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The role of ectomycorrhizal networks in plant-to-plant facilitation across climatic moisture gradients Bingham, Marcus Alan 2010

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THE ROLE OF ECTOMYCORRHIZAL NETWORKS IN PLANT-TO-PLANT FACILITATION ACROSS CLIMATIC MOISTURE GRADIENTS  by  Marcus Alan Bingham  A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Forestry)  The University of British Columbia (Vancouver)  December 2010  ©Marcus Alan Bingham, 2010  ii Abstract Common ectomycorrhizal (EM) networks are expected to facilitate conifer regeneration under abiotic stress, such as drought exacerbated by climate change. This study examined effects of climate, CO2 concentration ([CO2]), and EM networks on Douglas-fir seedling establishment. My objectives were (1) to determine the effects of regional climate (represented by a drought index) on EM network facilitation of Douglas-fir seedling establishment; (2) to separate genotypic effects from climatic effects; (3) to compare the importance of EM networks to 3-year-old outplanted nursery seedlings versus 1st year seedlings germinated in the field; (4) to parse the competitive from facilitative effects of residual Douglas-fir trees on small seedlings; and (5) to determine the interaction between soil water and [CO2], in their effects on EM network-facilitated seedling establishment and C-transfer between different sized Douglas-fir seedlings.  Survival was maximized when seedlings were able to form an EM network in the absence of root competition, both in growth chambers and in the field for the medium moisture provenance. When drought conditions were greatest, growth of these same seedlings increased when they could form an EM network with nearby trees in the absence of root competition, but it was reduced when they were unable to form a network. Overall, survival was greatest for these seedlings relative to those from the wet or dry provenances, but decreased with summer heat:moisture index more rapidly. I found no evidence of C transfer between seedlings through growth chamber 13CO2 labeling, but D2O labeling and natural abundance H218O measurements are suggestive of increasing water transfer from donor to receiver seedlings as receiver water deficiency increased.  iii Preface  In chapter 2, I co-designed the experiment and helped obtain funding with Dr. Suzanne Simard. I conducted the research, including data collection, quantitative components, and manuscript writing. Dr. Suzanne Simard initiated funding, proposed the initial concept to be tested, and assisted in manuscript preparation.  In chapter 3, I co-designed the experiment and helped obtain funding with Dr. Suzanne Simard. I conducted the research, including data collection, quantitative components, and manuscript writing. Dr. Suzanne Simard initiated funding and assisted in manuscript preparation.  In chapter 4, I co-designed the experiment and helped obtain funding with Dr. Suzanne Simard. I conducted the research, including data collection, quantitative components, and manuscript writing. Dr. Suzanne Simard initiated funding and assisted in manuscript preparation. Dr. Robert Guy assisted with developing methodology for and interpretation of isotope labeling data.   iv Table of contents Abstract .............................................................................................................................. ii Preface ............................................................................................................................... iii Table of contents .............................................................................................................. iv List of tables...................................................................................................................... vi List of figures .................................................................................................................. viii Acknowledgements ........................................................................................................... x 1. Introduction ............................................................................................................... 1 Scientific background ................................................................................................... 1 Mycorrhizas and facilitation ....................................................................................... 1 Mycorrhizal networks and seedling establishment ..................................................... 3 Facilitation and drought .............................................................................................. 7 Global change and environmental stress ..................................................................... 9 Problem statement and objectives ............................................................................. 11 2. Ectomycorrhizal networks of old interior Douglas-fir trees facilitate establishment of conspecific seedlings under drought ................................................. 15 Introduction ................................................................................................................. 15 Methods ........................................................................................................................ 19 Experimental design.................................................................................................. 19 Measurements ........................................................................................................... 21 Data analysis ............................................................................................................. 25 Results .......................................................................................................................... 27 Discussion..................................................................................................................... 29 3. Ectomycorrhizal networks and seedling genetics act independently to improve conifer regeneration with increasing drought .............................................................. 49 Introduction ................................................................................................................. 49 Methods ........................................................................................................................ 52 Experimental design.................................................................................................. 52 Measurements ........................................................................................................... 54 Data analysis ............................................................................................................. 58 Results .......................................................................................................................... 60 Discussion..................................................................................................................... 62 Mycorrhizal network effects ..................................................................................... 62 Cohort effects ............................................................................................................ 63 Provenance effects .................................................................................................... 64 Conclusions ............................................................................................................... 65 4. Water, but not carbon, translocation through ectomycorrhizal networks affects survival and growth of interior Douglas-fir seedlings ................................................. 75 Introduction ................................................................................................................. 75 Methods ........................................................................................................................ 79 Experimental design and treatments ......................................................................... 79 Soil ............................................................................................................................ 81 Pulse-labeling ............................................................................................................ 82 Seedling measurements ............................................................................................. 83 Data analysis ............................................................................................................. 85  v Results .......................................................................................................................... 86 Discussion..................................................................................................................... 88 5. Conclusions ............................................................................................................ 102 Summary .................................................................................................................... 102 Strengths and limitations ......................................................................................... 110 Future directions ....................................................................................................... 113 Literature cited .............................................................................................................. 117 A. Appendix ................................................................................................................ 133   vi List of tables Table 1 Biogeoclimatic unit and climatic parameters of each site. Sites were classified according to Lloyd et al. (1990). Climatic variables have been interpolated for each plot using ClimateBC (Hamann and Wang 2005, Wang et al. 2006), then calculated across time and averaged across plots for each site. Minima and maxima for each site were selected at the resolution of year by plot. ......................................................................... 36 Table 2 Logistic regression testing for the probability of seedling survival in response to mesh treatments, distance from established tree (m), June-July heat:moisture index (JH:M), seedling outplanting date (number of days after May 1st, 2006), and soil texture. Odds ratios and their p-values are given for each continuous variable, category and interaction. The p-value tests the null hypothesis that the odds ratio equals one. ............ 40 Table 3 Analysis of covariance testing for response of the natural logarithm of proportional increase in biomass of seedlings to mesh treatments, distance from established tree (m), and summer heat:moisture index (SH:M), after adjustment for initial seedling height (cm) and total soil N (ppm extracted). NOTE: The coefficient signs are only given for continuous variables. ................................................................................. 42 Table 4 Analysis of covariance testing for response of seedling stem natural abundance 13C to mesh treatments, distance from established tree (m), and summer heat:moisture index (SH:M), after adjustment for the natural logarithm of final seedling stem biomass and total soil N. NOTE: The coefficient signs are only given for continuous variables. . 43 Table 5 Logistic regression testing for the probability of survival of nursery seedlings in response to mesh treatment, seedling provenance (xeric, mesic, hydric), 2006 June-July heat:moisture index (JH:M), and seedling planting date (number of days after May 1st, 2006). The mesh factor was not significant and was therefore removed from the original model................................................................................................................................. 66 Table 6 Logistic regression testing for the probability of survival of field-germinated seedlings in response to mesh treatments, seedling provenance and climate factors. Mesh and provenance factors were not significant and therefore were removed from the original model................................................................................................................................. 67 Table 7 Analysis of covariance testing for response of the natural logarithm of proportion increase in biomass of  nursery seedlings  to mesh treatment, seedling provenance, and summer heat:moisture index (SH:M), after adjustment for total N and its interaction with provenance. NOTE: The coefficient signs are only given for continuous variables. ....... 68 Table 8 Analysis of covariance testing for response of the natural logarithm of total biomass of field-germinated seedlings to mesh treatments, seedling provenance, and 2007-8 summer heat:moisture index (SH:M), after adjustment for total N and its interaction with SH:M. NOTE: The coefficient signs are only given for continuous variables. ........................................................................................................................... 69 Table 9 Analysis of covariance testing for outplanted nursery seedlings natural abundance 13C response to mesh treatments, provenance, and SH:M, after adjustment for the natural logarithm of proportion increase in seedling stem biomass and whether the soil was a Gleysol. NOTE: The coefficient signs are only given for continuous and dummy variables. .............................................................................................................. 70 Table 10 Analysis of covariance testing for response of field-germinated seedling stem natural abundance 13C to mesh treatments, provenance, and 2007-8 summer  vii heat:moisture index (SH:M). NOTE: The coefficient signs are only given for continuous variables. ........................................................................................................................... 71 Table 11 Logistic regression testing for the probability of seedling survival in response to, CO2 concentration (ppm), mesh treatments, soil moisture regime, temperature regime, and photosynthetically active radiation (µmol/m2/s) (PAR). Temperature was removed from the original model. ................................................................................................... 93 Table 12 Analysis of covariance testing for response of the natural logarithm of total biomass of seedlings to CO2 concentration (ppm), temperature regime, soil moisture regime and mesh treatment after adjustment for photosynthetically active radiation (µmol/m2/s) (PAR) nested within run. NOTE: The coefficient signs are only given for continuous variables.......................................................................................................... 94 Table 13 Analysis of covariance testing for response of the natural logarithm of 13C of seedlings to CO2 concentration (ppm), soil moisture regime, temperature regime and mesh treatment, after adjustment for growth chamber nested within run. NOTE: an adjustment for growth chamber nested within run was made to minimize variation intrinsic to individual growth chambers, thus P-values are conservative. No growth chamber by trial combinations were significant, and thus are not shown. ....................... 