). More N was mineralized over the winter than over the summer for the high shrub

site only (season: F2,28 = 13.11, P = 0.001; Table 2-1). There was no significant site by

season interaction (site x season: F2,28 = 1.11, P = 0.34). Net N-nitrification and the

percent of mineralized N that was nitrified did not differ among sites or seasons (data

not shown).

Snow manipulation

The snow fence produced a maximum (ambient plus addition) snow pack in our

plots that was, on average 149, 158, and 172 cm deep in 2006 and 166, 175, and 133

deep in 2007 for the low, medium and high shrub sites, respectively (Fig. 2-1). In both

years there was a significant difference between treatments within the same site but no

35

difference across sites within the same treatment (Two-way ANOVA, 2006 site: F2,18 =

0.26, P = 0.78; treatment: F1,18 = 430.39, P < 0.0001; 2007 site: F2,8 = 1.54, P = 0.27;

treatment: F1,8 = 76.02, P < 0.0001). There was however a significant site by treatment

interaction for 2006 data only (2006 site x treatment: F2,18 = 11.61, P < 0.001; 2007 site

x treatment: F2,8 = 2.12, P = 0.18). Snow addition increased average winter soil

temperatures by 3, 1, and 0.5°C (-4.8 ± 0.11, -2.7 ± 0.72, -2.9 ± 0.82; Mean ± SE) and

summer soil temperatures by 0, 1, and 2°C (4.9 ± 0.44, 7.0 ± 0.62, 8.0 ± 0.33; Mean ±

SE) in the low, medium, and high shrubs sites, respectively (Fig. 2-1; Winter site: F2,14 =

17.33, P = 0.0001; treatment: F1,14 = 15.44, P < 0.01; site x treatment: F2,14 = 2.80, P =

0.09; Summer site: F2,14 = 8.00, P < 0.01; treatment: F1,14 = 5.63, P = 0.03; site x

treatment: F2,14 = 2.16, P = 0.15). Soil moisture in the snow addition treatments was

only measured in June 2006 and showed a trend of increased soil moisture (5.9 ± 1.6,

4.2 ± 0.3, and 5.5 ± 0.3 g H2O.g-1 soil; mean ± SE) at the low, medium, and high shrub

sites) compared to the ambient snow sites (3.1 ± 0.4, 4.0 ± 0.3, and 5.1 ± 0.4 g H2O.g-1

soil; mean ± SE) with the low shrub site having the largest increase in soil moisture (2-

Way ANOVA, site F2,29 = 1.4, P = 0.25; treatment F1,29 = 3.6, P = 0.07; site x treatment

F2,29 = 1.9, P = 0.17).

The addition of snow increased N-mineralization in the summer (F1,2 = 6.0, P =

0.02) but did not affect N-mineralization in the winter (F1,2 = 1.0, P = 0.29) and did not

interact with site (Summer F1,2 = 0.9, P = 0.42, Winter F1,2 = 0.2, P = 0.80; Fig. 2). Net

nitrification was not altered by the addition of snow during either season, but within the

snow treatment more mineralized N was nitrified in the low site during the summer (χ22

= 8.05, P = 0.02). With the addition of snow, the C:N ratio became negatively correlated

36

with summer net n-mineralization (r2 = 0.303, p = 0.01); while, the positive relationship

found in the control between summer nitrate pool, C pool, soil moisture and net N-

mineralization no longer held true (Appendix A-3).

Reciprocal soil core transplant experiment

During both summer and winter, SOM quality, but not soil microclimate, had a

significant effect on N-mineralization rates (soil origin effect: Summer F 2,6 = 32.2, P =

0.001, Winter F2,6 = 0.5, P = 0.0008). There was no significant interaction between soil

origin or site of incubation during either season (Summer F 1,2 = 1.4, P = 0.24, Winter

F2,6 = 0.5, P = 0.71). Soils from the high shrub site mineralized more N than soils from

the medium and low sites, regardless of where they were incubated (Fig. 2-3). Soils

from the high shrub site also mineralized more N in the winter when compared to the

soils from the medium and low shrub sites but only when high shrub soils were

incubated at the medium and high shrub sites (Fig. 2-3). Net nitrification and the

percent of mineralized nitrogen that was nitrified were also not significantly different

when incubated in the other sites (data not shown).

Discussion

Snow addition effects on N availability

Contrary to the snow-shrub hypothesis, I was unable to detect an effect of snow

addition on winter net N-mineralization at any of my experimental sites despite the fact

that the addition of snow increased winter soil temperatures by an average of 3, 1, and

0.5°C at the low, medium, and high shrub sites, respectively. Surprisingly, I found that

summer, not winter, net N-mineralization was positively affected by adding snow at two

of my three sites. The effect of snow on summer mineralization was microclimatic: by

transplanting soil from ambient snow plots to snow addition plots in spring, I controlled

37

for any changes to the SOM that may have occurred by winter snow additions, isolating

the effects of moisture and temperature.

The effects of added winter snow on summer mineralization have been variable in

other arctic studies. Schimel et al. (2004) found that after 4 years of adding snow at

levels 5 times ambient, soils that had experienced increased snow cover over the entire

experiment switched from immobilizing to mineralizing N during the growing season. By

contrast, Borner et al. (2008), after 6 years of adding snow at 6 times ambient, found no

effect of added snow on summer N-mineralization rates. These studies differ from mine

in that they measured the integrated effects of winter and summer snow addition, while I

isolated the microclimate effects of snow addition by moving ambient soils into snow

addition plots, showing that on a short time scale changes in microclimate can result in

changes in soil nutrient turnover.

The lack of response of N-mineralization rates to winter soil warming was

unexpected and may be explained by a number of different factors. First, inorganic N

released during mineralization may have been immediately remobilized by microbes

during the winter incubation or at the time of spring thaw (Schimel et al. 2004). Second,

the moderate increase in temperature may not have been enough to drive significant

changes in microbial activity and result in an increase in N mineralization rates. Even

with the addition of snow, soil temperatures were still very cold and remained at or

below -5°C for most of the winter at all three sites, reaching as low as -8°C at the

medium and high sites and -10°C at the low sites. Schimel et al. (2004) measured an

increase in winter N-mineralization with the addition of snow, but, their total snow pack

of 3 m maintained soil temperatures at -5°C or above for most of the winter.

38

I was surprised to find that during the winter of 2006 the ambient snow depth at my

high shrubs site was actually lower than the ambient snow depths at the low and

medium shrub sites, which is counterintuitive to the idea that taller shrubs trap more

snow. I analyzed two additional years following the transplant experiment and found

that out of all four years only in 2006 was the snow depth lower at the high shrub site

compared to the low shrub site. In 2007 and 2008 there was no difference in snow

depth across the sites and in 2009 the high shrub site had substantially higher depths

than both the medium and high shrub site (Fig. A-1). Shrubs “snow trapping abilities”

may vary interannually depending on the amount of snow fall and the number or

intensity of wind events that can redistribute snow. Regardless of the differences in

ambient snow depths, I have shown that when the snow level is brought up to the same

depth across all sites it has no effect on winter and a positive effect on summer net N-

mineralization at least two of my sites.

My net N-mineralization values are higher than what others have reported for in

situ incubations from similar ecosystem type within this region (Giblin et al. 1991,

Schimel and Bennett 2004, Schimel et al. 2004, Borner et al. 2008) and may reflect

differences in methodologies used to determine net N-mineralization. The buried bag

technique can provide lower rates of net N-mineralization than the intact soil core

method (Hart and Firestone 1989) because inorganic nutrients released from

mineralization cannot be leached from the soil and are therefore available to be

remobilized by soil microbes. In addition, the length of incubation can influence net N-

mineralization rates because nutrients are simultaneously being mineralized and

immobilized during the incubation making it difficult to compare rates across studies.

39

My data suggest that on a short time scale moderate snow addition is important in

influencing N dynamics not because of the direct effects it may have on winter soil

temperatures but because of the carryover effect winter snow has on summer soil

microclimate. Warmer soil temperatures may help explain why I found higher rates of

net N-mineralization in the snow addition treatment. Warmer winter soil temperatures

under deeper snow could be carried over into the summer, resulting in warmer summer

soil temperatures because less energy is required to warm and thaw the soil during the

summer (Seppala 1994, Stieglitz et al. 2003, Sturm et al. 2005). Although, there was no

significant difference in mean summer soil temperature between treatments there were

differences in treatments early in the season following spring thaw. Because my soil

cores were incubated for the entire season I am unable to separate out the effect that

early season warming may have on net N-mineralization. Alternatively, the higher rates

of N mineralization measured in the snow addition site may have been driven by

differences in soil moisture associated with adding snow or an interaction between soil

temperature and moisture. Soil moisture in June was greatest in the snow addition

treatment at the low shrub site supporting this potential mechanism. How this realistic

snow increase will influence N-mineralization over a longer time scale is unknown.

Soil organic matter quality effects on N availability

In my reciprocal soil transplant experiment, I isolated the SOM from each plant

community and incubated it in another microenvironment to separate out the short term

microclimate effects on N dynamics from more long term SOM effects. Given the

difficulties of measuring and separating out microbial community from SOM quality, I

chose to consider the SOM as an integrative factor which includes nutrient pool size,

relative decomposability as indexed by C:N ratio, and microbial community. Across all

40

sites and over both seasons, soil from the high shrub site mineralized more N than the

medium and low shrub sites regardless of where it was incubated. Mineralization rates

were low for the medium shrub soil across all the sites it was incubated, despite having

a similar soil C:N ratio as the high shrub site. This difference may be attributed to

differences in C quality between the soils and/or differences in microbial communities or

activity. Although not significant, the high shrub soils mineralized more N in the low

shrub site in the summer, but during the winter mineralized more in their site of origin.

This suggests that the summer soil microclimate at the low shrub site may be more

favorable for mineralization once SOM quality limitations are removed.

Soil microclimate versus soil organic matter quality effects on N availability

My study demonstrates that differences in SOM quality can drive larger differences

in net N-mineralization than changes in soil microclimate of the magnitude of what I saw

across our three sites. Microclimate differences across my sites were small. The soil

temperatures at the medium and high shrub sites were 1° C warmer in the summer and

4° C warmer in the winter compared to the low shrub site. This small change in

microclimate was not enough to increase net N-mineralization rates once we controlled

for differences in SOM quality. The changes in soil temperature I saw with the addition

of snow were similar in magnitude to the differences I saw across our unmanipulated

sites. With the addition of snow, winter soil temperatures increased by 3°C at the low

shrub site resulting in soil temperatures close to soil temperatures in the ambient high

shrub site. If net N-mineralization was limited by temperature alone I would expect that

net N-mineralization rates in the snow addition at the low shrub site to be similar to net

N-mineralization rates at the ambient high site. This is not the case. Results from both

my snow manipulation and my SOM reciprocal transplant experiments both suggest that

41

net N-mineralization is more strongly limited by SOM quality than temperature. This is

consistent with incubation studies, which show larger effects of site SOM quality on N

mineralization than temperature (Nadelhoffer et al. 1991, Weintraub and Schimel 2003).

Across our sites, I was unable to control for differences in topography. Changes in

topography can influence microclimate, hydrology, nutrient movement, and snow

distribution, potentially influencing vegetation growth and nutrient cycling (Schimel et al.

1985, Burke 1989); indeed, changing water movement on an arctic slope was found to

alter plant productivity and nutrient content (Chapin et al. 1988, Oechel 1989). My

reciprocal soil core transplant results suggest that the quality of SOM has stronger

influence on net N-mineralization rates than the small differences in microclimate that

might be due to differences in topography among the sites. These findings are

consistent with other studies comparing mineralization rates across vegetation types

incubated at different temperatures (Nadelhoffer et al. 1991) and along a toposequence

(Giblin et al. 1991). In my study, I did not separate site effects from species effects on

soil organic matter quality. It is likely that both of these effects are important at different

time scales. Topographical influences on hydrology, microclimate, and snow

distribution may lead to favorable habitat (such as better soil drainage, warmer

temperatures, and greater snow cover) for initial establishment of deciduous shrubs.

However, over time vegetation influences on the abiotic and biotic controls over

biogeochemical cycling of nutrients may ensure that shrubs will remain in that area via

positive feedbacks to nutrient cycling, such as better quality litter, SOM, and soil

microclimate. Understanding these controls is becoming more important with current

and future warming within the Arctic because warmer arctic temperatures may increase

42

growth of already existing shrubs, but also lead to changes in topography (due to

thawing of permanently frozen soil), that may promote the establishment of deciduous

shrubs (Schuur et al. 2007).

Implications for feedbacks to climate change

Results from my experimental manipulations suggest that changes in arctic

deciduous shrub abundance can affect N dynamics through both abiotic and biotic

controls. These controls, however, play out at different time scales. On a short time

scale, microclimate had an effect on soil N dynamics, but only during the summer. The

summer season is when plants are most actively taking up nutrients, so a stimulation of

summer N mineralization rather than winter mineralization should increase the coupling

of plant and soil processes by synchronizing nutrient availability and plant growth.

Thus, the addition of snow could lead to changes in plant communities by shifting the

timing of nutrient availability.

Although my study was short term, my results can still be useful to make

inferences about how snow and shrubs can influence N mineralization in a changing

climate. If these sites are representative of the transitions that might occur in a warming

arctic, then my study suggests that increased dominance of large shrubs could lead to

an increase in plant productivity and aboveground biomass, which can increase uptake

of atmospheric CO2 and the storage of more C aboveground in woody tissue or

belowground in rhizomes (Shaver and Chapin 1991), roots, and ectomycorrhizal fungi

(Clemmensen et al. 2006). These effects could result in a negative feedback to climate

change. My results also suggest, however, that increased dominance of large shrubs

may also lead to increased decomposition of SOM and thus a faster cycling of C and N.

Thus, there is also a potential for a positive feedback to warming with increased shrub

43

cover if decomposition of SOM occurs at a faster rate than plant uptake of atmospheric

CO2. Because the arctic stores 20-50% of the total amount of terrestrial soil-bound C

(McGuire et al. 2009) it is important to better understand the mechanisms behind these

potential feedbacks to climate change. In addition, climate change scenarios predict an

increase in arctic winter precipitation (ACIA 2004) which could complicate these

feedbacks. My results suggest that on a short time scale, increases in winter

precipitation can lead to higher rates of summer net N-mineralization and nutrients

available for plant uptake, potentially leading to greater shrub abundance and a positive

plant-soil-microbial feedback that favors shrub dominance. How an increase in winter

precipitation will influence N and C cycling on a longer time scale is still unknown.

Here I conclude that on a short time scale, shrub interactions with snow may play

a role in increasing plant available N, primarily through effects on the summer soil

microenvironment that increase N availability when plants are most active. The quality

of SOM matter, however, which can be linked to species specific traits such as litter

allocation and litter quality, may be more of a limiting factor in determining

mineralization rates of N. Assuming that our natural shrub gradient represents the

structure and function of future climate-driven shrub communities, I would expect a

shrubbier arctic to have greater aboveground and belowground biomass, higher soil

temperatures, and higher quality of SOM that favors higher rates of N fluxes. More

research is needed to better understand the differences in SOM quality across shrub

communities and their controls on N turnover.

44

Table 2-1. Vegetation and soil characteristics for three arctic plant communities located near Toolik Lake, AK. Means (±SE).

Superscript letters indicate a significant difference (p<0.05)

Sites

Ecosystem Characteristics Low Med High

Vegetation Canopy height (cm) 4.4 (0.94) 33.6 (1.92) 51.9 (3.23) Deciduous Tall Shrub Biomass (g/m2) 123a (13) 407b (74) 927c (68) Other Biomass (g/m2) 551 (32) 275 (63) 202 (24)

Total Aboveground Biomass (g/m2) 674a (29) 682a (66) 1128b (72)

Ambient snow depth (cm) 69.7 (4.6) 68.5 (1.5) 52.6 (4.7)

Soil Properties

Thaw depth (cm) 48.4 (1.04) 33.7 (1.46) 55.2 (2.13) Surface organic layer depth (cm) 15.6 (2.86) 11.5 (1.11) 14.1 (1.16) Mineral layer depth (cm) 15.6 (4.11) 13.5 (1.55) 11.9 (1.34) pH (mineral) 4.7a (0.08) 5.2b (0.14) 5.1b (0.10) Organic layer soil moisture (gH20/g ode soil)

4.7 (1.03) 3.4 (0.25) 4.4 (0.30)

Annual Soil Temperature at 0-5 cm (°C)

-5.5a (0.13) -1.8b (0.63) -1.7b (0.51)

Summer (69 days) 5.0a (0.48) 5.9ab (0.85) 5.9ab (0.39) Winter (267 days) -8.3a (0.11) -3.8b (0.56) -3.6b (0.58) Annual N mineralization (g N/m2/yr) 0.6 (0.13) 1.8 (0.35) 3.8 (0.85) Summer 0.1b (0.10) 0.8ab (0.19) 1.3a (0.34) Winter 0.4ab (0.16) 1.4a (0.18) 3.2c (0.75)

45

Table 2-2. Soil characteristics from the top 10 cm of the organic soil at three sites near Toolik Lake, AK that varied in shrub abundance (low, medium, and high).

