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Interspecific boreal shrub growth response to climate, fertilization and herbivory. Grabowski, Meagan M. 2015

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INTERSPECIFIC BOREAL SHRUB GROWTH RESPONSE TO CLIMATE, FERTILIZATION AND HERBIVORY  by Meagan M. Grabowski  B.Sc., The University of British Columbia, 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2015  © Meagan M. Grabowski, 2015 ii   Abstract  Substantial evidence indicates a circumpolar ‘greening’ trend largely attributed to an increase in tall shrub abundance. Shrub expansion creates positive feedbacks to the global climate, involving the release of stored carbon due to shifts in permafrost stability and nutrient cycling. Understanding drivers of shrub growth is therefore essential to predicting how northern ecosystems will respond to climate change. After a decade of research into circumpolar shrub expansion, there remain large gaps in our understanding of shrub expansion below treeline in the boreal forest biome. In the Kluane region of southwest Yukon, there has been an increase in the aboveground standing biomass of boreal shrubs by one and a half to two times over the past 27 years. I used dendroecological methods to assess the relative impacts of climate, fertilization, and herbivory on three boreal understory shrub species (Betula glandulosa, Salix glauca, and Shepherdia canadensis) and found interspecific variation in growth response. I used a mixed effects model selection analysis to determine which factors were strongest in determining annual ring width. B. glandulosa had increased ring widths with added nutrients, S. glauca was not climate or nutrient sensitive, and S. canadensis responded to precipitation and drought index climate variables. When the same growth analysis was performed on canopy trees in the same area, Picea glauca ring widths were found to be more climate and nutrient sensitive than the shrubs growing beneath them. However, the response of shrub growth to the aforementioned factors was not strongly associated with canopy cover by trees. This indicates there is a difference between trees and shrubs in their growth response other than the influence of canopy species on understory light availability. If shrubs continue to increase, and boreal trees decline in iii   health (drought, insects, fire) as predicted, there could be a shift from a more tree-dominated to a more shrub-dominated system. This would influence local habitat for key wildlife species and global climate feedbacks via carbon storage or release. The boreal forest is one of the largest forests in the world, and understanding vegetation change in this biome is essential for adapting to climate change.    iv   Preface  Fieldwork for this research was conducted at the Kluane Lake Research Station (Arctic Institute of North America) in the Yukon Territory, Canada. Approval for this research to occur in the Yukon Territory was under Yukon Department of Tourism and Culture Scientists and Explorers Act Licenses (14-10S&E and 15-01S&E).   This research was stimulated by observations by Dr. Charles Krebs of increased shrub densities, and further developed with guidance from Dr. Isla Myers-Smith. I was responsible for background research, developing research questions, conducting field work to collect data, processing and analysis of samples, and manuscript preparation. Additional data for analysis (snowshoe hare abundances, past biomass surveys, and original fertilization experiment setup) was supplied by Dr. Charles Krebs. The tree ring data for Chapter 3 was supplied by Dr. Rudy Boonstra and Emily Lomax (Dr. Lori Daniels).   Versions of Chapter 2 and 3 will be submitted for publication and co-authored by Dr. Krebs, Dr. Myers-Smith, and in addition for Chapter 3 Dr. Rudy Boonstra and Dr. Lori Daniels.   v   Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents .......................................................................................................................... v List of Tables ............................................................................................................................... vii List of Figures ............................................................................................................................. viii Acknowledgements ...................................................................................................................... ix Chapter 1: Introduction ..........................................................................................................................1 1.1 Study System ............................................................................................................................1 1.2 Shrub Dendroecology ..............................................................................................................3 1.3 Study Objectives ....................................................................................................................10 Chapter 2: Contrasting interspecific boreal shrub growth responses to climate, fertilization and herbivory ...........................................................................................................................................11 2.1 Introduction ............................................................................................................................12 2.2 Methods ..................................................................................................................................14 2.3 Results ....................................................................................................................................21 2.4        Discussion ..............................................................................................................................28 Chapter 3: What controls the growth rates of trees and shrubs in the boreal forests of southwestern Yukon? .............................................................................................................................34 3.1 Introduction ............................................................................................................................34 vi   3.2 Methods ..................................................................................................................................37 3.3 Results ....................................................................................................................................39 3.4 Discussion ..............................................................................................................................43 Chapter 4: Conclusion ...........................................................................................................................48 Literature Cited .......................................................................................................................... 52 Appendices ................................................................................................................................... 62   vii   List of Tables  Table 2.1 Summary of climate variables used in analysis for all sampling sites; extracted from ClimateWNA (data from 1976-2014). .......................................................................................... 17 Table 2.2 Summary of shrub-ring series between species and treatments.. ................................. 19 Table 2.3 Mixed effect model AIC comparison results for the canopy comparison (5years ring width means), climate models (using all years ring widths testing a null model, each of the 8 variables separately, and full model with all 8 variables), fertilization model (all years ring widths), herbivory model (all years ring widths).. ........................................................................ 22  Table 3.1 Summary of tree and shrub-ring series between species and treatments (after 50% Measurement Repeatability Index exclusion).. ............................................................................. 37  Table 3.2 Mixed effect model AIC comparison results for climate models (null model, each of the 8 variables separately, and full model with all 8 variables) and all factors model.. ............... 40! Table 3.3 Mixed effect model results for fertilization treatment (factored by “pre”, “during”, and “post” treatment time periods) and interactive term for fertilization treatment and average June/July summer temperature…………………………………………………………………..44!    viii   List of Figures  Figure 1.1 Map of sites where shrub individual stems were sampled. .......................................... 2!Figure 1.2 Cross-section of a 16-year old B. glandulosa stem taken with microscope; sampled September 5, 2014 on Grizzly (fertilized) grid (see Figure 1.1). .................................................... 5 Figure 2.1 Dimensions of shrubs species sampled demonstrating difference in height (length of longest stem) in relation to stem diameter (of largest live stem) of A) B. glandulosa, B) S. glauca and C) S. canadensis. .................................................................................................................... 20 Figure 2.2 Ring width (mean ring width for each individual for 2009-2014), for each treatment and species, in relation to canopy openness (% open). ................................................................. 24 Figure 2.3 Mean biomass and ring width for time periods pre (1974-1986), during (1987-1994), and post (1995-2014) fertilization treatment. ............................................................................... 26 Figure 2.4 A) Trend in normalized ring width for each species, B) fall hare density, C) mean June/July summer air temperature, and D) Drought index ........................................................... 27 Figure 3.1 Mean ring width (plus or minus SE) for A) P. glauca, B) B. glandulosa, C) S. glauca and D) S. canadensis, for the time periods pre (before 1987; number of years varies by age of individual), during (1987-1994), and post (1994-2014) fertilization treatment ........................... 42 Figure 3.2 Linear model R-squared outputs for each individual shrubs most recent 10 years’ annual radial growth (2004-2014), tested against average June/July summer temperature of that years growth, by percent openness of tree canopy. ...................................................................... 