95 Table 14 Analysis of covariance testing for response of the percent reservoir D2O taken up by receiver seedlings to mesh treatments and CO2 concentration (ppm), after adjustment for 18O of donor seedling xylem water, 18O of receiver seedling xylem water, percent reservoir water taken up by donor seedlings, labeling period, and run. NOTE: The coefficient signs are only given for continuous variables. Run was analyzed as a random effect, but not shown due to lack of significance. ........................................ 96 Supplementary table 1 Ectomycorrhizal (EM) morphotypes on interior Douglas-fir (Pseudotsuga menziesii var. glauca) trees and seedlings (outplanted nursery and field- germinated) at the nine site located across a climatic moisture gradient (biogeoclimatic zones PP, IDF, and ICH) in southern interior British Columbia, Canada. Sampling was conducted in September 2007 (nursery seedlings) and 2008 (germinants). ................... 133    viii List of figures Figure 1 Location of possible transect positions (represented by stars at 2.5 m) around a residual tree (brown circle), with an example of a transect at 260 (stars along transect representing seedling positions on transect). Each position along the transect represents an experimental unit. ......................................................................................................... 44 Figure 2  Probability of seedling survival as a function of planting date under the three mesh treatments. ............................................................................................................... 45 Figure 3  Odds ratio values (logarithmic y-axis) for (a) mesh treatment and (b) distance in the logistic regression model predicting survival. ........................................................ 46 Figure 4  Relationship of summer heat:moisture index and the natural logarithm of proportional biomass increase after adjusting for covariates at different distances from the established tree under the no mesh treatment (a), the 0.5-µm mesh treatment (b) and the 35-µm mesh treatment (c). All regression lines among distances under the 35-µm mesh treatment are significantly different (P0.5). The regression lines in the 0.5-µm and no mesh treatments do not differ in slope or altitude. ............................................................ 48 Figure 5  Location of seedling positions (represented by stars at 2.5 m) around a residual tree (brown circle). Each position represents an experimental unit. ................................. 72 Figure 6  Probability of outplanted nursery seedling survival as a function of precipitation within first three months after planting (mm) for the three provenances. ... 73 Figure 7  Relationship of 2006-7 summer heat:moisture index and stem natural abundance 13C  of outplanted nursery seedlings after adjusting for covariates among provenances. Regression lines for provenance were tested for difference in slope and altitude (P < 0.05), and consequently combined for the dry provenance and wet provenance provenances. .................................................................................................. 74 Figure 8  The general arrangement for donor and receiver seedlings in each root box. In this example, the 35-µm mesh treatment has been applied to the receiver seedling. ....... 97 Figure 9  Odds ratio values (logarithmic y-axis scale) for (a) mesh treatment and (b) the interaction of CO2 concentration and soil moisture in the logistic regression model predicting survival of receiver seedlings. The odds ratio for a category is the odds of survival of a seedling in that particular category relative to seedlings not in that category, after adjusting for covariates. ............................................................................................ 98 Figure 10  Unadjusted means for total receiver seedling biomass under (a) various combinations of temperature regime and CO2 concentration and (b) various combinations of soil moisture regime and mesh treatments. Bars with different letters are significantly different at P ≤ 0.05. Error bars are 1 s.e. ......................................................................... 99 Figure 11  Means for 13C of seedlings among natural abundance controls, labeled donors, receivers and post-label controls. Error bars are 1 s.e. ...................................... 100 Figure 12  Unadjusted means for the percent reservoir water taken up by receiver seedlings (calculated from D) under various combinations of CO2 concentration and mesh treatments. Bars with different letters are significantly different at P ≤ 0.1. Error bars are 1 s.e. ................................................................................................................... 101 Supplementary figure 1  Map showing the locations of seed trees (colored by site) and seed sources (labeled with seedlot number) with respect to the biogeoclimatic unit (colored by area) and seed planning zones (outlined and labeled in white) in which they occur. ............................................................................................................................... 136  ix Supplementary figure 2  Relative abundance of EM morphotype colonization of trees and seedlings for outplanted nursery seedlings (a) and field-germinated seedlings (b). 137 Supplementary figure 3  (previous page) Four perspectives of a three-dimensional graph of the relationship of drought (summer heat:moisture index) and ln(growth) (natural logarithm of proportional biomass increase) after adjusting for covariates at different distances from the established tree under the no mesh treatment (blue), the 0.5-µm mesh treatment (red) and the 35-µm mesh treatment (green). ................................................. 139  x Acknowledgements  While the first 2.5 years of this journey were smoothly-paved highway, the highway transitioned to ungraded logging road, yet Dr. Suzanne Simard persistently expressed confidence in my capabilities and guided me through the nuances of BC forest research the entire trip, even in illness and physical absence, and has especially advocated for me from a financial perspective. Her willingness to take big risks is also invaluable in giving her graduate students the latitude to design large, elaborate projects that allow us to view the soil-fungus-plant-atmosphere continuum from an unconventional perspective. I’m also grateful to Dr. Robert Guy for extensive input on the physiological components of the project, tutoring and use of his instruments and self-made blown-glass distillery, and his seemingly inhuman ability to hold the department together when chaos threatened to ensue. Both he and Dr. Sally Aitken had the wisdom and decisiveness to stop me in the beginning from creating an even larger project, and were both instrumental in ensuring the growth chamber experiment proceeded by making their growth chambers available. Sally and Dr. Melanie Jones have both given much high-quality critical feedback on aspects of the project design and analysis, and Melanie has been a wealth of EM knowledge. And, although not on my committee, I’d like to make special mention of Dr. Cindy Prescott, who has been another important advocate for graduate students in the Faculty of Forestry, and especially the Belowground Ecosystem Group when other professors have been absent. This includes taking the time to give me feedback on a manuscript, despite her weighty administrative duties, and her generous hosting of the BEG graduate students in her home and on beer outings.  xi  I cannot think of the growth chamber trials without thanking Dr. Pia Smets, who was instrumental in ensuring that growth chambers were reserved for me, that they were running correctly and that there were no interruptions in my experiment. She also provided valuable logistical feedback and gave input in designing the water delivery system for the experiment. I also received important methodological feedback on aspects of the field and lab work from Dr. Francois Teste and Brendan Twieg. And, most importantly, I could not have accomplished this research without the toil and suffering of the many paid assistants in the field, growth chamber and laboratory. Amanda Schoonmaker, Dreena Lindstrom and Haemee Lee all made important intellectual contributions to the methodology, in addition to their high-quality hands-on work. Meghan Anderson, Tiffany Cobb, Helena Fleming, Wallis Johnson, Yosune Miquelajauregui and Anita Norman; I thank you all for your diligence and critical role in completing the fieldwork, and the many long, stimulating conversations on the road to the field sites. Cathryn Hale, Asalatha Manda, and Deborah Martell also deserve special mention for their diligent work on the isotope labeling and sample processing.  1 1. Introduction  Scientific background   Mycorrhizas and facilitation  Mycorrhizal fungi are integral to successful seedling establishment when soil nutrients are limiting (Perry et al. 1989, Smith and Read 1997), as mycorrhizas facilitate plant uptake of water and other soil nutrients (Duddridge et al. 1980, Landhausser et al. 2002, Rillig et al. 2002). Evidence of their role in conifer nutrient uptake has been known since at least the 1930s (Hatch 1937), and since the 1970s, their importance to Douglas- fir (Pseudotsuga menziesii) regeneration has been apparent (Stack and Sinclair 1975). It is also known that establishing plants may benefit when growing near previously established plants under these same stressful conditions, when intraspecific competition is low (Greenlee and Callaway 1996, Choler et al. 2001, Pugnaire and Luque 2001, Bertness and Ewanchuk 2002, Callaway et al. 2002, Maestre et al. 2003, Gómez- Aparicio et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006).  Furthermore, there is evidence that EM (EM) networks may play a role in this plant-to-plant facilitation (Dickie et al. 2002, Querejeta et al. 2002, Querejeta et al. 2003a, b, Dickie and Reich 2005, Dickie et al. 2005).  With increasing average annual temperature over much of the globe as a result of atmospheric carbon dioxide (CO2) rise (Hamann and Wang 2005), foresters are concerned about forest regeneration following disturbance. The interaction of EM networks with other biotic, climatic, and topoedaphic factors in regulating forest  2 recruitment needs to be understood if foresters are to design silvicultural practices that ensure forests regenerate successfully under increasing drought, particularly those near the forest-grassland interface. To help predict the effects of climatic and atmospheric conditions on mycorrhizas, and in turn, the effects of EM networks on plant community dynamics, it would be beneficial to assess plant-fungus-plant interactions at several field locations that differ in climate. Several papers  have discussed mycorrhizas in light of economics, suggesting that mycorrhizas are an ideal relationship for testing the biological market model of mutualism (Koide and Elliott 1989, Tinker et al. 1994, Johnson et al. 1997, Schwartz and Hoeksema 1998, Hoeksema and Bruna 2000, Hoeksema and Kummel 2003, Hoeksema and Schwartz 2003, Jones and Smith 2004).  Even more importantly, these models can be used to predict the most efficient allocation of resources among organisms, which can then be used to develop hypotheses as to when trading would be advantageous. This is based on asymmetry between two organisms in their resource acquisition and resource requirement ratios. When this theoretical approach is applied to the role of EM networks in water-limited ecosystems, the “market price” of water in exchange for C (in other words, how much C the plant is “willing” to trade the fungus for one unit of water) should be the primary factor in determining the importance of EM networks in plant facilitation (Cavieres et al. 2006). Water budgets are expected to shift not only in response to changing precipitation and temperature regimes, but also to changes in the atmospheric partial pressure of CO2 (pCO2), due to decreased stomatal conductance. While an increase in temperature would be expected to increase the market price of water through increased evapotranspiration (and therefore decrease in quantity supplied),  3 increases in precipitation and pCO2 could counter this via increases in soil available water and water use efficiency for C3 plants, respectively.  Mycorrhizal networks and seedling establishment The importance of mycorrhizas to plant establishment in nutrient-deficient environments appears to originate with their facilitation of colonization of the land by bryophytic species during the Ordovician period (438-505 Ma BP) (Simon et al. 1993, Redecker et al. 2000, Halling 2001, Wilkinson 2001, Brundrett 2002).  While this colonization involved arbuscular mycorrhizas (AMs), EMs likely did not arise until at least 200 Ma later in gymnosperms (Halling 2001).  Ectomycorrhizas are prevalent in forests dominated by conifers due to their ability to utilize ammonium (NH4+) and organic nitrogen (N), their dominance in hygric and mesic biomes, their ability to store nutrients in their extensive mycelia during periods of dormancy, and ultimately their evolutionary history with gymnosperms (Allen et al. 1995).  Hence, EMs are not a trivial component of the functioning, particularly nutrient cycling, of temperate forest ecosystems. Simard et al. (1997b) challenged some basic tenants of plant community ecology when they detected C transfer between Douglas-fir and paper birch in the field, with more transferred through EM network than soil pathways. The relative importance of these pathways, however, is still unclear (Robinson and Fitter 1999, Simard and Durall 2004; Philip 2006).  If, in fact, C transfer through EM networks between chlorophyllous plants is common, then the prevailing forest stand dynamic models (e.g. Oliver and Larson 1996) may need to be modified to allow for plant interactions other than simply  4 competition (Simard et al. 1997ab, Read 2002).  Applying this paradigm shift in practice has potentially profound implications for forest management. Greenhouse studies have indicated that EM seedlings are able to transfer C and water through EM networks (Brownlee et al. 1983, Simard et al. 1997a).  However, the importance of EM networks in facilitative-competitive relationships between plants is still unknown (Simard and Durall 2004, Taylor 2006).  While it has been unequivocally shown that certain achlorophyllous species of plants rely on photoassimilate from nearby conifers for survival using EM networks as a conduit (Cullings et al. 1996), C-transfer between chlorophyllous plants has also been shown to occur in the field (Simard et al. 1997b, Lerat et al. 2002).  In chlorophyllous systems, however, the significance of C- transfer to plant community dynamics, and the relative importance of EM network versus other belowground pathways for this transfer, both require further research (Robinson and Fitter 1999).  