Bulk Soil Low Medium High F P

Bd* (g/cm3) 0.03a (0.01) 0.06b (0.02) 0.05ab(0.01) 4.42 0.03 C (%) 39.62a (1.28) 38.95a (0.77) 37.77a (0.81) 0.94 0.40 N (%) 1.01a (0.07) 1.81b (0.05) 2.10c (0.08) 65.50 <0.001 C:N 40.98a (3.13) 21.65b (0.58) 18.36b (1.00) 43.20 <0.001 Pools (g/m2) C 1331.60a (39.36) 2211.00b (43.50) 1873.07c (40.08) 116.95 <0.001 N 34.57a (2.18) 102.84b (3.05) 104.24b (4.04) 23.36 <0.001

*Bulk density

46

Figure 2-1. Soil temperature and snow depth across three sites near Toolik Lake, AK. a)

Weekly mean, maximum, and minimum air temperature at 5 m. b) Weekly mean (± SE) soil temperature measured at 5 cm depth within the organic layer under ambient and snow addition treatments at each site (n = 3-4). c) Mean (± SE) ambient and manipulated snow depth during the winter of 2005-2006 and 2006-2007.

a)

b)

Summer 2006 Winter 2006-2007

Air

Tem

p a

t 5m

(oC

)

-5

0

5

10

15

20Max

Mean

Min

-20

-15

-10

-5

0

5

10

-20

-15

-10

-5

0

5

10

Time (Weeks)

Sep Nov Jan Mar May

-20

-15

-10

-5

0

5

10

Low

Soil

Tem

p a

t 5cm

(oC

)

0

2

4

6

8

10

12Ambient Snow

Snow Addition

Medium

Soil

Tem

p a

t 5cm

(oC

)

0

2

4

6

8

10

12

High

Time (Weeks)

Jul Aug

Soil

Tem

p a

t 5cm

(oC

)

0

2

4

6

8

10

12

-50

-40

-30

-20

-10

0

10

20

Sn

ow

de

pth

(cm

)

0

40

80

120

160

200Ambient

Snow AdditionLow

Sn

ow

de

pth

(cm

)

0

40

80

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160

200

Medium

Year

2006 2007

Sn

ow

de

pth

(cm

)

0

40

80

120

160

200High

c)

47

Figure 2-2. Mean (± SE) net N-mineralization in intact, resin capped organic soil cores incubated in the control and snow addition treatments at each of the three sites during the summer (mid June-Sept 2006; 74 days) and winter (Sept 2006-June 2007; 280 days). Capital letters indicate a significant difference between control treatments across sites. Lower case letters indicate a significant difference between snow treatments across sites. Asterices indicate a significant difference between treatments within each site.

Site

Low Medium High

Net

N-M

inera

lization

(ug N.g

soil-1.. d

-1)

0

2

4

6

8

10

Site

Low Medium High

Ambient

Snow Addition

Summer 2006 Winter 2006-2007

Organic

A

AB

B

aab

b

*

*

A

ab

AB

ab

B

b

48

Figure 2-3. Mean (± SE) net N-mineralization in intact, resin capped organic soil cores incubated in the control treatment or reciprocally transplanted and incubated in one of the other sites during the summer (June-Sept 2006; 74 days) and winter (Sept 2006-June 2007; 280 days).

Site of Incubation

Low Med High

Net

N-M

inera

lized

(u

g N. g

so

il-1. d

ay

-1)

0

2

4

6

8

10

Summer 2006

Site of Incubation

Low Med High

Low

Med

High

Winter 2006-2007

Soil OriginOrganic

49

CHAPTER 3 CONTROLS OVER LITTER DECOMPOSITION IN THREE ARCTIC PLANT

COMMUNITIES

Background

Temperatures in the arctic region are warming at almost twice the rate of the rest

of the globe (Overpeck et al. 1997, ACIA 2004, Serreze and Francis 2006, Kaufman et

al. 2009, Screen and Simmonds 2010) and are expected to cause an increase in the

availability of soil nutrients by warming soils and stimulating microbial breakdown of soil

organic matter (SOM) (Nadelhoffer et al. 1991, Hobbie 1996). In experimental

manipulations within the Alaskan arctic, an increase in plant available N can lead to an

increase in plant productivity and a shift in species composition: from graminoid to

deciduous shrub dominance (Chapin et al. 1995, Chapin and Shaver 1996, Shaver et

al. 2000, Mack et al. 2004). Increased photosynthetic activity, as detected by time

series of normalized differenced vegetation index (NDVI) from satellite images, has

occurred in the Alaskan, Canadian, and Siberian arctic tundra systems over the past

two decades and some of this enhanced activity has been attributed to an increase in

growth of shrubs (Jia and Epstein 2003, Stowe et al. 2004, Jia et al. 2009, Forbes et al.

2010). In addition, repeat aerial photography comparing historic with current photos in

Northern Alaska show that deciduous shrubs have expanded their cover over the last

50 years (Tape et al. 2006). Understanding the mechanisms that drive shrub expansion

are important because increasing shrub dominance will affect feedbacks between the

arctic land surface and regional to global climate (Chapin et al. 2005).

An increase in shrubs could lead to an increase in plant productivity and biomass

resulting in more uptake of atmospheric CO2 and the storage of more C aboveground in

woody tissue or belowground in rhizomes (Shaver and Chapin 1991), roots, and

50

ectomycorrhizal fungi (Clemmensen et al. 2006) resulting in a negative feedback to

climate change. In addition, more woody stems associated with shrubs could mean

more C stored in the soil because stems decompose slowly (Hobbie 1996). However,

shrub-lands have a lower albedo than tundra which can lead to an increase in absorbed

solar radiation during the snow-free period, resulting in a positive feedback to climate

change (Chapin et al. 2005). In addition, deciduous shrubs in fertilized tundra cycle C

and N faster than in unfertilized tundra resulting in a net loss of deep soil C over a 20

year period (Mack et al. 2004). Soils in naturally occurring shrub tundra similarly store

less C than those in graminoid tundra (Ping et al. 1998). Thus, positive feedbacks to

warming are possible, if increased shrub cover alters ecosystem structure and function

so that soil C stocks decrease. Since the arctic stores 20-30 percent of the total amount

of terrestrial soil-bound C (McGuire et al. 2009) it is important to understand better the

mechanisms behind these potential feedbacks to climate change.

The mechanisms that drive shrub expansion are not well understood. It has been

hypothesized that shrubs may enhance their dominance and growth by altering abiotic

and/or biotic controls over litter decomposition-a key control over the recycling of

nutrients within terrestrial ecosystems because it converts N in plant litter to organic and

inorganic forms that can be readily taken up by plants. The snow-shrub hypothesis

suggests that shrubs can alter the abiotic soil environment via their interactions with

snow (Sturm et al., 2001a). Tundra areas with taller and more abundant shrubs

accumulate greater snow depth due to greater retention of snow fall (e.g., less snow lost

to wind events) and trapping of wind distributed snow than tundra areas with fewer

shrubs (Sturm et al., 2001a; Pomeroy et al., 2006). It has not been directly tested

51

whether the amount of snow trapped by deciduous shrubs alters the environment

enough to stimulate litter decomposition and increase litter nutrient release. Previous

studies have shown that snow acts as an insulator that can increase soil temperature

(Brooks et al., 1996, 1998; Grogan and Jonasson, 2003; Schimel et al., 2004; Wahren

et al., 2005) and the availability of water to soil microorganisms (Coxson and Parkins,

1987; Romanovsky and Osterkamp, 2000; Mikan et al., 2002) and therefore, potentially

regulate the rate at which microbes and fungi can break down litter over the winter.

Indeed, litter decomposition has been found to occur in the winter and under snow

(Stark 1972, Hobbie and Chapin 1996, Uchida et al. 2005, McLaren and Turkington

2010) and to be higher in areas that have deeper snow cover (Baptist et al. 2010). In

only the Stark (1972) study was it clear that biological activity was responsible for mass

lost in the winter with the finding that 80 % of the first year’s mass loss of Jeffry pine

(Pinus jeffreyi) litter occurred during the winter months by black fungal hyphae and other

animals and bacteria living under the snow at 0 to -1 C. In the rest of the studies it is

unclear whether biological activity or physical processes such as fragmentation or

leaching were responsible for winter mass loss. If faster turnover of N bound in litter

resulted in an increase in plant-available N then snow accumulation by shrubs could

indirectly influence N availability by maintaining warmer soil temperatures in fall and

winter, allowing a longer window for microbial breakdown of litter substrates, increased

N release, and higher rates of N supply to plants resulting in a positive plant-soil

feedback that promotes further shrub expansion.

Shrubs may also have effects on decomposition and N release that are

independent of their effects on winter soil temperatures and may differ in the direction of

52

their effects. In moist acidic tundra the deciduous shrub, Betula nana, allocates 79

percent of its total biomass to new and old stems (Shaver et al. 2001), which

decompose three times slower than their leaves and one to eight times slower than

leaves and stems from graminoids and evergreen shrubs found within the Alaskan

arctic tundra (Hobbie 1996). Thus a species composition shift to more deciduous shrub

dominance may alter nutrient turnover through biotic controls by producing larger

quantities of litter that is of lower quality (Hobbie, 1992; Buckeridge et al., 2010),

possibly slowing nutrient turnover in litter resulting in a decrease in plant-available N

and a negative plant-soil feedback that does not promote further shrub expansion.

These two potential mechanisms lead to the question of whether changes in

microclimate or changes in litter chemical composition (quality) and the amount of litter

(quantity) will have a stronger control on litter decomposition and nutrient release. In

arctic and boreal systems, differences in litter quality among species and the quality of

the soil organic matter (SOM) across different vegetation types can have a larger effect

on N mineralization than differences in temperature (Flanagan and Van Cleve, 1983;

Giblin et al., 1991; Nadelhoffer et al., 1991; Hobbie, 1996). This pattern has also been

seen in alpine tundra of the southwestern French Alps, where Baptist (2010) found that

species specific differences in litter quality had a stronger control over decomposition

rates than differences in timing of snowmelt, which is associated with differences in

snow depth and soil temperature. Although there is much evidence to suggest that

shrubs can influence their environment to alter key biogeochemical processes that

control plant nutrient supply, there have been no published studies to date that have

directly tested the effect of added snow (at the depth that would be trapped by shrubs)

53

or increased shrub cover on litter decomposition. In addition, within the Alaskan arctic

all the decomposition studies have largely occurred in graminoid dominated moist acidic

or non-acidic tundra, therefore we know nothing about how the environment in shrub

dominated tundra may influence litter decomposition.

The goal of our study was to understand the mechanisms through which arctic

deciduous shrubs affect N dynamics. This was done by investigating how differences in

soil microclimate, litter quality, and nutrient availability influence litter decomposition

across three different arctic plant communities where snow depth was experimentally

manipulated. Our objectives were three fold: (1) to test whether realistic levels of snow

accumulation altered the environment for decomposition enough to stimulate rates of

litter mass and N release; (2) to compare how litter C quality and the relative availability

of C to N influenced the rate of litter mass and N loss; and (3) to better understand how

changes in species composition could influence community decomposition and nutrient

turnover. We hypothesized that across the three plant communities that 1) the addition

of snow would slow temperature decline in the winter and lead to faster decomposition

and net N release from litter, 2) decomposition and net N release would covary

positively with lignin:N, and 3) community weighted decomposition rates would be

lowest in the shrub dominated sites because of the greater abundance of less

decomposable litter.

To test our hypotheses, we measured litter quality, quantity and decomposition

across three plant communities that represented natural variation in shrub abundance

across the landscape. We used a common substrate to test for site and snow treatment

effects on decomposition and net N release. We decomposed litter that varied in litter

54

quality in a common environment to control for differences in microclimate and directly

test the effect of litter quality on decomposition. We then combined these studies with

litter production estimates to calculate a community weighted decomposition rate and

determine how changes in species composition could alter ecosystem decomposition

and nutrient turnover.

Methods

Study Area

All sites are located near Toolik Field Station at the Arctic Long Term Ecological

Research (LTER) Station (Lat. 68°38’N, Long. 149°38’W, elevation 760m) in the

foothills region on the north slope of Brooks Range, Alaska, USA. This area is a

younger landscape glaciated during the late Pleistocene and includes large areas of

Itkillik I (deglaciated ca. 60 000 yr) and Itkillik II (deglaciated about 10 000 yr) glacial

drifts (Hamilton 1986). The entire foothills region of the Brooks Range is treeless and

underlain by continuous permafrost, 250-300 m thick (Osterkamp and Payne 1981).

Mean annual air temperature is around -10°C, with average summer temperatures from

7-12 °C. Annual precipitation is 318 mm, with 43% of falling in the winter

(http://ecosystems.mbl.edu/ARC). Average snow depth is 50 cm, although snow

distribution can be variable due to redistribution by wind. Snow melt occurs in early

May.

In the fall of 2005, three sites were selected for the snow manipulation experiment

that varied primarily in deciduous shrub abundance, hereafter referred to as low,

medium and high shrub abundance sites and are described in detail in DeMarco et al.

(2011). In short, sites were chosen to have similar state factors (climate, relief, parent

55

material, time) but varied in the abundance of deciduous shrubs (Jenny 1994). The

same species of deciduous shrubs (Betula nana, and Salix pulchra) are found at all

three sites (except S. richardsonii, which is found only at the medium shrub site);

however, their percent cover increases from 15 to 94 % and their canopy height

increases from 4 cm to 50 cm across sites. Our sites are within 1 km of each other, and

have similar parent material, time since last glaciation (Itkillik I, deglaciated ca. 60 000

yr), and regional climate, although microclimates vary across sites due to differences in

slope and aspect. Elevation changes from about 764 m at the low shrub site to 741 m at

the medium and high shrubs sites.

Our low shrub abundance site is located in moist acidic tussock tundra where the

vegetation consists of approximately equal biomass of graminoids (Eriophorum

vaginatum and C. bigelowii), dwarf deciduous shrubs (B. nana, Vaccinium uliginosum,

and S. pulchra), evergreen shrubs (Ledum palustre ssp. decumbens and V. vitis-idea),

and mosses (Hylocomium splendens, Aulacomnium turgidum, Dicranum spp., and

Sphagnum spp.) (Shaver and Chapin 1991). In our medium shrub abundance site,

vegetation consists of graminoids (primarily C. bigelowii), deciduous shrubs (B. nana, V.

uliginosum, S. pulchra and S. richardsonii), and mosses (H. splendens and Dicranum

spp.). Our high shrub abundance site has predominantly deciduous shrubs (B. nana, S.

pulchra, and some Potentilla fruticosa) with some evergreen or wintergreen shrubs (V.

vitis-idaea and Linnaea borealis), forbs (Polygonum bistorta, Petasites frigidus, Stellaria

longipes, Valeriana capitata, and Artemisia alaskana), graminoids (Poa arctica, C.

bigelowii, and Calamagrostis canadensis), and mosses (Sphagnum spp. and H.

splendens).

56

Snow Manipulation

To determine the influence of increased snow depth on litter decomposition, snow

fences that represented maximum regional shrub height (1.5 m high and 62 m long)

were set up in the fall of 2005 at the low and medium sites to manipulate snow depth

(DeMarco et al. 2011). Two treatments (control = ambient snow and drift = manipulated

snow) were set up at each site. For all sites, subplots on the drift side of the fences

were located in the zone of maximum snow accumulation, which was relatively uniform.

Within each treatment, 18 2 by 10 m plots, with 1 m buffer strips between, were

established. For this study, six plots per treatment (n = 6) were randomly assigned to

measure litter decomposition. Remaining plots were used for additional experiments.

Soil temperature at 5 cm within the organic layer, was measured continuously (1-3

h intervals) from July 2006-May 2009 in each study plot (n = 3-4) by using Ibutton

temperature data loggers (IButtonLink, LLC, East Troy, WI). Mean daily soil

temperatures were calculated for all plots within each treatment and site for each year.

Mean growing season and winter temperatures were calculated from the mean daily

temperatures from each plot within each treatment and site. Growing season included

measurements taken from July 1st to August 1st of that year and includes years 2006,

2007, and 2009. Winter growing season includes measurements taken from September

1st through May 1st of the following year and includes winters from 2006-2007, 2007-

2008, and 2008-2009.