43   ix   Acknowledgements  Thank you to my committee Dr. Charles Krebs, Dr. Isla Myers-Smith and Dr. Jedediah Brodie for their thoughtful instruction and support. Dr. Krebs provided essential guidance and humour throughout this project, and I greatly value his continued contributions to the Yukon science community. Growing up in northern Canada I did not consider graduate school an option until opportunities and motivation were provided to me by Dr. Myers-Smith; without her continued mentorship and friendship none of this would be possible. Thank you to field assistance from: Rebecca Irish, Hannah Schmidt, Clara Flintrop, Patti Grabowski, and Yukon Youth Conservation Field Corps (Y2C2 Red Crew and Blue Crew 2014). This work was made possible by support from the W. Garfield Weston Foundation, Wildlife Conservation Society Canada, Northern Scientific Training Program, and Yukon Research Centre’s Northern Research Endowment Fund. During the completion of this study, the author was supported by NSERC, the W. Garfield Weston Foundation, the Association for Canadian Universities for Northern Studies, and the Yukon Foundation. Many thanks to Kluane Lake Research Station, especially Sian Williams and Lance Goodwin, for logistical support, Don Griffiths (Imaging Technician) at the Spencer Entomological Collection, Beaty Biodiversity Museum, UBC for guidance and use of the Leica microscope, Diana Hastings and Dr. Simon Ellis at UBC Forestry for guidance and use of the sledge microtome, and Dr. Lori Daniels and lab for guidance on belt sanding and scanning. Thank you to the Champagne-Aishihik First Nation and Kluane First Nation for allowing this research on their traditional lands. Lastly, I feel very fortunate to have a network of incredible friends, family and mentors. Thank you to everyone who listened, helped, motivated, x   and distracted me when necessary. Special thanks to Andrew Cook and Natalie Caulk (TMD) for surviving the first eight months with me, to Rebecca Irish for true friendship and trudging through Grizzly grid, and to my family, especially my parents Tony and Patti Grabowski, for their continued support.       1 Chapter 1:!Introduction  There are currently rapid vegetation changes occurring in the Arctic and Subarctic due to global climate change (Myers-Smith et al. 2015, Tape et al. 2012, Sturm et al. 2001, Wilmking et al. 2004). Applying our knowledge of tundra shrubs to the boreal understory shrubs, especially when many of the same species are present in both ecosystems, is an important step to understanding the connections between boreal and tundra systems. Boreal systems are expanding in geographic range into tundra systems (Tape et al. 2015, Harsch et al. 2009, Danby and Hik 2007, Zhang et al. 2013), therefore understanding the relative importance of climate, nutrients, and herbivory to boreal shrub growth could be relevant as trees colonize the Arctic and alpine.  1.1! Study System  Shrubs are a dominant vegetation type in both the boreal forest and tundra biomes. In the boreal understory, shrubs form a second canopy layer and in many tundra ecosystems, tall shrubs are the dominant canopy-forming species. Shrubs are an important component of boreal and tundra ecosystems and food webs (Powell et al. 2013). Specifically, in the boreal forest shrubs are a key winter food source for snowshoe hares, moose, and ptarmigan (Smith et al. 1988). Vegetation in the Kluane region in southwest Yukon Territory (61° N, 156° W), is dominated by white spruce (Picea glauca) boreal forest, with the understory mainly composted of grey willow (Salix glauca) and bog birch (Betula glandulosa; Turkington et al. 1998).   2  The dominant mammalian species in this region is the snowshoe hare (Lepus americanus), which has eight to ten-year population cycles (Krebs et al. 2014). S. glauca and B. glandulosa terminal branches are browsed by hares as important winter food, however, B. glandulosa is the preferred food with over 80% of five millimeter twigs being browsed in the 1981-82 hare peak (Smith et al. 1988).    Figure 1.1 Map of sites where shrub individual stems were sampled.   3 From 1988-1994, two-one kilometer squared plots near Kluane Lake, underwent nitrogen-potassium-phosphorus (NPK) addition and were monitored for eight years after for: 1) annual shrub growth according to five millimeter twigs and 2) biomass (Turkington et al. 1998, Melnychuk and Krebs 2005). Annual shrub growth increased for both S. glauca and B. glandulosa in the eight years following the start of the fertilization treatment, and even greater increases were found four to eight years after fertilization had ceased (Turkington et al. 1998, Melnychuk and Krebs 2005). These studies demonstrate that boreal shrub growth is enhanced with nutrient addition, but how stem branch tip growth relates to radial growth remains unknown.  Quantitative evidence is lacking to determine if boreal shrubs are increasing in the subarctic forest biome, via increased annual growth and expansion, similar to what has been observed in tundra ecosystems (Sturm et al. 2001, Tape et al. 2012, Myers-Smith et al. 2011). Repeat aerial photography and dendroecological evidence suggest that trees are invading boreal grasslands in southwest Yukon over the last 60-80 years, but the contribution of shrubs to this spatial shift is largely unknown (Conway and Danby 2014). There are several natural history observations of increased shrub density and height (Krebs pers. comm.), however a quantitative assessment has not yet been produced.   1.2! Shrub Dendroecology  The majority of shrub expansion studies thus far have been conducted in tundra ecosystems (Tape et al. 2006, Myers-Smith et al. 2011, Sturm et al. 2001, Epstein et al.  4 2013). Above treeline, there is a distributional limit of shrubs, or shrubline, and these elevational limits vary with latitude and aspect. Shrubs at the shrubline are generally shorter and/or exhibit a prostrate growth form (Myers-Smith et al. 2011). However, shrubs in the boreal understory have a tall growth form. The three main drivers of shrub expansion suggested in the literature are: 1) increased temperature, 2) increased nutrient cycling, and 3) snow dynamics, disturbance and permafrost thaw (Jørgensen et al. 2015, Holleson et al. 2015, Tape et al. 2012, Myers-Smith et al. 2015, Epstein et al. 2013, Sturm et al. 2005). But in addition to these abiotic factors, biotic factors, and in particular herbivory, are also at play (Tape et al. 2015, Christie et al. 2015, Bernes et al. 2015, Oloffson et al. 2013, Tape et al. 2010).  Dendrochronology is one of the most informative tools available for understanding changes through time in tree and shrub growth in seasonal ecosystems (Kolishchuk 1990, Schweingruber and Poschlod 2005).  Although traditionally used with trees, dendrochronology has also proved useful in measuring growth in tall and dwarf shrub species especially in circumpolar regions (Myers-Smith et al. 2011). Shrub dendrochronology can be used to quantify the sensitivity of growth to various factors including: climate (Myers-Smith et al. 2015, Ropars et al. 2015, Blok et al. 2011), fertilization (Paradis et al. 2014, Zamin and Grogan 2012), and herbivory (Christie et al. 2015, Bernes et al. 2015, Tape et al. 2010, Olofsson et al. 2009).   5  Figure 1.2 Cross-section of a 16-year old B. glandulosa stem taken with microscope; sampled September 5, 2014 on Grizzly (fertilized) grid (see Figure 1.1). The darker rings are caused by declined growth during winter months and the distance between dark rings gives a quantitative measurement of annual radial growth. These rings can be used to determine impacts of various factors on annual growth through time.  Climate Warmer growing season temperatures are acknowledged to be one of the main drivers of Arctic shrub expansion (Jørgensen et al. 2015, Myers-Smith et al. 2015, Holleson et al. 2015, Ropars et al. 2015, Tape et al. 2012). Globally, land surface air temperature has increased 0.25°C from 1979-2012 (IPCC 2013). In Canada, annual mean temperature  6 increased 1.5°C from 1950-2010 (Vincent et al. 2012). In southwestern Yukon the increase in spring temperature (April-June) from 1975 to 2001 was 2°C (Réale et al. 2003). Increased temperatures may lead to positive or negative growth responses in circumpolar and circumboreal vegetation (Aerts 2006, Myers-Smith et al. 2015), but the relative growth responses of boreal trees and shrubs compared to eachother are unknown.  In interior Alaska, some studies indicate decreases in the annual growth of white spruce associated with increased temperatures (Barber et al. 2000, Lloyd and Bunn 2007). This is comparable to subarctic NDVI literature that indicates a ‘browning’ trend of the boreal biome (Beck et al. 2011, Verbyla 2008).  These growth responses contrast with many alpine and arctic shrub and tree-line studies in which radial growth was positively correlated to warmer summer temperatures (Myers-Smith et al. 2015, Wilmking et al. 2004, Harsch et al. 2009, Garfinkle and Brubaker 1980). Subarctic climate change may promote growth with increased temperatures, yet in semi-arid regions diminishing growth could be caused by drought stress or nutrient limitations (Barber et al. 2000). These interacting processes complicate boreal dendroclimatology (Jacoby and D’Arrigo 1995).  Fertilization The growth of shrub species in northern ecosystems is nutrient limited (DeMarco et al. 2014, Zamin and Grogan 2012, Melnychuk and Krebs 2005). There is strong experimental evidence from fertilization trials that nutrient availability is a stronger determining factor of shrub growth than temperature in tundra regions (DeMarco et al. 2014, Zamin and Grogan 2012, Chapin et al. 1995). Positive feedbacks are created by  7 shrub expansion wherein ground insulation due to increase snow depth where shrub canopies capture snow, could lead to alterations in soil temperature (Myers-Smith and Hik 2013, Lawrence and Swenson 2011) and potential increases in nutrient cycling (Sturm et al. 2001, Buckeridge et al. 2010). Shrubs are potentially facilitating their own expansion, via positive feedbacks.   Fertilization experiments in both tundra shrubs and boreal trees have demonstrated increased radial growth (Zamin and Grogan 2012, Malik and Timmer 1996) and productivity (DeMarco et al. 2014, Gough et al. 2012). Tree seedlings that are nutrient-loaded treatment grow more in both height (15-18%) and biomass (16-39%) compared to standard fertilization treatments (Malik and Timmer 1996). Extended increases in nutrients can also cause species composition shifts, for example dwarf birch becoming more dominant in certain tundra plant communities during 11 years of fertilization treatment (Gough et al. 2012). However, these inter-annual growth and productivity increases with fertilization could have interactive effects with other factors including climate (Jørgensen et al. 2015, Myers-Smith et al. 2015, Holleson et al. 2015) and herbivory (Bernes et al. 2015, Christie et al. 2015).    