In their study, Simard et al. (1997b) developed a method for dual C isotope labeling and found two-way C transfer between EM Douglas-fir and EM paper birch (Betula papyrifera) shoots in the field, using AM western redcedar (Thuja plicata) as a control, while Lerat et al. (2002) found C transfer from AM Erythronium americanum to AM sugar maple (Acer saccharum) shoots in the spring, using EM yellow birch (Betula alleghaniensis) as a control.  However, in both studies the method used did not conclusively show that C travelled the entire root-to-root distance through fungal hyphae, nor did they demonstrate any effect of transfer on fitness-related traits. Addressing these two questions is complicated by the fact that processes within the soil are complex, minute, and not directly observable. Further research by our group also showed that  a portion of transferred C traveled the entire interplant distance via EM  5 networks, and that a substantial portion was transferred to the recipient seedlings’ shoots, but this transfer did not conclusively affect survival and growth of those seedlings (Teste et al. 2009, Teste et al. 2010). These experiments all occurred at mesic sites during wet years. Other studies have demonstrated C transfer from root to root in AM systems, but in all but one study (Watkins et al. 1996) the C did not usually travel to shoots, and transferred C may have remained in fungal structures (Fitter et al. 1999, 2000, Robinson and Fitter 1999, Fitter and Robinson 2000).  These AM studies also did not adequately address transfer pathway or fitness questions. More recently, Pfeffer et al. (2004) used shoot-excised transgenic carrots in Petri dish experiments to show that C does not leave fungal structures when transferred through AM networks, but one drawback of this study is that removal of shoots likely altered source-sink strength in plant pairs. In addition to C, there is evidence that networks in AM and mixed AM-EM systems can facilitate plant-to- plant transfer of water (Duddridge et al. 1980, Brownlee et al. 1983, Querejeta et al. 2002, Querejeta et al. 2003a, b).It is thus conceivable that EM networks could facilitate water transfer in purely EM systems. In both EM and AM systems where EM network facilitative effects on seedling physiology or growth have been examined, few field experiments have adequately controlled for the occurrence of EM networks. Facilitation between plants in EM or mixed EM-AM systems has been demonstrated in the field (e.g. Dickie et al. 2002, Booth 2004, Dickie et al. 2005, Nara 2006, McGuire 2007, Booth and Hoeksema 2010), but only a few of these studies controlled for the opportunity for EM networks to form, and none examined C transfer.  6 Earlier work established that competition for light and soil water, as well as mediation of resource availability through soil organisms (particularly mycorrhizas) and established EM networks, were important determinants of seedling growth in Douglas-fir forests, but that these relationships changed as stands developed and varied across ecosystems (Simard et al. 1997c, Simard and Sachs 2004, Simard and Vyse 2006). Proximity, density, species and age of neighbors, the presence of an established EM network, as well as forest productivity (as determined by climate and site), were all important factors in the performance of establishing seedlings (Simard 2009). Once seedlings are established, EM networks can continue to affect resource allocation patterns (Simard et al. 1997b), but competition for light, nutrients and water may become relatively more important as stands develop (Simard and Sachs 2004, Dickie et al. 2005, Simard and Vyse 2006). Regardless of the shifting importance of interspecific interaction mechanisms with time, EM networks may profoundly affect stand development and productivity by facilitating establishment of new seedlings, however it remains to be shown that this occurs through interplant nutrient transfer and varies with abiotic stress gradients. In EM systems, EM networks with mature trees may be important in facilitating seedling survival and growth. The three proposed causes for this are:  (1) C or soil nutrients that are transferred from tree-to-seedling through the hyphal network; (2) colonization of the seedling by the mycelia of the tree; and  (3) connection of the seedlings into the extensive preestablished network for nutrient uptake hosted by the tree (Jones et al. 2003).   7 Facilitation and drought Increases in facilitation of establishing plants by nurse plants with environmental stress have been described at length in the literature (Greenlee and Callaway 1996, Choler et al. 2001, Pugnaire and Luque 2001, Bertness and Ewanchuk 2002, Callaway et al. 2002, Maestre et al. 2003, Gómez-Aparicio et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006).  In this section, I will discuss mechanisms potentially underlying this pattern, and the mechanism that I will be testing in this study.  Plant establishment requires rapid access to resources required for growth and survival. This is typically accomplished through: (1) rapid growth of roots and, in most plants, establishment of a mycorrhizal network into the surrounding soil where nutrients are available; followed by (2) development of leaves for C fixation. There are three primary ways in which other plants can facilitate this process:  (1) provide protection from herbivores; (2) make the abiotic environment more favorable for plant functioning; and/or (3) make the soil biotic community more favorable to the establishing plant (Greenlee and Callaway 1996).  There has been renewed attention to how these facilitative processes occur, and under what circumstances they are most prevalent (see Michalet 2006).  None of the published studies to date, however, have looked at EM networks as a mechanism for plant-to-plant facilitation under increasing environmental stress.  Most studies have found an increase in facilitation with environmental stress (Choler et al. 2001, Pugnaire and Luque 2001, Bertness and Ewanchuk 2002, Maestre et al. 2003, Gómez-Aparicio et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006), but a few have not (e.g. Tielboerger and Kadmon 2000, Bellot et al. 2004, Maestre and Cortina  8 2004, Maestre et al. 2005).  Facilitation has usually been attributed to microclimatic factors, especially soil moisture. Bertness and Callaway (1994) were among the first to suggest that facilitation ought to increase in importance as environmental stress intensifies, but a recent meta-analysis challenged this hypothesis by analyzing data sets from field and common garden studies in arid and semi-arid environments (Maestre et al. 2005).  Lortie and Callaway (2006) countered with their own meta-analysis, concluding that lack of rigorous data selection criteria, lack of standardization among key variables and statistics, selection of data from inappropriate studies, and lack of peer review were the reasons for Maestre et al.'s (2005) results, and that the reanalysis supported the theoretical model previously proposed by Bertness and Callaway (1994).  All studies testing facilitation by nurse plants under the facilitation-stress model have thus far assumed that nurse plant effects on seedling microenvironment underlie the facilitation mechanism under increasingly stressful conditions. One study in old-growth ponderosa pine (Pinus ponderosa) forest found evidence of  hydraulic redistribution of water from tree root systems to seedlings via EM pathways (Warren et al. 2008).  Deuterated water and acid fuchsin dye were applied to freshly cut tree stumps and their redistribution then monitored over a period of weeks, resulting in reuptake by seedlings 1-2m away. Uptake was 1.8x greater in seedlings able to form an EM network excluding roots than in those that were not able to form an EM network. Egerton-Warburton et al. (2007) found that water was transferred between oak (Quercus agrifolia) seedlings via EM networks when one seedling was able to access water. The complexity of water movement through soil in forests was illustrated by Schoonmaker et al. (2007) in partially harvested interior Douglas-fir forests, where they found that  9 enrichment of interior Douglas-fir seedlings by hydraulically redistributed deuterated water was highly variable, regardless of distance (within 5 m) and ability to form EM networks with conspecific trees, with some seedlings being enriched and some not.  Global change and environmental stress Because of the long life expectancy and wide environmental tolerance of mature trees (Peterson 1998), responses to climate change will be most readily seen in regeneration.  Difficulties in regenerating tree populations with climate change are likely to be most dramatic in ecotones (Chhin and Wang 2002), especially where ecotones are between forest and grassland (the forest-grassland interface). Mature stands may persist in the face of a changing climate, but a dramatic change in the regenerating plant community can take place after a disturbance (e.g., fire or clearcutting) due to kin- regeneration difficulties in response to the new microclimate or due to competition from invading plants seeding in from adjacent areas. This can be accelerated by a shift in the microbial community following plant death and the subsequent negative feedback to the establishing plants (Perry et al. 1989).  While increasing temperature-induced drought is predicted for the grassland-forest ecotones in the southern interior of British Columbia (Hamann and Wang 2005), rising pCO2 may also affect plant and soil microbial physiological processes (Hoorens et al. 2003).  Although results have been variable, it has been experimentally shown that pCO2 affects plant growth in ecosystems (Mooney et al. 1999, Norby and Luo 2004). The partial pressure of CO2 has been shown to affect transpiration, N allocation, and photosynthetic rate of Douglas-fir seedlings and saplings in a series of studies  10 utilizing sunlit, environmentally controlled growth chambers (Lewis et al. 2002, 2004, Tingey et al. 2003). All of these physiological processes are related to C flux within the plant, and between the plant and its environment (Mooney et al. 1999).  Over a 21-month growing period, these studies showed that increases in pCO2 resulted in decreased transpiration, increased instantaneous transpiration efficiency, increased foliar C/N ratio, and declining net photosynthetic rates of seedlings. Despite this photosynthetic acclimatization, net photosynthesis was still enhanced by increased [CO2] after 21 months (Lewis et al. 2004), probably due to inhibition of photorespiration and increased RuBP regeneration mediated by the electron transport chain. While transpiration declined with increasing [CO2] at constant temperature, it increased with increases in both [CO2] (increased 179 µmol mol-1 above ambient) and temperature (increased 3.5°C above ambient), suggesting that increasing [CO2] will not be enough to counteract water deficiency in Douglas-fir seedlings in response to rising temperatures in southern British Columbia (Lewis et al. 2002). Using the same growth chamber facilities as Lewis and group, Rygiewicz et al. (2000) assessed the four-year response of Douglas-fir EM morphotype community structure to increasing [CO2] and temperature, using native, low-N forest soils.  During the first two years, morphotype numbers increased with [CO2], but then plateaued for the final two years. Morphotype richness also increased with temperature. Greater EM fungal richness increases the probability that fungal species favorable for forming EM networks will be present, possibly increasing the importance of established parent trees to facilitation of seedling establishment (Eriksson 2000, Dickie et al. 2002, 2005, Jones et al. 2003, Simard and Durall 2004, Dickie and Reich 2005).  11 Given these climate change predictions, regeneration near the forest-grassland interface may require changes in silvicultural practices to accommodate the increased water deficiency. Summary:  (1) There is evidence that facilitation of plant establishment by previously established plants increases in importance with water deficiency, and that this facilitation may arise through EM networks. There are a number of mechanisms by which hyphal linkages could mediate this facilitation. (2)  With the forecasted changes in climate and [CO2], understanding the quantitative effects of these factors on plant community dynamics will be critical for creating models for predicting forest regeneration under a range of silvicultural systems. (3)  There are few studies that have investigated at the role of EM networks in plant-to-plant facilitation, and none have examined EM network facilitation along a stress gradient.  Problem statement and objectives This study examines the importance of EM networks associated with mature trees in facilitation of conifer seedling establishment in water- deficient forest ecosystems. A better understanding of competitive and facilitative effects of residual trees on seedling recruitment across a range of stand structures and climatic regions is needed to design silvicultural systems for a range of forest types and changing climatic conditions. To that end, I proposed field and growth chamber experiments examining Douglas-fir seedling establishment as a function of regional climate (as classified by biogeoclimatic (BGC) subzone), links into EM networks with residual trees, distance from residual trees, seedling cohort (3-year-old outplanted nursery seedlings versus 1st  12 year seedlings germinated in the field), seed provenance, and atmospheric CO2 concentrations [CO2]. To predict climatic change effects on seedling recruitment, I assessed the interaction of these factors at field locations (except [CO2]) in different interior BGC subzones, using spatial climatic variability as a proxy for climate change. I predicted that residual trees and linkage into an EM network would be of increasing importance to seedling recruitment in BGC zones with greater summer drought, and hence in regions that will experience greater drought with climate change. The experimental design, which includes replication within stands and climatic regions, provides basic information for further ecological research into the role of EM networks in ecosystems, as well as information relevant to the design of silvicultural systems across multiple scales. Additionally, ambient CO2 levels are predicted to nearly double by the year 2100 (Houghton et al. 2001).  Inferences about stand dynamics in response to climate change will be more accurate when changes in atmospheric CO2 are taken into account, given that ambient CO2 levels affect basic plant physiology (Hoorens et al. 2003, Lewis et al. 2004, Norby and Luo 2004, Handa et al. 2005), and therefore competitiveness with neighbors.  