Alpine Plant Communities of the White Mountains of New Hampshire

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2019 Alpine Plant Communities of the White Mountains of New Hampshire: Aboveground Plant Diversity and Abundance Correlated to Belowground Factors Timothy Maddalena-Lucey Antioch University of New England

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Department of Environmental Studies

THESIS COMMITTEE PAGE

The undersigned have examined the thesis entitled:

Alpine Plant Communities of the White Mountains of New Hampshire: Aboveground Plant Diversity and Abundance Correlated to Belowground Factors

Presented by: Timothy Maddalena-Lucey Candidate for the degree of Master of Science and hereby certify that it is accepted.*

Committee chair name: Peter Palmiotto, D.F. Title/Affiliation: DF Professor, MS Program Director, Academic Adviser

Committee member name: Charles Cogbill, Ph.D. Title/Affiliation: Harvard Forest Associate

Date Approved by all committee members:

*Signatures are on file with the Registrar’s Office at Antioch University New England.

Alpine Plant Communities of the White Mountains of New Hampshire: Aboveground Plant Diversity and Abundance Correlated to Belowground Factors

A Thesis Presented to the Department of Environmental Studies Antioch University New England

In Partial Fulfillment of the Requirements for the Degree of Master of Science

By Timothy Maddalena-Lucey April 2019 Dedication

In loving memory of Linda Ann Maddalena-Lucey A creative inspiration and caring soul

Acknowledgments

I would like to thank Dr. Peter Palmiotto and Dr. Charles V. Cogbill for their support and advice throughout the process of this thesis. Without the foundational work of Dr. Charles V. Cogbill in

1993 I would have had little framework for a repeatable method to base this study on. Without the guidance and encouragement of Peter Palmiotto this study would not have been possible. I would also like to thank Dr. Lisabeth Willey for the statistical support needed to complete as well as Dr. Rachel Thiet for soil science support. I would like to thank Dr. David Peart of the

Moosilauke Advisory Committee and Dr. Daniel Sperduto, Erica Roberts, and Robert A. Colter of White Mountain National Forest Research Division and Regional Forest Supervisor Clare

Mendelsohn for allowing me to conduct research on the summit of Mount Moosilauke.

I would also like to thank Dr. Laura Spence, David S. Gilligan, and Richard Smyth

Naturalist Emeritus for encouraging, guiding, and overseeing the work I conducted on

Moosilauke in 2015 as part of my undergraduate research capstone. Without their continued support and correspondence this thesis would not have been possible.

Organizations and institutions I have been in correspondence with regarding sponsorship, networking, and support include AMC research, Beyond Ktaadn, White Mountain National

Forest Research, Dartmouth College, The Waterman fund, UMASS Amherst research department and the Center for Circumpolar Studies. Notable field researchers, past and present, that were very helpful throughout the process of this study include: Dr. Lawrence C. Bliss, and

Dr. William Dwight Billings, Dr. Robert Capers, Dr. Steven Young, Dr. Michael T. Jones, and

Doug Weihrauch from the AMC. I would also like to thank my family for their constant support throughout this process.

Abstract

Alpine plant communities are fragile complex systems that may be threatened by climate change. Patterns of climate-driven soil warming are shown to have lasting effects on ecosystem nutrient cycles, alpine plant phenology, and belowground soil biotic activity. There are concerns in the alpine mycorrhizal research community as to how belowground fungal biomass will react to climate-driven soil warming. To address these concerns I asked in this study what are the correlations between belowground factors such as collective ectomycorrhizal (EcM) and ericoid mycorrhizal (ErM) percent colonization, pH, and soil depth to bedrock and aboveground plant diversity and richness. In 2018, on Mount Moosilauke, the southernmost peak in the White Mountain National Forest, I resampled 71m² plots along five transects initially established in 1993 and resampled in 2015. In each plot both vascular and non-vascular plant species richness, bare soil, exposed rock, and dead organic matter were assessed via percent cover and soil depth was measured. Soil was extracted (organic and mineral material) in nine plots per transect for pH testing and EcM/ErM analysis. The most significant increase in species richness, which was found in the Western fellfield community may indicate a shift in composition. The most significant negative relationship was between soil depth and species richness, and the most significant positive relationship was between soil pH and EcM/ErM colonization in the Southeast heath (T3) community. Soil pH, especially when associated with long-term acidification from atmospheric nitrogen deposition, can be a driver for reducing EcM/ErM diversity and richness in Northern temperate and boreal forest ecosystems. Given these relationships, one could assert that a combination of continued nitrogen deposition will drive the pH levels in alpine plant communities even lower and subsequently drive EcM/ErM diversity down leaving an opportunity for the more acidophilic mycorrhizae and their specialist conifer partners perhaps providing a mechanism for tree line encroachment into the alpine. The effects of climate warming and ongoing nitrogen deposition on plant productivity, phenology, and composition are of great concern to the alpine research community. More research is needed in these areas to help guide future conservation needs.

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TABLE OF CONTENTS

Page DEDICATION i ACKNOWLEDGEMENTS ii ABSTRACT iii

TABLE OF CONTENTS...... iv LIST OF TABLES v

LIST OF FIGURES vi INTRODUCTION 1 Abiotic Factors 4 Ectomycorrizae 6 Ericoid Mycorrhizae 8 METHODS 12 Study Site 12 Sampling Design 13 Sample Collection 14 Lab Methods 15 Data Analysis 18 RESULTS 35 Aboveground Change 36 Belowground 36 DISCUSSION 47 Aboveground Changes 47 Soil Depth and EcM/ErM 51 Soil pH 53 Future Changes and Conservation Initiatives 54 LITERATURE CITED 66 Appendix A 75 Appendix B 77 Appendix C 79

List of Tables

Table 1. Environmental data for 2018 survey. Includes transect length, quadrat interval, and soil quadrat placement 22

Table 2. Summary statistics for two aboveground variables (response) and three belowground variables (predictor) by five plant communities and in total for the full survey (n=71). The ‘Mean’ column reads as ‘ ± Std (‘n’) and the ‘Range’ column displays the minimum and maximum values of the corresponding dataset. Bolded values represent minimum and maximum values in their respective datasets. 23

Table 3. Paired t-test comparison of mean plant species richness and diversity from 2015 to 2018 by transect and for the full survey (n=71). Alpha was set to .05 and all statistically significant p- values were bolded. 39

Table 4. Comparison of mean variances of aboveground and belowground variables at the soil survey level (n=45) by community type/ transect (n=9). Bolded values represent the variance that stands out at least one power/decimal point higher or lower than all others. 41

Table 5. Single factor ANOVA table for EcM/ErM percent colonization (n=45) by transect/community (n=9). 42

Table 6. Results of a Shapiro-Wilk’s test of normality for all variables on both the full survey (n=71) and soil survey (n=45) level. Bolded p-values depict variables that fit the normality assumption. 43

Table 7. Mean species percent cover by transect/community for 2015 and 2018. The ‘X’ represents a mean percent cover of zero or less than .1%. Species are listed top to bottom by morphological class (i.e., Tree, shrub, graminoid, forb, bryophyte, and lichen). 43

Table 8. Results for species richness values in four categories: vascular plants, non-vascular bryophytes, lichenous vegetation, and all plants. Richness values were calculated using percent cover totals for each category through a “Welch Two-Sample” t-test. Values were compared for each of the five plant communities as well as all communities as a whole. Significant differences in richness (p <0.05) are indicated in bold (Maddalena-Lucey 2015). 59

Table 9. List of all species identified in the study from 1993 to 2018. Species are listed top to bottom by morphological class (i.e., Tree, shrub, graminoid, forb, bryophyte, and lichen). 75

List of Figures

Figure 1. Regional map of the Mount Moosilauke area including Dartmouth Outing Club property in purple and surrounding WMNF land outlined in red. The summit is marked with a red triangle, secondary roads in brown, and the DOC Ravine Lodge is marked in green. 24

Figure 2. Line diagram depicting the placement direction, and setup of transects used in the above ground vegetation survey. Dotted lines indicate the tree line boundary, dashed lines indicate the major trail intersections, arrows indicate the transects, the cross hatch symbol indicates the absolute summit, and grid squares represent quadrats. 25

Figure 3. Map of Mount Moosilauke alpine zone with transect and soil sample markers. Checkered flags represent the start and stop points and orange flags represent the soil sample quadrats. 26 Figure 4. Images of the five transects surveyed in 2018. Position is aligned with the azimuth of each start point. 27 Figure 5. Soil depth was measured by driving a 5/16 steel rod into the ground until bedrock was reached. The rod was then flagged at the surface and carefully removed to be measured to the nearest tenth of a cm with either the transect tape or tape measure. 28 Figure 6. Soil subsample taken with Oakfield soil probe. The majority of soils had a profile most akin to either histosols or spodosols. 28

Figure 7. Depiction of the rejection criteria when taking soil samples in a quadrat. Starting at 1 and working clockwise than to center. From center, worked clockwise along outside from step 6 until step 9. If no soil is present by step 9, rejected quadrat and moved on. 29 Figure 8. Mineral soils from nine soil quadrats being placed in the drying oven prior to pH analysis. 29

Figure 9. Subsample of fine root material being rinsed in tepid water for one minute prior to extraction for EMF/Ericoid percent colonization analysis. 30

Figure 10. Measuring and placement of freshly rinsed fine root subsamples for EMF/Ericoid mycorrhizal analysis. 30

Figure 11. Side-by-side comparison of EMF colonized root of Abies balsmea (right) and Ericoid mycorrhizal colonized root of Vaccinium vitis-idaea (left). 31

Figure 12. Dried mineral soil subsamples being cleaned through a 1mm sieve prior to weighing for pH analysis. 32

Figure 13. Mineral soil subsamples being mixed for pH analysis while meter is calibrated for 0-7 range. 32

Figure 14. Scatterplot matrix of all variables at the soil survey scale (n=45) with trend lines highlighting the strongest visual relationships between species richness, soil depth, and EcM/ErM percent colonization. These trends were later assessed with a non-parametric linear regression. 33

Figure 15. Boxplots of the five variables (n=45) in best transformations for linear regression analysis. 33

Figure 16. Boxplots showing the distribution of species diversity, species richness, and soil depth at the full survey (n=71) scale. Outliers in diversity and soil depth were later determined to be accurate and not influential (cook’s distance <.5) to the linear model. 34

Figure 17. Plot of a Pearson’s Product-Moment correlation of species richness from 2015 to 2018 (n=71). Gray margins along the trend line represent the 95% confidence interval. Points of the same value were ‘jittered’ for easier interpretation. ‘R’ = Correlation Coefficient. 44

Figure 18. Plot of a Pearson’s Product-Moment correlation of species diversity from 2015 to 2018 (n=71). Gray margins along the trend line represent the 95% confidence interval. Points of the same value were ‘jittered’ for easier interpretation. ‘R’ = Correlation Coefficient. 44