Due to the cold climates and short growing seasons of high-latitude ecosystems, nutrients are the main limiting factor for plant growth (Sistla et al. 2013, Aerts 2006, Chapin et al. 1995). With warming temperatures and longer growing seasons, experiments have demonstrated increased decomposition rates, litter decomposition, and greater nutrient availability due to shifts in species composition (Sistla et al. 2013, Gough et al. 2012,  8 Aerts 2006, Chapin et al. 1995). Therefore, it is important to consider the interactive effects of climate and nutrients to account for warmer and more nutrient-rich future scenarios (Aerts 2006, Hobbie et al. 2002).   Herbivory A largely overlooked mechanism potentially inhibiting shrub expansion is herbivory. The majority of the shrub expansion literature has focused on bottom-up controls to growth, but herbivores, especially when at high densities and in high-latitudes, can have a large impact on the aboveground biomass of shrubs (Tape et al. 2015, Christie et al. 2015, Tape et al. 2010, Olofsson et al. 2009). Dendrochronological evidence, especially variation in inter-annual growth and scarring, can be used to infer changes in some animal populations (Speed et al. 2011, Boudreau et al. 2003, Myers-Smith et al. 2015, Morneau and Payette 2000). The main herbivores impacting circumpolar shrubs are snowshoe hares, caribou, moose, and ptarmigan (Christie et al. 2015, Tape et al. 2015, Zamin and Grogan 2012, Tape et al. 2010). Some studies indicate herbivory inhibits shrub growth (Tape et al. 2015, Kaarlejärvi et al. 2015, Tape et al. 2010, Speed et al. 2011, Zamin and Grogan 2012), whereas others indicate a potential rebound effect of browsing resulting in an enhancement of shrub growth (Christie et al. 2015, Danell et al. 1994, Krebs 2011, Smith et al. 1988).  There is dendrochronological evidence of herbivory limiting shrub growth (Speed et al. 2011). Based on an exclosure experiment, Speed (2011) found evidence that sheep herbivory in alpine Norway limited radial growth of birch, and that this overrode the  9 effect of temperature on radial growth. Therefore, not only can evidence of browsing appear in shrub-rings, but dendrochronology provides an opportunity to test the relative strengths of several factors such as temperature or herbivory on growth.  Zamin and Grogan (2012) found increased soil nutrients decreased the amount Betula nana was able to exhibit compensatory growth after large browsing events. Browsed plants usually react with compensatory growth, and after heavy browsing events shrubs can have dramatic rebound growth, as suggested in Krebs (2011). However, if plants are not nutrient-deprived because herbivores are introducing nutrients back to the soil, rebound effects could be diminished (Zamin and Grogan 2012).   In the Kluane boreal forest, Salix glauca and Betula glandulosa terminal branches are browsed by hares as important winter food (Smith et al. 1988). B. glandulosa is the preferred food, with over 80% of 5mm twigs being browsed in the 1981-82 hare peak (Smith et al. 1988). In winter, if B. glandulosa and S. glauca availability is low, hares will also browse white spruce (Picea glauca) and soapberry (Shepherdia canadensis; Smith et al. 1988). Variability in snow depth, both seasonally and inter-annually, could be impacting the extent to which snowshoe hares have access to branch tips, potentially magnifying the impact on lower stems (Brodie et al. 2012). In turn, herbivory by snowshoe hares and other species can structure the abundance and growth of shrubs (Tape et al. 2010, Dale and Zbigniewicz 1997). From 1987-2008 the annual growth (terminal stem of less than 5mm) of S. glauca and B. glandulosa was measured with no  10 significant trend over time and high inter-annual variability (Powell et al. 2013). After high hare years, however, browsing did stimulate growth (Smith et al. 1988, Krebs 2011).   1.3! Study Objectives  The objectives of this research are to assess the increases in shrub abundance via conducting a repeat aboveground biomass survey, and to use dendroecological methods to disentangle the effects of climate, fertilization, and herbivory on boreal shrub growth. Additionally, I explored the interactions between overstory trees and understory shrubs. I tested the following hypotheses: 1) that open canopy cover increases the radial growth of boreal shrubs, 2) that warmer temperatures increase radial growth, 3) that increased nutrients increase radial growth, 4) that decreased herbivore abundance increases radial growth, 5) that climate, of climate, fertilization and herbivory, is the strongest factor influencing radial growth, and 6) that trees respond similarly to shrubs to climate, fertilization and herbivory.  11 Chapter 2:!Contrasting interspecific boreal shrub growth responses to climate, fertilization and herbivory  Warmer summer temperatures and reduced herbivore abundances have been linked to increased shrub growth in tundra ecosystems (Jørgensen et al. 2015, Holleson et al. 2015, Myers-Smith et al. 2015, Tape et al. 2015, Christie et al. 2015) but we do not know if these same factors are also contributing to shrub expansion below treeline in the boreal forest ecosystem. Repeat biomass surveys and local observation show the aboveground standing biomass of boreal shrubs has increased one and a half to three times from 1987-1996 to a repeat survey in 2014. I used dendrochronological methods to estimate the relative impacts of climate, fertilization and herbivory on three dominant boreal shrub species (Betula glandulosa, Salix glauca, and Shepherdia canadensis) and attempt to assess causation for this increase in biomass. I found interspecific growth responses, wherein B. glandulosa exhibited a negative growth response to summer temperature but positive growth response to fertilization treatment, S. glauca was not positively or negatively nutrient or climate sensitive, and S. canadensis had a negative growth response to spring precipitation and a negative growth response to fertilization treatment. The results of this research chapter fill important knowledge gaps in the understanding of growth responses of boreal shrubs, and demonstrate that there are differences between boreal shrubs and the more well-studied tundra shrubs.    12 2.1! Introduction  Increases in shrub growth and abundance have been observed in tundra ecosystems (Tape et al. 2006, Myers-Smith et al. 2011, Elmendorf et al. 2012), but boreal forest shrub growth dynamics remain understudied. In tundra ecosystems, shrub expansion has been linked to climate warming (Myers-Smith et al. 2015, Elmendorf et al. 2012) and tundra shrub growth has been demonstrated to vary with ungulate and avian herbivore densities (Olofsson et al. 2009, Tape 2012, Zamin and Grogan 2012), but the relationship between these factors and boreal shrub growth remain untested. Shrub cover in tundra ecosystems can affect key global climate feedbacks such as surface albedo, permafrost thaw, and release of stored carbon (Cahoon et al. 2012, Chen et al. 2012, Myers-Smith et al. 2011), and change in boreal forest biomass could also contribute to global climate feedbacks (Berner et al. 2015).   Boreal forest trees in Alaska have demonstrated a ‘browning’ trend, or a decrease in productivity largely observed by dendrochronology and satellite imagery, likely a result of drought conditions, forest fire, and insect outbreaks (Barber et al. 2000, Beck et al. 2011, Verbyla 2008), yet the change in productivity of understory species overtime has yet to be explored. Observational and biomass data show increases in abundance of boreal shrubs in southwest Yukon, including increases in distribution into grasslands (Krebs unpublished, Conway and Danby 2014). However, the potential factors responsible for these changes in boreal shrub biomass have not been identified.   13 Dendrochronological analysis can be used to examine growth trends of woody plants in response to climate and other factors (Schweingruber and Poschlod 2005, Myers-Smith et al. 2015, Speed 2011, Kolishchuk 1990). Increases in summer temperatures and decreases in herbivore abundance can increase annual ring width of shrubs (Speed 2011). Drought and increased herbivore abundance can decrease tree ring growth (Barber 2000, Speed 2011). Warmer temperatures have been shown to increase nitrogen in soil (Chapin 2015) and fertilization experiments facilitate increases in tundra shrub growth (Kaarlejärvi et al. 2015, Elmendorf et al. 2012). Though individual studies indicate the importance of different factors in driving growth, few studies have tried to compare the influence of multiple factors such as climate, fertilization and herbivory on shrub growth in high-latitude ecosystems.  In this chapter, I explore the influence of climate, fertilization and herbivory on boreal forest shrub growth using annual ring widths of the three dominant understory shrubs of southwestern Yukon. This chapter combines dendrochronological data with long-term ecological monitoring data of climate, a fertilization experiment (1987 to 1994), and snowshoe hare abundance (1976 to 2014). I tested the following hypotheses: 1) that open canopy cover increases the radial growth of boreal shrubs, 2) that warmer temperatures increase radial growth, 3) that increased nutrients increases radial growth, 4) that decreased herbivore abundance increases radial growth, and 5) that climate is the strongest factor of the four potentially influencing radial growth.    14 2.2! Methods  2.2.1! Study Area  The field sites are located near Kluane Lake, in southwest Yukon Territory (61 º N, 156 º W). Vegetation in this region is dominated by white spruce (Picea glauca) boreal forest, with the understory mainly composted of grey willow (Salix glauca) and bog birch (Betula gladulosa) (Turkington et al. 1998). Another key shrub species for Grizzly Bear (Ursus arctos) food is soapberry (Shepherdia canadensis; MacHutchon and Wellwood 2003). The four main sites of this study, Silver, Sulphur, Grizzly, and Flint, are 1 km2 in size and composed of continuous or patchy forest (45-60% canopy cover, approx. 140-220 stems/ha) with open areas being shrub dominated (Krebs 2014 pers comm., Turkington et al. 1998).   2.2.2! Fertilization treatments  Two of the four sites underwent a fertilizer treatment as part of the Kluane Project between 1987 and 1994. The fertilized sites, Grizzly and Flint, were nitrogen fertilized in 1987 and then NPK fertilized at the beginning of each growing season from 1988-1994 (Turkington et al. 1998, Boutin et al. 2001). To ensure a uniform distribution of fertilizer granules throughout the plot, plastic bags (1 m2) were placed at 15 locations in each plot on the day fertilizer was applied and the amount of fertilizer pellet weighed after fertilizer drop (Turkington et al. 1998). I have divided this time series of shrub growth data into  15 three periods reflecting the intensity of fertilization treatment: pre (before 1987), during (1987-1994), and post fertilization (1994-2014).  