The snow fence produced a snow pack in our plots that was, on average 87, 96,

and 104 cm deeper than ambient snow depth for the low, medium and high shrub sites,

respectively. Snow addition increased average winter soil temperatures by 3°C and

summer soil temperatures by 2°C in the high shrub site; the medium and low shrub sites

57

showed similar trends, although the differences between treatments were smaller in

magnitude (DeMarco et al. 2011).

Common Substrate Experiment

To test directly the effect of microclimate and snow addition on litter

decomposition rates we incubated the senesced leaves from a common substrate,

Betula papyrifera var. neoalaskan, in the ambient and snow manipulated plots across all

three sites. Senesced leaves were collected while still attached to the trees but when

the petiole had already started to abscise. Leaves were air dried at 45°C, well mixed,

and then subsampled for litter bags. One gram of leaves was sewn into two mm mesh

bags, 8 x 8-cm in size. Litter bags were incubated beneath the live moss and litter layer

in early June of 2006. The moss and litter in this system are well mixed so bags were

inserted in this layer. Four identical bags were strung together for four time points to be

removed annually. Bags were replicated six times per treatment per site with an

additional three replicates per each of the six plots. Bags were removed after one, two,

and three years in July of 2007, 2008, and 2009 and were kept frozen for approximately

6 months until they could be processed.

At time of processing, bags were thawed and then gently rinsed with deionized

(DI) water to remove dirt and loose litter attached to the outside of the bag. All original

leaf litter was removed, dried at 45°C for a minimum of 48 hours and weighed. To

determine the percent C and N of the litter, samples were ground on a Wiley-mill, with a

#40 mesh screen, and then analyzed using an ECS 4010 elemental analyzer (Costech

Analytical, Valencia, California, USA). Percent of initial mass remaining was calculated

by dividing the incubated mass by the initial mass and multiplying by 100. Percent of

initial C and N remaining was calculated by the following equation:

58

ICR = (t1mass x t1C) ÷ (t0mass x t0C) x 100

100 100

Common Environment Experiment

To compare the effect of the individual plant species on litter decomposition

rates, we collected litter from 13 different species at seven different sites located in the

arctic foothills region on the north slope of Brooks Range, Alaska, USA. Three of the

sites were the control plots at the low, medium, and high shrub sites. Four sites were

dominated by alder shrubs (Alnus crispa): two near the Sagavanirktok River and two

along the Dalton Highway ~ 32 km north of Toolik Field Station. In addition, we

decomposed litter, collected from seven different species growing in long term

fertilization experiments, in a mesh bags in a common environment to understand better

the influence of litter quality on litter decomposition rates. All three sites (Historic,

LTER, and Species Removal) are located in moist acidic tundra and were fertilized

annually with both 10 g of N and 5 g of P m-2 year-1 for 20, 14, and five years,

respectively (Chapin et al. 1995, Bret-Harte et al. 2001). These bags were installed in

the field in July of 2003, removed in one, two, four, and five years in July of 2004, 2005,

2007, and 2008 and were kept frozen until they could be processed.

Across all sites senesced leaves were collected while still attached but when the

petiole had already started to abscise. Stems from deciduous shrubs Betula nana, Salix

pulchra, S. richardsonii, and Alnus crispa were also collected. Leaves and stems were

air dried, well mixed, and then subsampled for litter bags. One gram of litter was sewn

into 1.6 mm mesh bags, 4 x 8-cm in area. Litter bags were incubated beneath the live

moss and litter layer, in early June of 2006. Four identical bags were strung together for

59

four time points to be removed annually. Leaf bags were replicated six times per site

and stem bags were replicated three times per site. Bags were removed in one, two,

and three years in July of 2007, 2008, and 2009 and were kept frozen until they could

be processed. Bags were processed in the same manner as the litter bags for the

common substrate experiment.

Calculations

The exponential decay constant, k, was determined by assuming a single

exponential decay model (Olson 1963):

Mt = Moe-kt

Where Mt = litter mass at time t, and MO = initial mass. The slope of regressions

of the proportion of initial mass remaining plotted against time was used to determine

decay constants for each substrate at each site.

Initial Litter Quality

A subsample of all samples of leaf and stem litter collected was analyzed for C,

N, and C-fractions to determine the quality of the litter substrates prior to decomposition.

Carbon and N was determined on samples that had been ground to a fine powder on a

Wiley-mill, with a #40 mesh screen, and then analyzed using an ECS 4010 elemental

analyzer. C-fractions were determined using fiber forage techniques on an ANKOM

fiber analyzer (Ankom Technology, Macedon, N. Y.). The following C fractions were

determined: cell soluble (carbohydrates, lipids, pectin, starch, and soluble protein),

hemicelluloses plus bound proteins, cellulose, and lignin plus other recalcitrants (Ryan

et al. 1990).

60

Community Weighted Decomposition

To determine how changes in species composition (litter quality/quantity) alter

community decomposition, we used biomass harvests collected in July of 2007 from the

ambient treatment across all three sites (DeMarco et al. 2011). Total aboveground net

primary production (ANPP) (g/m2/yr) per plot (n = 12) was calculated by summing new

biomass (g/m2) for that year for each plot. We calculated species specific ANPP per

plot by summing all the new biomass per species per plot. The proportion that each

species contributed to the total community ANPP was then calculated by dividing

species ANPP by total ANPP per plot. This proportion was then multiplied by the

species specific decomposition constant, k, to get a species weighted k value. All

species weighted k values for each plot were summed to obtain a decomposition k

constant for the entire community. Community weighted k values were calculated for

each replicated plot within each site.

To determine how changes in species composition (litter quality/quantity) and

environment altered community decomposition, we used the same calculated species

specific k values, weighted by the proportion that each species contributed to total

community ANPP, for each plot but then adjusted them for site-specific differences in

decomposition rates indexed by the common substrate decomposition experiment. Site

mean k values for the common substrate, Betula papyrifera var. neoalaskan, were

subtracted from the mean common garden k value for that substrate. Species-specific k

values were multiplied by this difference and then the adjusted values were summed to

obtain a decomposition k constant for the entire community. The community weighted k

values for all 12 plots within each site was calculated. This method assumes that all

species and parts respond the same way as Betula papyrifera var. neoalaskan when

61

decomposed in different environments and does not take into account any site by

species interactions.

The ANPP calculations were only for vascular plants and thus do not include

mosses and lichen or any belowground parts such as rhizomes or roots. We estimated

the contribution of secondary stems to ANPP by multiplying the ANPP of species likely

to produce woody tissue by a proportion determined by Bret-Harte et al. (2002). These

were 0.158, 0.181, and 0.079 for Betula nana, Salix pulchra, and Ledum palustre ssp.

Decumbens, respectively. We followed the methods outlined in Hobbie and Gough

(2004) and assumed that Cassiope tetragona and Vaccinium uligonosum resembled L.

palustre in their proportional secondary growth and S. reticulate, S. glauca, and S.

richardsonii all resembled S. pulchra in their proportional secondary growth. We also

assumed that Andromeda polifolia, Arctostaphylos alpina, Dryas integrifolia, Empetrum

nigrum, Rubus chamaemorus, V. vitis-idaea, and Linnaea borealis had negligible

secondary growth.

We did not decompose all the species found at our sites in our common garden

site, so we used k values from other published studies within the region or substituted k

values for species with similar growth forms. These species contributed less than 7% of

the total ANPP. For all forbs, we used the decay constant for Polygonum bistorta from

Hobbie and Gough (2004) which was also decomposed in the same environment as our

common garden. For Calamagrostis spp., Poa arctica, and Juncus we used an average

of k values from Carix spp. and Eriophorum spp. (k = 0.18) from our common garden.

For Empetrum, L. borealis, and Cassiope leaves we used an average of k values from

Ledum and V. vitis-idaea from our common garden. For Empetrum, L. borealis, and

62

Cassiope stems we used an average of k values from B. nana and S. pulchra stems (k

= 0.08). We excluded Equisetum from our NPP and weighted k values because it was

found in only one plot and only contributed 2 g/m2/yr in that plot.

Statistical Analysis

To test our hypotheses regarding snow additions on decomposition we used two-

way Analysis of Variances (ANOVA) with site and snow addition as the main effect and

site by snow addition as the interaction term (JMP 7.0, 2007, SAS). To test our

hypothesis regarding site effects on decomposition we used two-way ANOVAs with site

and species as the main effect and site by species as the interaction term. To test our

hypotheses regarding the effect of species and fertilization on initial litter quality indices

and decomposition we used two-way ANOVAs with species and treatment (control vs.

fertilized) as the main effect and species by treatment as the interaction term. The

effect of time since fertilization on initial litter quality indices and decomposition was

tested using a one-way ANOVA with time since fertilized (0, 5, 14, or 20 years) as the

main effect. Relationships between the litter decay constant, k, and initial litter quality

indices for nine of the species decomposed in the common garden were tested using

regression analysis with the litter decay constant, k, as the dependent variable, as the

litter quality indices of interest as the independent variable. The effect of litter

quality/quantity on community decomposition and the effect of litter quality/quantity and

environment on decomposition were tested using two separate one-way ANOVAs with

site as the main effect. Tukey’s multiple comparisons test were used when significance

was obtained from ANOVAs. Data were tested for normality (Shapiro-Wilks), and ln-

transformed when necessary to meet the assumptions of ANOVA.

63

Results

Effect of added snow on soil temperature

Snow addition significantly increased winter soil temperatures by an average of

1-4 °C across all three sites. Winter soil temperatures across sites and among years

were also significantly different (Appendix B, Table B-1 and B-2). There was a

significant interaction between vegetation type and year. Snow addition had no

significant effect on growing season soil temperatures for any of the three years

measured; however, there was a significant difference in growing season soil

temperature across the three sites with warmer temperatures at the medium and high

shrub sites compared to the low shrub site. There was also a significant effect of year

on soil temperature but no significant interactions across all combinations (Appendix B,

Table B-1 and B-2).

Common Substrate Experiment

After three years, the addition of snow resulted in a significant interaction between

site and treatment (F2,30 = 3.5, P = 0.04) but no significant main effect of snow addition

on litter mass loss at any of the three sites (F1,30 = 1.5, P = 0.23). Mass remaining was

greater in the control treatment than the snow treatment in all sites except the low site

where the mass remaining in the snow treatment was greater than mass remaining in

the control treatment. Snow addition also had no significant effect on litter C loss at any

of the three sites and there was no significant interaction between site and treatment.

There was also a significant interaction between site and treatment on litter N loss but

no significant main effect. In contrast to mass remaining, litter N remaining was

greatest in the snow treatment compared to the control for all sites except the Medium

shrub site where N remaining was greater in the control compared to the snow

64

treatment. Snow addition had no effect on C:N or litter decay rates at any of the three

sites and there was no significant interaction between site and treatment (Fig.3-1, Table

3-2).

After three years of incubation, Betula papyrifera var. neoalaskan leaf litter

incubated in the low shrub site lost 10 and 6 % more mass when incubated in the low

shrub site than in the medium and high shrub sites, respectively (Fig. 3-1, Table 3-1;

F2,30 = 13.2, P <0.0001). Initial carbon (C) remaining followed the same pattern as initial

mass remaining with 8 and 6 % more C loss occurring at the low shrub site compared to

the medium and high shrub sites, respectively (Fig. 3-1 and 3-2, Table 3-1 and 3-2).

Initial nitrogen (N) remaining followed a very different pattern as initial mass and C

remaining with mineralization of litter N occurring at the low site only, but immobilization

occurring at both the medium and high shrub sites (Fig 3-1, Table 3-1). The proportion

of initial C:N remaining decreased with an increase in shrub abundance. The litter

decay rate, k, was highest at the low shrub site compared to the medium and high shrub

sites (Table 3-1).

Common Environment Experiment

Deciduous shrub litter collected across the shrub gradient sites

After three years of incubation, neither site of origin nor species had an effect on

the % initial mass remaining or decay rate of leaf and stem litter from Betula nana and

Salix pulchra, despite significant differences in their initial litter quality (Appendix B,

Table B-3 and Table B-4). The percentage of leaf cell solubles, cellulose, and lignin all

significantly differed by site while the percentage of leaf C and cellulose differed

significantly by species. For stem litter, only hemicellulose was significantly different

65

among sites while both percent C and cellulose in stem litter significantly differed

between species (Table B-4).

Initial litter quality and decay constants across species with and without fertilization

Initial litter quality significantly differed across species for all indices measured,

although species within the same growth form were not always similar in their initial litter

quality (Appendix B, Table B-5). After five years of fertilization, initial litter quality

changed in all the indices measured except cell solubles and lignin. Deciduous shrubs

had up to two times more N in their leaves than evergreen shrubs, graminoids, and

mosses. Leaf litter N increased with fertilization in all species except R. chamaemorus

(Fig. 3-3; two-way ANOVA, species: F6,65 = 72.4, P < 0.0001, treatment: F1,65 = 146.7, P

< 0.0001, species x treatment: F6,65 = 4.5, P < 0.001). Evergreen shrubs had the

highest percent C in their leaves followed by deciduous shrubs (except R.

chamaemorus), graminoids, mosses, and R. chamaemorus. Fertilization decreased

percent C but only for B. nana (Appendix B, Table B-5; species: F6,65 = 172.6, P <

0.0001, treatment: F1,65 = 13.8, P < 0.001, species x treatment: F6,65 = 3.5, P < 0.01).

Evergreen shrubs, mosses, and the graminoid, E. vaginatum, all had high C:N ratios in

their leaves; while the deciduous shrubs R. chamaemorus and V. uliginosum had the

lowest C:N ratios. The C:N ratio in leaf litter decreased in four out of seven species with

added N (species: F6,65 = 82.6, P < 0.0001, treatment: F1,65 = 172.2, P < 0.0001,

species x treatment: F6,65 = 11.4, P < 0.0001). Both evergreen and deciduous shrubs

had similar percentages of cell solubles that were almost double that of graminoids and

remained unchanged by fertilization (species: F6,65 = 105.5, P < 0.0001, treatment: F1,65

= 0.5, P = 0.49, species x treatment: F6,65 = 2.1, P = 0.06). Graminoids had 1.5 to 4

66

times more hemicellulose in their leaves compared to evergreen and deciduous shrub

leaves. Fertilization increased percent hemicellulose in both C. bigelowii and V.

uliginosum (species: F6,65 = 165.6, P < 0.0001, treatment: F1,65 = 7.3, P < 0.01, species

x treatment: F6,65 = 3.6, P < 0.01). Graminoids also had the highest percentage of

cellulose in their leaves that were 2 to 4 times greater than evergreen and deciduous

shrubs. B. nana had the least percentage of cellulose in their leaves compared to all six

other species. E. vaginatum decreased in percent cellulose with added N while all other

species remain unchanged (species: F6,65 = 217.2, P < 0.0001, treatment: F1,65 = 8.8, P

< 0.01, species x treatment: F6,65 = 3.7, P < 0.01). B. nana also had the highest

percentage of lignin, followed by evergreen shrubs and the deciduous shrubs, R.

chamaemorus and V. uliginosum. Graminoids had the least amount of lignin in their

leaves. Fertilization had no significant effect on percent lignin for any of the seven

species (species: F6,65 = 94.3, P < 0.0001, treatment: F1,65 = 1.5, P = 0.22, species x

treatment: F6,65 = 0.4, P = 0.86). The evergreen shrubs and the deciduous shrub B.

nana had lignin:N ratios that were two to four times higher than the deciduous shrubs,

R. chamaemorus and V. uliginosum, and the graminoids. Fertilization decreased the

lignin:N ratio of Ledum decumbens, V. vitis-idaea, and B. nana leaf litter (species: F6,65

= 54.1, P < 0.0001, treatment: F1,65 = 45.2, P = < 0.0001, species x treatment: F6,65 =

3.4, P < 0.01).

After five years of incubation, Rubus chamaemorus decomposed one and half to

six times faster than litter from the other six species of vascular plants and three species

of mosses collected at the same moist acidic tundra site and decomposed in the same

common garden (Fig. 3-4, see also Appendix Figs. B1-4). Although there was a

67

significant difference in litter decay constants across species (species: F6,65 = 15.8, P <

0.0001, treatment: F1,65 = 0.3, P = 0.58, species x treatment: F6,65 = 0.9, P = 0.52),

species within the same functional group did not always follow the same pattern in their

rates of decomposition (Fig. 3-4). The deciduous shrubs, Betula nana and Vaccinium

uliginosum had similar decay constants as the evergreen shrub Ledum decumbens and

the graminoid Eriophorum vaginatum, and all had higher decay constants than the

evergreen shrub V. vitis-idaea and the graminoid Carex bigelowii. Mosses had the

lowest decay constants compared to the other seven vascular plant species

decomposed in our experiment. After five years of fertilization, there was no significant

difference in litter decay constants (Appendix B, Figs. B1-4).