Figure 19. Plot representing the Spearman’s Rank Correlation between Species Richness and Soil Depth at the full survey scale (n=71). Gray margins along the regression line represent the 95% confidence interval and ‘R’ = rank correlation coefficient. 45

Figure 20. Plot representing the Spearman’s Rank Correlation between EcM/ErM percent colonization and soil depth at the soil survey scale (n=45). Gray margins along the regression line represent the 95% confidence interval and ‘R’ = rank correlation coefficient. 45

Figure 21. Plot representing the Spearman’s Rank Correlation between EcM/ErM percent colonization and species richness in the East peak (T4) community (n=9). Gray margins along the regression line represent the 95% confidence interval and ‘R’ = rank correlation coefficient. 46

Figure 22. Plot representing the Spearman’s Rank Correlation between EcM/ErM percent colonization and soil pH in the Southeast heath (T3) community (n=9). Gray margins along the regression line represent the 95% confidence interval and ‘R’ = rank correlation coefficient. 46

Figure 23. Comparison of mean percent cover of Empetrum nigrum from 2015 to 2018 for the full survey (n=71). 2015 mean cover was .03 % ± .02 SE, 2018 mean cover was .5 % ± .4 SE with a mean increase of .5% ± .4 SE. Results of a paired t-test (p > .05) as well as Pearson’s product correlation (p < .05) show this is not a significant difference. 60

Figure 24. Comparison of mean percent cover of Dead Organic Matter (DOM) from 2015 to 2018 in the East peak (T4) community. 2015 mean percent cover was 3.6% ± 2.2SE, 2018 mean

vii percent cover was 17.7% ± 4.96 SE with a mean increase of 14.1 % ± 4.4 SE. Paired t-test results show this to be a significant increase ( t13= 4.3, p= .0008). 60

Figure 25. Comparison of mean percent cover of all vascular plants from 2015 to 2018 in the East peak (T4) community. 2015 mean percent cover was 117.6 % ± 7.3 SE, 2018 mean percent cover was 83.6 % ± 6.3 with a mean decrease of 34 % ± 9.7 SE. Paired t-test results show this to be a significant decrease (t13= -3.5, p= .004). 61

Figure 26. Comparison of mean percent cover of all graminoids and forbs (excluding ferns and lycopods) across all five plant communities from 1993 to 2018. Overall, total mean percent cover in 1993 was 6.4% ± 2.9 SE and total mean percent cover in 2018 was 12.1% ± 4.4 SE with a mean increase of 5.7% ± 1.9 SE. The three species that showed a decrease in mean cover were Betula cordifolia (-4.8% ± 1.6 SE), Solidago macrophylla (-.1% ± .08 SE), and Vaccinium uliginosum (-.8% ± 1 SE). The decrease in B.cordifolia was significant (p<.05). 62

Figure 27. Comparison of mean percent cover of vascular plants by species from 2015 to 2018 in the Western fellfield (T1) community. The highest mean increases were attributed to Carex bigelowii (16.8% ± 5.2 SE) and Juncus trifidus (12.3% ± 6.3 SE) respectively. 63

Figure 28. Comparison of mean percent cover of all vascular plants from 2015 to 2018 in the Western fellfield (T1) community. 2015 mean percent cover was 25 % ± 10.5 SE, 2018 mean percent cover was 32.7 % ± 11.9 SE with a mean increase of 7.7% ± 2.5 SE. Paired t-test results show this to be a significant increase (t16= 3.61, p= .002,Table 4, pg. 40). 63

Figure 29. Comparison of mean percent cover of *Cladonia arbuscula from 1993 to 2018 in the Southeast heath (T3) community (n=18). 1993 mean cover was 18.5% ± 2.4 SE, 2018 mean cover was 1.14% ± .7 SE with a mean decrease of 12% ± 2.4 SE. t-test results show this to be a significant decrease (p< .01). 64

Figure 30. Comparison of mean percent cover of Carex bigelowii and Juncus trifidus in 1993, 2015, and 2018 in the Western fellfield (T1) community. Carex bigelowii showed a mean increase of 17.9% ± 2 SE from 1993 to 2015 and an overall increase of 34.7% ± 4.3 SE from 1993 to 2018. Juncus trifidus showed a mean increase of 38.2% ± 3.6 SE from 1993 to 2015 and an overall increase of 50.7% ± 4.5 SE from 1993 to 2018. T-test results showed these increases to be highly significant (p>01). 64

Figure 31. Plot representing the Spearman’s Rank correlation between soil depth and vascular species richness (lichens and bryophytes removed) in the East peak (T4) community (n=14). Gray margins along the regression line represent the 95% confidence interval and ‘R’ = rank correlation coefficient. 65

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Introduction

Given the results of climatic data over the last few decades regarding the distribution, tree line migration, and phenological variability of plant communities in the alpine ecosystems of North

America (Spear 1989; Waser & Price 1998; Capers & Stone 2011), it’s quite evident that changes in Mean Annual Temperatures (MAT) and Mean Annual Precipitation (MAP) in alpine ecosystems may have dramatic effects on the current and future distribution and conservation status of circumpolar and circumboreal alpine plants (Thuiller et al. 2005). For the White

Mountain National Forest (WMNF) of New Hampshire, home to the largest expanse of alpine tundra in the Eastern United States (Willey & Jones 2012), these potential threats to plant diversity are of great concern.

Understanding the effects of climate-driven soil warming on belowground microbial and fungal communities and their subsequent effect on aboveground plant community dynamics is a popular topic of concern in the alpine research community (Fujimura et al 2008; Solley et al

2017). Some experimental studies on fungal response to climate-driven soil warming have indicated increases in fungal biomass and abundances along with subsequent effects on aboveground plant trait responses to warming (Bunn et al. 2009; Mohan et al. 2014). Solley et al

(2017) suggested that an increase in climate-driven soil warming in nitrogen deficient alpine plant communities may enhance the proliferation of mycorrhizae that specialize in nitrogen mineralization causing a shift in fungal composition that favors these taxa with potential ecosystem level feedbacks on nitrogen cycling and carbon storage. In order to fully understand these potential climatic repercussions and what it means for the alpine plant communities of

WMNF and Mount Moosilauke, one must first understand the relationships between soil biotic and abiotic factors and aboveground plant richness.

Building on the research of Cogbill (1993), Capers & Stone (2011), and my own undergraduate research in alpine plant community ecology (Maddalena-Lucey 2015, henceforth

“the 2015 survey”) this research focuses on the aboveground below ground relationships on

Mount Moosilauke. The need to better understand the changes in relationships between aboveground diversity and richness and belowground variables, both biotic and abiotic, is pivotal to the future of alpine plant conservation research. To determine these changes and relationships the research questions I addressed were: Were there any short-term and significant changes in alpine plant community richness (number of species) and diversity (Simpson’s Diversity Index) from 2015 to 2018? What is the correlation between belowground factors such as collective ectomycorrhizal (EcM) and ericoid mycorrhizal (ErM) percent colonization, pH, and soil depth to bedrock and aboveground plant diversity and richness? I addressed these relationships in five plant communities representative of the alpine zone on Mount Moosilauke in Benton, NH. If these relations existed I asked at what intensity? (estimated r²) and how do they contribute to the ecological functions of those alpine plant communities?

Potential climate-driven threats to alpine plant communities were the catalyst for a 2011 symposium that focused on the need for long-term consistent baseline data on alpine plant assemblages in the Greater Northern Appalachian Bioregion (GNAB). At this symposium priorities and protocols were set in place to coordinate, in a multi-disciplinary manner, research to support the identified need (Capers et al. 2013). It has been noted that in the past, alpine research in the GNAB region has been poorly coordinated and scattered across fields of study with little if any priorities established (Capers et al. 2013). However, at the 2011 symposium and workshop series, 39 alpine researchers representing various fields of study convened to discuss and establish a list of important research areas in which to focus (Capers et al. 2013).

Of those prioritized areas, snowbed ecology, tree line ecology, and phenological variabilities were among the top listed. Results from across the amphi-Atlantic1 region have shown that rising tree line migration, higher species richness in the alpine-tree line ecotone, changes in phenology, and higher abundance of shrub species were apparent (Kullman 2002;

Pauli et al. 2012). These shifts in tree line, phenology, and composition are accentuated in alpine ecosystems as the biophysical boundaries governed by insular biogeography have further isolated populations in these alpine communities (Walker et al. 2001). Evidence of homogenization of plant communities (Robinson et al. 2010) along with an increase in frequency and abundance of woody plants in summit communities (Capers & Stone 2011) has already been reported in New York and Maine respectively.

In addition to the top three areas of research need mentioned above, there was also a call to resample surveys done historically in alpine sites for a more comprehensive and quantitative assessment of changes in diversity and richness over time. Although there have been other long- term and cross-historical monitoring studies and analyses conducted over the last few decades in the WMNF region, there is cause for concern over the lack of publication and open-source sharing of the results in the alpine research community (Capers et al. 2013). In order to progress as a functional multi-disciplinary proactive conservation-driven entity, the alpine research community in the WMNF as well as GNAB as a whole must strive to standardize research protocols, goals, and ultimately decision-making influence to foster real change in an uncertain future. The following review describes the context and relevance of the aforementioned abiotic and biotic belowground factors as well as their importance in alpine plant community ecology.

1 ‘Amphi-Atlantic’ distribution implies that a species can be found on both sides of the Atlantic Ocean but is absent from the Pacific side of the Northern hemisphere (Hultén & Fries 1986). 3

Abiotic Factors

The Northern Appalachian Mountains, which include the White Mountain range, originated during the late Ordovician period approximately 440 million years ago and the majority of bedrocks have been assigned to the Silurian Rangley, Perry Mountain, Smalls Falls, Madrid, and

Devonian Littleton formations (Eusden et.al 1996; Siever & Press 1997). The bedrock of the

White Mountains ranges from low-grade mica schist to high grade gneiss with small intrusions of quartzite and pegmatite (Bliss 1963). On Mount Moosilauke, the majority of bedrocks are gneiss and mica schists of the Littleton formation (Cronan 1980). The mineralogy of these metamorphic basement rocks leads to slow weathering rates, a lack of mineral richness, and shallow acidic soils (Siever & Press 1997). Acidic substrates, which are common in alpine systems, create a specialized niche for acid-loving or “acidophilic” plants, among which are a number of Ericaceous shrubs such as Vaccinium uliginosum L. and Rhododendron groenlandicum Oeder.

‘Edaphology’, a term often associated with the soil sciences is used to describe the influences of soils on biota (often plants) and how soils relate to certain assemblages or communities (Bruce 1939). Historically, edaphic factors were considered to be one of three main environmental factors used in determining the ecology of plant communities wherein soil type, topography, drainage, water content, hygroscopicity, porosity, temperature, and solutes were all aspects of the edaphic nature of a soil (Bruce 1939). As soil science has developed, the term has been replaced by or rather incorporated into the “CLORPT” formula to be used as a framework for studying the interactions between soils and the many factors that affect them (Jenny 1941;

Kent 1996). “CLORPT” is an acronym and formula coined in 1941 by pedologist Hans Jenny and used to described the fundamental factors of climate, organisms, relief (topography), parent

4 material, and time and their subsequent effect on the structural, biological, and ecological properties of healthy soils (Jenny 1941).