2.2.3! Herbivore population monitoring  The dominant mammalian species in this region is the snowshoe hare (Lepus americanus), which shows eight to ten-year population cycles (Krebs et al 2014). S. glauca and B. glandulosa terminal branches are browsed by hares as important winter food, although B. glandulosa is the preferred food, with over 80% of five millimeter B. glandulosa twigs being browsed in the 1981-82 hare peak (Smith et al. 1988). Herbivore populations have been monitored as part of the Kluane Project and ongoing monitoring since 1975 using live trapping and mark/recapture methods in the spring and fall. I used autumn season densities for this thesis because it best represents hare abundance entering the winter shrub feeding season.  2.2.4! Spruce beetle disturbance  During the 1990’s there was an outbreak of spruce bark beetles that led to substantial tree mortality in the Kluane region (Krebs et al. 2014, Berg et al. 2006). Some of these spruce beetle killed trees have since fallen, creating openings in the forest canopy. In order to account for this potential change, I measured canopy openness above the location where each shrub stem section was collected. Canopy openness measurements were based on circular hemispheric photographs (Nikon Coolpix P5000 with FC-E8 Fisheye lens and  16 UR-E20 adapter) of the canopy from standing height at each shrub and analyzed with Gap Light Analyzer (Frazer 1999) for percent canopy openness. Digital circular hemispheric photography is an effective tool for measuring canopy-openness compared to both film cameras and spherical densiometers (Englund 2000, Fiala 2006, Paletto 2009).  2.2.5! Fire  The boreal forest has a fire disturbance regime, but in the Kluane region there have been no large and only occasional small, fires in the past 200 years (Boonstra et al. 2008). The Flint site was burned in approximately 1924 and has younger spruce trees in a more open habitat, and the Grizzly grid was burned in approximately 1870 and has a less open, older spruce tree habitat (Boonstra et al. 2008).   2.2.6! Climate data  The climate data used for this study were extracted from the ClimateWNA data set, which corrects for gaps in meteorological station data for elevational differences between station data from the region and the study site location (www.climatewna.com). The climate data was extracted for one location (61.01 N, -138.31 W) and used for all sites. From the 26 potentially relevant climate variables I chose 7 climate variables that are suggested to be important in controlling shrub growth (Myers-Smith et al. 2015, Hollesen et al. 2015, Jørgensen et al. 2015, Barber et al. 2012, Elmendorf et al. 2012) and that were  17 not highly correlated with each other: mean annual temperature, growing degrees above 5°C, Hargreaves climate moisture deficit (mean June/July), spring temperature (mean April/May), spring precipitation (mean April/May), summer precipitation (mean June/July), and summer temperature (mean June/July). The models tested were: a null model, each climate variable separate, and a full model with all climate variables (Appendix A).  Table 2.1 Summary of climate variables used in analysis for all sampling sites; extracted from ClimateWNA (data from 1976-2014). Variable! Symbol! Months! Mean!Mean%Annual%Temperature%% MAT% annual% .2.5°C%Growing%Degree%Days%above%5°C%% DD.5% annual% 699.1%growing%degree.days%Hargreaves%climatic%moisture%deficit% CMD_jj% June/July% 52.49%mm%Average%Spring%Temperature% Tave_am% April/May% 18.37°C%Spring%Precipitation%Average%Summer%Temperature%Summer%Precipitation%%%%%%Prec_am%Tave_jj%Prec_jj%April/May%June/July%June/July%10.76%mm%11.70°C%38.16%mm% 2.2.7! Biomass  The shrub biomass data was collected from 1987-1996 and then again in 2014 as per protocols created by the Kluane Project (Krebs 1989). Rectangular clip plots 20 cm by 10 m were laid out at random on all the sampled grids and all aboveground shrub biomass was clipped, separated by size (< or > 5 mm diameter), and weighed. Samples were dried at 60°C for more than 72 hours to measure dry mass.  18  2.2.8! Shrub Ring Widths  In 2014, 300 shrub stems were sampled in the four 1km2 sites, 25 of each species per site for a total of 75 per site in four sites. Shrubs were selected based on a random grid points within the plots. Shrub individuals of the same species were always sampled greater than 30m apart. The coordinates of all individuals were recorded by use of a GPS (global positioning system) unit before destructively sampling the largest stem (DBH) with pruning shears or a handsaw. The largest stem was sampled because larger stems usually have longer records of growth and can be the most responsive to climate change (Karlsson et al. 2004).   Additional shrub variables At the time of sampling the following variables were measured for each shrub: height, width, number of stems, diameter of the largest stem and canopy openness. For individuals with very high stem numbers, I recorded when the stem number exceeded 40. Height was measured from ground level at the stem base to the end of the longest stem. Width was measured at the widest point of the shrub. The diameter was measured at the base of the largest stem using calipers.    Ring width measurements Stem sections were thin-sectioned using a sledge microtome (UBC Forestry) and photographed with a Leica C205 microscope (Beaty Biodiversity Museum). Cross- 19 sections too large to microtome (>6cm) were belt sanded sequentially to 400 grit, then imaged using a high-resolution scanner (UBC Forestry). Ring width was then measured manually in ImageJ 1.47v (Schneider et al. 2012). Four radii were completed (bark to core) at 90˚ and the first radii was repeated to create a measurement repeatability index (correlation of first radii re-measured). If four radii could not be measured due to growth abnormalities, three were measured separated by at least 90˚.   Table 2.2 Summary of shrub-ring series between species and treatments. Species,!Treatment! N!shrubs!Time!span!rings!Mean!age!2014! Measurement!Repeatability!Index!(SD)!B.#glandulosa,%Fertilized% 38% 1962.2014% 30.9%±%8.2% 0.72%±%0.29%B.#glandulosa,%Control% 39% 1955.2014% 28.9%±%11.2% 0.73%±%0.23%S.#glauca,%Fertilized% 32% 1955.2014% 30.0%±%9.1% 0.59%±%0.35%S.#glauca,%Control% 40% 1972.2014% 25.1%±%6.6% 0.65%±%0.23%S.#canadensis,%Fertilized% 38% 1974.2014% 21.6%±%7.3% 0.73%±%0.25%S.#canadensis,%Control% 45% 1980.2014% 19.9%±%5.8% 0.82%±%0.21%   20  Figure 2.1 Dimensions of shrubs species sampled demonstrating difference in height (length of longest stem) in relation to stem diameter (of largest live stem) of A) B. glandulosa, B) S. glauca and C) S. canadensis and the lack of differences between control and fertilized sites. Each point represents an individual shrub whose stem was sampled for ring width measurement. Trendlines indicate linear models for each treatment.    2.2.9! Data Processing and Analysis  Once radii were measured, they were cross-dated within the individual. When the shrub sample had partially missing or unmeasurably narrow rings, I included a measurement of 0.0001 mm to represent the lower limit of our measurement precision and to complete a more accurate ring width average age. After cross-dating within an individual, correlations were created comparing each of the radii and a measurement repeatability index comparing the repeated first radii. Then the four radii were averaged to create a  21 chronology for each individual shrub. Samples with a measurement repeatability index (correlation between repeated measurements of one radii) of less than 50% were excluded from the mixed effect model analysis (total 69 samples or 23% of total 300 samples collected).  Data analysis was carried using R (Version 0.98.1103. R Development Core Team 2014). The first five years of growth were removed from analysis to account for age-related growth trends, as younger rings are wider and older rings are narrower on average in many shrub individuals (Table 2.2, Myers-Smith et al. 2015). Individual shrub stem was the experimental unit, ring width was the independent variable, and the shrub individual nested within site was a random factor. Fixed effects included climate variables, fertilization treatment, herbivore density (snowshoe hare), as well as an interaction term for fertilization and temperature. Model selection was conducted using Akaike’s Information Criterion (AIC) in the model comparison analysis (Anderson 2008). For full models tested see Appendix A.  2.3! Results  2.3.1! Climate  Variation in growth of two of the three shrub species corresponded with climate, but to different variables (Table 2.3). B. glandulosa was negatively related to mean summer temperatures (June/July), whereas S. canadensis growth was positively related to drought  22 and negatively related to precipitation (Hargreaves moisture deficit and mean summer precipitation). However, S. glauca growth was not related to any of the climate models investigated in this analysis, and the B. glandulosa and S. canadensis climate relationships are not strongly significant (ΔAIC 2.58 to 3.83).   Table 2.3 Mixed effect model AIC comparison results of ring width to canopy (5 years ring width means), climate models (using all years ring widths testing a null model, each of the 8 variables separately, and full model with all 8 variables), fertilization model (all years ring widths, data subset by treatment), herbivory model (all years ring widths). Only includes parameters above null in model selection based on AIC (for climate only list models above null, fertilization and hare are just slope, t-value). Species Analysis Model Parameters ΔAIC Estimate tHvalue B.#glandulosa# Canopy%%%%Climate%Fertilization%%%Hare Canopy%*%treatment%%%Canopy%Tave_jj%Fertilization%%%. Canopy%Treatment%Canopy%*%treatment%Canopy%Tave_jj%Pre%fertilization%During%fertilization%Post%fertilization%. %7.44%%%2.99%% .0.005%±%0.001%.0.351%±%0.103%0.007%±%0.002%.%.0.098%±%0.043%0.406%±%0.076%.%.%. .3.43%.3.40%3.41%.%.2.25%5.34%%%. S.#glauca Canopy%%%%Canopy%*%treatment%%%Canopy%+%treatment%Canopy%Treatment%Canopy%*%treatment%Canopy%%4.22%%3.89%0.003%±%0.001%0.070%±%0.075%.0.003%±%0.002%0.001%±%0.001%2.37%0.92%.1.54%1.69% 23 Species Analysis Model Parameters ΔAIC Estimate tHvalue %%%Climate%Fertilization%Hare %Treatment%Canopy%.