Consequently, I manipulated CO2 levels, along with climatic variables, in a growth chamber experiment. The proposed research further assessed the facilitation-stress model as it applies to increasing climatic aridity, testing the hypothesis that nurse tree effects on the soil biotic community, specifically EM networks, are more important in facilitation than are the independent effects of microclimate. I proposed that nurse (residual) tree facilitation would increase with climatic aridity, and that the mechanism for this facilitation would be seedling linkage into the nurse tree’s EM network. Questions that follow are:  (1) Do EM  13 seedlings receive C nutrition or water from trees or other seedlings of the same species via EM networks? (2) Does C or water transfer through EM networks have an important effect on plant establishment in EM plant communities? (3) Will the importance of EM networks in EM plant-to-plant facilitation increase with climate change or ecosystem stress (i.e., does it increase where seedlings are water-limited and the market price for water is high)? (4) If EM networks are more important under water-limited conditions, do they affect the seedling-to-residual tree distance at which competitive and facilitative interactions are balanced? (5) How do pCO2 and climatic factors interact to affect Douglas-fir tree growth, water deficiency and facilitation of seedling establishment through EM networks? There were five main objectives that were addressed in four chapters as follows: (1) To determine the effects of regional climate (represented by precipitation and temperature) on EM network facilitation of Douglas-fir seedling establishment (Chapters 2 and 3); (2) To separate genotypic effects from climatic effects (Chapter 3); (3) To compare the importance of EM networks to 3-year-old outplanted nursery seedlings versus 1st year seedlings germinated in the field (Chapter 3); (4) To parse the competitive from facilitative effects of residual Douglas-fir trees on small seedlings by determining the effect of Douglas-fir residual tree distance on seedling establishment, and how this changes with climate and connection through a EM network (Chapter 2); and  14 (5) To determine the interaction between soil water and pCO2 in their effects on EM network-facilitated seedling establishment and C-transfer between different sized Douglas-fir seedlings (Chapter 4).  15 2.  Ectomycorrhizal networks of old interior Douglas-fir trees facilitate establishment of conspecific seedlings under drought  Introduction  Successful seedling establishment in stressed ecosystems is strongly influenced by mycorrhizal fungi (Perry et al. 1989, Smith and Read 1997), as well as proximity to previously established plants (Greenlee and Callaway 1996, Choler et al. 2001, Pugnaire and Luque 2001, Bertness and Ewanchuk 2002, Callaway et al. 2002, Maestre et al. 2003, Gómez-Aparicio et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006).  Mycorrhizas aid plants in water uptake and provide other benefits that are critical to plants under water deficiency (Duddridge et al. 1980, Landhausser et al. 2002, Rillig et al. 2002). Evidence that EMs aid conifers in nutrient uptake appeared as early as the 1930s (Hatch 1937). Since the 1970s, they have been known to be important to Douglas-fir (Pseudotsuga menziesii) regeneration (Stack and Sinclair 1975). Some basic tenets of plant community ecology were challenged in 1997 when net C transfer between Douglas-fir and paper birch (Betula papyrifera) was demonstrated in the field, with more transferred through EM networks  than soil pathways (Simard et al. 1997b). The relative importance of these transfer pathways, nevertheless, remains debated (Robinson and Fitter 1999, Simard and Durall 2004; Philip et al. 2010).  If, in fact, C transfer through EM networks is common, then traditional models of plant community dynamics (e.g. Tilman 1988) need modification to allow for a multiplicity of  16 interactions among plants, rather than simply competition determined by resource ratios (Simard et al. 1997a, b, Read 2002, Brooker and Callaway 2009, Heijden & Horton 2009). Plant-to-plant facilitation  has been shown to be most important when environmental stress is high, as predicted by the stress-gradient hypothesis (Greenlee and Callaway 1996, Callaway et al. 2002, Castro et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006), and EM networks may play a role in this facilitation through either interplant transfer of nutrients or water, or through mycorrhizal fungal colonization of establishing plants (Dickie et al. 2002, Querejeta et al. 2002, Querejeta et al. 2003a, b, Dickie and Reich 2005, Dickie et al. 2005; Nara 2006).  With recent summer droughts in southern interior British Columbia (BC), and the predicted increase in average annual temperature and decrease in summer precipitation for this region with rising atmospheric CO2 concentrations, concerns are increasing about forest recruitment following harvest or natural disturbance (Hamann and Wang 2006, Nitschke and Innes 2008, Spittlehouse 2008). Ensuring that forests regenerate successfully under shifting climate, particularly those in the most vulnerable ecosystems (e.g., forest near its climatic limits (Hamann and Wang 2006)), requires that we design silvicultural systems using a sound understanding of the climatic, site and biotic factors regulating recruitment. Understanding the role of EM networks in forest regeneration in stressed ecosystems is, therefore, of increased importance in designing mitigation and adaptation strategies for climate change. To elucidate the effects of climate change on interactions among plants in EM forest communities, plant-fungus-plant interactions should examined at several field locations that differ in regional climate. A better understanding of competitive and facilitative  17 effects of residual trees on seedling recruitment across a range of stand structures and climatic regions is needed to design silvicultural systems for a range of forest types and changing climatic conditions. Current management of interior Douglas-fir is based on the assumption that interactions between Douglas-fir seedlings and trees are the same regardless of regional climatic differences. However, inter-tree competitive and facilitative interactions have been shown to be highly variable across precipitation gradients of interior Douglas-fir forests, and that EM networks can mediate them (Simard 2009). Carbon (C) and N transfer between EM seedlings is known to occur, but the magnitude and direction of net transfer depends on tree species composition, mycorrhizal type, inter-tree competition for resources (especially light), tree phenology, tree size, growth rate, and soil disturbance level (Arnebrant et al. 1993, Simard et al. 1997b, Teste et al. 2010, Philip et al. 2010). There is also evidence that both seedlings and mycorrhizal mycelia receive hydraulically redistributed water from mature trees in a number of ecosystems, that this transfer is facilitated by EM networks, and that this facilitation is likely to occur in interior Douglas- fir forests (Querejeta et al. 2002, Brooks et al. 2002, Querejeta et al. 2003a, b, Brooks et al. 2006, Schoonmaker et al. 2007). Although our group has shown that the same genets of Rhizopogon spp. link all mature interior Douglas-fir trees with understory regenerating seedlings in uneven-aged forests, and that access to both roots and mycelial networks of mature trees facilitates long-distance transfer of C and N to seedlings, the precise circumstances under which EM network-mediated facilitation of seedling establishment is most likely to occur have not been established (Beiler et al. 2009, Twieg et al. 2009, Teste et al. 2010).  18 The main objective of this study was to determine whether interior Douglas-fir seedling establishment is affected by access to a EM network of mature residual trees, and whether this varies by regional climate and distance from the residual tree. Interior Douglas-fir was chosen for five reasons:  (1) it has a broad geographic range and environmental tolerance across the North American Cordillera; (2) its range is likely to expand upward in elevation and latitude with warming, but contract at its driest boundaries (e.g., forest-grassland interface); (3) regeneration success has been shown to depend on mycorrhization in the field; (4) it is one of the most studied tree species in the world; and (5) it is one of the most important tree species commercially and ecologically in southern BC.  To predict climatic effects on the interaction between EM network and distance factors, nine field sites were established along a climatic moisture gradient, where spatial climatic variability served as a proxy for climate change. The following hypotheses were tested:  (1) seedling survival and growth are enhanced by EM colonization via EM networks because of greater access to soil resources, (2) EM network facilitation of seedling establishment should be greatest at intermediate distances from residual trees, where EM network facilitation outweighs competition from tree roots, and (3) facilitation of seedling recruitment by the tree via the EM network increases with drought, in keeping with the stress gradient hypothesis, and thus should manifest in the interactive effects of regional climate, EM networks and distance from mature trees on seedling establishment. We tested these hypotheses in very dry, dry and moist interior Douglas-fir forests of BC, where a strong precipitation gradient is generated by the rain shadow of the Coast Mountain massifs in the west, and orographic lift of the Columbia Mountain highlands in the east. These forests interface with the northern extension of the  19 sagebrush steppe of North America, and hence are predicted to experience dramatic climatic shifts over the next century (Spittlehouse 2008).  Methods  Experimental design The field study was conducted at nine sites located across a climatic gradient that varied according to drought (i.e., the ratio of mean annual potential evapotranspiration to precipitation) (Supplementary Figure 1). Interior Douglas-fir was the dominant tree species at all sites, and we attempted to establish three sites each in each of the Very Dry Hot Interior Douglas-fir (IDFxh) subzone, Dry Cool Interior Douglas-fir (IDFdk) subzone, and Moist Warm Interior Cedar Hemlock (ICHmw) subzone (ref); however, the sites ultimately chosen did not all belong to these three biogeoclimatic units. The three sites within each subzone were at least 2 km apart. Each site had been ‘clearcut with reserves’ (i.e., clearcut leaving mature residual trees scattered throughout the opening) within 10 years of the study initiation in 2005. Clearcut with reserves is the most common harvesting method practiced in British Columbia, comprising 80% of cutting methods in 2009 (BC Ministry of Forests 2009). At each site, the 14 largest, solitary, residual Douglas-fir trees were selected for study, differing by as much as 2 km horizontal and 300 m vertical distance within sites. Climatic, environmental and stand history characteristics for each site are summarized in Table 1. The experiment was planned as a nested 3x3x5 factorial design, where EM network access (3 levels) and distance (5 levels) were nested within regional climate (3  20 levels x 3 sites per level). Each EM network x distance combination was replicated seven times per site (9 sites x 3 EM network treatments x 5 distances x 7 replications = 945 seedlings). Access to a EM network was controlled by planting one-year-old nursery stock seedlings (seedlot # FDI 42192; 50° 01' 00" N, 120° 49' 00" W, 1000m; Thompson Okanagan Dry seed planning zone; Supplementary Figure 1) into soil or mesh bags made of sturdy plain-weave nylon (Plastok, Birkenhead, UK). The three ‘mesh treatments’ were: (1) no mesh, where seedlings were planted directly into soil and thus could form hyphal and rhizomorph EM networks, and their roots were free to intermingle with tree roots; (2) 0.5-µm mesh, where hyphae, roots and invertebrates were restricted from accessing seedlings, and (3) 35-µm mesh, which allowed hyphae to access seedlings and form EM networks, but restricted access by roots and invertebrates) (after Teste and Simard (2008) and Johnson et al. (2001)). For the two mesh bag treatments, a cylinder of soil was removed from the ground, placed into the mesh bag, and seedlings were planted into this soil. For the no-mesh treatment, a cylinder of soil was disturbed in the same manner as that of the mesh treatments. This method allowed us to separate soil water, EM network and root/invertebrate pathways of water and nutrient flow without creating treatment differences in the degree of soil disturbance. The five distances at which seedlings were planted from the residual trees were: 0.5, 2.5, 5, 10 and 15 m from the mature tree bole. The 0.5-m treatment was immediately under the tree canopy, the 2.5-m treatment was usually at the canopy dripline, and the remaining distances were outside the mature tree canopy (i.e., under open sky). One or two transects were randomly assigned to one of nine bearings from the tree (20, 60, 100, 140, 180, 220, 260, 300, or 340 degrees) (Figure 1), and mesh treatments were randomly assigned to each distance  21 position along the transects. Interior Douglas-fir seedlings were planted into these treatments over a period of 6 weeks in May and June of 2006. Competition from understory plants was eliminated by applying an herbicide containing glyphosate and surfactant (Credit (Nufarm Agriculture Inc., Calgary, AB, Canada) at a rate of 17 mL / m2) in a 3-m radius around each planted seedling early every growing season. During application, every seedling at a tree was covered with an upside-down waste-bin.  Measurements  Seedlings Following completion of planting in June 2006, each seedling was monitored for survival at two-week intervals during two growing seasons, until harvest in September 2007. Following harvest, mesh bags were examined for tears, root penetration and hyphal penetration. If the bag had a tear, the seedling was removed from the analysis. Roughly half of all bags had some minor root or mycelial strand penetration at the seams, but upon inspection it was concluded that the magnitude was low relative to the total number of root tips on the seedling and hyphae passing through 35-µm pores. Growth over two growing seasons of surviving seedlings was measured by oven-dry weight (65°C for 48 hours) of root and shoot biomass. A random subsample of three whole oven-dry seedling stems per treatment combination per site were ground using a Thomas Wiley® mini-mill (Thomas Scientific®, NJ, USA), MM 200 ball mill (Retsch® Newtown, PA, USA), and mortar and pestle, then  analyzed for 13C by mass spectrometer (PDZ Europa, UK) at the University of California Davis Stable Isotope Facility.   22 Climatic conditions Photosynthetically active radiation was measured once at the top of every seedling using a Sunfleck PAR Ceptometer (Model SF-80, Decagon Devices, Pullman, WA), and compared to open conditions.  Measurements were only taken when the sun was obscured by clouds, and thus the light was diffuse, the reference being the ability to see one’s shadow.  