Edaphic variables in alpine tundra ecosystems tend to be related to exposure of substrates to topographic and climatic variables, and the passage of time in recently deglaciated terrains

(Matthews 1992). The interaction of these variables will result in changes in the distribution and abundance of plant species assemblages. Historic and recent climate change may also contribute to the edaphic drivers of plant distribution and abundance (Takahashi & Murayama 2014). In the

White Mountains, edaphic variables along with climatic variables are often the limiting factors that determine tree line and thus mark the physical boundaries between forested systems and tundra systems (Zwinger & Willard 1972).

Soils in the White Mountains are mainly developed from late-glacial frost wedging and surface weathering of silica-rich rock (Spear et al. 1994), and are therefore typically sandy and nutrient poor. These foundational soils known as “Lithosols” form in thin rocky substrates often devoid of any horizonation under snowbanks and outcrops (Johnson & Billings 1962).

“Podzolization” is a reference to the soil order “Spodosols,” which define the process in which mineral base cations are “leached” from the A horizon by eluviation leaving behind a distinct characteristic grey “E” horizon ( Little et al. 1990; Bockheim 2015). There are two dominant types of alpine soils along the ridges and cirques of the White Mountains. Alpine bog soils have an A-B-C profile wherein the A horizon is organic, the B horizon is mineral soil, and the C horizon is composed of glacial till or bedrock parent material (Bliss 1963). These soils are mesic, the surface of which is rich in humus which is mostly composed of Sphagnum peat. The other type of alpine soil is alpine turf soil, a type of “Histosol” which also holds the majority of the organic content in the A horizon (Bliss 1963; Reiners & Lang 1979; Huntington et al. 1990). In a

5 recent study of the pedogenic processes of alpine soils along the Munroe Flats in the Presidential range, it was noted that although post-glacial bedrock weathering was a component of the process, the majority of pedogenesis occurs in a homogeneous ‘mantle’ of glacial till deposited in the late Wisconsin glaciation (Schide & Munroe 2015). To assist in the development of these organically rich soils and the capture and mineralization of nutrients, plants will often form fungal partnerships with mycorrhizae (Allen 1991).

Ectomycorrhizae

By today’s standards, mycorrhizae have been classified into seven major groups: ecto- (EcM), arbuscular (AMF), ericoid (ErM), ectendo-, arbutoid, monotropoid, and orchid mycorrhizae

(Smith & Read 2008). Some recent estimates suggest that there are approximately 50,000 species of fungal species that form mycorrhizal associations with approximately 250,000 plants worldwide (Heijden et al. 2015). Mycorrhizae tend to share phenological cycles with the host plant (Allen 1991) and ectomycorrhizae (EcM) have often been sampled for study in mid to late summer between June and August (Lilleskov et al. 2001; Langley et al. 2003).

Initial EcM infection of plant roots begins with spores or hyphae of the fungal symbiont found in the soil around the feeder roots or ‘rhizosphere’. Growth of these fungi is stimulated by exudates from the roots. Fungal mycelia, strands of hyphae bound together, grow over the feeder root surfaces and form an external mantle, eventually growing intercellularly (between the cells) forming what is called a ‘Hartig net’ (Allen 1991), a network of hyphae around the roots and cortical cells. The fungi do not enter the host plant cells, but rather grow around them. The

Hartig net may completely replace the tissue between cortical cells, but does not go deeper than the cortex or actively dividing meristematic cells at the root tip. The Hartig net, though not exclusive to it, is the major distinguishing feature of EcM (Allen 1992). Hyphae that form on

6 short roots often radiate from the mantle into the soil, greatly increasing the absorbing potential of the roots (Ruehle & Marx 1979).

Mycorrhizae can increase absorption of nutrients such as nitrogen, phosphorous, potassium, and calcium passing these on to the host plant and greatly aiding those plants growing in infertile, harsh, or adverse sites (Pope 1993). This unique ability to aid in nutrient retention is important in boreal and montane ecosystems where nutrient availability is a limiting factor due to the young age of soils, which is relevant to the alpine soils of the White Mountains given their sandy loam texture and poor nutrient retention (Bliss 1963). Additionally, mycorrhizae and other soil fungi play a key role in carbon sequestration and respiration in boreal forest ecosystems

(Clemmensen et al. 2013). Clemmensen et al. (2013) indicated that for the majority of soil organic horizons sampled in 30 forested lake islands in three size classes (10 Small (<.1ha), 10

Medium (.1 to 1.0ha), and 10 Large (>1.0ha)) in Northern Sweden, the depth at which root- associated and mycorrhizal fungi dominated the soil profile showed the most difference in carbon sequestration, thus accentuating the role these organisms play in soil organic matter dynamics.

Ectomycorrhizae are formed by fungi belonging to the divisions Basidiomycetes

(mushrooms and puffballs) and Ascomycetes (cup fungi and truffles). These divisions form the subkingdom Dikarya, often referred to as the “higher fungi” (Pope 1993). These are the most advanced group of true fungi that have co-evolved with plants on land and utilize a diet of complex organic substances (Mukerji 2012). One study suggests that a high percentage of non- arbuscular mycorrhizal (including EcM and ErM) richness exists in the alpine as compared to arbuscular mycorrhizal (AMF) richness (Becklin et al. 2012). These results are intriguing because a large portion of true alpine plant communities are composed of one or more species of

7 grass, sedge, or rush which tend to form AMF associations (Willey & Jones 2012). Additionally,

Bunn et al. (2009) conducted an experimental study measuring the effects of increased air and soil temperatures on AMF function in grassland soils and found that a significant increase in both internal and external fungal biomass in response to soil warming could be a driver of plant resilience (increased fitness) to climate-driven soil warming. Mohan et al. (2014) suggest that over 60% of studies conducted on the capacity of mycorrhizae to mediate plant response to warming showed an increase in mycorrhizal abundance along with a decrease in mycorrhizal activity. There was little literature specifically on EcM and it’s responses to soil warming in alpine ecosystems.

Ericoid Mycorrhizae

Ericoid mycorrhizae (ErM) are formed by fungi belonging to the Ascomycetes and occasionally

Basidiomycetes (Heijden et al. 2015), specifically with members of the plant family Ericaceae

(Crayn & Quinn 2000) and thus considered to be a more advanced and specialized group of mycorrhizae (Allen 1991). Ericoid mycorrhizae are a type of epidermal ‘ecto-related’ mycorrhizal fungi that colonize ericaceous plants via the epidermal cells of distal roots (Smith &

Read 2008). Although the major distinguishing factor for identifying ErM is the presence of fine hyphal coils in the cortical cells (Vohnik & Albrechtova 2011), there are also epidermal hyphal structures present in the rhizosphere that indicate ErM colonization.

Ericoid mycorrhizae have often been associated with plant assemblages found in nutrient and organic matter poor substrates such as those of sand dune and chaparral habitats (Cowles

1901, E. B. Allen & Allen, 1990). Additionally, ErM have been noted to predominate in alpine tundra ecosystems forming close associations with plants in the Erica and Vaccinium genera alongside EcM colonized plants in the Salix and Betula genera (Allen 1991). One study of ErM

8 in European species of the Rhodendron genus determined that of the six species tested, root hair samples showed the presence of ErM and dark septate endophytes (DSE)2 and lack of EcM and

AM associations (Vohnik & Albrechtova 2011).

In other harsh and nutrient poor habitats like moorlands, some species in the Calluna genus have become completely dependent upon the hyphal networks of their ErM partners for the uptake of nitrogen (Read & Leake 1983). Additionally, one study suggested that evidence from study site environments contaminated by heavy metals and other pollutants showed a clear presence of ErM and associated DSE signifying potential resistance to such harsh conditions

(Cairney & Meharg 2003). Additionally, the review paper by Cairney and Meharg (2003) cited numerous studies suggesting that in order to break down and absorb phosphorous and nitrogen proteins (including chitin) from the more complex and floristically inaccessible organic compounds like phospho- monoesters and diesters found in harsh and nutrient poor conditions,

ErM use a specialized suite of enzymes including protease, chitinase, and phosphatase, carbohydrolase, and other hydrolyzers and phenol-oxidizers. Some of these enzymes specialize in low pH environments and have been shown to have resistance to heavy metal ions such as aluminum (Al3+) and iron (Fe2+) (Cairney & Meharg 2003). The alpine plant communities of the

WMNF, which tend to be harsh, nutrient poor, and acidic (Bliss 1963; Spear et al. 1994; Kimball

& Weihrauch 2000), may fit the criteria for the presence of these specialist ErM as well as EcM that form plant associations in harsh and nutrient poor conditions (Allen 1991; Pope 1993;

Bardgett & Wardle 2011).

2 With a characteristic network of melanized hyphae, dark septate endophytes (DSE) are a type of sterile fungi that form a sort of commensalism with the host plant and are likely associated with the Ascomycetes occurring in a range of habitats from tropic to alpine (Jumpponen & Trappe 1998).

Global, regional, or local studies of abiotic and biotic interactions of aboveground and belowground associations in alpine ecosystems are sparse. There is a critical need for more research regarding these relationships (Sutherland et al. 2013). Additionally, the literature shows a significant gap specifically in research on EcM communities in alpine ecosystems (Ryberg et al. 2009) and local research in these two areas of focus, particularly in the White Mountains of

New Hampshire, is very limited. Belowground factors play an important role in aboveground ecosystem functions and the impacts of climate-driven and anthropogenic changes such as soil warming, compaction, and various nutrient depositions (Lilleskov et al. 2001) can be better understood through alpine plant community ecology.

Few examine the changes in soil microbial communities and their driving environmental factors along an elevational gradient (Siles & Margesin 2016; Kernaghan & Harper 2001). In a study of relationships between soil bacterial and fungal diversity, richness, and abundance and elevation in the Italian Alps, Siles & Margesin (2016) noted that the increased abundance of soil bacteria and fungi at higher elevations showed a significant positive relationship with high amounts of carbon and other mineral nutrients associated with large amounts of soil organic matter (hereafter ‘SOM’) in substrates. Additionally, Siles & Margesin (2016) noted that although soil pH did not show a significant correlation with bacterial and fungal abundance, it appeared to be one of the driving factors in fungal richness and diversity along an elevational gradient. Given the confirmation of these aboveground-belowground relationships in alpine plant communities in other parts of the world, the intention of this study was to investigate those relationships in the alpine plant communities of Mount Moosilauke and discern their significance in ecosystem response to regional changes in climate.

Though there was a strict limit to the amount of soil material I could remove for testing, there is an abundance of information that can be extracted from even a small amount of soil. For example, even a 20 centimeter core sample can harbor hundreds of fungal “operational taxonomic units” (OTUs) (Pickles & Pither 2014). This study was conducted with previously tested methods and survey designs to extract the most data from a limited amount of collected material. Alpine ecosystems in New England are fragile and dynamic and thus conducting research in those sites must be done with the utmost care and respect.