%.%. Treatment%Treatment%Canopy%.%.%. %2.81%2.87%.%.%. .0.043%±%0.021%.0.046%±%0.021%0.002%±%0.001%.%.%. .2.05%.2.22%2.03%.%.%. ###S.#canadensis###### Canopy%%%%%%Climate%%Fertilization%Hare Canopy%*%treatment%%%Canopy%+%treatment%%Treatment%CMD_jj%Prec_jj%.%. Canopy%*%treatment%Canopy%Treatment%Canopy%Treatment%Treatment%CMD_jj%Prec_jj%.%. 8.05%%%2.68%%4.1%2.58%3.13%.%. 0.002%±%0.001%.0.001%±%0.001%.0.065%±%0.033%.0.0003%±%0.001%0.0212%±%0.010%0.024%±%0.01%0.121%±%0.054%.0.133%±%0.056%.%0.01%.2.51%0.05%.0.81%2.14%0.01%2.25%.2.36%.%.   24 Figure 2.2 Ring width (mean ring width for each individual for 2009-2014), for each treatment and species, in relation to canopy openness (% open). Each point (black) represents mean of the most recent five years of ring width of one individual stem. Trendlines only shown when p < 0.05.  25  2.3.2! Fertilization  Shrub biomass increased from an average of 1987-1996 to 2014 in both fertilized and unfertilized sites by 135%, however due to spatial variation in trends this difference is not statistically significant (Figure 2.3, p = 0.6). However, for ring width only B. glandulosa of the three shrub species responded most strongly to fertilization (ΔAIC 5.04 to full model, Table 2.3). S. glauca had the lowest AIC value for the null model (ΔAIC 0.98 to fertilization), similarly to S. canadensis which has the lowest AIC value for the null model (ΔAIC 1.47 to fertilization).   The response to fertilization interacted with canopy cover (Figure 2.2). B. glandulosa (ΔAIC 7.44) and S. canadensis (ΔAIC 8.05) responded to the interactive term of fertilization and canopy, whereas S. glauca did not (ΔAIC <2).   2.3.3! Herbivory  None of the three boreal shrub species showed a positive or negative radial growth correlation with the fall density of snowshoe hares through time from 1976-2014 (Table 2.3, ΔAIC <2). However, there is observational and photographic evidence of snowshoe hare browse (Krebs, pers. comm.).   26  Figure 2.3 Mean biomass and ring width for time periods pre (1974-1986), during (1987-1994), and post (1995-2014) fertilization treatment.   27  Figure 2.4 A) Mean normalized ring width for each species, B) fall hare density, C) average June/July air temperature, and D) Climate WNA derived Drought Index (Hargreaves Climate Moisture Deficit).     28 2.4! Discussion   Our study demonstrates that the growth of two of the three dominant boreal shrub species found in the Kluane region of southwest Yukon was related to climate, but not to the same climatic variables. B. glandulosa radial growth had a negative relationship with summer temperature and S. canadensis radial growth had a positive relationship with summer drought and a negative relationship with precipitation. Interactive effects between fertilization and canopy cover of trees were found for the all three species, but more so with the same two species (B. glandulosa and S. canadensis). Variation in growth was not related to herbivore density for any of the three species over the time period 1976-2014. Relationships among growth and climate varied among these three species, illustrating the need to use species-specific projections of shrub growth over time. The growth of boreal shrubs in response to fertilization was influenced by canopy cover. This finding demonstrates a key difference between boreal forest understory species and tundra species where tree canopy does not constrain shrub growth responses. Lastly, the lack of evidence for herbivore-limited shrub growth in this study contrasts with tundra shrub studies, which highlight herbivory (i.e. wild caribou, domestic reindeer or sheep, and ptarmigan) as a major influence on tundra shrub expansion in some areas (Olofsson et al. 2009, Speed et al. 2011, Tape et al. 2010, Zamin and Grogan 2012). Our study demonstrates that, although the same factors are influencing the growth of boreal shrubs as tundra shrubs (i.e. warmer summer temperature, increased soil nutrients, and fluctuations in herbivore densities), the growth relationships differ.    29  2.4.1 Climate  Growth of two of the three dominant shrub species were climate sensitive, which mirrors the boreal forest literature indicating increases and decreases in boreal forest productivity with climate change (Barber et al. 2000, Beck et al. 2011, Beck and Goetz 2012). Barber et al. (2000) found negative climate-growth relationships in P. glauca trees in Alaska indicating drought-stress in boreal forests. However, Beck et al. (2011) found evidence of both increasing productivity at the boreal-tundra ecotone and decreasing forest productivity in interior Alaska when comparing tree ring and satellite data. The Kluane region sampled for this study is in the rain-shadow and may be susceptible to drought, however, only S. canadensis growth was correlated with spring precipitation and an annual drought index. Similar to the boreal forest tree literature, this boreal shrub data indicates evidence of both positive and negative drought growth relationships and contrasting productivity trajectories contingent on species.  Growth of tundra shrubs has been demonstrated to be climate sensitive at sites around the tundra biome (Jørgensen et al. 2015, Myers-Smith et al. 2015, Holleson et al. 2015, Ropars et al. 2015, Tape et al. 2012). Growing season conditions, especially air temperature and growing season degree days (Myers-Smith et al. 2015, Boudreau and Villeneuve-Simard 2012), have been demonstrated to be key drivers of tundra shrub growth, but so too have winter air temperatures (Hollesen et al. 2015, Jørgensen et al. 2015). Experimental warming has also been shown to increase tundra shrub growth (Elmendorf et al. 2012, Chapin et al. 1995). Though multiple lines of evidence indicate high climate sensitive of tundra shrub growth, this high level of climate  30 sensitivity might not hold true for boreal forest shrubs. The boreal shrubs tested in this study were not strongly climate-sensitive only being somewhat influenced by summer temperature (B. glandulosa) and spring precipitation (S. canadensis), indicating there are other factors determining growth in this region.  2.4.2 Fertilization  Fertilization treatments could simulate the impact of permafrost thaw and increased nutrient cycling in northern systems (DeMarco et al. 2014, Chen et al. 2012, Chapin et al. 1995), therefore testing the effect of increased nutrients on dominant boreal forest shrubs is a test of hypothesized future climate change scenarios. Climate change increases temperatures and extends the growing season, which has been shown to encourage growth of thermophilic plant species (Elmendorf et al. 2015). Species composition shifts may lead to increased litter deposition and decomposition rates (DeMarco et al. 2014, Aerts 2006).  The insulating effects of shrubs in the winter due to increased snow depth also contribute to potential positive feedbacks, wherein warmer, insulated ground can undergo higher rates of decomposition (Pearson et al. 2013, Sturm et al. 2001). These hypothesized positive feedbacks in tundra systems are thought to be contributing to the shrub expansion in tundra ecosystems (Pearson et al. 2013, Sturm et al. 2005), but only one of the three boreal shrub species tested, B. glandulosa, responded to fertilization treatment which simulates these nutrient-enriched future scenarios. This response differs from that found measuring terminal stem growth, which found extended increased growth responses from the fertilization treatment (Melnychuk and Krebs 2005).    31 The combined factor of canopy and fertilization indicates B. glandulosa is influenced by both the influence of trees via nutrient competition and canopy shading. In this study, I sampled the three dominant shrub species in the boreal forest understory in the Kluane Region. Spatial variation in canopy cover is determined by large-scale disturbance, namely fire and spruce beetle outbreaks. When fertilization treatment occurred in areas of open canopy, growth was enhanced (Figure 2.2), indicating light availability could be a determinant of growth for the shorter-stature shrubs. Strong (2011) found tree canopy cover in the Yukon influenced the species and abundance of understory species due to canopy profile area and indicators of shading. However, my canopy study was based on the most recent five-years growth under the assumption canopy had not changed, and was not a strong relationship. This study demonstrates the potential interactive effects of canopy cover and nutrient-availability on the growth of one species of boreal forest shrub, B. glandulosa, which being the main winter food source for snowshoe hares is a key component of the boreal foodweb.  2.4.3 Herbivory  Although tundra shrub growth can be controlled by herbivore densities (Christie et al. 2015, Bernes et al. 2015, Kaarlejärvi et al. 2015, Speed et al. 2011, Olofsson et al. 2011, Zamin and Grogan 2012), our study did not find similar results for boreal shrubs. Speed et al. (2011) found densities of sheep populations interacting with temperature caused decreased ring width growth.  Reindeer populations limiting shrub distribution is geographically context-dependent (Bernes et al. 2015) but both ungulates, rodents and avian browsers have been shown to limit tall shrub expansion (Bernes et al. 2015, Kaarlejärvi et al. 2015, Ravolainen et al. 2014, Zamin and Grogan  32 2012, Tape et al 2010, Olofsson et al. 2009, Speed et al. 2011). Herbivores can limit the abundance and distribution of shrubs, but also the morphology of shrubs by browsing certain parts of stems stimulating alternate growth patterns such as increased root growth (Ravolainen et al. 2014, Tape et al. 2010). There is both photographic evidence and observations of stem scarring that snowshoe hare abundance influences the morphology of B. glandulosa (Krebs, unpublished), but I did not find snowshoe hare cycles and herbivory influenced B. glandulosa radial growth in this study (Figure 2.4).  2.4.5 Conclusions  This study found contrasting interspecific growth responses to climate, fertilization and herbivory. All three dominant boreal shrub species tested, B. glandulosa, S. glauca and S. canadensis, had radial growth that correlated with the interactive effect of canopy cover and fertilization treatment (Table 2.3). B. glandulosa and S. canadensis have negative growth relationships with treatment and canopy, whereas S. glauca had a positive growth relationship with treatment and canopy (Figure 2.3). B. glandulosa radial growth was positively correlated with fertilization treatment and negatively with summer temperature, whereas S. glauca did not have significant growth correlations to any of the eight climate variables tested. S. canadensis growth positively correlated with drought and negatively correlated with spring precipitation (Table 2.3). This demonstrates species-specific responses for dominant boreal shrub species of the same functional group, and adds complexities to considerations of climate change impacts on the boreal biome.   