Weather and climate data for the period of 1950 to 2008 were obtained from the Global Historical Climatology Network (GHCN) (Vose et al. 1992) for the climate stations that were closest to the nine sites. Individual tree latitude, longitude and altitude were used to generate climate data from the web-based tool ClimateBC, which interpolates weather parameters for the period 1950-2002 (Hamann and Wang 2005, Wang et al. 2006). We developed regression equations to estimate monthly precipitation and mean monthly maximum temperature from GHCN for each tree for the duration of the experiments. Two distinct variations on the summer heat:moisture index were then calculated, and they varied depending on the time period relevant to the seedling performance variable modeled. For modeling seedling survival, the index used was: [1] JH:M = (Mean maximum 2006 June-July temperature) / ((2006 May-July precipitation) / 1000), where JH:M is the June-July heat:moisture index. For modeling seedling growth and isotope abundance, the index used was: [2] SH:M =(Mean maximum 2006-7 July temperature)/((mean 2006-7 summer (May- Sept.) precipitation)/1000), where SH:M is the growing season heat:moisture index.   23 Soils Soil temperature and volumetric soil moisture content were recorded every 20 minutes throughout the year during 2007 and 2008 using HOBO Smart Temp (S-TMB-M002, Onset Computer Corp., Bourne, MA, USA) temperature sensors and EC-20 (S-SMC- M020, Decagon Devices, Pullman, WA, USA) capacitance moisture sensors connected to HOBO Micro Stations (H21-002, Onset Computer Corp., Bourne, MA, USA). At each site, the two sensors were buried at 16 cm depth in the mineral soil at a distance of 5 m from three selected residual trees. Thetaprobe Soil Moisture Sensors (Delta-T Devices Ltd, Cambridge, England; Model #ML2) were also used to measure volumetric soil water content via time domain reflectometry, along with temperature, monthly during the 2007 growing season. This was done at five distances from the trees, near the corresponding experimental seedlings. Three soil pits were excavated to the bottom of the B horizon, if present, at each site to characterize the total thickness and color of the forest floor (L, F and H) and mineral (A and B) horizons, as well as mineral soil texture, root abundance, root restricting layer, humus form, depth to water table, coarse fragment content, and soil order (Canadian System of Soil Classification 1998). Soil cores were also taken at the five seedling distances from three randomly selected trees per site to a depth of 32 cm and pooled for each distance by site. The pooled samples were analyzed for total C (Tiessen & Moir 1993), total N (McGill & Figueiredo 1993), and available P (Kalra & Maynard 1991) at the BC Ministry of Forests, Research Branch Analytical Laboratory in Victoria, BC.    24 Residual trees Diameter at breast height, total height, crown height, slope, aspect, latitude, longitude and elevation were recorded for each mature residual tree. All residual trees were alive and healthy when selected in May 2006. During the course of the study, a number of residual trees died at various sites due to windthrow, accidental harvest or undetermined causes.  Ectomycorrhizal community Ectomycorrhizal fungal communities on seedling and conspecific tree root tips were sampled and characterized following Teste et al. (2006) and Twieg et al. (2009). After completion of harvest (September 2007 for distance experiment and outplanted nursery seedlings; September 2008 for field-germinated seedlings), seedlings were morphotyped for EM fungi on 25 root tips, if 25 root tips were present (many seedlings from field- germinated seedlings had fewer than 25 root tips). In addition, a soil core 13cm x 13cm x 32cm deep was taken 2.5m distance from three randomly selected trees at each site, and then 25 Douglas-fir tree roots were morphotyped, provided 25 were available. Roots from trees and seedlings were rinsed in cold water, cut into 1-cm fragments, placed in a baking dish with distilled water and randomly subsampled from the baking dish. Morphotypes were characterized using Ingleby et al. (1990), Agerer (1993), and Goodman et al. (1996). The morphotypes were identified via sequencing of the rDNA internal transcribed spacer region (ITS) by University of British Columbia Okanagan Fragment Analysis & DNA Sequencing Services using the methods of Twieg et al. (2007) and Teste et al. (2010). Most morphotypes sequenced to multiple species.   25 Data analysis Many seedlings died from cattle trampling or bears pulling up mesh bags, thus reducing replication substantially. Seedlings that died of causes other than water deficiency, or that were growing near a dead residual tree, were excluded from the analysis (19% seedlings lost to these factors). Effects of the treatments on seedling survival, growth and stem natural abundance 13C were analyzed using the SAS System for Windows, V9.2 (2009). Because the sites did not fit neatly into the predetermined biogeoclimatic units, climate factor were treated as continuous variables in all statistical models. Logistic regression analysis was used to determine whether seedling survival was associated with mesh treatment, climatic variables, distance from tree, site variables or planting date (SAS PROC LOGISTIC) (Tabachnick and Fidell 2001). The general form of the model was: [3] p(Y) = exp(β0 + β1x1 + β2x2+ …+ βkxk)/1 + exp(β0 + β1x1 + β2x2+ ….…+ βkxk), where p(Y) is the probability of survival, β0 is the intercept, β1 ….βk are estimated coefficients, and x1…xk are independent treatment, climatic, site, or planting date variables. The predictive factors were allowed to enter the model if they improved the overall fit, but were ultimately removed from the model if they did not meet the criteria of P  0.05, with the stipulation that the treatment factors and interactions had to be retained until all remaining covariates were significant. An odds-ratio (i.e., the logarithmic change in probability of survival when the predictive factor is increased by one unit) was calculated for each factor included in the final model. In logistic regression there are no assumptions regarding normality, linearity or homogeneity of variance. The probability of correct classification of a pair of cases from every outcome category selected at random, c, is provided as a measure of strength of association.  26 All growth and isotope analyses were performed  using analysis of covariance (ANCOVA) for a factorial set of treatments using initial seedling height (which was positively correlated to growth), final stem biomass (which was positively correlated to 13C), and site variables as covariates in a completely randomized design using SAS PROC MIXED (Milliken and Johnson 2002). By default, PROC MIXED uses the restricted maximum likelihood (REML) approach for parameter estimation, which produces unbiased parameter estimates when data are unbalanced. The general form of the model was: [4] Yijk  = μ + δi + τj + k + (δτ)ij + (δ)ik + (τ)jk + (δτ)ijk + β 1(X 1ijk – 1X ..) +… β n(X nijk – nX ..) + εijk, where Yijk is the response variable (growth or 13C); μ is a general mean, δi, τj, k, (δτ)ij, (δ)ik, (τ)jk, and (δτ)ijk are the fixed effects parameters for the treatment factors, including SH:M, mesh treatment, and distance from tree and their interactions; β 1 ….β n are estimated coefficients; X 1…X k are covariates, and εijk is the residual (Steel et al. 1980). The procedure for entry and retention of the covariates was the same as that of the survival analysis, except that treatment factors and interactions were retained regardless of P-value. Growth parameters were logarithmically transformed to conform to the assumptions of ANCOVA. All seedlings were assessed for proportional increase in biomass ((final biomass / initial biomass) – 1). Akaike’s Information Criterion (AIC) is provided as a measure of goodness-of-fit, but was not used in model selection.  The EM community was characterized with the intent of assessing treatments effects on EM community similarity between trees and seedlings, however, many genera were very difficult to distinguish visually, and some of the most important genera  27 observed on the root tips did not amplify and sequence successfully. Thus, a general characterization of EM colonization is given without any statistical analysis.  Results Of the 766 outplanted nursery seedlings that were retained in the analysis, a total of 412 seedlings survived to harvest. The logistic model predicting seedling survival included mesh treatment, distance from tree, JH:M, soil texture, and planting date as significant predictors of survival, along with a significant interaction between planting date and mesh treatment (Wald 2 = 167.5, df = 27, P < 0.0001, c = 0.883) (Table 2). All other variables and interactions tested did not enter the model at P<0.05. Seedling survival decreased by 4% with every day after May 1st they were planted in the 35-µm mesh treatment, while survival increased by 4% with every day after May 15th they were planted in the 0.5-µm mesh treatment (Figure 2). Survival was not affected by planting date when seedlings were planted in the no-mesh treatment, after adjusting for other variables in the model. With the covariate adjustments, seedlings were more than seven times as likely to survive when growing in the 35-µm mesh treatment relative to the other mesh treatments combined, while those growing in the 0.5-µm mesh treatment were only 20% as likely to survive (Table 2, Figure 3a). Survival was substantially higher in the 35- µm treatment when seedlings were planted within the first 6 weeks of the start of the planting season, but was lower relative to the other treatments when planted in late June (Figure 2). Survival was greatest at 15 m from residual trees, and lowest at 0.5 m distance, or immediately under the crown of residual trees (Figure 3b). Seedling survival was  28 highest in soils with a silty texture. The probability of survival decreased 4% for every 1 unit increase in June-July heat:moisture index. Growth of surviving seedlings was affected by an interaction of mesh treatment, distance from tree, and summer heat:moisture index, when initial seedling height and total soil N were included as covariates (Table 3). Growth of seedlings in the no mesh (Figure 4a) and 0.5-µm (Figure 4b) treatment decreased with SH:M regardless of their distance from residual trees. When seedlings were grown in the 35-µm mesh, however, the regression lines diverged (Figure 4c). At the lowest SH:M, growth of seedlings in 35- µm mesh was lowest at 0.5 m, increasing with distance from the tree. At the highest SH:M, the reverse was true. In the 35-µm mesh, growth of seedlings 15 m from the tree was strongly negatively correlated with SH:M, while growth of seedlings 0.5 m from the tree was positively correlated with SH:M. Seedling stem 13C was positively correlated with SH:M (P = 0.0034), when the natural logarithm of stem biomass and total soil N were included as covariates (Table 4). None of the other treatment factors affected 13C.  A total of 16 EM morphotypes comprised of 20 fungal taxa were identified in tree and seedling root systems (Supplementary Table 1). Trees, outplanted nursery seedlings and field-germinated seedlings hosted 12, 12 and 11 EM morphotypes, respectively. Outplanted nursery seedlings had 9 morphotypes in common with trees (60% shared) and field-germinated seedlings had 10 morphotypes in common with trees (77% shared). More unique taxa were found on trees and outplanted nursery seedlings than on field- germinated seedlings. The five most abundant EM morphotypes were Rhizopogon/Suillus, Wilcoxina rehmii, Cenococcum geophilum, Amphinema byssoides and Lactarius  29 rubrilacteus (Supplementary Figures 2a and 2b). Sixty-seven percent of shared morphotypes on outplanted nursery seedlings and 60% of shared morphotypes on field- germinated seedlings had a relative abundance of >5% on root tips. Overall, community distance between trees and seedlings was low (D=0.11).  Discussion Seedling survival decreased when seedlings grew in 0.5-µm mesh and were planted early in the spring, supporting our first hypothesis that survival increases with EM colonization through access to the EM network of residual trees. Effects of EM networks on growth were more complex, however. Ectomycorrhizal networks were beneficial to seedling growth only when root competition was excluded by a mesh bag, and only when seedlings were growing furthest from the tree when drought was low, and closest to the tree when drought was high. These results suggest that EM networks facilitate seedling establishment primarily when seedlings are under drought. The potential benefit of networks is further evidenced by the dramatic increase in seedling survival when they were able to form a network compared to when they were not, provided they were planted early in the growing season. At later planting dates, increasing drought appeared to cross a threshold where EM network facilitation was insufficient to improve survival probability.  While we found no evidence that remediation of water deficiency accounted for most of the variation in growth, given the lack of response of 13C to mesh treatments, there was clearly an effect on water deficiency with regard to survival. Delta carbon-13 was positively correlated to the drought index, and it is well established that water  30 deficiency decreases photosynthetic discrimination against 13C via reduced stomatal conductance (Farquhar et al. 1989). While nutrient deficiency can increase 13C discrimination via a reduction in photosynthetic capacity, total soil N was negatively correlated with 13C, which was the opposite expected if nutrient availability accounted for the substantial variation in 13C of our seedlings. The alleviation of water deficiency could have occurred through EM uptake of soil water independent of the established tree, or through translocation of water to the seedling that was hydraulically redistributed to the EM network by the tree. Survival and growth generally increased with distance from residual trees, rejecting our second hypothesis that seedling establishment would be greatest at intermediate distances. These results suggest that seedling performance was regulated primarily by competition from the residual trees. At the driest sites, however, seedling growth was greatest in close proximity to large trees provided they had access to the EM network in the absence of root competition (i.e., in the 35 µm mesh). The benefit of networks to seedling establishment under drought and large nearby trees is consistent with the stress-gradient hypothesis of facilitation (Greenlee and Callaway 1996, Choler et al. 2001, Pugnaire and Luque 2001, Bertness and Ewanchuk 2002, Callaway et al. 2002, Maestre et al. 2003, Castro et al. 2004, Gómez-Aparicio et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006). That we generally found increasing survival and growth with distance from mature trees in most environments contrasts with previous studies (Dickie et al. 2007; McGuire 2007; Teste and Simard 2008). For example, Teste and Simard (2008) found that interior Douglas-fir seedling growth reached a plateau at an intermediate distance  31 (2.5m) from mature trees when they grew in the no mesh treatment. In their study, Teste and Simard (2008) tested distances as far as 5 m from residual trees, where tree root competition was still important, whereas our study tested distances to 15 m, where residual tree roots were no longer suppressive to seedling growth.  