In a comparison within one species, B. nana, across four different times since

fertilization (0, 5, 14, and 20 years), initial litter quality changed in five out of seven

indices measured. Percent C, C:N ratio, and lignin:N ratio decreased after five years of

fertilization than remained unchanged at 14 and 20 years of fertilization. Percent

hemicelluloses increased after five years but became similar to the control at 14 and 20

years. Percent N significantly increased with the length of fertilization. Percent lignin

was only significantly different after 20 years of fertilization (Fig. 3-5). The decay

constant, k, significantly increased with fertilization but only after 20 years of fertilization

(Table 3-3, Fig. 3-5).

The relationship between initial litter quality and decay constants

In a comparison with all nine species decomposed in our common garden, leaf

litter decay rates were only weakly related to some of the litter quality indices we

measured. The percentage of C and cell solubles were positively correlated with decay

rates (Appendix B, Fig. B-5; r2 = 0.23, n = 21, p = 0.03; r2 = 0.23, n = 21, p = 0.03); while

68

the percent of cellulose in the leaves was negatively correlated with decay rates

(Appendix B, B-5; r2 = 0.23, n = 21, p = 0.04). For stem litter, the percentage of

cellulose was the only litter quality index that was found to correlate with decay rates

and it explained 55 % of the variation in stem decay rate (Appendix B, B-6; r2 = 0.55, n

= 9, p = 0.01).

Community Weighted Decomposition

Community weighted k constants that took into account differences in litter

quality and quantity across sites were marginally higher in the medium and high shrub

sites compared to the low shrub site (one-way ANOVA; F2,21 = 3, p = 0.06). Community

weighted k constants that took into account both differences in litter quality/quantity and

environment were significantly higher at the low shrub site compared to the medium and

high shrub sites (one-way ANOVA; F2,21 = 65, p < 0.0001).

Discussion

Microenvironment controls over litter decomposition

Surprisingly, we were unable to detect any effect of adding snow on litter

decomposition of our common substrate after three years of incubation, even though

soil temperatures were warmed by up to 2°C during the growing season and up to 4°C

during the winter. There are only a few studies that directly compare the effects of snow

addition on litter decomposition. Walker et al. (1999) also did not find an effect of

deeper snow on litter decomposition after two years of decomposing Betula nana leaf

litter under ambient and snow addition of up to 3 meters in tussock tundra near Toolik

Lake, Alaska. In contrast, Baptist et al. (2010) found a trend for greater litter mass loss

in late snow melt sites presumably due to warmer soil temperatures; although, this was

69

not significantly different. Using the same species of litter as our experiment, Hobbie

and Chapin (1996) found differences in litter mass loss between microsites in which

summer soil temperatures differed by 4°C, with greater mass loss occurring in the

warmer microsites. In addition, litter mass loss in lab incubations that included warming

treatments of either 2 or 6°C above the ambient growing season temperatures resulted

in an increase in mass loss with an increase in temperature (Hobbie 1996, Jonasson et

al. 2004). Two of the three studies had temperature differences that were twice as high

as our study, which may explain why they found significant differences in litter mass

loss with change in temperature while we did not.

Interestingly, litter decomposed faster in the low shrub site compared to the

medium and high shrub sites, even though the ambient soil temperatures at the medium

and high shrubs sites were actually warmer than at the low shrub site in both the

growing season and the winter. Soil temperature differences across these sites are of

the same magnitude as the differences in soil temperature we saw when we added

snow. This suggests that other factors such as moisture, soil nutrients, litter substrate

quality, or the decomposer communities may be more important than small (<4°C)

changes in temperature for driving decomposition at our sites. Soil moisture was not

measured over the three year incubation period, but measurements in June of 2006

showed no difference in soil moisture across sites and trend for greater moisture at the

low shrub site compared to the medium and high shrub site with the addition of snow

(DeMarco et al. 2011).

Our data suggests that soil nutrients play an important role in controlling litter

decomposition and nutrient release at our sites. Over our three year incubation period,

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litter decomposed at the low shrub site mineralized litter N while immobilization of N

occurred at the medium and high shrub sites. Previous research from these sites have

shown that bulk soil N at the medium and high shrub sites is twice as high as compared

with bulk soil N at the low shrub site (DeMarco et al. 2011) and is highly correlated with

initial N remaining, the C:N ratio, and litter k values (Fig. 7). Thus, sites with greater soil

N have lower rates of litter decomposition and higher retention of N on the leaves.

Greater soil N availability has been found to stimulate (Hobbie 1996, Aerts et al. 2006b),

repress (Prescott 1995, Magill and Aber 1998, Aerts et al. 2006b), or have no effect

(McClaugherty et al. 1985, Prescott 1995, Hobbie 1996, Aerts et al. 2003, Aerts et al.

2006b) on litter decomposition rates and can lead to N immobilization in some systems

(Gallardo and Merino 1992, Magill and Aber 1998, Hobbie 2005, Aerts et al. 2006b) but

see (McClaugherty et al. 1985).

Varying responses of litter decomposition to external N may be attributed to initial

litter quality. In a meta-analysis of 24 litter decomposition studies in which external N

was experimentally manipulated, (Knorr et al. 2005) found that external N availability

and litter quality interact to influence decay rates with N additions stimulating

decomposition of high-quality litters (<10% lignin content), while inhibiting decay of low-

quality (>20% lignin content) litters. The “microbial N mining” hypothesis suggest that

this pattern occurs because some microbes use labile C to decompose recalcitrant

organic matter in order to acquire N (Fontaine and Barot 2005, Moorhead and

Sinsabaugh 2006). Therefore we would expect “microbial N mining” to increase

decomposition of low quality litter when it is incubated in soils with low soil N and be

suppressed when litter is incubated in soils with high N. Indeed there is evidence that

71

this can occur, and this mechanism may help explain why we saw a linear pattern of

decreased rates of decomposition and an increase in N immobilization with increasing

soil N at our sites (Craine et al. 2007). The Betula papyrifera var. neoalaskana litter we

used for our study was of low quality with both a high C:N ratio (72 ± .06) and percent

lignin (19 ± 2) content.

In addition, high nutrient content in soils can suppress the production of fungal

ligninase (Carreiro et al. 2000, Sinsabaugh et al. 2002), which is induced by low N

availability (Keyser et al. 1978), resulting in lower rates of decomposition. Low quality

litters can also contain higher levels of tannins that can bind to N and become

incorporated in the lignin fraction, decreasing decomposition and increasing

immobilization of N (Gallardo and Merino 1992, Aerts et al. 2003). Fungal communities

between tussock tundra and shrub tundra soils sampled near Toolik Lake, AK differ at

the phyla and subphyla level; however, we do not know whether the species

responsible for breaking down lignin or the production of ligninase differs between these

two plant communities (Wallenstein et al. 2007). These tussock tundra soils are

dominated by slow growing microbes that have higher affinities for low quality and

quantity C substrates. In contrast, shrub tundra soils are dominated by microbes that

have high growth rates with high nutritional requirements for higher quality and quantity

C (Fierer et al. 2007, Wallenstein et al. 2007). It is possible that microbes at the low

shrub site are better at decomposing litter that is of low quality than the microbes at the

high shrub site, and microbes at the high shrub site immobilize more N because they

have a higher nutrient demand when mineralizing C. Although we did not measure

tannins in our litter others have found that Betula spp. leaves can have more

72

polyphenols when compared to Salix spp. and Populus spp. leaves (Palo 1984). In

addition, litter of Betula papyrifera grown under elevated CO2 increased its tannin

content by as much as 81% and when decomposed in a common garden had lower

rates of decomposition and higher N immobilization compared to litter from ambient CO2

conditions that had lower tannin levels (Parsons et al. 2004).

Our common substrate experiment demonstrates that on a short time scale

interactions between soil nutrient content, litter quality, and microbial community

composition play a greater role than small changes in soil temperature in regulating

nutrient cycling at our sites. The litter quality of B. papyrifera var. neoalaskana was

lower than the deciduous shrub litter native to these sites, thus we do not know whether

native litter decomposed at the high shrub site would respond in the same way as our

common substrate.

Litter quality controls over litter decomposition

Data from our common garden experiment suggest that changes in litter C

quality, and not N content, may be more important in regulating decomposition rates.

We found differences in litter N within the same species both across our sites (for Betula

nana and Salix pulchra) and among litter that had been fertilized for five years; but, we

found no differences in decomposition rates nor a significant correlation between initial

litter N and decomposition rates. It was not until after 20 years of fertilization that led to

a significant decrease in lignin content did we see an increase in decomposition rates.

Thus, it appears that lignin content, rather than litter N, controls decomposition rates

when incubated in the same low soil N environment. This idea agrees with data from

Hobbie (1996) who found that initial C fractions correlated more with litter decay

constants than initial N for similar species, as our study and with data from Baptist et al.

73

(2010) who showed that plant species with higher lignin content in their leaves

decomposed more slowly than leaves with lower lignin content. In contrast, when

mosses were excluded Hobbie and Gough (2004) found no relationship between initial

leaf litter chemistry and the litter decay constant for the same species studied in the

Hobbie (1996) study. Hobbie (2005) found a significant relationship between litter N

and decomposition rates across litter from eight species that varied in their initial N by

2.5 %, however litter N was also tightly correlated with litter phosphorus (P) and

potassium (K) concentrations, so it is unclear whether decomposition was directly

influenced by N or P and K. All species mineralized litter N, regardless of their litter

quality. Since our common garden was in a low nutrient soil, this suggests site

microenvironment may be more important than litter quality in controlling litter N

mineralization/immobilization dynamics.

Comparisons in litter decomposition among species and functional groups

suggest that decomposition rates cannot be generalized using functional group

designations, since species within the same functional group did not always follow the

same pattern of decomposition and this in contrast with other decomposition studies

within this region (Hobbie 1996, Hobbie and Gough 2004), although our study included

more species of deciduous and evergreen shrubs. Decomposition rates varied

significantly among species. Of the seven species we decomposed, Rubus

chamaemorus had the highest quality litter and the faster rate of decomposition, losing

about 70 % of its mass over a three year period. Aerts et al. (2006) also found that R.

chamaemorus decomposed more quickly than three other sub-arctic bog species. In

contrast, mosses had the lowest decomposition rate, only losing about 30 % of their

74

mass over the same period. Although, there were some differences in decomposition

between the rest of the species, which included deciduous shrubs, evergreen shrubs,

and graminoids, these differences were relatively small. A change in species

composition within the arctic could lead to alterations in community decomposition rates

and nutrient turnover, if the change includes an increase in the relative abundance of R.

chamaemorus and a decrease in mosses as seen in fertilized tussock tundra in Alaska

where R. chamaemorus dominates the understory and moss cover is reduced (Chapin

et al. 1995).

Litter quality/quantity versus microclimate controls over litter decomposition

Based on our results, changes in litter quality and quantity via changes in species

community composition will have relatively little impact on the total community

decomposition rate, however changes in the soil microenvironment could lead to

substantial differences in decomposition rates across plant communities. Hobbie and

Gough (2004) also found that site differences had a larger control over community-level

decomposition compared to changes in species composition alone. We did not include

mosses and lichens in this estimate although, we know that their distribution changes

across our sites and is expected to change with climate warming. In addition, we are

assuming that all species will respond similarly to B. papyrifera when incubated across

our sites. It is possible that differences in litter quality may interact with site

environment at the high nutrient site resulting in a different pattern than what we saw

with B. papyrifera as Knorr et al. (2005) have shown that species that differ in their litter

quality respond differently to high soil N environments.

Our study suggests that on a short time scale small changes in soil temperature

and moisture associated with additional snow trapping by shrubs is unlikely to influence

75

litter nutrient turnover enough to drive positive snow shrub feedbacks as proposed by

Sturm et al. (2001). However, long term changes in litter quality inputs associated with

different dominant plant species could lead to alterations in soil nutrients and microbial

communities, which, in turn, can significantly alter litter decomposition processes.

Assuming that all species respond similarly to Betula papyrifera when incubated at the

medium and low shrub sites, an increase in deciduous shrub cover could actually lead

to slower rates of litter decomposition and nutrient turnover with concomitant increase in

C sequestration. Retaining N in litter may be beneficial for soil organic matter (SOM)

decomposition and could help explain why we see more soil N and greater N

mineralization at the medium and high shrub sites.

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Table 3-1. Initial mass remaining (IMR), initial carbon remaining (ICR), initial N remaining (INR), and decay constants (n = 6) for the common substrate, Betula papyrifera var. neoalaskan, decomposed at each site with each treatment and calculated for the entire three year decomposition period. Means ± SE, n = 6).

Table 3-2. Two-way ANOVA results comparing k, INR, ICR, and the proportion of ICR:INR after three years of incubation across all sites and treatments.

K (1/yr) df F-ratio p-value

Site 2 18.91 <0.0001

Treatment 1 1.45 0.24

Site X Treatment 2 2.09 0.14

Initial C Remaining (%)

Site 2 7.27 <0.01

Treatment 1 1.49 0.23

Site X Treatment 2 2.72 0.08

Initial N Remaining (%)

Site 2 101.64 <0.0001

Treatment 1 0.48 0.49

Site X Treatment 2 4.07 0.03

Initial C:N Remaining

Site 2 36.21 <0.0001

Treatment 1 2.00 0.17

Site X Treatment 2 1.63 0.21

Site Treatment IMR (%) ICR (%) INR (%) K (1/yr)

Low Ambient 42.7c (1.46)

45.4b (1.47)

78.9c

(1.68) 0.292a (0.01)

Snow Addition

45.4bc (1.33)

47.9ab (1.37)

85.3c (1.81)

0.277ab

(0.01) Medium Ambient 52.5a

(1.17) 53.9a (1.34)

102.5b (2.89)

0.208c (0.006)

Snow Addition

49.2ab (1.43)

51.1ab (1.58)

96.6b (2.58)

0.232bc (0.01)

High Ambient 48.7ab (0.88)

51.8ab (1.34)

112.5a (2.62)

0.238bc (0.009)

Snow Addition

45.6bc (1.45)

47.5ab (1.98)

115.9a (1.58)

0.259ab (0.01)

77

Table 3-3. Initial litter quality of senesced leaves of Betula nana collected in both

unfertilized plots and plots that had been fertilized for five, 14, and 20 years. Results are from one-way ANOVA’s comparing each variable across years fertilized (Means ± SE; n = 13, 6, 4, and 4).

Years Fertilized ANOVA Results Foliar traits 0 5 14 20 df F-stat p-value

N (%) 1.64a (0.04)

2.41b

(0.11) 2.09c (0.09)

2.08c (0.07)

3 29.95 <0.0001

C (%) 44.44a (0.08)

43.33b (0.08)

43.44b (0.18)

43.39b (0.18)

3 28.99 <0.0001

C:N 27.29a (0.59)

18.17b (0.89)

20.93b (0.97)

20.95b (0.73)

3 35.91 <0.0001

Cell soluble (%)

62.49 (0.52)

60.86 (0.74)

62.66 (1.13)

63.80 (0.96)

3 1.26 0.31

Hemicellulose (%)

10.77a (0.29)

12.71b (0.39)

12.63ab (0.86)

11.28ab (0.35)

3 4.33 0.01

Cellulose (%) 7.79 (0.31)

7.94 (0.19)

7.68 (0.55)

8.59 (0.42)

3 0.57 0.64

Lignin (%) 18.54a (0.46)

18.17ab (0.70)

16.60ab (0.76)

15.79b

(0.31) 3 4.37 0.01

Lignin:N 11.35a (0.29)

7.63b (0.55)

8.03b

(0.65) 7.63b (0.33)

3 25.41 <0.0001

K (1/yr) 0.177a (0.001)

0.204ab (0.009)

0.206ab (0.017)

0.249b (0.029)

3 3.93 0.02

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Figure 3-1. Initial mass, C, and N remaining of the common substrate (Betula papyrifera var. neoalaskana) from litter bags incubated over three years in the ambient and snow addition treatments at the low, medium, and high shrub sites. (Mean ± SE, n = 6)

Year

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2006 2007 2008 20090.2

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Figure 3-2. Decay constants, initial C and N remaining, and the proportion of initial C:N remaining for the common substrate, Betula papyrifera var. neoalaskana), decomposed at each site and treatment and calculated for the entire three year decomposition time period. Different letters within a graph represent significant differences at the p < 0.05 level. (Mean ± SE, n = 6)

Site

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port

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f In

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Ambient

Snow Additiona

ab

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bcbc

ab

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abab

ab

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Figure 3-3. Percent initial leaf litter N collected from plants that were unfertilized and fertilized with 10 g N/m2/yr for 5 years. Statistical results are from a two-way ANOVA. Different letters within the graph represent significant differences at the p < 0.05 level. (Mean ± SE, n = 6)

Species

Betnan Rubcha Vaculi Leddec Vacvit Carbig Erivag

Initia

l le

af

litte

r N

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Fertilized

de

a

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gh

def

h

defefg

cd

h

fg

Species: F6,65 = 72, p < 0.0001

Treatment: F1,65 = 147, p < 0.0001

S x T: F6,65 = 5, p < 0.001

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Figure 3-4. Leaf litter decay constants (k) vs. leaf percent C, cell solubles, and cellulose for 11 vascular plants species collected across nine sites and decomposed for three to five years in the same common garden (n = 3-6). Percent C includes three moss species collected at one site and incubated for five years in the same common garden (n = 5-6). (Mean ± SE)

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Figure 3-5 (Left). Percent initial leaf litter lignin of Betula nana collected from four sites that have been fertilized for zero, five, 14, or 20 years. (Right). Decay constant, k, of Betula nana leaves collected from four sites that have been fertilized for zero, five, 14, or 20 years and incubated in a common garden for five years. Statistical results are from one-way ANOVAs. Different letters within a graph represent significant differences at the p < 0.05 level. (Mean ± SE, n = 6)

Years Fertilized

0 5 14 20

Initia

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a

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bTime: F3,23 = 4, p = 0.02 Time: F3,23 = 4, p = 0.02

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Figure 3-6. Comparison of the influence of litter quality/quantity and litter quality/quantity with microenvironment in mean community weighted litter decay constant (k, 1/yr) across three arctic plant communities. Different capital letters represent significant differences at the p < 0.05 level across sites in influence of litter quality/quantity on decomposition. Different lower case letters represent significant differences at the p < 0.05 level across sites in influence of litter quality/quantity and environment on decomposition.