33 Comparing boreal shrub growth response to three different environmental factors demonstrates the variability in response between species in the same functional group.  All three species studied fulfill similar roles in the ecosystem as habitat and forage for animal species and yet their response to a changing climate is not uniform. It is therefore vital to consider species-specific responses to shifting herbivore densities and nutrient availability when creating projections of boreal forest change under climate change scenarios. Our results suggest that boreal shrubs respond differently than tundra shrubs under the same changing climate, and thus further research is required to understand the changing role of understory shrubs across the boreal biome.     34 Chapter 3:!What controls the growth rates of trees and shrubs in the boreal forests of southwestern Yukon?  Arctic circumpolar tundra shrub expansion is a growing concern due to the potential positive feedback to global climate change involving energy balance and carbon storage. However, little is known about shrub expansion below treeline in the boreal forest. This chapter examines the different responses of the dominant tree species (Picea glauca) versus three dominant shrub species (Betula glandulosa, Salix glauca and Shepherdia canadensis) to climate and nutrient addition. I compared boreal shrub ring widths of the three species with tree ring widths using model comparison and hierarchical mixed effect models. Using the same suite of models, I found that canopy species P. glauca radial growth was more strongly positively correlated with climate (summer temperature) and nutrients (via a fertilization experiment) than three understory shrub species. Our findings demonstrate growth response differences between canopy and understory species, and the importance of environmental factors when dendrochronology is used to reconstruct climate. Understanding the response of forests to climate and nutrient addition is essential for predicting how factors such as habitat quality, fuel load, and insect infestations will be altered by global change.  3.1! Introduction  The vast boreal forest is instrumental in global climate, energy balance and carbon feedbacks (Bonan et al. 1992, Lindroth et al. 1998). The impacts of climate change on the boreal forest include increased fires, pathogen outbreaks, and increased growth because of warmer growing  35 conditions and increased growing season length (Girardin et al. 2008, Hurteau and North 2008, Soja et al. 2007), and these factors can influence tree productivity (Barber et al. 2010, Nelson et al. 2014). Quantifying how different components of the forest, including trees and understory shrubs, react and interact with the changing climate and nutrient addition is essential to predicting the future role of the forest in global climate feedbacks (Allen et al. 2010).   Northern systems are generally nutrient poor and typically nitrogen-limited (Aerts 2006, Chapin et al. 1995, DeMarco et al. 2014). With increases in air and soil temperatures and growing season length, experiments have shown that the sub-Arctic may become more nutrient rich (Aerts 2006, DeMarco et al. 2014, Sturm et al. 2005, Chapin et al. 1995, Jarvis and Linder 2000). Fertilization experiments have demonstrated that the boreal forest trees respond to increases in nutrients with increased growth (Boonstra et al. 2008, Linder et al. 1987, Nilsen 1995), but other dendrochronological studies have shown that tree-ring width decreases with climate warming due to an observed reduction in precipitation during peak growing season despite potential increases during the Spring (Girardin et al. 2008, Barber et al. 2000, Jacoby and D’Arrigo 1995). These contrasting effects of warming and nutrient addition could confound predictions of climate change impacts on the boreal forest, especially when the response of understory shrubs in the boreal forest to climate warming and nutrients compared with trees is understudied (Hart and Chen 2006).  Many studies demonstrate the influence of canopy trees on understory plants, however few studies outline the inter-annual growth response of both to climate and nutrients. Much of forest research focuses on trees (Barber et al. 2000, Nilsen 1995), whereas some studies indicate that  36 understory vegetation may drive structure and disturbance regimes by influencing tree seedling regeneration, soil nutrient and moisture regimes, and forest succession (Duclos et al. 2013, Hart and Chen 2006, Nilsson and Wardle 2005). At larger scales, in a frequently disturbed landscape like the boreal forest, an increase in biomass of understory shrubs could have detrimental impacts to tree regeneration on a larger scale (Eis 1981). In tundra systems shrubs have shown to self-facilitate their own expansion via winter insulation and warming soil temperatures that increase available nutrients (Myers-Smith 2011). If boreal shrub growth is more positively correlated to warmer temperatures and increased nutrients than trees, shrubs could outcompete tree seedlings and shift the community to a shrubland system (Duclos et al. 2013, Putz and Canham 1992). However, if boreal tree species show greater responses to climate and nutrient addition then they could outcompete understory shrubs for light and nutrients similar to step-wise forest succession (Johnstone et al. 2004).  In this study, I revisited a fertilization experiment (1987-1994) in the Kluane region of southwest Yukon to test the impact of nutrients and climate change on the ring widths of both shrub and tree species (Turkington et al. 1998, Melnychuk and Krebs 2005, Boonstra et al. 2008, Nilsen 1995).  A comparison of canopy vs. understory species is a way to test if different structural components of the same system respond to climate changes similarly. I tested the following hypotheses: 1) that climate sensitivity of trees is greater than understory shrubs in the same forest and 2) that fertilization response of trees is less than that of shrubs. Understanding the growth responses of trees and shrubs to climate change and potentially shifting nutrient regimes enables better predictions of the future of the boreal forest biome.   37 3.2! Methods  3.2.1! For Study Area, Climate Data, Shrub Ring Widths see Chapter 2 (2.2.1, 2.2.6, and 2.2.8)  3.2.2! Tree ring width data  Tree ring width data was available from trees sampled by Boonstra et al. (2008) and Lomax (2011, unpublished report, UBC Zoology) on the same fertilization grids and surrounding area control grids (Table 3.1).   Table 3.1 Summary of tree and shrub-ring series between species and treatments (after 50% Measurement Repeatability Index exclusion). Note for analysis all data was cut to years 1977-1997. For additional information on P. glauca data see Boonstra et al. 2008. Species,!Treatment! N!! Time!span!rings!Mean!age!2014!Measurement!Repeatability!Index!(SD)!P.#glauca,"Fertilized"P.#glauca,"Control"B.#glandulosa,"Fertilized"120"135"37"197751997"197751997"196252014"5"5"30.9"±"8.2"5"5"0.72"±"0.29"B.#glandulosa,"Control" 39" 195552014" 28.9"±"11.2" 0.73"±"0.23"S.#glauca,"Fertilized" 32" 195552014" 30.0"±"9.1" 0.59"±"0.35"S.#glauca,"Control" 40" 197252014" 25.1"±"6.6" 0.65"±"0.23"S.#canadensis,"Fertilized" 38" 197452014" 21.6"±"7.3" 0.73"±"0.25" 38 Species,!Treatment! N!! Time!span!rings!Mean!age!2014!Measurement!Repeatability!Index!(SD)!S.#canadensis,"Control" 45" 198052014" 19.9"±"5.8" 3.82" ±"0.21""3.2.4 Analysis  Data analysis was performed with R (Version 0.98.1103. R Development Core Team 2014). Shrub rings, climate, and hare data were truncated to fit the available tree ring data by year to the period 1977-1997 (Table 3.2). Three analyses were completed: 1) model selection using Akaike’s Information Criterion (AIC) testing ring width with 8 climate variables, 2) fertilization treatment mixed effects model, and 3) fertilization treatment interactive effect with average June/July temperature mixed effects model. The climate model selection involved 10 models then ranked by ΔAIC from the null model (i.e. ring width by 1, or a redundant model that if tested stronger than other factors indicates ring width not correlated with any particular factor) testing normalized ring widths (mm) of each species with the following normalized climate variables: null model, each climate variable independently (i.e. ring width by average summer temperature; ring width by growing degree days, etc.), and then a full model with all climate variables (i.e. ring width by average summer temperature plus growing degree days etc.). Random effect for these models was year. The fertilization model tested raw ring width for each species fertilization treatment as a fixed effect (factor arranged as time period of “pre”, “during”, and “post” treatment) and year, site and treatment as random effects). The third model tested normalized ring width with an interactive effect of fertilization treatment and summer temperature as the fixed effect, with the same random effects as the fertilization model. Lastly, the impact of canopy cover on the growth of understory species was tested by comparing the  39 climate sensitivity of each shrub species with amount of canopy cover, using a linear model looped for ring width and average June/July temperature.   3.3! Results  3.3.1! Climate  P. glauca responded to climate more strongly than all three shrub species, and to more climate variables, when tested under the same mixed effect model selection  (Table 3.2, ΔAIC 8.23).  The best models (i.e., highest ΔAIC from the null model) included average June/July temperature in the same year (ΔAIC 8.23) and growing degree days above 5 °C (ΔAIC 6.95). In the full climate model with all 8 climate variables as fixed effects, spring precipitation (average April/May) was the most significant determinant of ring width (p=0.03), followed by average June/July temperature (p=0.09; Appendix A).   When tested with the same climate variables, only B. glandulosa growth responded significantly to climate, in particular average June/July temperature (ΔAIC 6.42). S. glauca and S. canadensis did not respond significantly to climate (ΔAIC < 2.00 from null model).        40     Table 3.2 Mixed effect model AIC comparison results for climate models (null model, each of the 8 variables separately, and full model with all 8 variables) and all factors model. The table only includes parameters >2 above the null model in model selection based on AIC (for climate models above null are listed). All data for the period 1977-1997.  