Our results also contrast with McGuire (2007), who studied conspecific facilitation of EM Dicymbe corymbosa seedling establishment via EM networks of EM conspecifics within mixed forest dominated by AM species. In her study, seedling survival decreased with distance from conspecifics. McGuire (2007) also found that survival was lowest when seedlings were unable to form a network, but that there was no difference between seedlings growing in 20µm mesh versus no mesh. However, she did find that survival was lower in 0.45µm than 20µm mesh (i.e., in the treatment where seedlings could not form a network versus where they could), which is consistent with the survival difference we found between 0.5µm and 35µm mesh. Seedlings able to form EM networks with other EM root systems may benefit from access to a larger volume of soil or nutrient transfer from connected trees (Read 2002; Smith and Read 1997). Four reasons underlie the major differences between our experiment and McGuire’s (2007) as follows: 1) our seedlings were planted in areas with low overstory canopy coverage, while her seedlings were planted in forest with high canopy coverage and thus shade; 2) our seedlings were grown in greenhouses for one year prior to planting, while her seedlings were naturally regenerated from seed; 3) competition from AM plants within three meters of our seedlings was removed by application of glyphosate, while her seedlings were grown under native competitive conditions; and 4) only a minority of our sites were monodominant stands of EM conspecifics (at the drier end of our gradient) and  32 had plant communities dominated by AM plants.  These factors, especially the removal of AM grass and forb competition in our experiment, reduced the competition under which our seedlings were grown compared to McGuire’s (2007), and may account for the disparity in distance effects between the two studies. Moreover, grass and forb density was observed to generally increase with distance from trees at our sites, whereas AM competition was more uniform in McGuire’s forest. This may also account for the difference  between our results and those of Dickie et al. (2007), who found that Quercus ellipsoidalis seedling survival decreased with distance from conspecific trees in an AM plant matrix. The difference between our studies may also reside in the fact that the maximum distance of seedlings from conspecifics in Dickie et al.’s (2007) study was over twice that of ours. The lower survival of our seedlings growing in no-mesh versus 35-µm mesh must be due to root competition, since all other factors were equal (see Teste et al. 2009). This contrasts with Teste and Simard (2008), who found that survival of seedlings was highest when they were grown in no-mesh. This difference may result from the fact that their experiment was conducted only at mesic sites, and during unusually wet years, thus increasing overall resource availability to seedlings compared to our study. Greater resource availability would have increased seedling survival probability regardless of residual root competition, while potentially reducing the beneficial effects of EM fungi on the seedlings. Increasing drought (SH:M) interacted with mesh treatment and distance factors to affect growth (Figure 4 and Supplementary figure 3). Under the no mesh and 0.5-µm treatments, growth was most sensitive to SH:M when seedlings grew close to trees. By  33 contrast, growth of seedlings in 35-µm mesh, where they were able to form an EM network with reduced root competition, increased with SH:M at high proximity to trees. When seedlings grew close to trees under high drought, those in the 35-µm mesh exhibited substantially greater growth than in the other treatments. This supports our third hypothesis that EM network facilitation of seedling establishment increases under drought due to climatic factors and increasing proximity to residual trees (from increasing root competition). In contrast to their interactive effects on growth, drought, mesh treatment and distance from tree affected survival independently of one another. When seedlings were planted early in the growing season, survival was greatest in the 35-µm mesh, all other things being equal, and lowest when growing in the 0.5-µm mesh. It is likely that survival increased when seedlings were colonized by mycorrhizal networks (i.e., in the 35 µm mesh treatment) prior to the onset of the summer drought. The lack of an interaction between mesh and distance, however, suggests that seedling survival was not influenced by EM networks of distant trees, and this may result from the increasing probability of breaks in the network (e.g., due to grazing by soil fauna or mechanical disruption) with distance. The lack of a decrease in survival of seedlings grown in 0.5-µm mesh with later planting dates is perplexing at first glance. However, seedlings planted early may have been able to develop a more extensive root system within the 0.5-µm mesh bag prior to the onset of drought dormancy, which would cause them to deplete the water within their bag more rapidly during later periods of growth, without the compensatory effect of a mycorrhizal fungal mycelium extending out from the bag. The 0.5-µm mesh seedlings  34 planted just prior to the summer drought, however, would deplete their soil water less rapidly during later growth periods than their early-season cohorts, while being less affected by root competition than late-season seedlings planted directly in soil. The lack of a mesh or distance effect on seedling stem natural abundance 13C contradicts our finding of mesh and distance effects on survival and growth, as water deficiency should cause seedling enrichment in 13C (Farquhar et al. 1989), and distance and mesh should both affect water deficiency. However, precipitation and growth were both major confounding factors in this parameter. Despite the inclusion of stem biomass as a covariate to subtract greenhouse-grown tissue and field-growth effects from the additive model, initial stem biomass was not measured due to the destructive method. As a result, tissue generated in the greenhouse still had some influence on measured isotope content of seedling tissue, as well as growth effects in the field. Additionally, if variation in climatic drought among sites was far greater than that caused by distance and mesh treatments, variation in stomatal conductance within sites may not have been great enough for us to detect mesh and distance effects on fractionation. More likely, though, a covariate that was influencing 13C was not adequately captured in our model, possibly PAR. Additionally, replication was much lower for the isotope data than for the growth and survival data, thus the power to detect differences was substantially reduced (Tabachnick and Fidell 2001). In summary, we found that EM networks of residual trees were particularly important in facilitating regeneration of Douglas-fir seedlings when they were establishing under drought and root competition of nearby trees. As a result, managers need to consider retaining residual trees for their facilitative effects when harvesting  35 forests near the forest-grassland interface. To maximize regeneration of interior Douglas- fir in dry forests, we suggest retaining a low density of old residual trees to ensure the maintenance of robust EM networks for colonization of new seedlings and possibly redistribution of water or nutrients. While caution should be taken in generalizing these results to other forest ecosystems, it is likely that the EM network effects observed in this study, and affiliated studies in the same forest type (Schoonmaker et al. 2007; Teste and Simard 2008; Simard 2009; Teste et al. 2009 and 2010; Beiler et al. 2010) apply to most dry EM forests and woodlands.  36  Table 1 Biogeoclimatic unit and climatic parameters of each site. Sites were classified according to Lloyd et al. (1990). Climatic variables have been interpolated for each plot using ClimateBC (Hamann and Wang 2005, Wang et al. 2006), then calculated across time and averaged across plots for each site. Minima and maxima for each site were selected at the resolution of year by plot. Site Heffley Creeka Red Lakeb Opax Mountainc Nesbitt Laked Cannell Creeke McConnell Lakef Pisima Lakeg Honeymoon Bayh John Creeki Seed Planning Zone  TOA TOA TOA TOA TOA TOA SA SA SA Biogeoclimatic Unit   PPxh2  IDFxh2  IDFdk1  IDFdk2  IDFdk2  IDFdk1  ICHmw3  ICHmw3  ICHmw3  Altitude (m) mean 734 937 1064 1227 1281 1242 815 1190 1125  a Partially harvested through time. Heavily grazed by cattle. b At the forest grassland interface. Partially harvested through time. Moderately grazed by cattle. c Partially cut in 1956-7, and again in 2005. Moderately grazed. d Clearcut in 1999. Site prepared in 2000.Moderately grazed. e Clearcut in 2002. Site prepared in 2003. Moderately grazed. f Clearcut in 2000. Site prepared in 2000. Lightly grazed. g Clearcut in 2006. Site prepared in 2008. Lightly grazed. h Clearcut in 2006. Site-prepared in 2007. Lightly grazed. i Clearcut and site-prepared in 2006. Lightly grazed.  37 Table 1 Biogeoclimatic unit and climatic parameters of each site. Sites were classified according to Lloyd et al. (1990). Climatic variables have been interpolated for each plot using ClimateBC (Hamann and Wang 2005, Wang et al. 2006), then calculated across time and averaged across plots for each site. Minima and maxima for each site were selected at the resolution of year by plot. Site Heffley Creeka Red Lakeb Opax Mountainc Nesbitt Laked Cannell Creeke McConnell Lakef Pisima Lakeg Honeymoon Bayh John Creeki  range 645-775 893-987 1031-1083 1197- 1302 1243-1375 1163-1284 668-941 1120-1248 1056-1199 Annual Precip. (mm) mean 340 376 436 373 461 433 774 607 607  range 242-441 247-532 307-576 249-516 318-625 297-586 533-1032 432-771 432-791 Growing Season Precip. (mm) mean 163 186 212 176 223 216 328 264 271  range 69-270 65-373 83-377 77-345 84-415 85-380 170-546 146-425 150-434 Annual Snowfall (cm) mean 100 115 135 131 159 140 224 220 226  range 53-201 55-237 71-257 73-253 84-297 77-254 97-490 131-392 137-395  38 Table 1 Biogeoclimatic unit and climatic parameters of each site. Sites were classified according to Lloyd et al. (1990). Climatic variables have been interpolated for each plot using ClimateBC (Hamann and Wang 2005, Wang et al. 2006), then calculated across time and averaged across plots for each site. Minima and maxima for each site were selected at the resolution of year by plot. Site Heffley Creeka Red Lakeb Opax Mountainc Nesbitt Laked Cannell Creeke McConnell Lakef Pisima Lakeg Honeymoon Bayh John Creeki Annual Temp. (°C) mean 5.6 4.5 4.3 3.7 3.3 3.8 5.3 3.3 2.9  range 3.1-7.9 1.8-6.7 1.8-6.4 0.8-5.7 0.6-5.5 1.3-6.1 2.6-8.1 0.9-5.6 0.4-5.1 Growing Season Temp. (°C) mean 14.1 12.8 12.5 11.8 11.5 12.0 13.8 11.6 11.0  range 12.4-16.3 11.3- 15.3 11.0-14.8 10.1-14.2 9.9-13.7 10.4-14.2 12.1-16.0 9.9-13.9 9.2-13.3  Min. Jan. Temp. (°C) mean -10.7 -11.1 -11.2 -11.3 -11.7 -11.2 -10.8 -11.9 -12.7  range -21.8--3.7 -21.9-- 3.9 -21.6--4.1 -22.3--4.2 -22.4--4.6 -21.7--3.9 -23.7--3.2 -24.6--4.7 -25--5.7  39 Table 1 Biogeoclimatic unit and climatic parameters of each site. Sites were classified according to Lloyd et al. (1990). Climatic variables have been interpolated for each plot using ClimateBC (Hamann and Wang 2005, Wang et al. 2006), then calculated across time and averaged across plots for each site. Minima and maxima for each site were selected at the resolution of year by plot. Site Heffley Creeka Red Lakeb Opax Mountainc Nesbitt Laked Cannell Creeke McConnell Lakef Pisima Lakeg Honeymoon Bayh John Creeki Growing Degree Days (>5°C) mean 1604 1346 1296 1160 1110 1194 1535 1129 1034  range 1220- 2111 1041- 1774 1001-1715 823-1582 794-1502 904-1618 1104-2146 793-1553 692-1450 Frost-Free Period (days) mean 111 94 92 85 78 85 109 83 70  range 75-143 49-128 50-128 43-122 29-120 42-125 68-149 33-123 0-115  40 Table 2 Logistic regression testing for the probability of seedling survival in response to mesh treatments, distance from established tree (m), June-July heat:moisture index (JH:M), seedling outplanting date (number of days after May 1st, 2006), and soil texture. Odds ratios and their p-values are given for each continuous variable, category and interaction. The p-value tests the null hypothesis that the odds ratio equals one. Logistic Regression: c = 0.879 Likelihood ratio P < 0.0001 Effect Odds ratios DF Wald 2 P > 2 JH:M 0.953229 1 82.8954 <.0001 0.5 m 0.218428 4 49.3901  <.0001 2.5 m 0.876604 5 m 1.613329 10 m 1.539257 15 m 2.103074 0.5-µm mesh 0.197899 2 8.7755 0.0124 35-µm mesh 7.380933 No mesh 0.684614 Seedling outplanting date 0.924964 1 33.0201 <.0001 Planting date*0.5-µm mesh 1.03977 2 7.8328 0.0199 Planting date*35-µm mesh 0.955997 Planting date*no mesh 1.006028 clay loam 1.025008 6 75.7604 <.0001 fine 0.095198 gritty silty 2.045414  41 Table 2 Logistic regression testing for the probability of seedling survival in response to mesh treatments, distance from established tree (m), June-July heat:moisture index (JH:M), seedling outplanting date (number of days after May 1st, 2006), and soil texture. Odds ratios and their p-values are given for each continuous variable, category and interaction. The p-value tests the null hypothesis that the odds ratio equals one. Logistic Regression: c = 0.879 Likelihood ratio P < 0.0001 Effect Odds ratios DF Wald 2 P > 2 loam 1.46404 sandy loam 0.494702 silty 21.98147 silty clay loam 0.31468   42  Table 3 Analysis of covariance testing for response of the natural logarithm of proportional increase in biomass of seedlings to mesh treatments, distance from established tree (m), and summer heat:moisture index (SH:M), after adjustment for initial seedling height (cm) and total soil N (ppm extracted). NOTE: The coefficient signs are only given for continuous variables. ANCOVA: AIC = 646.1 Effect Coefficient F-value P > F Mesh N/A 1.12 0.3266 Distance N/A 1.69 0.1533 Mesh*Distance N/A 2.31 0.0209 SH:M  - 36.73 <.0001 SH:M *Mesh N/A 0.3 0.7444 SH:M *Distance N/A 0.49 0.7439 SH:M *Mesh*Distance N/A 2.08 0.0375 Covariates: Total N + 34.68 <.0001 Initial seedling height + 20.01 <.0001  43  Table 4 Analysis of covariance testing for response of seedling stem natural abundance 13C to mesh treatments, distance from established tree (m), and summer heat:moisture index (SH:M), after adjustment for the natural logarithm of final seedling stem biomass and total soil N. NOTE: The coefficient signs are only given for continuous variables. ANCOVA: AIC = 619.2 Effect Coefficient F/t-value P > F/|t| Mesh N/A 0.05 0.9476 Distance N/A 0.13 0.9723 Mesh*Distance N/A 1.15 0.3338 SH:M + 8.36 0.0043 SH:M *Mesh N/A 0.08 0.9254 SH:M *Distance N/A 0.06 