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Litter quality/quantity

Litter quality/quantity and Environment

A

B AB

a

c

b

Quality/quantitySite: F2,21 = 3, p = 0.06

Quality/quantity + EnvironmentSite: F2,21 = 65, p < 0.0001

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Figure 3-7. Soil % N for each site vs. decay constant (k), initial C remaining, initial N remaining, and the proportion of initial C to N remaining of the common substrate, Betula papyrifera var. neoalaskana), decomposed at each site over a three year period. (Mean ± SE, n = 6)

Soil % N

0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

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p < 0.01, r2 = 0.74 p = 0.02, r2 = 0.64

p < 0.001, r2 = 0.80 p < 0.0001, r2 = 0.88

85

CHAPTER 4 PLANT AND ECOSYSTEM RESPONSE TO LONG TERM EXPERIMENTAL

WARMING AND NUTRIENT ADDITIONS IN ARCTIC SHRUB TUNDRA

Background

Temperatures in the arctic have increased by 1.5°C over the last century and

should continue to increase at a faster rate than the rest of the globe (Overpeck et al.

1997, Serreze and Francis 2006, Kaufman et al. 2009). Warmer temperatures can

stimulate plant productivity directly in arctic plant communities by providing a warmer

environment for plant growth (Chapin et al. 1995, Michelsen et al. 1996, Aerts et al.

2006a) or indirectly by stimulating microbial decomposition of organic matter and

releasing more nutrients for plant uptake and growth (Nadelhoffer et al. 1991, Chapin et

al. 1995, Schmidt et al. 2002, Aerts et al. 2006a).

The Arctic tundra biome includes a diverse array of vegetation communities which,

due to the variety of plant functional types that dominate, may differ in their response to

environmental change (Chapin and Shaver 1989, Baddeley et al. 1994, Schmidt et al.

2002, Walker et al. 2006). Studies in North America tundra that have directly tested

plant community and ecosystem responses to environmental changes have been

concentrated in communities dominated by graminoids-such as tussock tundra, dry

heath tundra or wet sedge plant communities in the upland tundra of Alaska’s North

Slope. In contrast studies in Northern Europe have been concentrated in communities

dominated by shrubs, including subalpine dwarf shrub heath and fellfield in the sub-

arctic of Sweden. Plant productivity in Alaskan communities as well as dwarf shrub

communities in Sweden, respond strongly to nutrient additions and to a lesser degree to

temperature increase (Shaver and Chapin 1980, 1991, Parsons et al. 1994, Boelman et

al. 2003, Van Wijk et al. 2003). Nitrogen (N) or N in combination with phosphorus (P)

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can limit productivity in upland communities such as tussock moist acidic tundra

(Shaver and Chapin 1980, Chapin et al. 1995, Shaver et al. 2001), tussock moist non-

acidic tundra (Gough and Hobbie 2003), heath tundra (Gough et al. 2002), and dwarf

shrub communities (Baddeley et al. 1994). Wet sedge communities tend to be P-limited

(Shaver and Chapin 1995).

There are few, if any, studies that directly test temperature and nutrient controls

over productivity and carbon (C) storage in Alaskan shrub tundra, even though shrub-

dominated communities make up 22% of the land cover in the Arctic tundra ecosystems

of Alaska and 36.5% of the non-glaciated Pan-Arctic tundra biome (Walker et al. 2005).

Those studies that have tested temperature and nutrient controls over productivity in

shrub tundra communities have been concentrated in sub-arctic systems in Northern

Sweden where the dominate shrubs are evergreen and are compositionally more similar

to heath tundra communities in Alaska than riparian shrub communities (Parsons et al.

1994, Michelsen et al. 1996, Molau and Alatalo 1998). In contrast, shrub tundra

communities in Northern Alaska are dominated by deciduous shrubs, whose functional

traits may allow them to respond more rapidly to environmental change compared to

evergreen shrubs. Understanding how Alaskan shrub tundra communities will respond

to environmental change is becoming even more critical as these communities are

currently expanding (Tape et al. 2006, Forbes et al. 2010), and are expected to continue

to increase with future warming (Walker et al. 2006).

To our knowledge there have been no experimental studies reporting the effects of

either short or long-term environmental change on Alaska shrub tundra communities.

Furthermore, previous research has shown that the short-term (< 5 years) response of

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arctic plant communities to environmental manipulations is not always predictive of

long-term (> 9 years) response studies (Chapin et al. 1995, Boelman et al. 2003, Mack

et al. 2004). In addition, plant communities from different regions within the arctic do

not always respond similarly to the same environmental manipulations (Van Wijk et al.

2003). For example, in a meta-analysis comparing long-term ecosystem level

experiments at Toolik Lake, Alaska and at Abisko, Northern Sweden, Van Wijk et al.

(2003) found that communities from both regions responded to nutrient additions by

increasing aboveground plant biomass, particularly the biomass of deciduous and

graminoid plants. In addition to our limited knowledge on long-term ecosystem

responses, we know little about how environmental changes will influence belowground

biomass, C and nutrient storage, even though the arctic stores 20-30 percent of the total

amount of terrestrial soil-bound C (McGuire et al. 2009).

Much of what we know about North American shrub tundra communities comes

from observational studies. These studies show that shrub tundra communities are

found along gravelly river bars, well-drained floodplains, streams, and in water track

areas where the soil temperatures are warmer and nutrient availability is higher

(Matthes-Sears et al. 1988). These communities are dominated by deciduous shrubs-

willows (Salix spp.), birch (Betula spp.) or alder (Alnus spp.)-and have the highest plant

productivity when compared to other tundra plant communities (Matthes-Sears et al.

1988, Shaver and Chapin 1991). In addition, 70 percent of shrub tundra biomass is

stored belowground in rhizomes and roots (Chapin et al. 1980, DeMarco et al. 2011).

Shrub tundra soils have larger C and N pools (DeMarco et al. 2011) and cycle N in the

soil faster than other tundra communities (Weintraub and Schimel 2003, Buckeridge et

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al. 2010, Chu and Grogan 2010, DeMarco et al. 2011). At the plant level, deciduous

shrubs have higher transpiration (Bliss 1960), photosynthetic (Johnson and Tieszen

1976) and nutrient uptake (Kielland 1994) rates compared to other arctic plant growth

forms and have been found to respond more quickly to environmental change than

other functional groups (Baddeley et al. 1994, Chapin et al. 1995).

The objective of our study was to understand controls over plant productivity and

C and N storage in shrub tundra ecosystems in order to make inferences about how

these systems will respond to environmental change. To investigate whether plant

productivity is limited by temperature, nutrients, or an interaction between the two, we

examined the plant and ecosystem response from the longest running (18 years)

nutrient and warming experiment in Alaskan arctic riparian shrub tundra ecosystems. In

addition, we tested whether these experimental environmental changes altered total

ecosystem N and C storage. We hypothesized that productivity, and C and N pools in

plant biomass, would increase more strongly and consistently to the alleviation of

nutrient limitation than they would to a 1-3 °C increase in air temperature-an increase

chosen to mimic the expected increase in arctic air temperature by the middle of the 21st

century (ACIA 2004). In addition, we predicted that increasing plant productivity in

response to nutrients would lead to an increase in C and N pools in soil organic matter

due to increased inputs of plant litter.

Methods

Study Site and Treatments

This study took place in riparian shrub tundra located near Toolik Field Station at

the Arctic Long Term Ecological Research (LTER) site (Lat. 68°38’N, Long. 149°38’W,

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elevation 760m) in the foothills region on the North Slope of Brooks Range, Alaska,

USA. The entire northern foothills region of the Brooks Range is treeless and underlain

by continuous permafrost, 250-300 m thick (Osterkamp and Payne 1981). Mean annual

air temperature is around -10°C, with average summer temperatures from 7-12°C.

Annual precipitation is 318 mm, with 43% falling in the winter

(http://ecosystems.mbl.edu/ARC). Average snow depth is 50 cm, although snow

distribution can be variable due to redistribution by wind. Snow melt occurs in early

May.

In 1989, two replicate randomized blocks were established in riparian shrub

tundra with each block containing four 5 m x 10 m plots separated by 1 m buffer strips.

Within each block, plots were randomly assigned to the following treatments: control,

nutrient addition, elevated temperature, and nutrient addition with elevated temperature.

Nutrients have been manipulated annually since 1989 by adding 10 g N/m2 of Nitrogen

(N) as NH4NO3 and 5 g P/m2 of Phosphorus (P) as P2O5 in the spring immediately

following snow melt. Temperature was manipulated by placing a greenhouse over the

shrub tundra during the months of June through August. Greenhouses were built of

transparent 0.15-mm (6ml) plastic stretched over an A-shaped 2.4 m by 4.9 m wooden

frame. Plastic was removed each autumn prior to snow fall and replaced in the spring.

The treatments were similar to those described in Chapin et al. (1995). The sites are

currently being maintained by the Arctic Long Term Ecological Research Station and

still receive their annual nutrient additions and heating treatments.

Environment

Two profiles of soil temperature were measured at four depths (moss level-1, 10,

20, 40 cm) within each treatment in block two using copper/constantan thermocouple

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wires connected to a data logger (Campbell Scientific CR10, Campbell Scientific, Inc.,

Logan, Utah, USA). Soil sensors were read every 15 minutes and averaged every three

hours. Mean annual soil temperatures during the growing season (June-August), and

winter season (January-May plus September-December) were calculated using data

collected in 2007.

Biomass

We measured all aboveground and belowground stem biomass by destructively

harvesting all shrubs from three separate 50 x 50 cm quadrats within each plot for each

treatment within each block. Understory plants and mosses were collected from a 10 x

40 cm area nested within the 50 x 50 cm quadrat. Each quadrat was sorted into

species and then into tissue type (ex. inflorescences, new growth, and old growth). The

separated samples were then dried for a minimum of 48 hours at approximately 65 °C

and then weighed for biomass. Biomass measurements are expressed on a per meter

squared basis. Total biomass for each treatment was determined by first calculating the

mean total biomass across the three quadrats per treatment and block. The total

biomasses per block were then averaged to get a final value for each treatment. To

determine total biomass per functional group, biomass for each functional group was

averaged across the three quadrats per treatment and block and then averaged again

across the two blocks per treatment.

Rhizomes within the organic layer were removed from the same 10 x 40 cm

quadrats used to determine aboveground understory and moss biomass, according to

methods described in (Bret-Harte et al., 2008). Root biomass was measured by

removing by hand all live roots from soil cores collected from each quadrat within each

treatment and block. Two soils cores, adjacent to each other, were removed and

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combined to provide enough material for processing. Organic soils were removed in

five by five cm “brownies” down to the mineral soil and mineral soils were removed

using a five cm diameter mineral core down to the permafrost layer. Roots were

separated into two size classes; fine (≤ 2 mm in diameter) and coarse (> 2mm diameter)

and were dried at 60 C for a minimum of 48 hours before weighing. Roots and

rhizomes from the organic and mineral layer were combined to estimate total

belowground root and rhizome biomass. All biomass measurements are expressed on

a per-area basis.

Aboveground Net Primary Production

Net primary production (NPP) was calculated for aboveground vascular plants

only and did not include any old leaves, belowground parts, or rhizomes. Aboveground

production was separated by parts: leaves, new stems, secondary stem growth, and

inflorescence/fruit. Leaves included new leaves, all aboveground material for forbs, and

the blades/sheaths of the graminoids. Secondary stem growth was estimated for stems

that were produced in previous years, using estimated values of the percentage of

secondary growth that contributes to NPP calculated in Bret-Harte et al. (2002). For

Betula nana 16 percent secondary contribution to NPP was used, for all Salix species

an estimate of 18 percent, and for Ledum palustre 8 percent was used. A mean of

Betula nana and Salix pulchra estimates was used for Potentilla fruticosa and came to

16.95 percent. We assumed negligible secondary growth for Dryas integrifolia,

Empetrum nigrum, Rubus chamaemorus, Vaccinium vitis-idaea and did not include their

stem in the ANPP calculation.

92

Species Diversity

Biomass for each species was summed across all replicates within a treatment

for construction of rank-abundance curves. Species were ranked within each treatment

by their biomass, with number one being the most abundant species within that

treatment.

Soil Properties

A separate set of soil cores were removed in the exact same way as the root cores

in order to examine soil characteristics. Prior to analysis, soils were homogenized by

hand and the > 2 mm diameter fraction (e.g., roots, rhizomes, coarse woody debris, and

rocks) was removed. Soil water content was calculated by subtracting the weight of the

soil, after being dried at 60°C (organic soils) or 105 °C (mineral soils) for 48 h, from the

wet weight of the soil and then dividing by the dry weight of the soil. Soil bulk density

was determined by dividing the oven dry equivalent soil by the core volume.

Carbon and Nitrogen Pools

Dried plant and organic soil samples from all quadrats, treatments, and blocks

were ground to a fine powder on a Wiley-mill with a #40 mesh screen. Mineral soils

were hand ground using a mortar and pestle. Bulk C and N were determined on all

plant parts for each species and for both organic and mineral soil layers using an ECS

4010 elemental analyzer (Costech Analytical, Valencia, California, USA).

Pools of dissolved inorganic N (N-NH4+ and N-NO3

-) were measured by extracting

10 g of fresh soil with 50 ml of 0.5 M K2SO4. The soil slurry was agitated on a shaker

table for 2 h, allowed to sit overnight in a cooler, and then vacuum filtered through a

Whatman GF/A filter. Filtrate was frozen until analyzed colorimetrically, on a

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segmented flow autoanalyzer (Astoria analyzer, Astoria-Pacific, Inc, Clackamas,

Oregon, USA).

Statistical Analysis

Differences in aboveground biomass among treatments and among plant

functional groups were tested using a three-way ANOVA with fertilization (NP),

greenhouse (T), and plant functional group as the main effects and block as a random

effect. All possible interactions were also tested. Differences in all other variables were

tested using separate two-way Anova models with Treatment (C, NP, T, and NP+T) as

the main effect and block nested within treatment as a random effect. Tukey’s multiple

comparison tests were used to detect differences among groups when ANOVA’s

showed significance at a level of p < 0.10. We chose this level of significance because

of the low level of replication in our experiment. We are constrained by the initial set up

of the experiment and due to the long term nature of the experiment and the lack of

experimental manipulation studies in shrub tundra communities within the Arctic, we feel

that even a significance at the p < 0.10 level can provide useful information to make

inferences on how these communities will respond to environmental change. All

statistical analyses were performed using the software package JMP v. 8.

Results

Environmental Data

The greenhouse warming treatment significantly increased mean annual soil

temperature by 0.5°C at a depth of 10 cm compared to the non warming treatments

(One-way ANOVA with treatment as the main effect: F3,4 = 2.3, p = 0.06; Table 4-1).