Species! Model! Parameters! ΔAIC! Slope! SE! tFvalue! pFvalue! RFsquared!P.#glauca# Climate" Tave_jj" 8.23" 0.286" 0.08" 3.63" 0.001" 0.21"DD.5" 6.95" 0.271" 0.08" 3.34" 0.003" 0.21"All"8" 3.73" 50.194" 0.08" 52.44" 0.030" 0.21"B.#glandulosa# Climate" Tave_jj""6.42" 50.259" 0.08" 0.01" 0.005" 0.19"S.#glauca# Climate" 5" 5" 5" 5" 5" ns" 5"S.#canadensis# Climate" 5" 5" 5" 5" 5" ns" 5" 3.3.2! Fertilization and Interactive Effects  Picea glauca was the only species of all four whose annual ring width was significantly positively correlated to fertilization treatment (Table 3.3, p<0.001). All three shrub species did not have growth correlations to the fertilizer treatment. An interactive effect of fertilization and temperature (representative of climate) was not found, meaning the fertilized individuals were not more climate sensitive than the control site individuals.   41  Table 3.3 Mixed effect model results for fertilization treatment (factored by “pre”, “during”, and “post” treatment time periods) and interactive term for fertilization treatment and average June/July summer temperature. All data for the period 1977-1997. Year, site and treatment are random effects. Species! Model! Slope! SE! tFvalue! pFvalue! RFsquared!P.#glauca# Fertilization" 0.937" 0.11" 8.62" <0.001" 0.39"Fertilization*Temp"" 5" 5" 5" ns" 5"B.#glandulosa# Fertilization" 0.171" 0.01" 17.3" ns"(0.08)" 0.10"Fertilization*Temp" 5" 5" 5" ns" 5"S.#glauca# Fertilization" 0.443" 0.02" 28.6" ns"(0.93)" 0.10"Fertilization*Temp" 5" 5" 5" ns" 5"S.#canadensis# Fertilization" 0.163" 0.00" 25.1" ns"(0.64)" 0.07"Fertilization*Temp" 5" 5" 5" ns" 5"   42  Figure 3.1 Mean ring width (plus or minus SE) for A) P. glauca, B) B. glandulosa, C) S. glauca and D) S. canadensis, for the time periods pre (before 1987; number of years varies by age of individual), during (1987-1994), and post (1994-1997) fertilization treatment.   3.3.3! Canopy influence on understory species  Canopy cover had no correlation with the climate sensitivity models comparing normalized ring width with normalized average summer temperature, when considering the most recent 10 years’ ring widths (Figure 3.2).  Only 8 of 271 shrubs (or 2.9%) were climate sensitive in open canopy areas according to the R-squared values.   43  Figure 3.2 Linear model R-squared outputs for each individual shrubs most recent 10 years’ annual radial growth (2004-2014), tested against average June/July summer temperature of that years growth, by percent openness of tree canopy. Large majority of models did not show a correlation between climate sensitivity of understory shrubs and canopy cover (i.e. only 2.3% of models R-squared were greater than zero).   3.4! Discussion  This study demonstrates that canopy species are more climate sensitive than understory species (Table 3.2, Figure 3.1). P. glauca, the dominant canopy species in the boreal forest of southwest Yukon, radial growth was significantly positively correlated with both average June/July  44 temperature and growing degree days, whereas B. glandulosa was negatively correlated with temperature, and S. glauca and S. canadensis were not correlated with any of the eight climate variables tested. Of all four species only the growth of the canopy tree species P. glauca growth was positively correlated with fertilization treatment. All three understory species were not significantly correlated with fertilization treatment from 1977-1997 (Table 3.3). None of the species experienced an interactive effect of fertilization and climate, meaning climate sensitive shrubs were not more influenced by fertilization. Lastly, P. glauca percent cover did not influence the climate sensitivity of shrubs, or those shrubs growing in more open canopy areas with more light reaching the understory were not more positively correlated with growth than those growing in closed canopy areas (Figure 3.2). Understanding the difference in climate and nutrient sensitivity between components of the forest is essential for predicting viability of different functional groups with present and future climate change.   3.4.1! Climate  Increased summer temperatures and growing season length are positively correlated with radial shrub growth at and above treeline (Ropars et al. 2015, Myers-Smith et al. 2015). However, studies of radial tree growth at and below treeline in the boreal forest show both positive and negative growth responses to warming climate (Wilmking et al. 2004, Girardin et al. 2008). Below treeline, increased summer temperatures and drought may have a greater impact on trees (Jacoby and D’Arrigo 1995, Barber et al. 2000).  There are confounding warming climate correlations with tree growth due to seasonal differences, namely potential increased growth in spring, but decreased growth later in summer due to drought stress (Girardin et al. 2008). In the  45 southwest Yukon I found the trees had positive growth correlations with summer temperature, whereas shrubs growing in the same sites did not.  Climate sensitivity in both tree and shrub dendrochronology has been shown to be site specific and influenced by soil moisture (Levanič et al. 2008, Myers-Smith et al. 2015) and topography (Ropars et al. 2015). Additionally, there is species-specific climate sensitivity of trees based on dendrochronological data (Friedrichs et al. 2009). Therefore, it is not surprising there should be a difference between tree and shrub climate sensitivity in this study, whereas satellite-derived indices of tree growth change indicate more uniform trends (Barber et al. 2000).   3.4.2! Nutrients  What is surprising is the difference in nutrient sensitivity between trees and shrubs. In the literature there are positive correlations between ring width and fertilization experiments for both trees (Bergh et al. 1999) and tundra shrubs (DeMarco et al. 2014, Zamin and Grogan 2012). A few studies of fertilization on forests examine the influence on understory shrubs, and they demonstrate contrasting site and species-specific responses (Saarsalmi and Mälkönen 2001). The lack of response by shrubs compared to trees to nutrient addition could be related to competition, meaning perhaps that trees absorbed nutrients more readily and therefore left less available to the shrubs (Hedwall et al. 2013). The methods of fertilization (by air) may have also influenced the increased ability of canopy species to respond to nutrient addition, however the ground coverage was monitoring during treatment (Turkington et al. 1998). Overall, these contrasting growth  46 responses to fertilization and climate indicate a difference between trees and shrubs in the way they are controlled by multiple abiotic effects.  3.4.3! Influence of Canopy  Higher climate sensitivity of trees compared to shrubs could be related to their placement in boreal forest structure. The density and abundance of canopy species can influence light availability for understory species, and in forest management canopy openness is used as a tool to suppress understory species that might compete with trees for nutrients (Boudreau and Villeneuve-Simard 2012, Angelini et al. 2015). Canopy cover and density can also influence light availability, air temperature and humidity below the canopy (Strong 2011, Hart and Chen 2006, Sharpe et al. 1996). The creation of this microclimate, depending on the density of the canopy trees, could be buffering the shrubs in the understory from fluctuations in climate. However, this study did not find that canopy cover influenced the climate sensitivity of understory shrub species, therefore there must be another reason for the decreased boreal shrub climate sensitivity compared to trees.  Disturbance in the forest creates canopy openings and increase the amount of light availability to understory plants, therefore increasing the growth of shrubs similar to the usual step-wise forest succession perspective (Duclos et al. 2013). This could influence boreal forest structure as increased shrub growth has been shown to negatively influence the survival of canopy tree species seedlings via competitive exclusion (Duclos et al. 2013, Eis 1981, Gorchov and Trisel 2003). While the current canopy trees in this study experience positive growth relationships with  47 both temperature and nutrients, whether seedlings are also growing to form the next canopy or being outcompeted by shrubs in the understory may deserve further study (Grau et al. 2012, Eis 1981).  3.4.4! Conclusions  These results have implications for understanding and predicting the climate and nutrient sensitivity of boreal forest ecosystems. In this study, the dominant tree canopy species, P. glauca, was more climate and nutrient sensitive than three understory shrub species (B. glandulosa, S. glauca, and S. canadensis).  All of these species are key components of the boreal foodweb, providing food and habitat for snowshoe hares, red squirrels, moose, grizzly bears, and ultimately fuel and food for people (Krebs 2011). These results aid understanding of how future climate fluctuations will impact these key components of the boreal forest biome, and further the ability to adopt adaptive landscape management (Hart and Chen 2006, Chen et al. 1999).    48 Chapter 4:!Conclusion  In the past century, a ‘greening’ up of the tundra biome has been observed (Stow et al. 2004, Jia et al. 2003, Myeni et al. 1997) led by shrubs (Tape et al. 2006, Sturm et al. 2001, Lantz et al. 2012, Myers-Smith et al. 2011, Tremblay et al. 2012). Locally, tundra shrub expansion decreases the available habitat of alpine specialized species such as arctic ground squirrels and Dall sheep (Christie et al. 2015, Wheeler and Hik 2014, Laliberte and Ripple 2004). Globally, shrubification of the tundra is hypothesized to influenced climate feedbacks via snow trapping and winter ground insulation, leading to permafrost thaw (Nauta et al. 2014, Pearson et al. 2013, Blok et al. 2010, Sturm et al. 2000). Increased summer temperatures and growing season length correspond with variation in inter-annual growth of tundra shrubs and has been linked to their expansion (Myers-Smith et al. 2015, Ropars et al. 2015, Epstein et al. 2013, Blok et al. 2011). Tundra shrub expansion has also been shown to be limited by herbivory (Christie et al. 2015, Olofsson et al. 2009, Zamin and Grogan 2012, Tape et al. 2010).  However, much less is known about the climate, nutrient and herbivory sensitivity of boreal shrubs.  