0.9941 SH:M *Mesh*Distance N/A 1.28 0.254 Covariates: Total soil N - 9.76 0.002 Ln (stem biomass) + 57.42 <.0001  44  Figure 1 Location of possible transect positions (represented by stars at 2.5 m) around a residual tree (brown circle), with an example of a transect at 260 (stars along transect representing seedling positions on transect). Each position along the transect represents an experimental unit.      45  Estimated Probability 0.3 0.4 0.5 0.6 0.7 0.8 0.9 initialplantingdate 10 20 30 40 50 60 MN 0.5m 35m nm Figure 2  Probability of seedling survival as a function of planting date under the three mesh treatments. Number of days after May 1st, 2006 seedling was planted Probability of survival . -µm -µm o mesh Network treatment  46   0.1 1 10 0.5 -µm  m es h 35 -µm  m es h No  m es h O dd s ra tio  (l og  s ca le )  0.1 1 10 0.5  m 2.5  m 5 m 10  m 15  m O dd s ra tio  (l og  s ca le )  Figure 3  Odds ratio values (logarithmic y-axis) for (a) mesh treatment and (b) distance in the logistic regression model predicting survival. a b  47 ln_adjust -1 0 1 2 3 SMH 80 90 100 110 120 130 140 150 160 170 180 190 200 Mesh=No mesh Distanc 0.5 2.5 5.0 10.0 15.0 ln_adjust -1 0 1 2 3 SMH 80 90 100 110 120 130 140 150 160 170 180 190 200 Mesh=0.5-um Distanc 0.5 2.5 5.0 10.0 15.0 Ln (growth) Summer heat:moisture index 0.5-m 2.5-m 5-m 10-m 15-mDistan e  a b Ln (growth) Summer heat:moisture index 0.5-m 2.5-m 5-m 10-m 15-mDistance   48 ln_adjust -1 0 1 2 3 SMH 80 90 100 110 120 130 140 150 160 170 180 190 200 Mesh=35-um Distanc 0.5 2.5 5.0 10.0 15.0 Figure 4  Relationship of summer heat:moisture index and the natural logarithm of proportional biomass increase after adjusting for covariates at different distances from the established tree under the no mesh treatment (a), the 0.5-µm mesh treatment (b) and the 35-µm mesh treatment (c). All regression lines among distances under the 35-µm mesh treatment are significantly different (P0.5). The regression lines in the 0.5-µm and no mesh treatments do not differ in slope or altitude. c Ln (growth) Summer heat:moisture index 0.5-m 2.5-m 5-m 10-m 15-mDistance   49 3. Ectomycorrhizal networks and seedling genetics act independently to improve conifer regeneration with increasing drought  Introduction   Interior Douglas-fir (Pseudotsuga menziesii var. glauca) is dependent on EM fungi for establishment (Stack and Sinclair 1975). The greatest benefit is typically incurred when seedlings are unable to acquire adequate quantities of soil resources on their own (Schwartz and Hoeksema 1998). This is especially crucial if the resource is needed in large quantities to maintain tissue integrity, such as is the case with water (Hsiao 1973). This is a special problem for interior Douglas-fir, as it experiences pronounced growing-season drought annually over most of its range (Rehfeldt 1989). Genetic clines, however, do occur along moisture gradients within the range of Douglas- fir, with variation in water use efficiency within and between coastal and inland populations (Aitken et al. 2008). This raises the question of whether coevolution between the trees and their EM fungi has affected variation in EM fungal facilitation of interior Douglas-fir along genetic clines (Thompson 2005, Hoeksema 2010). While facilitation should vary along genetic clines with water deficiency, it is unknown what proportion of the variation originates from environment versus genotype of plants and fungi (Callaway et al. 2002, Hoeksema 2010). It is known that EM fungi can form networks that connect the root systems of two or more different woody plants (e.g., seedlings connected to large trees), and that these networks can facilitate seedling establishment in soils that are  50 depauperate of beneficial fungi (Dickie et al. 2005). The degree of this EM network facilitation of seedling establishment should vary clinally within interior Douglas-fir. Ectomycorrhizal fungi facilitate plants when the fungi are limited by C and the plant is limited by water or some other soil nutrient (Schwartz and Hoeksema 1998), and the EM fungi serve in increasing nutrient and water uptake of plants, as well as providing other benefits that are critical to plants under environmental stress (Duddridge et al. 1980, Landhausser et al. 2002, Rillig et al. 2002). In field studies of tropical and temperate forests, facilitation of EM seedlings via the EM network of established trees or woody plants has been shown to be particularly important in arid climates (Simard 2009; Teste et al. 2009) as well as where EM seedlings are establishing in an AM plant community matrix (Dickie et al. 2002, Dickie et al. 2005, McGuire 2007) or during primary succession (Nara 2006).  Forest harvesting practices across the range of Douglas-fir, however, generally rely on clearcutting, which leaves few if any large trees to play a role in EM network facilitation of seedling establishment. Research by our group in interior temperate forests containing a large component of interior Douglas-fir indicates that EM networks mediate interactions among plants, but how seedling genotype and age interact with EM networks is unknown. Teste et al. (2010) has already shown that net C transfer between interior Douglas-fir seedlings varies according to whether they were planted or naturally regenerated, but what this means for survival and growth is unknown. Evidence also suggests that naturally regenerated Douglas-fir seedlings develop root systems that can rapidly exploit EM networks via rhizomorphs, resulting in improved height growth (Halter and Chanway 1993, Teste et al. 2010). Furthermore, provenance experiments have found genotypic variation in Pinaceae  51 seedling responses to EM fungi, but they were conducted in controlled environments and have not included tests of EM network effects (Rosado et al. 1994, Karst et al. 2009). The main objective of this study was to determine whether Douglas-fir seedling establishment is affected by the presence of EM networks, and whether this varies by regional climate, seed provenance and seedling life history. To predict climatic effects on the interaction among EM networks, seedling provenance, and life history factors, nine field sites were established along a climatic moisture gradient, where spatial climatic variability served as a proxy for climate change. The following hypotheses were tested: (1) EM networks facilitate survival of seedlings by reducing water deficiency and increasing survival and growth; (2) EM network facilitation depends on seedling life history, and will be greater for field-germinated seeds than in outplanted nursery seedlings because of differences in age, size and nutrient content; and (3) EM network facilitation depends on seedling provenance and its interaction with climatic stress, where EM networks have the largest facilitative effects on seedlings of the driest provenance planted in the driest environment, and the smallest effect on seedlings from the driest provenance planted into the wettest environment. We tested these hypotheses in the very dry, dry and moist interior Douglas-fir forests of BC, where a strong precipitation gradient is generated by the rain shadow of the Coast Mountain massifs in the west, and orographic lift of the Columbia Mountain highlands in the east. These forests interface with the northern extension of the sagebrush steppe of North America, and hence are expected to experience dramatic climatic shifts over the next century (Spittlehouse 2008). The experimental design, which included replication within stands and climatic regions, provides basic information for designing  52 silvicultural systems across multiple scales that will facilitate adaptation of these vulnerable ecosystems to climate change.  Methods  Experimental design The field study was conducted at nine sites located across a climatic gradient that varied according to drought (i.e., the ratio of mean annual potential evapotranspiration to precipitation) (Supplementary Figure 1). Interior Douglas-fir was the dominant tree species at all sites, and we attempted to establish three sites each in the Very Dry Hot Interior Douglas-fir (IDFxh) subzone, Dry Cool Interior Douglas-fir (IDFdk) subzone, and Moist Warm Interior Cedar Hemlock (ICHmw) subzone; however, the sites ultimately chosen only approximated these three biogeoclimatic units. Each site had been ‘clearcut with reserves’ (i.e., clearcut leaving mature residual trees scattered throughout the opening) within 10 years of the study initiation in 2006. At each site, a minimum of 14 of the largest solitary residual Douglas-fir trees were selected for study. The three sites within each subzone were at least 2 km apart. Climatic, environmental and stand history characteristics for each site are summarized in (Table 1). The experiment was planned as a nested 3x3x2x3 factorial design, where EM network access (3 levels), seed provenance (3 levels), and seedling cohort (2 levels) were nested within regional climate (3 levels x 3 sites per level). Two interior Douglas-fir cohorts were planted near mature, residual Douglas-fir trees:  (1) one-year-old nursery- grown seedlings were planted over a period of 6 weeks in May and June of 2006, and (2)  53 unstratified seeds (field-germinated seedlings) were planted in September of 2006, and thus germinated the following growing season of 2007. Each EM network x provenance x cohort combination was replicated seven times per site (9 sites x 3 EM network treatments x 3 provenances x 2 cohorts x 7 replications = 1134 seedlings; including 567 seedlings of outplanted nursery seedlings and 567 seed groups representing field- germinated seedlings). Access to an EM network was controlled by planting one-year-old nursery stock seedlings or seeds into soil or mesh bags made of sturdy plain-weave nylon (Plastok, Birkenhead, UK). The three ‘mesh treatments’ were: (1) no mesh, where seedlings were planted directly into soil and thus could form hyphal and rhizomorph EM networks, and their roots were free to intermingle with roots of older trees; (2) 0.5-µm mesh, where hyphae, roots and invertebrates were restricted from accessing seedlings, and (3) 35-µm mesh, which allowed hyphae to access seedlings but restricted access by roots and invertebrates (after Teste and Simard (2008) and Johnson et al. (2001)). For the two mesh bag treatments, a cylinder of soil was removed from the ground, placed into the mesh bag, and seedlings were planted into this soil. For the no-mesh treatment, a cylinder of soil was disturbed in the same manner as that of the mesh treatments. This method allowed us to separate soil water, EM network and root/invertebrate pathways of water and nutrient flow without creating treatment differences in the degree of soil disturbance. The three seed provenances were from seed planning zones, Thompson Okanagan Arid (TOA: seedlot # FDI 45272; 50° 43' 00" N, 121° 06' 00" W, 1100m), Thompson Okanagan Dry (TOD: seedlot # FDI 42192; 50° 01' 00" N, 120° 49' 00" W, 1000m), and Shuswap-Adams (SA: seedlot # FDI 30461; 50° 38' 00" N, 118° 49' 00" W, 900m) (Supplementary Figure 1). For field-germinated seedlings, roughly 25 seeds were planted  54 for each seedling, and gradually thinned until only one germinant remained. An aluminum dish containing naphthalene balls was placed next to each seed location to deter predation, however, many experimental units did not germinate and we infer that ants were the culprit. The geographic origins of the three provenances decreased in regional precipitation in the order SA (Wet) < TOD (Medium) < TOA (Dry). All seedlings were planted 2.5 m from the conspecific dominant, which was usually at the canopy dripline. Seedlings were planted at nine bearings from the tree (20, 60, 100, 140, 180, 220, 260, 300 or 340 degrees) (Figure 5). Mesh, provenance and cohort treatments were randomly assigned to each position, across all conspecific dominants at a site. Competition from understory plants was eliminated by applying an herbicide containing glyphosate and surfactant (Nufarm Credit® at a rate of 17 mL / m2) in a 3-m radius around each planted seedling early every growing season.  Measurements  Seedlings Following completion of planting (June 2006 for outplanted nursery seedlings and September 2006 for field-germinated seedlings), each seedling was monitored for survival (cohorts 1 and 2) and germination (field-germinated seedlings) at two-week intervals during the growing season until harvest (September 2007 for outplanted nursery seedlings and September 2008 for field-germinated seedlings). Two-year growth of surviving seedlings was measured by oven-dry weight (65°C for 48 hours) of root and shoot biomass. A subsample of three oven-dry seedling stems per treatment combination were milled and analyzed for 13C by mass spectrometer (PDZ Europa, UK) at the  55 University of California Davis Stable Isotope Facility to assess cumulative water-use- efficiency during the field life of the seedling.  Climatic conditions Photosynthetically active radiation was measured once at the top of every seedling using a Sunfleck PAR Ceptometer (Model SF-80, Decagon Devices, Pullman, WA), and compared to open conditions when one’s shadow could not be seen as the result of clouds. Weather and climate data for the period of 1950 to 2008 were obtained from the Global Historical Climatology Network (Vose et al. 1992) for the climate stations that were closest to the nine sites. Individual tree latitude, longitude and altitude were used to generate climate data from the web-based tool ClimateBC, which interpolates weather parameters for the period 1950-2002 (Hamann and Wang 2005, Wang et al. 2006). We developed regression equations to estimate monthly precipitation and mean monthly maximum temperature for each tree for the duration of the experiments. We then predicted survival, growth and δ13C values of seedling cohorts 1 and 2 from variations in the summer heat:moisture index. For predicting survival of outplanted nursery seedlings, the index was: [1] JH:M (outplanted nursery seedlings)  = (Mean maximum 2006 June-July temperature) / ((2006 May-July precipitation) / 1000). For survival of field-germinated seedlings, the index was: [2] JH:M (field-germinated seedlings) = (Mean maximum 2007 June-July temperature) / ((2007 May-July precipitation) / 1000).  56 For predicting the growth and δ13C of outplanted nursery seedlings, the heat:moisture index was: [3] SH:M (outplanted nursery seedlings) =  (Mean maximum 2006-7 July temperature)/((mean 2006-7 summer (May-Sept.) precipitation)/1000). For growth and δ13C of field-germinated seedlings, the index was: [4] SH:M (field-germinated seedlings) =  (Mean maximum 2007-8 July temperature)/((mean 2007-8 summer (May-Sept.) precipitation)/1000).  Soils At each site, three soil pits were excavated to the bottom of the B horizon, if present, to characterize the total thickness and color of the forest floor, A, and B horizons, as well as overall texture, root abundance, depth to root restricting layer, humus form, presence of mottling, depth to water table, coarse fragment content, and soil order (according to the Canadian System of Soil Classification 1998). Soil cores were also taken at 2.5 m distance from three randomly selected trees per site to a depth of 32 cm and pooled by site. The samples were analyzed for total N (McGill and Figueiredo 1993) at the British Columbia Ministry of Forests, Research Branch Analytical Laboratory in Victoria, British Columbia.  