The warming treatment increased temperatures by 0.5-0.8°C during the growing season

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and 0.4°C during the winter season, although, only the winter season was significantly

different (F3,4 = 13.3, p = 0.02; Table 4-1). There was no significant difference among

treatments in soil temperature at either the moss layer or at 20 and 40 cm depths (data

not shown). Soil moisture did not significantly differ among treatments (Table 4-1).

Biomass

Total aboveground biomass increased by 43 percent with nutrient addition and

38 percent with increased warming, although only the nutrient treatment was statistically

significant (Fig. 4-1; Treatment: F3,16 = 5.5, P < 0.01). There was no significant nutrient

by temperature interaction, suggesting that the 74 percent increase in biomass seen in

the nutrient plus warming treatment was an additive effect of warming and increased

temperature (Table 4-3). There was no significant difference between blocks in

aboveground biomass. Deciduous shrubs made up the greatest biomass compared to

biomass from other growth forms for all treatments. Fertilization plus warming

significantly increased deciduous biomass compared to the control (F3,4 = 5.0, p = 0.08).

Biomass of all other functional groups, except graminoids, declined with the addition of

nutrients and temperature although, due to small sample size, only moss was

statistically different (F3,4 = 5.2, p = 0.07; Fig. 4-1, Table 4-4).

Aboveground Net Primary Production

Total ecosystem aboveground net primary production for vascular plants

increased in the nutrient plus temperature treatment, although this was only marginally

significant (Treatment: F3,4 = 3.7, p = 0.12). The largest increase in growth occurred in

secondary stem growth (F3,4 = 6.2, p = 0.06; Fig. 4-2). Both new leaves and new stems

increased in the nutrient addition treatments, although this increase was not statistically

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different. Production of inflorescences and fruits declined with nutrient addition and

warming (F3,4 = 5.0, p = 0.08; Fig. 4-2).

Species Diversity

The control site had a total of 21 different vascular plant species (Fig. 4-3 and

Table 4-2). After 18 years of adding nutrients or increasing temperature, species

diversity declined to 12 and 13 species in the nutrient addition and the warming

treatments, respectively. The greatest species loss occurred in the interaction

treatment-nutrient addition plus temperature, which resulted in only six different species

(Fig. 4-3 and Table 4-2). Deciduous shrubs Betula nana and Salix spp. were the most

abundant species across all four treatments. Warming and added nutrients resulted in

a decline in forb, graminoid and evergreen shrub diversity (Fig. 4-3 and Table 4-2).

Soil Properties

There was no significant difference in soil layer depth, bulk density, or

concentrations of ammonium among any of the four treatments (Table 4-1). Soil nitrate

concentrations significantly increased in the NP treatment only (F3,4 = 13.8, p = 0.01;

Table 4-1).

Carbon and Nitrogen pools

Total ecosystem C pool did not differ across treatments; however, there was a

marginally significant increase in aboveground C pool, with the nutrient plus

temperature treatment having the greatest aboveground C pool (Fig. 4-4). Belowground

C pool was not significantly different across treatments. Total ecosystem N pool was

also not different across treatments. There was however a significant increase in

aboveground N pool for the nutrient plus warming treatment (Treatment: F3,4 = 4.5, p =

0.09; Fig. 4-4). Belowground N pool was not significantly different across treatments.

96

The C to N ratio of the aboveground biomass was greater in the temperature and the

nutrient plus temperature treatments and this was marginally significantly different (F3,4

= 3.3, p = 0.14). Mean (± SE) C to N ratios ranged from 60 (5), 55 (10), 78 (5), and 72

(0.4) in the control, NP, T, and NP + T treatments, respectively.

Added nutrients plus warming resulted in an increase in C pools in shoots, woody

standing dead, and litter by four, four, and eleven times respectively, relative to the

control. N pool also increased two times in shoots, four times in woody standing dead,

17 times in litter, and one and half times in roots in the added nutrients plus warming

treatment relative to the control. There was no difference in C or N pools across

treatments for rhizomes, organic soil, or mineral soil (Table 4-5 and Fig.4-5).

Allocation

Warming increased biomass, C, and N allocation to aboveground stems

(Biomass: F3,4 = 27.1, p < 0.01; C: F3,4 = 30.0, p < 0.01; N: F3,4 = 60.0, p < 0.001) and

decreased allocation to belowground stems (Biomass: F3,4 = 5.1, p = 0.08; C: F3,4 = 7.1,

p < 0.01; N: F3,4 = 4.8, p = 0.08; Table 4-6). There was no effect of warming or nutrient

addition on biomass, C, or N allocation to leaves or roots (Fig.4-6). Nutrient addition

resulted in a decrease in C to N ratio in leaves (F3,4 = 4.5, p = 0.09). In contrast,

warming caused an increase in C to N ratio in aboveground stems (F3,4 = 4.6, p = 0.09).

There was no difference in C to N ratios in belowground stems or roots with warming or

nutrient addition (Fig. 4-7).

With warming and nutrient additions, deciduous shrubs allocated more biomass,

C, and N to aboveground stems (Biomass: F3,4 = 14.3, p = 0.01; C: F3,4 = 16.5, p = 0.01;

N: F3,4 = 14.4, p = 0.01) and decreased allocation to belowground stems (Biomass: F3,4

= 9.0, p = 0.03; C: F3,4 = 10.0, p = 0.03; N: F3,4 = 7.5, p = 0.04; Fig. 4-8). Allocation to

97

leaves did not change with treatments. In contrast, graminiods decreased biomass, C,

and N allocation to belowground stems (Biomass: F3,4 = 4.3, p = 0.10; C: F3,4 = 4.3, p =

0.09; N: F3,4 = 6.6, p = 0.05; Fig. 4-8). Allocation to leaves and aboveground stems did

not change with treatment. Biomass, C, and N allocation between plant parts within

forbs also did not change with treatments.

Discussion

Controls over biomass and productivity

As with other ecosystems in the region, the riparian shrub community responded

to long term nutrient additions by increasing biomass and ANPP; however, the

magnitude of the response was not as great as seen in moist acidic arctic plant

communities, suggesting that riparian shrub communities may not be as N limited as

moist acidic tundra communities (Chapin et al. 1995, Mack et al. 2004). It has been

estimated that shrub community ANPP requires ~ 4.4 g N per m2 per year compared to

2 g N per m2 per year for moist acidic tundra (Shaver and Chapin 1991); thus, our

annual addition of 10 g N per m2 year is only about double the annual requirement and

does not consider any immediate N loss from leaching and denitrification or N made

unavailable to the plants by immobilization by soil microbes. Therefore we may not be

adding enough N to increase production to the magnitude seen in moist acidic tundra,

which has a lower N requirement.

The warming treatment stimulated an increase in both biomass and ANPP.

However, the increase was relatively small; suggesting that the 0.5 °C increase in

annual soil temperature was not enough to relieve direct temperature limitation that may

be occurring in riparian shrub communities or to stimulate enough of an increase in

98

plant available nutrients. A similar response to warming addition was seen in moist

acidic tundra and moist non-acidic tundra which showed a decrease in aboveground

biomass with no change or a relatively small increase compared to added nutrients on

ANPP (Chapin et al. 1995, Gough and Hobbie 2003).

We were surprised to see such a dramatic increase in biomass and ANPP in the

nutrient plus warming treatment and to discover that this was an additive and not an

interactive effect. It is possible that warming caused an increase in plant available

nutrients and the additional nutrients plus the nutrients added with fertilization was

enough to relieve nutrient limitation, resulting in an almost doubling of plant biomass

compared with nutrient additions or warming alone. We were unable to detect an

increase in inorganic N in the soils in the warming treatment. However, any nutrients

made available could have been immediately taken up by plants. In addition, other

studies have shown that warming manipulations can result in early leaf expansion

(Chapin and Shaver 1996, Arft et al. 1999). This increased growth and photosynthetic

activity would require a higher demand for nutrients early in the growing season soon

after the plots were fertilized. Results from other tundra manipulation studies have

suggested that temperature can constrain early-season growth while nutrients constrain

late-season growth (Chapin and Shaver 1996). Thus, the potential warming response

of early leaf out and measured increase in growth in combination with the increased

nutrient availability from fertilization may have contributed to the stronger biomass and

ANPP response seen in the nutrient plus warming treatment. Such a large response to

nutrients plus warming compared to warming or nutrients alone has not been seen in

the few studies in other ecosystems in this region that have manipulated both nutrients

99

and temperature together (Chapin et al. 1995, Gough and Hobbie 2003) but has been

seen in sub-arctic systems in Sweden (Jonasson et al. 1999). The other ecosystems

studied in Alaska vary more in their plant functional type composition than our riparian

shrub system or the systems studied in Sweden, which were dominated by shrubs.

Shrubs have been shown to respond strongly to independent nutrient addition and

warming treatments (Jonasson et al. 1999, Dormann and Woodin 2002), suggesting

that if we have a system that is dominanted by a plant functional group that responds

positively to nutrients and warming it would make sense that we would see a greater

ecosystem response when nutrients and warming are manipulated together.

The increase in biomass seen with nutrient additions and warming resulted in an

amplification of the already dominant functional group, deciduous shrubs, a reduction in

both graminiod and forb biomass, and a complete loss of evergreen shrubs. Changes

in nutrients and temperature had no effect on belowground biomass and this lack of

response is similar to the few studies that have also measured belowground biomass

with nutrient additions (Mack et al. 2004) or with nutrients and warming (Gough and

Hobbie 2003).

Changes in C and N pools

Surprisingly, there was no detectable change in total ecosystem C or N pools

with added nutrients or with warming even though we saw an increase in C and N pools

in shoots, woody standing dead, and fine litter. Our low sample size and the

heterogeneous nature of the soil prevented us from being able to detect any differences

in soil C and N pools that may have occurred across our treatments. Mack et al. (2004)

also found that C and N pools in shoots, standing dead, and litter all increased after 19

years of added nutrients in moist acidic tundra located in the same region. However, in

100

contrast to our study, they were able to detect a decrease in C pools in deeper soil

layers that was substantially larger than the increase in aboveground C pools. Similar

to biomass and ANPP, the largest responses seen in C and N pools in our study were in

the nutrient plus warming treatment.

Species diversity

The loss of species diversity and functional group representation with increased

nutrients and warming is not surprising since this system is dominated by deciduous

shrubs that produce a dense canopy allowing only shade tolerant species to survive

below. Loss of species diversity with environmental manipulations has been seen in

other warming (Chapin et al. 1995, Gough and Hobbie 2003, Hollister et al. 2005) and

nutrient addition (Chapin et al. 1995, Gough et al. 2002, Gough and Hobbie 2003)

experiments, with greater loss seen in nutrient plus warming treatments for at least one

other experiment (Chapin et al. 1995). The complete loss of evergreens with

fertilization has also been seen in other nutrient addition studies, mostly in the Alaskan

arctic, and has been attributed to the strong growth and biomass response of deciduous

shrubs, resulting in the shading out of the understory evergreens (Chapin et al. 1995).

In contrast, in sub-arctic Sweden, evergreen shrub biomass has been shown to

increase while deciduous shrub biomass decreases with additional nutrients and

warming (Jonasson et al. 1999). These Sweden ecosystems were dominated by

evergreen shrubs and had few deciduous shrubs present to increase biomass enough

to result in shading of the evergreens.

Shifts in allocation

Nutrient additions and warming resulted in an increase in aboveground allocation

of biomass, C, and N to long-lived woody stems, with the greatest increase seen in the

101

nutrient plus warming treatments. This pattern was driven by changes in allocation

within the deciduous shrubs, which increased allocation to aboveground stems, while

changes in aboveground allocation were not detected in the other functional groups

present in this system. In contrast, belowground allocation of biomass, C, and N

decreased in belowground stems, primarily in the warming treatments. Again, this was

driven by changes in allocation within deciduous shrubs although graminoids decreased

allocation to belowground stems with nutrient additions as well. Interestingly, C

allocation to belowground stems decreased more in the warming only treatment.

Temperature-enhanced vegetation growth would require additional carbon and nutrient

reserves. If these additional requirements cannot be met by uptake of nutrients then

reserves from belowground rhizomes may be used, which could result in less allocation

of biomass, C, or N belowground to rhizomes (Chapin and Shaver 1996), explaining the

decline in belowground allocation seen in the warming treatments.

Changing C balance

An increase in woody production in response to warming and nutrients, as seen

in this study, could have important implications for ecosystem C cycling. Woody stems

store more C than non-woody plant material and can take longer to decompose

compared to other plant parts (Hobbie 1996). If there was no simultaneous decrease in

belowground soil C stores, future warming in conjunction with increased nutrients in the

North American arctic could result in a negative feedback, where more C is taken up by

shrub growth and stored in tissue that has a longer C turnover time. However, more

measurements of soil C and N dynamics in warming and nutrient treatments are needed

to better understand how the belowground component of the ecosystem will respond

and contribute to total ecosystem C and N and feedback to global C cycling.

102

Our study is another example of how different arctic vegetation types do not

always respond the same way to environmental change. Models that try to simulate

arctic vegetation response to global climate change need to include a full range of

vegetation types across regions and their individual responses. More long term data

sets that represent more of the dominant vegetation communities within the arctic are

needed to increase our understanding and predictive power of how the arctic will

respond to environmental change.

Riparian shrubs in the Alaskan arctic responded to long term environmental

changes of increased nutrients and warming by increasing biomass and productivity of

the dominant functional group, deciduous shrubs, resulting in an increase in C and N

stored in aboveground shoots, woody standing dead, and litter. In addition, nutrient

addition and warming shifted allocation of biomass, C, and N to aboveground stems and

reduced allocation to belowground stems. Species diversity and the representation of

other functional groups such as evergreen shrubs and forbs declined with

environmental manipulations. In all cases, the effects of environmental manipulations

were more pronounced in the nutrient plus warming treatments. A future arctic that is

warmer and has more nutrients has the potential to alter riparian shrub ecosystem

structure and function and should be considered when making predictions about arctic

vegetation responses to future climate change.

103

Figure 4-1. Aboveground biomass (g/m2) from shrub tundra harvested in the eighteenth year of treatment (C = control, NP = Nitrogen and phosphorus additions, T = warming manipulation, and NP + T = Nitrogen and phosphorus additions plus warming manipulation), separated by functional group and belowground biomass (g/m2) separated by roots and rhizomes. Different letters indicate significance among treatments in total aboveground biomass. (Mean ± SE, n = 2)

a

ab a

bTreatment: F3,16

= 5.5, p < 0.01

Bio

ma

ss (

g m

- 2)

0

1000

2000

3000

4000

5000

Deciduous

Evergreen

Forb

Graminoid

Moss

Lichen

Treatment

C NP T NP + T

Bio

ma

ss (

g m

- 2)

0

1000

2000

3000

4000

5000

Rhizome

Root

104

Treatment

C NP T NP + T

AN

PP

(g/m

2/y

r)

0

200

400

600

800

1000

1200

1400Inflorescence/Fruit

New leaves

New stems

Secondary stems

a

abab

b

Treatment: F3,4

= 3.7, p = 0.12

Figure 4-2. Aboveground vascular net primary production (ANPP) across treatments

and separated by plant parts. Different letters indicate significance among treatments in total ANPP. (Mean ± SE, n = 2)

Species Rank

0 5 10 15 20 25

LO

G (

BIO

MA

SS

)

-2

-1

0

1

2

3

4

5C: DDDDEEGGGGFFEFFFFDFFF

NP: DDDDGGFGGGFG

T: DDDFFFFEFGFFF

NP+T: DDDGGF

Figure 4-3. Vascular plant biomass dominance-diversity curves sampled 18 years after initiation of treatments. The sequence in abundance of growth forms represented by each species in each treatment is also shown, with the relative number of repeated letters indicating abundance: forb (F), graminoid (G), deciduous (D), and evergreen (E).

105

Figure 4-4. Total ecosystem C and N pools separated by above and belowground for each treatment. Different letters indicate significant differences between aboveground N pools across treatments. (Mean ± SE, n = 2)

Treatment

C NP T NP + T

N p

oo

ls (

g N

m--2

)

0

200

400

600

800

Treatment

C NP T NP + T

C p

oo

ls (

g C

m- 2

)

0

2000

4000

6000

8000

10000

12000

14000Belowground

Aboveground Treatment F

3,4 = 4.5, p = 0.09

a

ab

a

b

106

Figure 4-5. Ecosystem C and N pools after 18 years of experimental manipulation of nutrients and temperature. Different letters indicate significant differences across treatments within the same component. (Mean ± SE, n = 2)

Component

Shoots

Rhizomes

Total Roots

Woody s

tanding dead

Fine litter

Total organic soil

Mineral soil (

0-10 cm)

N p

oo

ls (

g N

m-2

)

0

10

20

30

40

50

100

200

300

400

C

NP

T

NP + T

C p

oo

ls (

g C

m-2

)

0

2000

4000

6000

8000

a

ab a

b

NS

NS

NSNS

a abbc c

aab b

c

NSNS

NS

NS

a

aba

b

aab b b

a

abb

c

a

a

107

Figure 4-6. Proportional allocation of vascular plant biomass, C, and N to different plant parts across each treatment. Different letters indicate significance across treatments within the same plant part.