Due to the extensive research and experimentation in the Kluane region of southwest Yukon, this study provided a unique opportunity to: 1) examine the growth response of boreal shrubs to climate, fertilization and herbivory via dendrochronological analysis, and 2) compare tree and shrub growth response to climate and fertilization. The main conclusions of my thesis are that boreal shrub biomass has increased, but that the growth response differed between species, and that overall, the three dominant boreal shrub understory species tested were less climate and nutrient sensitive than the canopy tree species growing above them in the same forest. My thesis  49 research helps to fill the knowledge gap regarding how climate sensitivity of shrubs and trees differs, and has the potential to aid in projections of vegetation change through time across the boreal forest biome.   One of the main difficulties of this study was the measurement of boreal shrub growth. Boreal shrub growth rings are not as consistent or clear as tree rings. However, now we have methods that have been developed to deal specifically with shrub dendroecology and a body of literature to compare and contrast between tundra shrub species (Speed et al. 2011, Myers-Smith et al. 2015, Ropars et al. 2015). Dendrochronology allows for historical ecological reconstruction which enabled the study of boreal shrub growth over the past several decades, and thus the study of linkages to accompanying long-term experimental and monitoring data such as the fertilization experiment (Melnychuk and Krebs 2005, Turkington et al. 1998, Nams et al. 1996) and snowshoe hare abundance (Krebs 2011).   Due to the impacts of climate change on boreal forests (Barber et al. 2000, Nelson et al. 2014, Allen et al. 2010, Verbyla 2008, Lloyd and Bunn 2007), there has been some discussion in the literature about how to adapt boreal forest landscape management practices in order to maintain biodiversity and productivity (Torssonen et al. 2015, Chen et al. 1999). If some boreal forest species are more drought-tolerant than others, or if boreal shrubs can contribute to stabilizing understory microclimates, this could mediate drought-risk areas. Likewise, wetter sites that are less impacted by drought may be good areas to preserve as refuges in warmed climate scenarios, and indeed shrubs and other woody species are moving into these areas (Klein et al. 2005). Overall, the trees in this study were climate sensitive, but not drought-affected which contrasts  50 with similar studies in interior Alaska (Barber et al. 2000, Verbyla 2008) indicating general boreal ‘browning’ and tundra ‘greening’ trends may not accurately portray species and site-specific climate sensitivities (Verbyla 2008, Lloyd and Bunn 2007).  There are several key questions that remain unanswered regarding boreal shrubification. 1)" What is the spatial extent of boreal shrubification? An additional study that could be done to assess the feasibility of using repeat aerial photos and satellite imagery to assess the relative distribution and abundance of boreal shrubs. This could be challenging due to the presence of a forest canopy, but a combination of technologies, including LiDAR, should be able to further our understanding of spatial expansion (Martinuzzi et al. 2009). 2)" How does increased shrub cover impact snowshoe hare’s ability to detect predators? Does increased shrub cover mean snowshoe hares can more effectively hide from predators, or does it mean their ability to detect predators is impeded? This could be done using visibility transects (boards divided into grids, distance from grid and percent visibility) in the early spring when leaves are absent, and in late July during peak productivity, to compare the seasonality of visibility (Wheeler and Hik 2014).  3)" Does an increased shrub biomass negatively effect the survival of P. glauca seedlings in the understory? Normal stepwise succession where disturbance creates canopy gaps, shrubs grow successfully in those gaps due to increased light availability, seedlings grow shaded by shrubs, and then seedlings eventually outcompete shrubs to become the canopy, may not progress as expected if understory shrub biomass has increased to an extent that shrubs outcompete seedlings for light and nutrients (Grau  51 et al. 2012). This could be tested by completing surveys of the long-term monitoring grids for the distribution and abundance of P. glauca seedlings (verified by dendrochronology as less than a given age) relative to the distribution and abundance of shrubs.   My thesis is one of the first studies to directly compare tree and shrub growth responses to climate, fertilization and herbivory. Overall, I found that boreal shrubs were much less climate and nutrient sensitive than anticipated based on tundra shrub literature, and that they were also less climate and nutrient sensitive than tree species. This information ties together components of past experiments and monitoring with new techniques for shrub dendroecology to identify the strength, or lack thereof, of interspecific species’ climate sensitivities. 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Birch shrub growth in the low Arctic: the relative importance of experimental warming, enhanced nutrient availability, snow depth and caribou exclusion. Environmental Research Letters 7: 034027.  Zhang, W., Miller, P. A., Smith, B., Wania, R., Koenigk, T., & Doscher, R. 2013. Tundra shrubification and tree-line advance amplify arctic climate warming:  results from an individual-based dynamic vegetation model. Environmental Research Letters 8: 034023.     62 Appendices  These Appendices contain additional information on the mixed effect models tested and factors (Appendix A.1 - A.3) and repeat photography (Appendix B).  Appendix A     A.1! Further analysis information  63 Table A.1.1: Details on datasets involved in analysis. These analyses were completed in R.  Ch.$ Analysis$ Variables$ Data$source$ Growth$form$Normalized?$ Years$ Fixed$effects$ Random$effects$2$$Canopy$Shrub$ring$width$(mean)$Most$recent$5$years’$rw$Shrub$$No$ 2009L2014$ Ring$width,$canopy$openness$(%)$Site/individual$Canopy$openness$(%)$ From$photos$ No$ 2014$Climate$Shrub$ring$width$(full)$All$years$ring$widths$ Yes$(individual)$1st$years$growth$L2014$Ring$width,$climate$variable$Year$Climate$data$(see$Table$2.2)$ ClimateWNA$ Yes$$1979L2014$Fertilization$ Time$periods$of$treatment$Time$periods,$subset$fertilized$sites$Yes$ 1979L2014$Ring$width,$fertilization$time$period$Year$Herbivory$Hare$abundance$(fall$density$in$hares$per$hectare)$Abundance$categories$ Yes$1979L2014$Ring$width,$hare$abundance$category$Year$3$$Canopy$ Shrub$ring$width$(mean)$Most$recent$10$years’$rw$Shrub$and$Tree$(canopy)$No$ 2004L2014$Ring$width,$average$June/July$temp$NA$(loop)$Climate$ Tree$ring$widths$$From$Boonstra$et$al.$2008$$Tree$Yes$ 1977L1997$Ring$width,$climate$variable$Year$Fertilization$ No$ 1977L1997$Ring$with,$fert$time$period$ Year,$site$Climate$ Shrub$ring$widths$$Data$cut$from$full$set$$Shrub$Yes$ 1977L1997$Ring$width,$climate$variable$Year$Fertilization$ No$ 1977L1997$Ring$width,$fertilization$time$period$Year,$site$ 64 Table A.1.2: The fixed effects for models used to analyze ring width data for Chapter 2. These analyses were completed in R.   Factor' Model' Model'in'R'(data'=''for'each'species)'Climate' Null' lme(nrw~1,'random=~1|xyear,cor=corAR1(),'method="ML"'' MAT' lme(nrw~nMAT,'random=~1|xyear,cor=corAR1(),'method="ML"'' NFFD' lme(nrw~nNFFD,'random=~1|xyear,cor=corAR1(),'method="ML"'' CMD_jj' lme(nrw~nCMD_jj,'random=~1|xyear,cor=corAR1(),'method="ML"'' Prec_am' lme(nrw~nPrec_am,'random=~1|xyear,cor=corAR1(),'method="ML"'' DD.5' lme(nrw~nDD.5,'random=~1|xyear,cor=corAR1(),'method="ML"'' Tave_am' lme(nrw~nTave_am,'random=~1|xyear,cor=corAR1(),'method="ML"'' Tave_jj' lme(nrw~nTave_jj,'random=~1|xyear,cor=corAR1(),'method="ML"'' All'factors' lme(nrw~nMAT+nNFFD+nCMD_jj+nPrec_am+nDD.5+nTave_am+nPrec_jj+nTave_jj+fert2+hare2,'random=~1|xyear,cor=corAR1(),'method="ML"'Fertilization' Null' lme(nrw~1,'random=~1|xyear,cor=corAR1(),'method="ML"'' Fert2'(pre,'during,'post)'lme(nrw~fert2,'random=~1|xyear,cor=corAR1(),'method="ML"'' Fert*Climate' lme(nrw~nTave_jj*fert2,'random=~1|xyear,cor=corAR1(),'method="ML"'Hare'Abundance'Hare2'(peak,'mid,'low)'lme(nrw~hare2,'random=~1|xyear,cor=corAR1(),'method="ML"'Canopy' Null' lme(rw~1,random=~1|xyear,cor=corAR1(),'method="ML"'' Canopy'openness' lme(rw~open.percent,'random=~1|xyear,cor=corAR1(),'method="ML"'' Canopy*fertilization'treatment'lme(rw~open.percent*treatment,'random=~1|xyear,cor=corAR1(),'method="ML"'  65 Table A.1.3: The fixed effects for models used to analyze ring width data for Chapter 3. These analyses were completed in R.   Factor' Model' Model'in'R'(data'=''for'each'species)'Climate' Null' lme(nrw~1,'random=~1|xyear,cor=corAR1(),'method="ML"'' MAT' lme(nrw~nMAT,'random=~1|xyear,cor=corAR1(),'method="ML"'' NFFD' lme(nrw~nNFFD,'random=~1|xyear,cor=corAR1(),'method="ML"'' CMD_jj' lme(nrw~nCMD_jj,'random=~1|xyear,cor=corAR1(),'method="ML"'' Prec_am' lme(nrw~nPrec_am,'random=~1|xyear,cor=corAR1(),'method="ML"'' DD.5' lme(nrw~nDD.5,'random=~1|xyear,cor=corAR1(),'method="ML"'' Tave_am' lme(nrw~nTave_am,'random=~1|xyear,cor=corAR1(),'method="ML"'' Tave_jj' lme(nrw~nTave_jj,'random=~1|xyear,cor=corAR1(),'method="ML"'' All'factors' lme(nrw~nMAT+nNFFD+nCMD_jj+nPrec_am+nDD.5+nTave_am+nPrec_jj+nTave_jj+fert2+hare2,'random=~1|xyear,cor=corAR1(),'method="ML"'Fertilization' Fert2'(pre,'during,'post)'lme(rw~fert2T1,'random=list(xyear=~1,'site=~1),'method="ML"'' Fert*Climate' lme(nrw~nTave_jj*fert2,'random=~1|xyear,'method="ML"'Canopy' Canopy'openness' lme(rw~open.percent,'random=~1|xyear,cor=corAR1(),'method="ML"'' Canopy*fertilization'treatment'lme(rw~open.percent*treatment,'random=~1|xyear,cor=corAR1(),'method="ML"'     66 A.2! Details on Fixed Effects  Table A2. The organization of the factors for fertilization treatment and hare density by time period. Factor' Fixed'effect' Symbol' Time'Fertilization' Treatment' fert2' Pre'(<1987)'During'(1987T1994)'Post'(1994T2014)'Hare'Density' #'Hares' hare2' Low'(<0.05'hares/ha)'Mid'(0.05T1'hares/ha)'High'(>1'hares/ha)' A.3! Data fields and availability  Table A.1.3 The available data field and examples. All data available from author on request (grabowski@zoology.ubc.ca), with permission on additional hare and tree ring data from owners (krebs@zoology.ubc.ca, boonstra@utsc.utoronto.ca)  Year' Individual'Shrub'Species' Site' Treatment' Ring'Width'(mm)'Hare2' Fert2' Climate'variables'i.e.1977' GB.A16.1b' BEGL' grizzly' fert' 0.2230' mid' pre' i.e.'MAT'for'1977'   67 Appendix B    This is one site from repeat historical photos from the area that confirm observations of increased shrub biomass below treeline in the Kluane region.      

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