Methods

Five different alpine plant communities were sampled to investigate short-term changes in aboveground species richness and diversity from 2015 to 2018 and relationships between aboveground and belowground variables. Richness and diversity were based on percent cover of all vascular plant, non-vascular cryptogam, and lichen vegetation. Communities were surveyed along five predetermined transects that were digitized into GPS points in 2015 based on triangulation, slope, and proximity notes from 1993 (Cogbill 1993). In this study, transects were setup using a combination of GPS points and photo documentation from 2015 and run down slope to tree line sampling a total of 71 one-meter squared plots for aboveground percent cover by species, bare soil, dead organic matter (DOM), and exposed rock. To assess the other belowground variables, a subset of nine plots per transect were sampled for soil organic and mineral material to be tested in the lab for pH and collective EcM/ErM percent colonization (see method details below).

Study Site

To the southwest of the main White Mountain Range, in the town of Benton in Grafton County,

NH lies Mount Moosilauke. The peak reaches 1480m (Eggleston 1900) and from 1450m to the summit, vegetation is classified as true alpine tundra. Tundra describes any ecosystem in which mean daily temperature in the warmest month never exceeds the 10⁰ C isotherm3 therefore preventing tree growth (Ives et al. 1974) and vegetation cover typically consists of low herbaceous, dwarf shrub or lichenoid vegetation (Billings 1974). Along the lower slopes of Mt.

Moosilauke, communities are composed of temperate deciduous forest dominated by Acer saccharum Marsh., Fagus grandifolia Ehrh., and Betula alleghaniensis Britt., from the base to

3 The 10⁰ C Isotherm is a line connecting locations of equal temperature. In this case, the equal mean temperature of 10⁰ C for July (Young 1989). 12 approximately 760m. From 760m to 1220m the composition changes to sub-alpine spruce-fir forest dominated by Picea rubens Sarg., Abies balsamea L., and Betula papyrifera Marsh. From

1220m to 1440m the upper sub-alpine zone is dominated by A. balsamea and from 1440m to

1450m, sub-alpine krummholz vegetation wanes into treeline (Lambert & Rieners 1979). A large southeastern portion of the mountain (approximately 1,860 hectares) is owned and managed by

Dartmouth College located in Hanover, NH (Dartmouth College 2017) (Figure 1).

The alpine plant communities of Mount Moosilauke consist mostly of “sedge-rush-heath meadows” as described by Sperduto and Kimball 2011. This is one of the most common plant assemblages in New Hampshire alpine ecosystems and is dominated by Carex bigelowii Torr.

(ex. Schwein) and Juncus trifidus L. (Sperduto & Kimball 2011). Communities noted in the 2015 survey included Bigelow’s sedge meadows, sedge-rush-heath meadows, felsenmeer barrens, alpine heath snowbanks, alpine herbaceous snowbank/rills, and early stage edaphic heath krummholz (Maddalena-Lucey 2015). The transect sites in this study corresponded to the aforementioned communities respectively: Southeast heath (T3); Western fellfield (T1); North peak (T5); East snowbank (T2); and East peak (T4) (Figures 2, 3, & 4). In regards to the conservation significance of these communities, Sperduto and Kimball (2011) noted that the four meadow and snowbank communities have the potential to provide safe refuge and important sources of food for the rare and endangered White Mountain arctic (Oeneis melissa semidea

Fabricius.) and White Mountain fritillary (Boloria titania montinus Esper.) butterflies in alpine communities throughout WMNF (Sperduto & Kimball 2011).

Sampling Design

The aboveground component of this study was based on the previously established sampling protocols implemented in the research I conducted in 2015 on plant community diversity and

13 abundance as well as Cogbill’s 1993 baseline vegetation research (Cogbill 1993, Maddalena-

Lucey 2015). Plant species inventory was recorded within the five predetermined alpine plant communities. Once the 71 pre-established one-meter squared quadrat4 plots (replicates) were relocated (Figures 2 & 3), and permissions from both Dartmouth College and WMNF were granted (Appendices B & C), new data were collected on bare soil, exposed rock, dead organic matter (DOM), vascular and non-vascular plant, and lichen species relative abundance via percent cover for response variable analysis (Maddalena-Lucey 2015).

Sample Collection

Using each existing transect as a sample unit for the five predetermined communities, soil samples were taken at varying intervals depending upon the overall length of each transect

(Table 1). For example, the Western fellfield (T1) was a full 50 meters (m) in length and quadrats were set at three-meter intervals whereas the East snowbank (T2) was 18m in length and quadrats were set at two-meter intervals (Table 1). Varying intervals were set to balance the number of replicates per community to maintain accuracy of the results in analysis. Transects were run from the pre-determined start point to treeline. This method, used in both prior surveys,

(Cogbill 1993; Maddalena-lucey 2015) explains the variation in transect length.

In every quadrat/plot, five soil depth measurements were recorded on each corner and at the center. A 5/16 steel bolt stock rod was driven into the soil until solid rock was reached and marked with an alligator clip at the surface. The rod was then carefully removed and measured with a metric tape measure to the nearest tenth of a centimeter (Figure 5). These five values were then averaged for mean soil depth to bedrock for each quadrat. All quadrat mean depth values were then averaged for each transect/community type. Additionally, SOM was sampled from at

4 Quadrat- divisions used for measuring abundance of sessile species in any vegetation, including aquatic macrophytes (Bullock, 1996). 14 least three microsites in nine quadrats per transect using an Oakfield soil probe tool to acquire approximately 55grams (gm) of soil for pH testing and EcM/ErM percent colonization analysis

(Figure 6).

The soil quadrats/plots were chosen at odd intervals (i.e., every odd quadrat starting at one) (Table 1) and flagged with orange during transect setup. Additionally, sampling criteria were set to determine whether a quadrat was suitable for soil organic matter5 (SOM) collection depending upon the amount of organic soil present in the quadrat. Beginning at the north-facing side of the quadrat and working clockwise, subsamples were taken from three corners. If there was no soil in one of the corners I sampled soil from the subsequent corners if available. If there was no soil in the final corner, I sampled the center of the quadrat. If there is no soil in the center of the quadrat, I continued working clockwise from the north-facing corner along the outside corners of the quadrat. If there was no soil outside the quadrat, I moved on to the next quadrat

(Figure 7). Ultimately, this process of selecting soil quadrats ensured a random and independent sampling pattern (Figure 3). Organic soil was defined as a soil with an active profile (O, A, and

B horizons). The O, A, and some of the B horizons of the microsite core samples were consolidated into a one quart freezer rated Ziploc ® bag per soil quadrat with date, name, and

GPS tags for refrigerated storage (Figure 6).

Lab Methods

A total of 45 root samples (nine per transect/community) were visually assessed through low level microscopy using a ‘Wolfe Stereo Zoom’ dissecting scope to quantify EcM and ErM abundance through a physical presence/absence count later calculated into a percentage of colonized fine root tips. The full samples were cleaned through a 4mm sieve then a 2mm sieve to

5 “The term soil organic matter refers to the whole of organic materials found in soils, including litter, light fraction, microbial biomass, water-soluble organics, and stabilized organic matter (humus) (Stevenson, 1994).” 15 remove mineral soil portions. Mineral soil portions were set aside for drying and pH analysis

(Figure 8). A subsample of the remaining root material was mixed in a beaker of tepid water for one minute before extracting fine roots (Figure 9). Mixing each subsample in tepid water

(approximately 50°C) not only aided in removing excess soil, it also caused non-root material

(e.g., pine needle duff) to separate to the top of the beaker.

A subsample of approximately 50 cm of fine roots (fine = approximately 1mm in diameter) (Brundrette 1994; Langley 2003) was extracted from the cleaned root material then separated and measured out on a 30cm plastic ‘Westcott’ ruler and tamped dry. The subsamples were then randomly dropped into a 15x100mm glass petri dish with 1x1cm2 paper grid backgrounds (Figure 10). Roots were randomly dropped into the dish using forceps in order to get a relatively even spread in the dish with little mechanical assistance. Using a tally counter, each dish was first assessed for a count of how many fine roots crossed the grid. Next, each dish was assessed for a count of how many times a colonized fine root crossed the grid.

Ectomycorrhizal colonized roots were identified through the presence of either ‘club-like’ root tips or external hyphae associated with a mantle. Ericoid colonized roots are ectendomycorrhizal and thus the only external component visible at low level microscopy was the presence of external hyphae associated with a mantle on roots of ericaceous shrubs such as Vaccinium vitis- idaea (Figure 11).

Root samples were assessed for percent colonization of EcM and ErM fungi using a gridline intersect method (GLI). The GLI method used for this study was adopted in part from a

1980 study on AMF colonization intensity (Giovannetti & Mosse 1980) and framework from methods used in a 1966 study (Newman 1966) which focused on the use of a new formula for estimating length of root material per unit volume of soil. The aforementioned studies use

16 gridlines for the purpose of counting for root length. Given the probability of a longer root to make more intersections with the grid on average, Newman (1966) suggested that the length of a

휋푁퐴 root can be estimated using the formula 푅 = wherein ‘R’ represents the total length of the 2퐻 root, ‘N’ represents the number of intersections of the root and straight lines, ‘A’ represents the area of the grid rectangle, and ‘H’ represents the total length of the straight lines.

Giovannetti & Mosse (1980) used a GLI method that incorporated the use of a petri dish with grid square background for calculating root length. In both cases, these prior studies mention the use of replication (albeit subjective) to get a mean value and standard error for each sample unit. This would make sense in the case of measuring for average root length per sample unit. However, in my study root samples were randomly placed and never rearranged for replication as this would introduce a form of implicit pseudoreplication into the experimental design that seemed unnecessary. To be clear, the use of replication to sample for root length seems reasonable, however, use of replication to sample for EcM/ErM percent colonization could introduce bias into the experimental design and potentially reduce the accuracy of the result. Additional methods adopted by Newman (1966) included the washing and storing of roots in a beaker of water prior to grid placement. Newman (1966) also noted in their discussion that a possible source of error in their GLI count method was the potential for crossed root material to conceal colonized material. My study was conducted using freshly rinsed roots that were still rigid, without the use of tocopherols, in order to account for that potential error (Figures 9 & 10).

pH was sampled using a ‘Hach SensIon 378’ counter-top pH meter that was calibrated for ranges between 0 and 7 pH at a ratio of one part soil (10 ± 0.1gm dried) to two parts distilled water (20mL) (Figure 13). Requirements for the pH test dictated the approximate amount of

‘raw’ mineral soil used (~30-50gm) to ensure enough dry material would be available as some

17 samples had significantly more (lighter) humic material than mineral material. Each mineral soil sample was labeled by transect, quadrat, and soil sample number then dried in a 48-82°C

‘Hamilton’ electric drying oven for approximately 24-48 hours at 65°C until dry (Figure 8). In lieu of using a constant weight method to ensure that samples were completely dry, samples were given an additional 24 hours to dry in the oven (often over a weekend) prior to pH testing.