Bio

ma

ss a

lloca

tio

n (

%)

0

20

40

60

80

100Belowground stems

Aboveground stems

Leaves

Carb

on

allo

ca

tio

n (

%)

0

20

40

60

80

100

Treatment

C NP T NP + T

Nitro

ge

n a

lloca

tio

n (

%)

0

20

40

60

80

100

108

Figure 4-7. Carbon to nitrogen ratios of plant tissues across treatments. (Mean ± SE, n = 2)

Treatment

C NP T NP + T

C:N

0

20

40

60

80

100

120

140

Leaves

Aboveground stem

Belowground stem

Roots

109

Figure 4-8. Percent biomass, C, and N allocation among leaves (■), aboveground stems (□), and belowground stems (■) within each functional group across treatments. Different letters indicate significance across treatments within the same plant part. For total biomass, evergreens, graminoids, and forbs are graphed on the same scale.

To

tal b

iom

ass g

/m2)

0

20

40

60

80

100

Treatment

C NP T NP + T

Forbs

To

tal b

iom

ass (

g/m

2)

0

1000

2000

3000

4000

5000

6000Deciduous

Ca

rbon

allo

ca

tio

n (

%)

0

20

40

60

80

100

Treatment

C NP T NP + T

Nitro

ge

n a

lloca

tio

n (

%)

0

20

40

60

80

100

Treatment

C NP T NP + T

GraminiodEvergreen

Treatment

C NP T NP + T

aab b b

a

abb

b

a ab bb

a

ab abb

a

ab

ab

b

a

ab

ab

b

110

Table 4-1. Soil properties measured across all four treatments after 18 years of manipulation. Different letters indicate significance across treatments.

Soil properties C NP T NP + T Soil temperature (°C) Annual -0.80 (0.10) -1.63 (0.50) -0.28 (0.05) -0.34 (0.03) Growing season 5.02 (0.16) 5.01 (1.01) 5.78 (0.16) 5.49 (0.30) Winter -2.77ab (0.19) -3.88b (0.32) -2.34a (0.02) -2.33a (0.14) Soil moisture (g H20/g soil) Organic 1.68 (0.38) 1.17 (0.08) 0.72 (0.26) 1.47 (0.87) Mineral 1.12 (0.43) 0.85 (0.26) 0.95 (0.50) 0.76 (0.18)

Soil layer depth (cm) Organic 15.56 (4.69) 9.17 (2.50) 7.21 (2.54) 11.04 (2.54)

Mineral 13.83 (2.42) 9.23 (2.35) 8.04 (3.13) 10.98 (5.35)

Bulk density (g/cm3) Organic 0.11 (0.01) 0.10 (0.03) 0.09 (0.03) 0.11 (0.02)

Mineral 0.33 (0.01) 0.46 (0.001) 0.40 (0.04) 0.47 (0.17)

N-NH4+ (ug/g soil)

Organic 56.63 (8.97) 165.71 (75.43) 65.54 (6.11) 94.24 (11.18)

Mineral 17.32 (7.69) 22.81 (6.62) 18.48 (2.56) 17.01 (2.99) N-NO3

- (ug/g soil)

Organic 1.68 (0.74) 146.48 (74.21) 0.50 (0.05) 6.78 (5.43)

Mineral 1.49 (0.28) 32.30 (26.99) 0.38 (0.18) 1.67 (0.91)

111

Table 4-2. Vascular plant species rank by treatment based on aboveground biomass with number one being the most abundant species; no number indicates that species was not present in the plot.

Functional group Species C NP T NP + T

Forb Aconitum delphinifolium 21

Anemone richardsonii 14

Artemesia alaskana 16 6

Petasites frigidus 19

Polygonum sp. 15 6

Polygonum bistorta 11 11 11

Pyrola secunda 17

Senecio lugens 9

Stellaria longipes 20 3

Valeriana capitata 12 7 12

Graminoid Arctagrostis latifolia 4

Calamagrostis canadensis 8 6 4 5

Calamagrostis lap 8 7

Carex bigelowii 9 12

Carex hypnophillum 10

Carex podocarpa 7 10

Carex vaginatum 9 Poa arctica 10 5 5 Deciduous shrub Betula nana 2 3 2 3

Potentilla fruticosa 4 4

Rubus chamaemorus 18

Salix glauca 1 2 3 1

Evergreen shrub Salix pulchra 3 1 1 2

Empetrum nigrum 5

Ledum palustre 13

Vaccinium vitis-idaea 6 8

112

Table 4-3. Aboveground biomass (g/m2) for the most abundant species and functional groups. (Mean ± SE, n = 2)

Total Biomass (g/m2)

Growth form/species C NP T NP + T

Forb Artemesia alaskana 0.5 ± 0.4 0 4 ± 4 0 Polemonium acutiflorum 0.7 ± 0.7 0 0 2 ± 2 Polygonum bistorta 2 ± 0.5 0.1 ± 0.1 0.2 ± 0.2 0 Valeriana capitata 1 ± 1 3 ± 2 0.03 ± 0.03 0 Total 6 ± 4 3 ± 2 5 ± 3 1 ± 1 Graminoid Arctagrostis latifolia 0 0 0 10 ± 10 Calamagrostis canadensis 3 ± 3 3 ± 0.1 18 ± 18 3 ± 3 Calamagrostis lapponica 0 2 ± 2 2 ± 2 0 Carex bigelowii 2 ± 2 0 0 0 Carex podocarpa 5 ± 5 0 0.2 ± 0.2 0 Poa arctica 2 ± 2 15 ± 15 5 ± 5 0 Total 11 ± 3 20 ± 18 25 ± 15 14 ± 14 Deciduous Betula nana 205 ± 41 390 ± 130 573 ± 573 86 ± 86 Potentilla fruiticosa 46 ± 46 31 ± 31 0 0 Rubus chamaemorus 0.4 ± 0.4 0 0 0 Salix glauca 338 ± 338 443 ± 133 109 ± 109 2116 ± 2116 Salix pulchra 169 ± 162 736 ± 230 761 ± 414 1268 ± 1255 Total 758 ± 262 1601 ± 201 1443 ± 269 3470 ± 947 Evergreen Empetrum nigrum 33 ± 33 0 0 0 Ledum palustre 1 ± 1 0 0 0 Vaccinium vitis-idaea 10 ± 7 0 1 ± 1 0 Total 43 ± 41 0 1 ± 1 0 Mosses Sphagnum spp. 32 ± 32 0 0 0 Non-sphagnum spp. 47 ± 2 1 ± 1 2 ± 2 0 Total 79 ± 34 1 ± 1 2 ± 2 0 Lichen 22 ± 22 0 0 0

113

Table 4-4. Three-way ANOVA results comparing aboveground biomass among treatments.

Main Effects df F ratio P value NP 1 7.8 0.05 T 1 6.2 0.07 NP x T 1 1.4 0.30 Growth form 6 54.3 < 0.0001 NP x Growth form 6 8.6 < 0.0001 T x Growth form 6 6.8 < 0.0001 NP x T x Growth form 6 1.5 0.22

Table 4-5. Two-way ANOVA results comparing N or C pool across treatments (C, NP, T, NP + T) within the same component. Block = 2

Component Pool (g/m2) F-stat df P value

Shoots Carbon 4.5 3 0.09 Nitrogen 4.3 3 0.10 Rhizomes Carbon 0.1 3 0.94 Nitrogen 0.5 3 0.71 Total roots Carbon 1.0 3 0.49 Nitrogen 5.2 3 0.07 Woody standing dead Carbon 9.0 3 0.03 Nitrogen 4.7 3 0.09 Fine litter Carbon 4.1 3 0.11 Nitrogen 3.7 3 0.12 Total organic soil Carbon 0.9 3 0.50 Nitrogen 1.0 3 0.48 Mineral soil (0-10 cm) Carbon 0.3 3 0.82 Nitrogen 0.3 3 0.80

Table 4-6. Two-way ANOVA results comparing biomass, C, or N allocation across

treatments (C, NP, T, NP + T) within the same plant part. Block = 2 F-stat df P value

Biomass allocation Leaves 1.0 3 0.49 Aboveground stems 27.1 3 < 0.01 Belowground stems 5.1 3 0.08 Roots 2.7 3 0.18 Carbon allocation Leaves 1.3 3 0.39 Aboveground stems 30.0 3 < 0.01 Belowground stems 7.1 3 0.04 Roots 2.6 3 0.19 Nitrogen allocation Leaves 0.6 3 0.65 Aboveground stems 60.0 3 < 0.001 Belowground stems 4.8 3 0.08 Roots 2.2 3 0.23

114

CHAPTER 6 CONCLUSION

Results from this dissertation showed that on a short time scale, shrub interactions

with snow may play a role in increasing plant available N, primarily through effects on

the summer soil microenvironment that increases N availability when plants are most

active. In contrast, small changes in soil temperature and moisture associated with

additional snow trapping by shrubs is unlikely to influence litter nutrient turnover enough

to drive positive snow-shrub feedbacks, as proposed by Sturm et al. (2001). However,

long-term changes in litter quality inputs associated with different dominant plant

species could lead to alterations in SOM quality, soil nutrients, and microbial

communities, which in turn, can significantly alter litter decomposition processes. In

addition, the quality of SOM matter, which can be linked to species-specific traits such

as litter allocation and litter quality, may be more of a limiting factor in determining

mineralization rates of N than small changes in temperature. Assuming that our natural

shrub gradient represents the structure and function of future climate-driven shrub

communities, I would expect a shrubbier arctic to have greater aboveground and

belowground biomass, higher soil temperatures, and higher quality of SOM that favors

higher rates of N fluxes. If all species respond similarly to Betula papyrifera when

incubated at the medium and low shrub site, an increase in deciduous shrub cover

could actually lead to slower rates of litter decomposition and nutrient turnover and,

thus, an increase in C sequestration. Retaining N in litter may be beneficial for soil

organic matter (SOM) decomposition and could help explain why we see more soil N

and greater N mineralization at the medium and high shrub sites.

115

This dissertation also showed that riparian shrubs in the Alaskan arctic responded

to long term environmental changes of increased nutrients and warming by increasing

biomass and productivity of the dominant functional group, deciduous shrubs, resulting

in an increase in C and N stored in aboveground shoots, woody standing dead, and

litter. In addition, nutrient addition and warming shifted allocation of biomass, C, and N

to aboveground stems and reduced allocation to belowground stems. Species diversity

and the representation of other functional groups such as evergreen shrubs and forbs

declined with environmental manipulations. In all cases, the effects of environmental

manipulations were more pronounced in the nutrient plus warming treatments. A future

arctic that is warmer and has more nutrients has the potential to alter riparian shrub

ecosystem structure and function, resulting in a system that has low plant species

diversity and is dominated by deciduous shrubs that allocate more biomass, C, and N to

long-lived woody stems. These responses should be considered when making

predictions about arctic vegetation responses to future climate change.

116

APPENDIX A SUPPLEMENTARY MATERIAL FOR CHAPTER 2

A.1 Additional methods: Characterizing ecosystem structure

Live aboveground biomass in each site was determined by harvesting all plant

species within 12 10 by 40 cm quadrats at the low shrub site. Due to the high

heterogeneity of the plant composition at the low shrub site, two quadrats were taken

next to each and then averaged. For both the medium and high shrub sites, the

understory was removed from six similar 10 by 40 cm quadrats nested within a 50 by 50

cm quadrat, from which the overstory was removed. All plant material was dried at

60C for a minimum of 48 hours and weighed. From the harvested species, we

categorized deciduous shrubs as those shrubs that have the physiological ability to

substantially increase their height and biomass, including: Betula nana, Salix pulchra, S.

glauca, S. richardsonii and V. uliginosum. Only total biomass and deciduous shrub

biomass are presented here. Rhizomes within the organic layer were removed from the

same 10 x 40 cm quadrants used to determine aboveground biomass according to

methods described in Bret-Harte et al., (2008). Root biomass within the organic layer of

each plot was measured in five by five cm soil monoliths that extended down to the

surface of the mineral layer. From the mineral surface to the permafrost layer, both root

and rhizome biomass was measured in a five cm diameter core. Roots and rhizomes

were hand-picked from soil samples and separated into fine (< 2 mm in diameter) roots,

coarse (> 2 mm in diameter) roots, and all rhizomes, and dried at 60C for a minimum of

48 hours and weighed. Roots and rhizomes from the organic and mineral layer were

combined to estimate total belowground root and rhizome biomass.

117

Figure A-1. Annual ambient snow depth for each plant community. (Mean ±SE)

Table A-1. Belowground biomass at each site. Mean (±SE)

Note: Lower case letters indicate a significant difference when p<0.05.

Site

Low Med High

Am

bie

nt

Sn

ow

De

pth

(cm

)

0

20

40

60

80

100

120

2006

2007

2008

2009

Sites

Belowground Biomass (g/m2) n Low Medium High

Organic Soil Fine roots 8 405.8 (109.1) 414.8 (78.2) 442.4 (83.2) Coarse roots 1-5 109.6 (55.9) 265.6 (NA) 341.9 (151.7) Rhizomes 8 611.0a (59.5) 1050.9ab (217.3) 1618.8b (233.1) Total 8 1071.6a (140.3) 1499.0ab (215.8) 2275.0b (338.5) Mineral Soil Fine roots 7-8 355.8 (128.0) 232.8 (39.4) 252.6 (62.2) Coarse roots 1-2 26.9 (NA) 54.8 (4.7) 33.1 (22.4) Rhizomes 3 410.4 (308.3) 108.3 (34.4) 109.1 (36.8) Total 7-8 535.5 (244.5) 287.1 (39.1) 308.8 (76.5)

118

Table A-2. K2SO4 extractable soil nutrients down to 10 cm within the organic layer. Values are means (± SE). DON and

MB-N were analyzed using a 2-way ANOVA; NH4+-N, and NO3

--N pools were analyzed using Kruskal Wallis. Lower case letters indicate significance among sites within a season, while asterisks indicate significance between seasons within the same site. Capital letters indicate significance between season and among sites and include interaction effects.