Soils were weighed using an ‘Acculab Vicon VIC-212’ digital balance calibrated to the nearest hundredth of a gram. Dried soils were cleaned through a 1mm ‘Keck’ field sieve (Figure

14) prior to final weighing to remove any remaining root material without completely disaggregating the sample. Each 10gm pH sample was mixed in 20mL of distilled water in 25mL beakers for ten minutes and allowed to settle for five minutes while calibrating the meter prior to testing. The first sample tested was allowed up to five minutes to stabilize and subsequent samples were given up to two minutes to stabilize prior to reading. Samples were assessed for pH in ‘batches’ (one transect=nine samples at a time) to ensure that each beaker was stirred enough within the ten minute window prior to probing.

Data Analysis

The proportion of collective EcM/ErM colonization per sample was determined by dividing the number of colonized root crossings by the number of total root crossings (e.g., 7 colonized root crossings / 65 total root crossings = .108 or 10.8% colonization). Species richness was calculated in “R”6 studio 1.1.383 (R Core Team 2017) using the ‘rich’ (Rossi 2011) statistical package wherein an average count of individual species of all vascular and non-vascular plants and

6 “R”- A language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories. This program provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, and graphical techniques, and is highly extendable. - © The R Foundation

18 lichens per sample (n=71) were calculated using a ‘bootstrap’ estimator method based on the

formula which is noted to reflect stronger estimates given a larger number of samples or quadrats (Smith & van Belle 1984). Species diversity was calculated using the ‘vegan’ (Oksanen et al. 2018) statistical package diversity (index= “simpson”) function.

Simpson’s Diversity index is calculated using “(D = ∑ (n/N)2)” in that (D) being diversity is equal to the sum of (n) or the number of individuals of a species within the data set divided by

(N) or the number of individuals for all species in the data set and then squared to get a percentage value (Maddalena-Lucey 2015). Observations were sorted and cleaned into two main datasets. The aboveground dataset included all 71 observations of species, bare soil, bare rock, and dead organic matter (DOM) percent cover, species richness, and species diversity as well as mean soil depth to bedrock. The belowground dataset was a 45 observation subset of the full survey which included soil pH, mycorrhizal percent colonization, species richness, species diversity, bare soil percent cover, bare rock percent cover, and mean depth to bedrock corresponding to the 45 soil quadrats.

To ensure that field methods were repeated properly in 2018 as compared to 2015, the two datasets were compared for diversity and richness as a whole and by transect using a

‘Welch’s two-sample’ paired t-test against the null hypothesis that there was no significant difference between the two sets as a way to affirm that, given a time frame of three years, there would be little significant change in diversity and richness in the alpine plant communities of

Mount Moosilauke. Additionally, diversity and richness were further analyzed in R studio using the ‘Pearson’s product-moment correlation function’ wherein they were tested against the null hypothesis that the true correlation coefficient was equal to zero or that there was no correlation

19 between the two datasets to further assess whether or not there were significant changes within a three year time period.

Data were also tested against the null hypothesis that there was no correlation, positive or negative, between soil depth, pH, and EcM/ErM percent colonization and plant diversity and abundance in any plant communities in the alpine zone of Mount Moosilauke. To test this hypothesis, a series of parametric statistical tests were used, including a scatter plot matrix of all variables, to find any significant linear relationships. If there were linear relationships between predictor and response, normality of data sets were tested using Shapiro-Wilks, Q-Q norm, and residual plots (R Core Team 2017) before comparing the strongest relationships with single- factor analysis of variance (ANOVA) test (Chambers et al. 1992). Further analysis was conducted with the linear regression function (Chambers et al. 1992) to determine by what intensity relationships were positive or negative (r²= 0-1). All normality and ANOVA tests were weighed against an alpha of 0.05. Further analysis on linear relationships was conducted using a

‘Spearman’s Rank Correlation’ non-parametric test with ‘cor.test’ (method = “spearman”) function (R Core Team 2017). The response variables in this study were plant species richness

(mean species per plot) and plant species diversity (Simpson’s Diversity Index). Predictor variables were soil depth to bedrock (centimeters), pH (integer), and EMF percent colonization

(decimal proportion).

To test potential relationships by community/transect, richness and diversity were run against soil depth using transect as a factorial predictor. However, species diversity was not normal (W= .805, p= 2.817e-5, Table 2), due in part to significant changes in diversity in the

East peak (T4) community, and residuals were not normal enough to fit the linear model (W=

.946, p= .004). Distributions (Figure 15) and residuals of the depth to richness model were

20 normal (W= .99, p= .696) and the linear model was fit. At the soil survey scale (n=45), results of the scatter plot matrix for all five variables (Figure 16) were assessed for homoscedasticity and the normality assumptions of the linear regression model and those that did not fit the assumptions were transformed (Figure 17). A Shapiro-Wilk’s test on the best transformed variables showed that soil depth and species richness met the assumptions of normality and that species diversity, soil pH, and EcM/ErM colonization violated the normality assumption (Table

2). Due to the relative non-normal distribution of data for three out of five of these variables, the

Spearman’s Rank Correlation was chosen to test for significant relationships.

Table 2. Results of a Shapiro Wilk’s test of normality for all variables on both the full survey (n=71) and soil survey (n=45) scales. Bolded p-values depict variables that fit the normality assumption.

Figure 3. Map of Mount Moosilauke alpine zone with transect and soil sample markers. Checkered flags represent the start and stop points and orange flags represent the soil sample quadrats.

T1 Western Fellfield T2 East Snowbank

(sedge-rush-heath meadow) (herbaceous snowbank/rill)

T3 South East Heath T4 East Peak (heath krummholz)

(Bigelow’s sedge meadow)

T5 North Peak (felsenmeer barrens)

Figure 4. Images of the five transects surveyed in 2018. Position is aligned with the azimuth of each start point. Permission to reuse by: Timothy Maddalena-Lucey

Figure 5. Soil depth was measured by driving a 5/16 steel rod into the ground until bedrock was reached. The rod was then flagged at the surface and carefully removed to be Figure 6. Soil subsample taken with Oakfield soil probe. measured to the nearest tenth of a cm with either the transect The majority of soils had a profile most akin to either tape or tape measure. Permission to reuse by: Timothy histosols or spodosols. Permission to reuse by: Timothy Maddalena-Lucey Maddalena-Lucey

Figure 7. Depiction of the rejection criteria when taking soil samples in a quadrat. Starting at 1 and working clockwise than Figure 8. Mineral soils from nine soil quadrats being placed in to center. From center, worked clockwise along outside from step 6 until step 9. If no soil is present by step 9, rejected the drying oven prior to pH analysis. Permission to reuse by: quadrat and moved on. Timothy Maddalena-Lucey

Figure 9. Subsample of fine root material being rinsed in tepid Figure 10. Measuring and placement of freshly rinsed fine root water for one minute prior to extraction for EMF/Ericoid percent subsamples for EMF/Ericoid mycorrhizal analysis. Permission to colonization analysis. Permission to reuse by: Timothy reuse by: Timothy Maddalena-Lucey. Maddalena-Lucey.

Figure 11. Side-by-side comparison of EMF colonized root of Abies balsmea (right) and Ericoid mycorrhizal colonized root of Vaccinium vitis-idaea (left). Permission to reuse by: Timothy Maddalena-Lucey.

Figure 12. Dried mineral soil subsamples being cleaned through a Figure 13. Mineral soil subsamples being mixed for pH analysis 1mm sieve prior to weighing for pH analysis. Permission to reuse while meter is calibrated for 0-7 range. Permission to reuse by: by: Timothy Maddalena-Lucey. Timothy Maddalena-Lucey.

Figure 14. Boxplots showing the distribution of species diversity, species richness, and soil depth at the full survey (n=71) scale. Outliers in diversity and soil depth were later determined to be accurate and not influential (cook’s distance <.5) to the linear model. Permission to reuse by: Timothy Maddalena-Lucey

Figure 15. Scatterplot matrix of all variables at the soil survey scale (n=45) with trend lines highlighting the strongest visual correlations between species richness, soil depth, and EcM/ErM percent colonization. These trends were later assessed with a non-parametric linear regression. Permission to reuse by: Timothy Maddalena-Lucey

Figure 16. Boxplots of the five variables (n=45) in best transformations for linear regression analysis. Permission to reuse by: Timothy Maddalena-Lucey

Results

Community composition in the five communities sampled in this study were confirmed as described by Sperduto & Kimball (2011). The mean percent cover of all vascular and non- vascular plants and lichens by species was analyzed for both 2015 and 2018 and each community showed a continued dominance of key characteristic species (Table 3). The Western fellfield

(T1) showed a dominance of Carex bigelowii and Juncus trifidus in mean percent cover. The

East snowbank community (T2) showed a dominance of Vaccinium cespitosum Michx.,

Deschmpsia flexuosa L., and Solidago macrophylla Pursh. as well as Empetrum nigrum L. in mean percent cover indicating that it exhibits the composition characteristics of both alpine heath snowbanks and alpine herbaceous snowbank/rills. The Southeast heath community (T3) showed a dominance of Vaccinium vitis-idaea L., Juncus trifidus L., and C. bigelowii mean percent cover indicating its composition aligns best with the Bigelow’s sedge meadow and sedge-rush-heath meadow community types. The East peak (T4) community showed a dominance of A. balsamea,

V. vitis-idaea, and Betula cordifolia Regel. mean percent cover indicating its composition aligns best with a heath krummholz community type. Lastly, the North peak (T5) community showed a dominance of V. vitis-idaea, J. trifidus, and Sibbaldiopsis tridentata (Aiton) Rydb. in the upper plots closer to the Western fellfield community and a dominance of lichen species Cetraria laevigata Rass., Cladonia stygia (Fr.) Ruoss., and Cladonia amaurocraea (Flörke) Shaer. from the middle plots to tree line indication that its composition aligns best with a sedge-rush-heath meadow and felsenmeer barren (Sperduto & Kimball 2011) (Table 3). Additional details on morphological classification and taxonomy of all species recorded from 1993 to 2018 can be found in the appendix A (Table 9).

Aboveground Change

The comparison of mean species richness from 2015 to 2018 across the five communities showed no significant change (2015= 15.2 spp/m² v. 2018= 15.7 spp/ m²; t70= .75, p= .46, Table

4). Mean species diversity (Simpson’s) however, showed a significant decrease from 2015 to

2018 (2015= 0.839 v. 2018 = 0.807; t70= -3.23, p= .002, Table 4). Though there was a significant decrease in diversity in 2018, the relative amount of diversity and richness in the plots remained consistent given the results of the correlation coefficient test which showed a significant relationship in species richness (r69= .299, p= .01, Figure 18) and diversity (r69= .301, p= .01,

Figure 19) from 2015 to 2018.

At the community scale, the Western fellfield community (T1) showed a significant increase in mean species richness by approximately four species per square meter from 2015 to

2018 (mean 2015: 12.06 spp./m² v. mean 2018: 16.12 spp./m²; t16= 3.61, p= .002, Table 4). All other community scale results comparing richness from 2015 to 2018 were not significant. Of the five plant communities, three showed a significant decrease in mean species diversity from 2015 to 2018 with the biggest decrease occurring in the East peak (T4) community (mean 2015: .816 v. mean 2018: .676; t13= -4.65, p= .0004). The Western fellfield community showed a significant increase in mean species diversity from 2015 to 2018 (mean 2015: .808 v. mean 2018: .836; t16=

3.43, p= .003). There was no significant difference in mean species diversity from 2015 to 2018 in the North peak community (T5) (Table 4).

Belowground

As a whole, the alpine zone of Mount Moosilauke (as represented by the five key community types) had an average pH of 4.14 ± 0.28 (n=45) with an average soil depth of 12.7cm ± 6.7

(n=71) and an average collective EcM/ErM colonization of 13.7% ± .1 (n=45) (Table 5).

Mycorrhizal percent colonization showed the highest overall sample value in the East Peak (T4) community at 53.3% with a mean of 25.5% ± .12 (n=9).The lowest overall sample value for mycorrhizal colonization in the East snowbank (T2) community was 0% with a mean of 11.4% ±

.08 (n=9). The highest overall sample value for pH in the Southeast heath (T3) community was

5.54 with a mean of 4.25 ± .53 (n=9). The lowest overall sample value for pH in the Western fellfield (T1) community was 3.61 with a mean of 4.06 ± .23 (n=9). The highest overall soil depth was also found in the East Peak (T4) community (41.5cm) which had a mean depth of

18.16cm ± 9 (n=14). The lowest overall sample value for soil depth (.92cm) was found in the

North peak (T5) community which had a mean depth of 7.82cm ± 3.6 (n=13) (Table 5).

As a whole, the depth of the soil showed no significant relationship to species diversity or species richness across the five communities. However, at the community level results of the soil depth to richness model were significant (R2 = .286, F (5,65)=5.23, p=.0004) and significant differences between transects were found in both the East snowbank (p= .018) and Southeast heath (p= .023) communities. At the soil survey scale, prior to fitting the ANOVA model, all variables were tested by community/transect for differences in variance (Table 6). Results of the

ANOVA comparing EcM/ErM colonization by transect showed a significant difference in means

(p<.01) between the five communities [F (4, 40) = 4.23, p = .005, Table 7].

Results of the Spearman’s rank test across all five communities (n=71) showed a significant weak negative relationship between soil depth and species richness (rs= -.25, p = .03,

Figure 20).There were no significant relationships between species diversity and soil depth at the full survey scale (p > .05). Results at the soil survey scale (n=45) showed a moderately positive relationship between soil depth and EcM/ErM percent colonization (rs= .30, p = .04, Figure 21).

All other soil survey scale results were not significant (p > .05). At the community scale (n=9)

37 there was a marginally significant strong negative relationship between EcM/ErM percent colonization and species richness (rs= -.65, p=.059, Figure 22) in the East peak (T4) community.

The strongest relationship in the analysis was a positive correlation found between soil pH and

EcM/ErM percent colonization in the Southeast heath (T3) community (rs= .77, p = .02, Figure

23). All other community scale results were not significant (p > .05).

Table 3. Mean species percent cover by transect/community for 2015 and 2018. The ‘x’ represents a mean percent cover of zero or less than .1%. Species are listed top to bottom by morphological class (i.e., Tree, shrub, graminoid, forb, bryophyte, and lichen).

Mean Percent Cover By Community 2015 and 2018 2015 2018 Species *WFF ESB SHE EP NP WFF ESB SHE EP NP Betula cordifolia x 0.1 x 2.2 2.2 x x x 1.5 0.4 Abies balsamea x 3.4 3.4 52.4 52.4 x 9.3 10.3 46.4 3.8 Rubus idaeus x 2.2 x x x x 3.3 x x x Vaccinium uliginosum 1.2 24.4 x x 11.2 4.3 21.4 1.7 x 14.1 Vaccinium cespitosum x 41.1 x x x 0.0 58.9 3.9 x x Vaccinium vitis-idaea 61.2 36.1 31.9 49.3 40.8 69.7 58.9 59.7 31.8 58.7 Empetrum nigrum x 0.2 x x x x 1.4 x x 1.9 Agrostis borealis x x x x x x 0.1 X x 1.9 Juncus trifidus 51.5 31.1 38.6 12.4 12.4 63.8 26.1 57.0 3.3 40.0 Carex bigelowii 17.9 2.5 2.8 1.4 1.4 34.7 2.2 9.2 X 3.8 Carex brunnescens x 0.0 0.2 x x x 0.6 0.5 0.4 X Deschmpsia flexuosa x 18.9 X x x x 35.7 x x X Trichophorum cespetosum.var. callosus x 42.2 X x x x 2.8 x x 1.9 Poa pratensis x 0.3 X x x x 0.2 x x 0.0 Lysimachia borealis x X 4.2 x x x 0.1 5.4 x x **Coptis trifolia x X X x x x 9.6 x x x Clintonia borealis x 2.2 X x x x 6.2 x x x Chamaepericlymenum canadense x 31.1 11.8 x x x 63.9 23.1 0.2 x Mainathemum canadensis x 26.1 X x x x 52.8 x x x Minuartia groenlandica 0.3 X 0.1 x 0.2 0.3 X 2.6 x 5.0 Sibbaldiopsis tridentata 18.0 2.2 X x 5.4 23.5 2.0 x x 6.6 Solidago macrophylla x 2.8 x x x x 0.3 x x x Dryopteris campyloptera x 1.9 x x x x 2.8 0.1 x x Spinulum annotinum.var. pungens x 6.2 x x x x 15.2 x x x **Dedrolycopodium obscurum x x x x x x 0.1 x x x Politrichastrum alpinum 12.9 12.8 0.9 20.6 7.5 14.8 16.0 1.1 4.1 8.7 Politrichum piliferum x x x 0.1 x 0.8 0.1 1.4 x x Politrichum juniperinum x x x x 1.9 x 0.2 0.4 0.4 0.8 Andreaea rupestris 0.1 3.9 0.5 0.6 4.2 0.9 1.2 0.3 0.2 3.6

Cetraria laevigata 52.4 23.4 46.1 56.5 37.3 75.3 25.6 75.9 24.7 62.7 Cladonia amaurocraea 14.9 10.7 34.7 21.3 26.2 17.5 1.2 47.5 3.1 32.3 Cladonia arbuscula x x x 5.5 x 0.9 0.4 1.5 1.2 0.2 Cladonia coccifera 0.4 x x 0.3 1.3 0.1 0.2 0.1 0.1 0.7 Cladonia stygia 9.6 1.9 17.3 10.2 22.4 18.3 0.2 21.3 0.5 33.0 Cladonia stellaris 0.7 0.1 1.2 1.4 1.2 1.9 2.0 8.3 0.8 10.8 Cladonia uncialis 7.0 x 13.2 13.5 1.5 6.6 0.3 11.7 1.9 6.8 Cladonia pleurota 0.6 2.4 1.3 14.3 1.0 0.9 4.5 1.2 2.7 0.2 Cetrariella delisei x 3.4 x x x 0.1 3.3 x x 1.5 Cladonia squamosa 0.4 0.7 x 1.9 0.6 1.0 0.6 x 0.1 0.9 Cladonia sulpherina x x x 1.3 x x x x 0.2 x Vulpicida pinastri x x x 1.5 x x 0.1 x 2.1 x Pleurozium shreberi x 32.2 5.0 x x 3.0 35.3 3.3 0.8 x Dicranum sp. X 3.7 0.1 4.8 x 0.2 x x 7.2 0.6 Ditrichum pallidum 0.2 x x x x x 0.1 0.3 4.3 0.2 Racomitrium sp. x x x x x 0.2 0.2 X 0.5 0.1 Ptillidium ciliare 2.5 x 0.3 0.9 4.7 10.0 1.2 0.2 5.8 8.5 Stereocaulon alpinum x x 0.2 x 0.8 0.4 x x x x Rhizocarpon geographicum 0.9 3.0 10.3 1.4 7.1 4.9 1.5 2.4 1.0 8.7 Parmeliopsis ambigua 0.1 x 0.6 x 4.0 0.1 0.1 0.2 0.4 0.1 Parmotrema hypotropum x x x x x 0.2 x 0.1 0.1 x Porpidia flavocaerulescens 0.1 2.8 5.7 3 2.3 0.2 0.1 0.4 0.1 2.2 Lecanora polytropa 0.2 0.3 4.1 4.0 1.6 2.1 0.9 1.2 2.5 3.8 Umbilicaria proboscidea 0.1 x x x 0.9 0.1 x x x 0.4 Flavocetraria cucullata 0.2 x x x 0.1 0.2 x x x 0.7 Aspicilia cinerea 1.5 3.7 7.7 5.2 4.6 5.7 2.8 2.4 3.4 9.9 Hypogymnia physodes 0.1 X 0.1 6.5 0.6 0.1 x x x 0.4 Protoparmelia badia x 0.3 3 x 4.4 1.8 0.3 0.3 x 5.4 Tremolecia atrata x x x x x 0.5 0.3 0.3 0.3 4.4 Arctoparmelia centrifuga x x x x x 0.9 0.1 x x 4.8 Ophioparma ventosa x x x x x 0.1 x x x x Ochrolechia frigida x x x x x 0.9 x x x 0.1 Coral.sp. x x x x x 0.4 0.2 x x x

Table 4. Paired t-test comparison of mean plant species richness (spp. per plot/ spp.m²) and diversity (Simpson’s) from 2015 to 2018 by transect and for the full survey (n=71). Alpha was set to .05 and all statistically significant p-values were bolded.

Table 5. Summary statistics for two aboveground variables (response) and three belowground variables (predictor) by five plant communities and in total for the full survey (n=71). The ‘Mean’ column reads as ‘ ± Std (‘n’) and the ‘Range’ column displays the minimum and maximum values of the corresponding dataset. Bolded values represent minimum and maximum values in their respective datasets.