June Sept

(ug/g soil) Low Medium High Low Medium High

DIN NH4

+-N 10.2a (2.10) 81.4b* (11.96) 194.2b* (32.99) 6.9a (1.06) 30.6b* (9.83) 70.4b* (25.84) NO3

--N 1.8 (1.82) 2.1 (1.03) 5.0 (2.02) 0.9 (0.67) 4.1 (1.64) 5.6 (2.89) DON 67.9a (18.34) 139.7a (29.01) 299.3b* (25.94) 81.9 (13.06) 96.3 (19.09) 178.4* (14.59) MB-N 503.2a (49.16) 631.9a (28.85) 807.3b* (53.19) 536.8 (51.05) 510.1 (40.95) 569.3* (76.45) Pools (g/m2) DIN NH4

+-N 0.03a (0.007) 0.46a* (0.068) 0.96b* (0.16) 0.025a (0.003) 0.17ab* (0.056) 0.35b* (0.13) NO3

--N 0.01 (0.006) 0.01 (0.006) 0.03 (0.010) 0.004 (0.003) 0.02 (0.009) 0.03 (0.014) DON 0.23A (0.062) 0.79AB (0.165) 1.48B (0.129) 0.27A (0.044) 0.55AB (0.108) 0.59A (0.19) MB-N 1.69A (0.165) 3.59AB (0.164) 4.00B (0.264) 1.80A (0.171) 2.90A (0.232) 1.41AB (0.65)

119

APPENDIX B SUPPLEMENTARY MATERIAL FOR CHAPTER 3

Year

2003 2004 2005 2006 2007 2008

Initia

l N

Re

ma

inin

g (

%)

0

20

40

60

80

100

Control

Fertilized

Initia

l C

Re

ma

inin

g (

%)

20

40

60

80

100

Initia

l N

Re

ma

inin

g (

%)

40

60

80

100

Betula nana

Initia

l M

ass R

em

ain

ing (

%)

20

40

60

80

100

Vaccinium uliginosum

Initia

l M

ass R

em

ain

ing (

%)

20

40

60

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g (

%)

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Initia

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Rubus chamaemorus

Year

2003 2004 2005 2006 2007 2008

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Control

Fertilized

Year

2003 2004 2005 2006 2007 2008

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Figure B-1. Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species. Bags were incubated over five years in a common site (moist acidic tussock tundra). (Mean ± SE; n = 6)

120

Year

2003 2004 2005 2006 2007 2008

Initia

l C

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g (

%)

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Eriophorum vaginatumInitia

l M

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(%

)

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40

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g (

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Initia

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Carex bigelowii

Initia

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(%

)

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Control

Fertilized

Initia

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g (

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Initia

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g (

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Rhododendron subarcticum

Year

2003 2004 2005 2006 2007 2008

Initia

l M

ass R

em

ain

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(%

)

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40

60

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100

Year

2003 2004 2005 2006 2007 2008

Initia

l N

Re

ma

inin

g (

%)

20

40

60

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Figure B-2. Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species. Bags were incubated over five years in a common site (moist acidic tussock tundra). (Mean ±SE; n = 6)

121

Figure B-3. Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species. Bags were incubated over five years in a common site (moist acidic tussock tundra). (Mean ± SE; n = 6)

Historic Site

Betula nana

Initia

l M

ass R

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%)

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40

60

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%)

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Initia

l C

Rem

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%)

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Vaccinium vitis-idaea

Initia

l M

ass R

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%)

20

40

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100Control

Fertilized

Initia

l N

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%)

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LTER Site

Betula nana

Year

2003 2004 2005 2006 2007 2008

Initia

l M

ass R

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Year

2003 2004 2005 2006 2007 2008

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2003 2004 2005 2006 2007 2008

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122

Figure B-4. Initial mass, C, and N remaining from litter bags that contained either natural or fertilized plant species. Bags were incubated over five years in a common site (moist acidic tussock tundra). (Mean ±SE; n = 6)

Year

2003 2004 2005 2006 2007 2008

Initia

l N

Rem

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(%

)

0

20

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60

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100

Mosses

Year

2003 2004 2005 2006 2007 2008

Initia

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(%

)

20

40

60

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Aultur Ctrl

Hyloc Ctrl

Sphag Ctrl

Dicr Fert

GPS Fert

Year

2003 2004 2005 2006 2007 2008

Initia

l C

Rem

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(%

)

20

40

60

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123

Figure B-5. Leaf litter decay constants (k) vs. leaf percent C, cell soluble, and cellulose for 11 vascular plant species collected across nine sites and decomposed for three to five years in the same common garden (n = 3-6). Percent C includes three moss species collected at one site and incubated for five years in the same common garden (n = 5-6). (Mean ±SE)

Carbon (%)

38 40 42 44 46 48 50 52

Deca

y C

on

sta

nt (k

, 1

/yr)

0.0

0.1

0.2

0.3

0.4

0.5

Cell solubles (%)

30 40 50 60 70 80

Deca

y C

on

sta

nt (k

, 1

/yr)

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Cellulose (%)

5 10 15 20 25 30

Deca

y C

on

sta

nt (k

, 1

/yr)

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

p = 0.03, r2 = 0.19

p = 0.03, r2 = 0.19

p = 0.04, r2 = 0.17

124

Figure B-6. Stem litter decay constants (k) vs. percent cellulose for four deciduous shrub species incubated for three years in a common garden. (Mean ± SE; n = 3)

Cellulose (%)

18 20 22 24 26 28 30 32

De

ca

y C

on

sta

nt

(k,

1/y

r)

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

p = 0.01, r2 = 0.55

125

Table B-1. Soil temperature at 5 cm during the growing season (62 days) and winter (272 days) for the three year period our litter decomposition bags were incubated. (Mean ±SE; n = 3-4)

Growing Season Winter

Vegetation Type

Treatment 2006 2007 2008 2006-2007 2007-2008 2008-2009

Low Ambient 5.0 (0.50)

6.6 (0.50)

2.7 (0.44)

-7.4 (0.68) -8.6 (0.36) -3.9 (0.43)

Snow Addition

5.0 (0.43)

6.6 (0.89)

3.6 (0.36)

-4.7 (0.36) -4.9 (0.06) -2.8 (0.15)

Medium Ambient 6.0 (0.86)

6.6 (1.45)

4.6 (1.83)

-3.8 (0.57) -3.6 (0.53) -2.7 (0.04)

Snow Addition

7.0 (0.63)

7.2 (0.68)

5.9 (1.77)

-2.7 (0.72) -3.4 (0.10) -1.7 (0.05)

High Ambient 5.9 (0.36)

7.9 (0.91)

5.7 (0.58)

-3.6 (0.58) -5.8 (0.81) -2.4 (0.80)

Snow Addition

7.8 (0.31)

8.7 (0.62)

6.7 (0.34)

-2.9 (0.82) -4.8 (0.08) -0.8 (0.54)

126

Table B-2. Three-way repeated measures ANOVA comparing differences in soil temperature between vegetation type, treatment, and year.

Growing Season Winter

Source df F Prob df F Prob

Vegetation Type 2 4.96 0.02 2 12.66 <0.01

Treatment 1 1.97 0.18 1 6.23 <0.01

Year 2 38.30 <0.0001

2 61.18 <0.001

Vegetation Type X Treatment 2 0.21 0.82 2 0.71 0.52

Vegetation Type X Year 4 2.17 0.10 4 6.08 <0.01

Treatment X Year 2 0.81 0.47 2 1.01 0.42

Vegetation Type X Treatment X Year 4 0.34 0.85 4 2.32 0.12

127

Table B-3. Initial litter quality for senesced leaves and stems of Betula nana and Salix pulchra collected in each of our three plant communities and incubated at a common site. Different letters within the same foliar trait and part indicate a significant result at the p < 0.05 level and are post-hoc results after running two-way ANOVA’s comparing each variable across sites and between species, including site x species interactions. (Mean ± SE; n = 6 for leaves, n = 3 for stems).

Site

Low Medium High Foliar traits Betula Salix Betula Salix Betula Salix

N (%) Leaves 1.16ab (0.02) 0.92b (0.03) 1.14ab (0.07) 0.93ab (0.06) 0.93b (0.09) 1.27a (0.16) Stems 0.90 (0.02) 0.90 (0.21) 1.06 (0.10) 0.95 (0.16) 1.37 (0.19) 1.01 (0.16) C (%) Leaves 49.34a (0.11) 48.96b (0.12) 49.42a (0.39) 47.04b (1.28) 49.25a (0.22) 48.96b (0.16) Stems 53.70a (1.41) 49.32bc (0.89) 52.81ab (0.95) 49.32c (0.42) 51.20abc (0.59) 48.00c (0.38) C:N Leaves 42.70c (0.92) 53.47ab (1.49) 44.68bc (3.61) 51.43bc

(4.42) 55.46a (5.46) 41.89c (5.29)

Stems 59.49 (0.48) 61.07 (13.26) 50.81 (4.61) 54.82 (9.17) 39.49 (4.98) 50.00 (7.51) Cell Solubles (%) Leaves 65.72a (1.51) 70.65a (1.25) 65.21ab (1.33) 66.44a (1.98) 63.88ab (0.70) 58.47b (2.94) Stems 23.13 (3.32) 21.96 (2.51) 27.04 (2.76) 27.14 (1.00) 20.81 (2.79) 22.28 (0.88) Hemicellulose (%) Leaves 11.52a (0.60) 9.32bc (0.26) 10.94ab (0.70) 10.35abc

(1.37) 10.92abc (0.25) 8.87c (0.39)

Stems 12.91ab (1.71)

11.37ab (1.47) 11.52ab (0.67) 11.31b (0.76) 14.34a (0.67) 12.49ab (0.85)

Cellulose (%) Leaves 8.27b (0.72) 8.22b (0.58) 8.17b (0.51) 9.91ab (0.98) 9.25ab (0.14) 11.57a (0.99) Stems 22.54ab

(3.04) 28.90a (1.43) 20.48b (1.07) 26.70ab

(0.24) 24.57ab (2.16) 30.15a (0.75)

Lignin (%) Leaves 14.40b (0.57) 11.67b (0.79) 15.56b (0.90) 13.18b (0.69) 15.88b (0.86) 21.08a (2.12) Stems 41.03a (1.58) 37.50ab (1.17) 40.83a (1.81) 34.35a (1.73) 40.15a (1.27) 34.80b (2.01) Lignin:N Leaves 12.51 (0.74) 12.72 (0.89) 14.31 (1.91) 14.20 (0.21) 17.95 (2.12) 16.95 (0.65) Stems 45.44 (0.92) 46.02 (9.14) 39.00 (2.20) 38.26 (7.14) 31.21 (4.75) 36.01 (4.96) IMR (%) after 3 yrs

Leaves 36.92 (2.55) 36.18 (6.57) 47.37 (2.58) 45.11 (7.35) 44.47 (3.91) 49.39 (4.99)

Stems 82.09 (4.44) 77.41 (3.94) 85.04 (0.79) 79.15 (2.65) 81.11 (2.33) 80.62 (1.38) K (1/yr) Leaves 0.33(0.02) 0.36 (0.06) 0.25 (0.02) 0.29 (0.07) 0.27 (0.03) 0.24 (0.03) Stems 0.07 (0.02) 0.08 (0.02) 0.05 (0.003) 0.08 (0.01) 0.07 (0.01) 0.07 (0.004)

128

Table B-4. Results from two-way ANOVA’s comparing leaves and stems of Betula nana and Salix pulchra collected in each of our three plant communities and incubated at a common site.

ANOVA Results

Foliar traits Plant part Source df F-stat p-value

N (%) Leaves Site 2 0.5 0.6

Species 1 0.1 0.7

Site x Species 2 5.5 < 0.01

Stems Site 2 1.8 0.2.

Species 1 2.0 0.2

Site x Species 2 0.5 0.6

C (%) Leaves Site 2 0.4 0.7

Species 1 8.7 < 0.01

Site x Species 2 1.2 0.3

Stems Site 2 3.1 0.08

Species 1 42.0 < 0.0001

Site x Species 2 0.4 0.7

C:N Leaves Site 2 0.7 0.5

Species 1 0.1 0.8

Site x Species 2 5.4 0.01

Stems Site 2 2.0 0.2

Species 1 1.0 0.3

Site x Species 2 0.2 0.9

Cell soluble (%) Leaves Site 2 10.2 < 0.001

Species 1 0.1 0.7

Site x Species 2 5.5 < 0.01

Stems Site 2 1.0 0.4

Species 1 0.3 0.6

Site x Species 2 0.6 0.6

Hemicellulose (%) Leaves Site 2 0.6 0.5

Species 1 22.6 < 0.0001

Site x Species 2 0.5 0.6

Stems Site 2 4.3 0.04

129

Species 1 4.6 0.06

Site x Species 2 0.01 1.0

Cellulose (%) Leaves Site 2 5.6 < 0.01

Species 1 2.5 0.1

Site x Species 2 1.5 0.2

Stems Site 2 2.7 0.1

Species 1 22.7 < 0.001

Site x Species 2 0.1 0.9

Lignin (%) Leaves Site 2 12.2 0.0001

Species 1 0.2 0.6

Site x Species 2 7.0 < 0.01

Stems Site 2 1.6 0.2

Species 1 5.8 0.03

Site x Species 2 0.9 0.4

Lignin:N Leaves Site 2 7.2 < 0.01

Species 1 0.4 0.5

Site x Species 2 0.2 0.8

Stems Site 2 2.9 0.1

Species 1 1.1 0.3

Site x Species 2 0.2 0.8

K (1/yr) Leaves Site 2 2.5 0.1

Species 1 0.1 0.7

Site x Species 2 0.5 0.6

Stems Site 2 0.5 0.6

Species 1 2.8 0.1

Site x Species 2 0.2 0.8

130

Table B-5. Initial litter quality for senesced leaves of seven species of vascular plants and three moss species collected from control and fertilized plots in a moist acidic tundra community and incubated in a common site. Different letters within the same foliar trait indicate a significant result at the p < 0.05 level and are post-hoc results after running two-way ANOVA’s comparing each variable across species and treatment. (Mean ± SE, n = 6 for each species).

Species N (%) C (%) C:N Cell soluble

(%) Hemicellulose

(%) Cellulose (%) Lignin (%) Lignin:N

Graminoids Carex big. C 1.34

efg (0.04) 42.39

def (0.20) 31.91

cd (1.14) 41.87

d (2.61) 28.39

b (1.25) 24.47

ab (0.90) 4.81

e (0.93) 3.60

g (0.64)

F 1.71cd

(0.05) 42.33ef (0.35) 24.80

def (0.70) 38.43

d (1.46) 32.65

f (1.61) 23.43

ab (0.92) 5.02

e (0.78) 2.94

g (0.47)

Eriophorum vag. C 0.87h (0.04) 42.15

ef (0.13) 8.90

ab (2.11) 35.79

d (2.38) 30.82

ab (0.66) 27.16

a (0.46) 4.30

e (0.37) 4.91

efg (0.43)

F 1.27fg

(0.09) 41.94fg

(0.06) 34.07c (2.87) 41.24

d (2.59) 28.19

b (1.24) 21.63

b (1.49) 4.48

e (0.82) 3.63

g (0.67)

Deciduous Shrub Betula nana C 1.67

de (0.06) 44.59

bc (0.15) 26.94

cde (0.93) 62.76

bc (0.75) 10.15

fg (0.47) 7.39

g (0.47) 19.44

a (0.31) 11.72

bc (0.29)

F 2.41a (0.11) 43.33

de (0.09) 18.17

f (0.89) 60.86

bc (0.74) 12.71

ef (0.39) 7.94

fg (0.19) 18.17

a (0.70) 7.63

def (0.55)

Rubus cham. C 1.90bc

(0.04) 40.53h (0.57) 21.32

ef (0.38) 64.98

ab (1.54) 17.87

c (1.12) 9.82

efg (0.58) 6.78

de (0.57) 3.54

g (0.22)

F 2.13ab

(0.09) 40.77gh

(0.20) 19.35f (0.89) 67.94

ab (2.26) 16.72

cd (1.51) 9.34

efg (0.67) 5.38

de (0.71) 2.55

g (0.34)

Vaccinium uli. C 1.74cd

(0.05) 43.63cd

(0.10) 25.15def

(0.79) 69.71a (0.11) 9.49

g (0.23) 10.52

cdefg (0.09) 8.35

d (0.36) 4.83

efg (0.33)

F 2.43ab

42.04defg

17.29f 67.11

a 13.89de 10.23

defg 7.31d 3.01

fg

Evergreen Shrub Ledum dec. C 1.03

gh (0.03) 47.83

a (0.30) 46.64

b (1.25) 63.29

abc (0.99) 8.14

g (0.41) 12.14

cdef (0.30) 16.08

b (0.36) 15.66

a (0.38)

F 1.57def

(0.05) 46.98a (0.13) 30.16

cd (1.10) 61.82

abc (1.35) 10.31

fg (0.26) 11.13

cde (0.45) 15.99

b (1.19) 10.2

bcd (0.63)

Vaccinium vit. C 0.81h

(0.03) 45.15b (0.16) 56.05

a (2.31) 60.93

c (0.75) 13.29

e (0.85) 13.22

cd (0.27) 11.19

c (1.45) 14.09

ab (2.26)

F 1.43def

(0.08) 45.23b (0.24) 32.22

cd (2.26) 59.31

c (1.42) 13.68

de (0.98) 13.97

c (0.72) 11.72

c (1.01) 8.43

cde (1.15)

131

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BIOGRAPHICAL SKETCH

Jennie DeMarco lived in Las Vegas, Nevada until she graduated from Western

High School in 1996. After high school, Jennie spent time traveling around the

southwest region of the United States, interior Alaska, and Montana. During this time

she was able to experience the diverse ecosystems within these regions and wanted to

learn more about them. She enrolled in an undergraduate environmental science

program at Northern Arizona University, and in 2002 she earned a Bachelors of Science

degree in environmental science, with an emphasis in biology, from NAU. Jennie’s

interests in ecosystem ecology began while she was an undergraduate. During this

time she was introduced to concepts that were essential to understanding not only how

these ecosystems function but how humans are altering these functions. While an

undergraduate, she had the unique opportunity to work directly with a graduate student,

Aimee Classen, on her research project. Aimee and her advisors, Dr. Steve Hart and

Dr. George Koch, introduced her to the concepts of ecosystem ecology and provided

her the opportunity to gain field and laboratory experience needed to address ecological

questions at the ecosystem scale. After completing her undergraduate degree, she

worked under the direction of Dr. Michelle Mack at the University of Florida as a

research and field technician. Her interests in the field of ecosystem ecology grew and

in 2006 she began a graduate degree with Dr. Mack as her advisor. She chose to

conduct her research in the arctic of Alaska because of the uniqueness of the

ecosystems present there and their high vulnerability to climate change.