Diversity (Index 0-1) Richness (Spp./plot) pH (dec. %) Soil Depth (cm) EcM/ErM col. (dec. %) Communities Mean Range Mean Range Mean Range Mean Range Mean Range T1 Western fellfield .836 ± .034 (17) .797-.92 16.12 ± 4.96 (17) 9-27 4.06 ± .228 (9) 3.61-4.38 12.31 ± 4.34 (17) 4.82-24.62 .113 ± .075 (9) .034-.241 T2 Eastern snowbank .873 ± .016 (9) .854-.904 19.78 ± 5.29 (9) 12-28 4.19 ± .204 (9) 3.82-4.42 15.32 ± 6.98 (9) 3.78-28.46 .114 ± .082 (9) 0-.222 T3 Southeast heath .822 ± .04 (18) .726-.883 13.11 ± 2.78 (18) 8-20 4.25 ± .533 (9) 3.83-5.54 11.12 ± 4.88 (18) 4.28-20.66 .093 ±.087 (9) .023-.244 T4 East peak .676 ± .099 (14) .524-.845 13.86 ± 3.06 (14) 10-18 4.17 ± .148 (9) 3.94-4.4 18.16 ± 9 (14) 5.98-41.5 .255 ± .122 (9) .143-.533 T5 North peak .847 ± .027 (13) .808-.882 17.92 ± 4.13 (13) 8-23 4.04 ± .142 (9) 3.84-4.3 7.82 ± 3.63 (13) .96-17 .108 ± .061 (9) .039-.226 All Transects .807 ± .085 (71) .524-.92 15.7 ± 4.57 (71) 8-28 4.14 ± .287 (45) 3.61-5.54 12.72 ± 6.7 (71) .96-41.5 .137 ± .104 (45) 0-.533

Table 6. Comparison of mean variances of aboveground and belowground variables at the soil survey scale (n=45) by community type/ transect (n=9). Bolded values represent the variance that stands out at least one power/ decimal point higher or lower than all others.

Table 7. Single factor ANOVA table for EcM/ErM percent colonization (n=45) by transect/community (n=9).

0.059

Discussion

Aboveground Change

The East snowbank (T2) community was the richest and most diverse plant community in 2015 and showed a slight increase in diversity in 2018. The most interesting contribution to that increase came from Empetrum nigrum L., a circumpolar shrub species previously noted by

Cogbill (1993) as threatened in New Hampshire and removed from that status in 2013 (New

Hampshire Natural Heritage Bureau 2013). This plant was noted in my 2015 survey in the North peak (T5) community as falling outside the transect at approximately five meters square in area

(Maddalena-Lucey 2015). In 2018, this plant was captured within the bounds of North peak transect and additionally, showed an increase of 11% cover in the East snowbank community and overall showed a mean increase of 0.5 % (Figure 24) at the full survey scale. Although these increases were nominal and not significant, given this plant’s importance as a dominant indicator species for alpine snowbank and cliff communities (Kimball & Weihrauch 2000) in the WMNF, along with its importance in the circumpolar alpine region as a known biomonitor species for heavy metal deposition (Monni et al. 2001), the status of this plant in the alpine communities of

Mount Moosilauke should be closely monitored in the future. Another notable change in the East snowbank community was the addition of Coptis trifolia Salisb. and Dendrolycopodium obscurum L. (Table 3) both known boreal forest understory species that were not recorded in this community in 1993. These two plants were not recorded in 2015 either and given the current trends in other parts of the world where boreal plant encroachment into snowbed communities has been increasing (Kullman 2010) these and other boreal plants should be closely monitored in the snowbed community in future surveys.

The East peak community (T4) was one of the most dynamic of the five communities sampled in 2018. There was a significant increase in dead organic matter by 14.4 % and a significant decrease in mean percent cover of vascular plants by 34% indicating a ‘die-back’ of vascular plants in a span of just three years. This may explain the decrease in species diversity from 2015 to 2018 by approximately two species. Part of the decrease in diversity could be explained by a 4.8% decrease in Betula cordifolia Regel. from 1993 to 2018 which was significant (Figure 27). Given this community’s proximity to hiking trails and potential appeal as a stopover or tent site for Appalachian trail thru-hikers, this die-back could be due in part to mechanical human disturbances.

The most significant short-term change in species richness, which was found in the

Western fellfield community, may signify a shift in community composition. Most notably, there was an overall 7.7% increase in mean vascular plant cover from 2015 to 2018 in that community

(Figure 28). The highest increases were attributed to Carex bigelowii (16.8%) and Juncus trifidus

(12.3%) (Figure 29) which correspond well with the dominant species of a “sedge-rush-heath meadow” (Sperduto & Kimball 2011). Given these richness results, along with an increase in species diversity, it seems the Western fellfield community showed the most short-term aboveground change of the five communities sampled. In addition to these short-term changes in richness, it continues the trend of significant increases in vascular plant richness noted from 1993 to 2015 (Table 8) in the East snowbank and Southeast heath communities (Maddalena-Lucey

2015). Although lichen and bryophyte richness in the 2015 study showed an overall significant increase since 1993, there was a significant decrease of 12% in mean percent cover of Cladonia arbuscula (L.) Rab., (one of the few lichens recorded 1993) from 1993 to 2018 in the Southeast heath community (Figure 30).

Given the additional decrease in lichen abundance in other meadow communities over the past 25 years, one mechanism driving this change could be long-term atmospheric nitrogen deposition in the meadow and heath communities of Mount Moosilauke. Changes in plant composition that favor fast growing species such as forbs and graminoids have been associated with fluxes of nitrogen (Bardgett & Wardle 2011). Increases in nitrogen in the soil are known to contribute to higher net primary productivity in these species causing a shift in composition

(Bardgett & Wardle 2011). In addition to the shift in graminoid abundance in the Western fellfield community over the last three years, there was a 34.7% increase in Carex bigelowii and

50.7% increase in Juncus trifidus from 1993 to 2018 affirming a long-term significant increase in abundance of these species over the past 25 years (Figure 31).

It’s well known that historically, atmospheric dissolved organic nitrogen (NO3) deposition has been recorded in the higher elevation spruce-fir forests of the WMNF region

(including Mount Moosilauke) and at higher rates than surrounding lowlands (Lovett & Kinsman

1990; McNulty et al. 1991; Dittman et al. 2007). Additionally, it should not be discounted that dry atmospheric biological nitrogen fixation (BNF) plays a role in the contribution of nitrogen

(N2) in nitrogen limited alpine tundra soils (Sievering et al. 1992). The potential effects of N could be drawn from a five year experimental study of the effects of nitrogen deposition on lichen and bryophyte abundance in alpine meadow and heath communities in the Northern

Swedish Lapland region (Jägerbrand et al. 2009). Jägerbrand et al. (2009) noted that in addition to significant increases in graminoid and forb abundance, there were also significant decreases in lichen and bryophyte abundance in response to added nitrogen fertilization (Jägerbrand et al.

2009). Additionally, Bowman et al. (2006) noted in their study on the effects of nitrogen deposition in alpine dry meadows of the Colorado Rockies that shifts in vegetation composition

49 may serve as a better short-term response over the more ‘traditional’ soil factors as indication of deposition. Further evidence of nitrogen deposition could be explained by the very low pH found in the soils of the Western fellfield community. Other studies have noted that alpine lichen and bryophyte species are just as sensitive if not more so than vascular plants as bio-indicators of N deposition (Capers & Slack 2016) especially in meadow, heath, and snowbed communities where studies abroad have shown marked decline in bryophyte and lichen abundance in relation to nitrogen deposition (Jägerbrand et al. 2009; Alatalo et al. n.d.).

The deluge of anthropogenic acid deposition across the United States and in the

Northeast in the late 20th century (Aber et al. 1989; Galloway et al. 1995; Holland et al. 1999) has shown marked signs of reduction with subsequent forest recovery (Fuss et al. 2015;

Lawrence et al. 2015; Li et al. 2016; Kosiba et al. 2018). In the forests surrounding Mount

Moosilauke, Bernal et al. (2012) have even estimated that over the last 46 years, stream nitrate measurement in the watersheds of Hubbard Brook Experimental Forest (located ~20 miles South of Mount Moosilauke), has dropped an estimated 125kg/ha (from 1977-2007). However, nitrogen, sulphur, and especially ammonia deposition continues to affect the forested plant communities of the Eastern United States as a whole (Fakhraei et al. 2016; McDonnell et al.

2018). In the Northeast, nitrogen deposition still remains well above pre-industrial levels

(Templer et al. 2015). In montane and alpine soils, another important indicator of nitrogen and sulphur deposition and acidification is calcium (Likens & Holmes 2016). Calcium is an important building block of vascular plant structure and its role as an acid-neutralizer is imbalanced by the deleterious effects of sulphate and nitrate dry and wet deposition from anthropogenically influenced acid precipitation (Likens & Holmes 2016). This chemical imbalance in soils is intensified as it opens a window for the mobilization and uptake of

50 dissolved aluminum which is highly toxic to plants. In the WMNF, aluminum toxicity has taken a heavy toll on calcium concentrations and overall NPP of Picea rubens Sarg. (Likens et al.

1996). The montane and alpine communities of the White Mountains may continue to be impacted by the effects of nitrogen deposition (Capers & Slack 2016) though continuous long- term data analysis of plant community composition changes, along with nutrient deposition monitoring (or manipulation) in the meadow and heath communities mentioned above is needed to tell us more.

Soil Depth and EcM/ErM

Soil depths have been known to range from 15cm to <70cm in other alpine tundra communities of the WMNF with some extreme depths still unknown (Schide & Munroe 2015). Given the survey scale results of the soil depth-to-richness factorial regression by community along with results of the community level rank correlation, it is clear that soil depth has a relationship with aboveground species richness. The negative correlation was intriguing as soil depth was noted in one study as a positive driver of plant species richness and diversity in prairie grasslands in New

York (Baer et al. 2003). However, there seems to be a general paucity of research specific to the effects of varying soil depths on plant richness and diversity in alpine meadows. The negative correlation between soil depth and species richness may have been influenced by lichen and bryophyte richness in the fellfield and felsenmeer barren communities given that plots with little to no soil and more exposed rock would reflect relatively shallow soils yet a high richness of lichens and bryophytes. With bryophytes and lichens removed from the analysis, there was a significant rank correlation between soil depth and species richness found in the East peak (T4) community (rs= .58, p=.02, Figure 32). Given the strong association of EcM with plant species it

51 is logical that studies noted soil depth as correlated with EcM diversity (Rosling et al. 2003;

Clemmensen et al. 2013).

EcM/ErM have be noted in this study as strongly correlated with species richness, however, a negative correlation with species richness makes little ecological sense and the strength of the correlation is questionable. In light of the rank correlation comparing soil depth to vascular plant richness, along with the correlation between soil depth and EcM/ErM colonization, one could speculate that shallow soils could lead to lower EcM/ErM diversity and subsequently lower species richness and vice versa. Rosling et al. (2003) noted that in boreal forest systems, soil depth and horizonation played an important role in defining ectomycorrhizal diversity and vertical distribution in the soil. Additionally, though Cairney and Meharg (2003) describe the important role that ericoid mycorrhizae play in enhancing the nutrient uptake for ericaceous plants even in soils saturated with toxic heavy metals, some go further to state that in the face of climate-induced soil warming (through community and ecosystem level manipulations), ericoid mycorrhizae in ericaceous alpine tundra habitats have shown the ability to allocate more carbon and dissolved organic carbon in response to soil warming and increased

CO2 (Olsrud et al. 2004). Given that depth of the soil has been shown to impact vascular plant species richness (including ericaceous dwarf shrubs), especially in the East peak (T4) community, it is likely that ErM abundance would also be impacted by shallower soils.

As was expected, there were significant differences in collective EcM/ErM percent colonization depending upon plant community type in the alpine zone of Mount Moosilauke.

Most notably, the highest EcM/ErM percent colonization (53.3%) was found in the East peak

(T4) community which is an edaphic-driven clearing with alpine plants surrounded by sub-alpine spruce-fir krummholz approximately 0.25 miles from the summit. This makes ecological sense

52 as there would likely be an increase in conifer-associated ectomycorrhizae in the ecotone of subalpine to alpine communities (Kernaghan & Harper 2001). Additionally, the result of the soil survey ranked correlation of EcM/ErM percent colonization to soil depth, along with the highest mean soil depth (18.16cm) affirm the positive relationship between these soil, fungal, and environmental variables in the alpine zone of Mount Moosilauke. As noted earlier, soil depth and horizonation play and important role in EcM/ErM diversity and relationships between depth and fungal composition have been determined in other boreal forests around the world (Rosling et al.

2003; Clemmensen et al. 2013)

Soil pH

In the Southeast heath community there was a strong connection with EcM/ErM percent colonization (abundance) and a slightly higher pH in the meadow communities of Mount

Moosilauke. The Southeast heath community had the highest pH value (5.54) and highest mean soil pH of the five communities and for the survey as a whole and this was the only significant relationship between pH and EcM/ErM abundance found in the analysis. Although 5.54 pH was still an acidic value, others have reported pH ranges between 3.7-4.8 (Reiners & Lang 1979) in surrounding parts of the WMNF and 3-3.8 (Huntington et al. 1990) in subalpine communities on

Mount Moosilauke.

The significance and effect of soil pH on fungal and bacterial abundance and diversity in sub-alpine and alpine ecosystems has been noted in a recent study (Siles & Margesin 2016) and one study even stated that soil pH could be considered one of the strongest drivers of EcM composition and turnover in sub-alpine conifer forests (Glassman et al. 2013). However, some studies have noted that soil acidity and nutrient-poor conditions have had little effect on the ability of ‘acidophilic’ EcM species to form associations with their tree symbionts (Slankis 1974;

Natel & Neumann 1992) while others have suggested that some EcM species, especially those considered “ammonia” fungi, have an optimal range of 5-6 pH for growth (Yamanaka 2003).

Additionally, in an experimental seed bank study in 2017 in the alpine and subalpine meadows of the Tibetan plateau it was noted that among the other environmental factors driving seed density, pH showed a strong negative correlation with both plant species richness and abundance (Ma et al. 2017). This correlation analysis supports the connection between species richness, soil pH, and mycorrhizal abundance in other alpine plant communities around the world.

The significance of the pH to EcM/ErM relationship makes ecological sense given that a lower soil pH, especially when associated with long-term acidification from atmospheric nitrogen deposition, has been noted as a driver for reducing EcM/ErM diversity and richness in

Northern temperate and boreal forest ecosystems (Wallenda & Kottke 1998; Lilliskove et al.

2002; Treseder 2008). In the 2015 study, much of the increase in richness across communities was attributed to changes in bryophyte and lichen richness (Table 8). Those results are difficult to interpret given the difference in recording effort as there were very few lichens or bryophytes recorded in 1993 and vascular plant richness showed significant increases in only two out of the five communities sampled.

Future Changes and Conservation Initiatives

There are concerns in the alpine mycorrhizal community as to how fungal biomass will react to climate-driven soil warming (Fujimura et al 2008; Solley et al 2017). In this study, the sensitivity of EcM/ErM to changes in soil pH across the alpine plant communities of Mount Moosilauke along with the positive relationship between EcM/ErM abundance and soil depth is apparent.

Additionally, given these relationships, one could assert that a combination of continued (though diminishing) nitrogen deposition will drive the pH levels in alpine plant communities even lower

54 and subsequently drive EcM/ErM diversity down leaving a window of opportunity for the more acidophilic mycorrhizae and their specialist conifer partners (Slankis 1974; Natel & Neumann

1992; Kernaghan & Harper 2001) to fill in the gaps. Higher rates of dissolved organic nitrogen in the soils of ecotones may reduce the competition for these nitrogen tolerant mycorrhizae and their conifer partners which in-turn could potentially lead to more islands of krummholz beyond tree line and potential tree line encroachment. Additionally, Harsch et al. (2009) noted that although there are other many other factors to consider, across the globe over the last century, there has been a moderately high frequency (52% of sites observed) of treeline study sites reporting advancement in response to increases in winter warming associated with climate change and most notably in sites with ‘diffuse’ tree lines. Although there have been no studies on

Mount Moosilauke that directly assess these treeline predictions, further examination of dissolved organic nitrogen concentration, baseline soil temperature, and local temporal changes in mean annual temperature (MAT), their correlation to pH, and potential correlation to mycorrhizal-associated conifer (e.g. Abies balsamea) seedling density and dispersal in these communities could be conducted to verify these predictions.

Additional aspects of change in alpine plant communities that are of serious concern to professionals in the field include impacts of climate warming on plant productivity, phenology, and composition (Prevéy et al. 2019; Capers & Slack 2016; Capers & Stone 2011; Harsch et al.

2009). The accelerated disappearance of snowbed and snowbank communities and their constituents around the world has been a top priority of concern in alpine ecology for the last few decades (Bjork & Molau 2007; Schob et al. 2009; Capers et al. 2013). Prevéy et al. (2019) noted that after analyzing over 46,000 tundra plant phenology observations from around the world they discovered that not only were warmer temperatures leading to shorter flowering windows, those

55 shorter flowering windows had the potential to cause trophic cascades in tundra plant communities.

There have been reports from around the circumpolar alpine research community regarding the migration and potential extirpation of Salix herbacea L. from snowbed communities that are changing due to climate warming (Wijk 1986; Alsos et al. 2008; Wheeler et al. 2015; Wheeler et al. 2016). Salix herbacea is a circumpolar plant in the Salicaceae that is characterized by an ‘Amphi-Atlantic’ distribution wherein it can be found on both sides of the

Atlantic ocean but is absent from the Pacific side of the Northern hemisphere (Hultén & Fries

1986). This species was noted to have genetically diverse meta-populations in the alpine communities of Europe and predictive models showed that though Southern meta-populations had experienced a very different proglacial history and were predicted to lose approximately five percent genetic diversity via climate induced warming, Northern European meta-populations were predicted to lose a range of 46-57 percent of genetic diversity by 2080 (Alsos et al. 2008).

Although Salix herbacea has not been recorded in the alpine zone of Mount Moosilauke, its recent history in response to climate change should be considered when surveying other sensitive circumpolar and circumboreal alpine snowbed plants of conservation concern in the WMNF like

Empetrum nigrum, Potentilla robbinsiana Oakes ex Rydb., and Sibbaldia procumbens L.

(Cogbill et al. 1993; Williams 2002; Schide & Munroe 2015; Sperduto et al. 2018).

Drawing on what was learned in this study, it is clear that short-term changes in plant species diversity and richness remain relatively the same across all communities. However, at the community level, decreases in diversity were found in association with potential die-backs of vascular plants and increases in plant species richness in the meadows were associated with increases in graminoid cover. In the long-term, there were increases in both richness and

56 diversity across all communities and these increases were associated with long-term increases in vascular plant abundance in the meadow communities. Though the relationship between soil depth and species richness was negative across all communities, there were strong positive correlations at the community level. There were strong positive relationships between ErM/EcM colonization and soil depth at both the community level and across all communities and a strong positive relationship between ErM/EcM colonization and soil pH at the community level. All of these relationships, both aboveground and belowground, contribute in some way to alpine plant community dynamics of Mount Moosilauke. However, these connections are never as clear cut and exemplary as one would anticipate given that there are mechanisms both abiotic and biotic that can cause long-term and short-term effects on plant community and soil community dynamics. Some of these mechanisms could be human induced disturbances, atmospheric deposition, and or climate-induced changes. Though there were community level changes in richness and diversity in the short-term that reflect a decrease, in the long-term these trends are positive. The relationship between soil depth and plant richness and diversity in alpine communities is complex and heterogeneous and requires further study to fully understand. There is a clear and definitive relationship between soil pH and EcM/ErM abundance on Mount

Moosilauke. However, the long-term drivers of pH in those communities sampled should be explored more closely to determine a root causality and a clearer path to what can be done to preserve these fragile communities.

Based on what this study has shown, there are changes in composition in the meadows that favor annual graminoids and previously unrecorded boreal forbs are showing up in the snowbed community. The latter may indicate that although richness appears to be increasing in light of climatic changes, in the alpine, this happens on an elevational gradient. As communities

57 along the ecotone become richer, the competition for space may influence circumpolar plants and they will have nowhere to go but up. The increase in graminoid abundance in the meadows especially in J. trifidus and C. bigelowii may indicate a homogeneity of sedge-rush-heath meadow across these communities. Understanding the relationships between aboveground richness and diversity and belowground factors is a good foundation for understanding the broader climatic influences on plant community composition and phenology in the years ahead.

It is only when we play the part of conservation stewards in a way that reflects our deep connection with and influence on the natural world can we truly understand how to effect real and lasting conservation initiatives in an uncertain future.

Figure 24. Comparison of mean percent cover of Dead Organic Matter (DOM) from 2015 to 2018 in the East peak (T4) community. 2015 mean percent cover was 3.6% ± 2.2SE, 2018 mean percent cover was 17.7% ± 4.96 SE with a mean increase of 14.1 % ± 4.4 SE. Paired t-test results show this to be a significant increase ( t13= 4.3, p= .0008).

Figure 26. Comparison of mean percent cover of all graminoids and forbs (excluding ferns and lycopods) across all five plant communities from 1993 to 2018. Overall, total mean percent cover in 1993 was 6.4% ± 2.9 SE and total mean percent cover in 2018 was 12.1% ± 4.4 SE with a mean increase of 5.7% ± 1.9 SE. The three species that showed a decrease in mean cover were Betula cordifolia (-4.8% ± .2 SE), Solidago macrophylla (-.1% ± .08 SE), and Vaccinium uliginosum (-.8% ± 1 SE). The decrease in B.cordifolia was significant (p<.05).

Figure 27. Comparison of mean percent cover of all vascular plants from 2015 to 2018 in the Western fellfield (T1) community. 2015 mean percent cover was 25 % ± 10.5 SE, 2018 mean percent cover was 32.7 % ± 11.9 SE with a mean increase of 7.7% ± 2.5 SE. Paired t-test results

show this to be a significant increase (t16= 3.61, p= .002,Table 5, p. 41).

Figure 28. Comparison of mean percent cover of vascular plants by species from 2015 to 2018 in the Western fellfield (T1) community. The highest mean increases were attributed to Carex bigelowii (16.8% ± 5.2 SE) and Juncus trifidus (12.3% ± 6.3 SE) respectively.

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Appendix A

Table 9. List of all species identified in the study from 1993 to 2018. Species are listed top to

bottom by morphological class (i.e., Tree, shrub, graminoid, forb, bryophyte, and lichen).

Appendix B

Appendix C