Residual trees Diameter at breast height, total height, crown height, slope, slope aspect, latitude, longitude and altitude were recorded for each residual tree. All residual trees were alive and healthy when selected in May, 2006. During the course of the study, a number of  57 trees died at various sites due to windthrow, accidental harvest or undetermined causes. In addition, many seedlings died from cattle trampling or bears pulling up mesh bags, thus reducing replication substantially.  Ectomycorrhizal community Ectomycorrhizal fungal communities on seedling and conspecific tree root tips were sampled and characterized following Teste et al. (2006) and Twieg et al. (2009). After completion of harvest (September 2007 for distance experiment and outplanted nursery seedlings; September 2008 for field-germinated seedlings), seedlings were morphotyped for EM fungi on 25 root tips, if 25 root tips were present (many seedlings from field- germinated seedlings had fewer than 25 root tips). In addition, a soil core 13cm x 13cm x 32cm deep was taken 2.5m distance from three randomly selected trees at each site, and then 25 Douglas-fir tree roots were morphotyped, provided 25 were available. Roots from trees and seedlings were rinsed in cold water, cut into 1-cm fragments, placed in a baking dish with distilled water and randomly subsampled from the baking dish. Morphotypes were characterized using Ingleby et al. (1990), Agerer (1993), and Goodman et al. (1996). The morphotypes were identified via sequencing of the rDNA internal transcribed spacer region (ITS) by University of British Columbia Okanagan Fragment Analysis & DNA Sequencing Services using the methods of Twieg et al. (2007) and Teste et al. (2010). Most morphotypes sequenced to multiple species.     58 Data analysis Seedlings that died of causes more complex than simple water deficiency, or that were growing near a dead tree, were excluded from the analysis (leaving n=458 for outplanted nursery seedlings, n=134 for field-germinated seedlings). Effects of the treatments on seedling survival, growth and stem natural abundance 13C were analyzed with least likelihood methods using the SAS System for Windows, V9.2 (2009). Climate factors were treated as continuous variables in all statistical models because the sites did not fit neatly into the predetermined biogeoclimatic units. The two cohorts were analyzed separately because variation between them was so great that it obscured variation of other factors. For each cohort, logistic regression analysis was used to determine whether seedling survival was associated with mesh treatment, climatic variables, provenance, site variables or planting date (outplanted nursery seedlings only), where the individual seedling served as the basic experimental unit (SAS PROC LOGISTIC) (Tabachnick and Fidell 2001). Site variables included all measurements that were taken at the site. The general form of the logistic regression model was: [5] p(Y) = exp(β0 + β1x1 + β2x2+ …+ βkxk)/1 + exp(β0 + β1x1 + β2x2+ ….…+ βkxk), where p(Y) is the probability of survival, β0 is the intercept, β1 ….βk are estimated coefficients, and x1…xk are independent mesh treatment, climatic, provenance, site or planting date variables. The predictive factors were allowed to enter the model if they improved the overall fit, but were ultimately removed from the model if they did not meet the criteria of P  0.05, with the stipulation that the treatment factors and interactions had to be retained until all remaining covariates were significant.  59 Analysis of covariance (ANCOVA) was used to predict growth and isotope responses to the factorial set of treatments, where final stem biomass, climatic, and site variables (variables that are intrinsic to the site) were used as covariates for a completely randomized experimental design analyzed in SAS PROC MIXED (Milliken and Johnson 2002). Individual seedlings were the experimental units in these regressions. Initial seedling height was included as a covariate in the growth analysis because it positively affected final stem biomass. Final stem biomass was included as a covariate in the isotope analysis to adjust for effects of growth on 13C values; this is because 13C of greenhouse-generated biomass differed substantially in 13C from field-generated biomass as a result of radically different growing conditions. The general form of the regression model was: [6] Yijk  = μ + δi + τj + k + (δτ)ij + (δ)ik + (τ)jk + (δτ)ijk + β 1(X 1ijk – 1X ..) +… β n(X nijk – nX ..) + εijk, where Yijk is the dependent variable (growth or 13C); μ is a general mean; δi, τj, k, (δτ)ij, (δ)ik, (τ)jk, and (δτ)ijk are the fixed effects parameters for the mesh treatment, climatic, and seed provenance factors, and their interactions; β 1 ….β n are estimated coefficients; X 1…X k are climatic, site or planting date covariates; and εijk is the residual (Steel and Torrie 1980). The procedure for entry and retention of the covariates was the same as that of the survival analysis, except that the requirement for retention of covariates was P  0.1, and treatment factors and interactions were retained regardless of P-value. Akaike's information criterion (AIC) is presented for each model as a measure of goodness of fit, but was not used in the process of model selection. Growth parameters were logarithmically transformed to conform to the assumptions of ANCOVA. All seedlings in  60 outplanted nursery seedlings were assessed for proportional increase in biomass ((final biomass / initial biomass) – 1), while those in field-germinated seedlings were assessed for total biomass.  The EM community was characterized with the intent of assessing treatments effects on EM community similarity between trees and seedlings, however, many genera were very difficult to distinguish visually, and some of the most important genera observed on the root tips did not amplify and sequence successfully. Thus, a general characterization of EM colonization is given without any statistical analysis.  Results Of the 458 planted nursery seedlings that were retained in the analysis (i.e., that did not die from causes other than drought), a total of 197 survived to harvest. The logistic model predicting seedling survival for outplanted nursery seedlings included provenance, JH:M, and planting date as significant predictors, along with a significant interaction between JH:M and provenance (Wald 2 = 92.0, df = 8, P < 0.0001, c = 0.835) (Table 5). All other variables and interactions tested did not enter the model at P<0.05. Seedling survival decreased with JH:M regardless of provenance. Survival of the middle provenance (middle provenance) seedlings declined the most sharply with increasing JH:M, but they also had the highest average survival (Figure 6). As expected, the dry provenance (driest provenance) seedlings performed best on the most drought- prone sites (i.e., with the highest JH:M values). The wet provenance seedlings (wet provenance) had the poorest performance, but survival was similar to that of the middle provenance seedlings at the highest JH:M sites.  61 Of the 134 field germinants that were retained for the analysis, 87 survived. Survival of field-germinated seedlings was predicted by JH:M only (Wald 2 = 22.9, df = 1, P < 0.0001, c = 0.749) (Table 6). The probability of survival decreased by 2% for every 1-unit increase in JH:M. Growth of outplanted nursery seedlings was affected by SH:M and provenance when total soil N and its interaction with provenance were included as covariates (Table 7). Growth of outplanted nursery seedlings decreased with SH:M, and was greatest for the wet provenance (wet) provenance. Summer heat:moisture index was the only treatment factor that affected growth of field-germinated seedlings, after including total soil N and its interaction with SH:M as covariates (Table 8). Growth of field-germinated seedlings decreased with SH:M. Natural abundance 13C of outplanted nursery seedlings was affected by an interaction between SH:M and provenance (P=0.031) (Table 9). Seedling 13C increased with SH:M, but seedlings from the middle provenance were the least responsive (Figure 7), after adjusting for the natural logarithm of seedling stem biomass and whether the soil order was a Gleysol. Regression lines for dry provenance and wet provenance did not differ, and thus were combined. For field-germinated seedlings, natural abundance 13C was not significantly affected by the treatment variables (Table 10).       62 Discussion  Mycorrhizal network effects Survival, growth and 13C were not affected by EM networks in either cohort, failing to support our first hypothesis. The lack of a detectable effect may have been caused by substantial reduction in replication combined with the confounding variance of the provenance factor. At such close proximity to the tree lack of new germinants under the canopy of mature trees is common in these forests (Simard, 2009), and bears, cattle, and deer spend disproportionately more time under solitary trees in openings. Furthermore, in other studies (e.g. Dickie et al. 2005, Teste and Simard 2008) facilitation was greatest at further distances from dominant conspecifics (outside the dripline but still within its rooting zone). The lack of an effect in our study contrasts with Booth and Hoeksema (2010), who found that survival of Pinus radiata seedlings was greatest when they were able to form an EM network without root competition, and lowest in seedlings able to form an EM network in the presence of root competition. They also found that 13C was lower in seedlings that were able to form an EM network. A major difference between our studies is that theirs was conducted in a single plot, while ours was conducted at nine field sites across a climatic gradient, with plots dispersed as far as 2 km apart at individual sites. Thus, our study is more realistic and generalizable, while their results appear to be a special case. An alternative reason for the lack of EM network effect in our study relative to theirs is that our mesh bags allowed seedling roots to explore a greater volume of soil  63 (17-cm diameter x 32-cm depth) than did their PVC cylinders (15-cm diameter x 20-cm depth). Furthermore, the mesh treatments should have affected root competition in addition to the extent of EM mycelial exploration, even if an EM network had not formed or nutrient transfer through the EM network had not occurred. However, most of the mortality occurred within 6 weeks of planting, before networks or roots had time to develop. Additionally, precipitation and growth were both confounding factors in seedling stem natural abundance 13C. This suggests that drought affected growth more than did EM networks, possibly obscuring the effects of the mesh treatments. Moreover, we were unable to reduce the variation in 13C or growth caused by initial seedling size because biomass measurements are destructive. Any tissue generated in the field might be more enriched relative to tissue generated in the greenhouse under ideal growing conditions, due to water deficiency.  Cohort effects As indicated by logistic regression, survival of field-germinated seedlings was affected by JH:M, not provenance or mesh. The seedlings likely closed their stomata when the August drought set in, regardless of provenance. At this early phase of development, the seedlings are highly susceptible to drying out because their root systems are poorly developed, even at the wetter sites along our gradient. Thus, seedlings from all three provenances may be equally effective at reducing water loss when the growing season moisture deficit strengthens. By contrast, provenance was important in determining survival of the older, outplanted nursery seedlings seedlings. The root  64 systems of outplanted nursery seedlings were better developed, which may increase their sensitivity and hence expression of selective pressure against drought.  Provenance effects Climatic drought (JH:M or SH:M) and provenance interacted to affect survival and 13C in outplanted nursery seedlings, but the potential to form an EM network was not important in our models. Thus, we reject our third hypothesis that mesh effects on survival would be greatest on dry provenances grown in dry environments, and smallest on dry provenances grown in wet environments. Our provenance results are consistent with those of Booth and Hoeksema (2010), who found that seedling source population did not interact with mesh treatment to affect measured seedling variables for Pinus radiata growing in the understory of a monodominant conspecific forest. It is common for a single mycorrhizal fungal species to colonize multiple tree species simultaneously, providing them with a diverse and therefore more secure C source; as such, co-evolving genotypic specificity may be disadvantageous. Furthermore, high gene flow within Pinaceae species via wind-borne pollen distribution, and in basidiomycete and ascomycete fungi via wind-borne spore distribution, would tend to counter coadaptation at low spatial scales (Aitken et al. 2008). The most important predictors of seedling performance in our study varied substantially among seedling response variables. Provenance and drought index interacted to affect survival in outplanted nursery seedlings, but only drought index was important in influencing survival of field-germinated seedlings, and mesh treatment had no effect on survival of either cohort. The greater influence of provenance on nursery  65 grown than seed-origin seedlings may reflect their greater age and hence degree of genetic expression at the time of measurement, higher measurement to error ratio with larger seedlings, or even selection.  Conclusions We found that performance of interior Douglas-fir seedlings growing at the crown edge of mature trees was affected by climatic drought and seed provenance, but not access to EM networks, and these effects differed by age and life history of the seedling. The major conclusions from this study are that: (1) EM networks do not affect survival, growth or stem 13C values of interior Douglas-fir seedlings growing at the dripline of conspecific trees; (2) the effect of provenance on seedling performance is evident in outplanted nursery seedlings but not field-germinated seedlings; and (3) all effects are mediated by climatic drought conditions. It is important to consider that the seedlings in this study were planted in close proximity to the mature tree where competition for soil and light resources was intense. Other studies showing evidence for EM network facilitation and/or coevolution have done so in the context of lower plant proximities and/or other functional forms, and thus these phenomena may be of higher importance in less competitive environments (e.g. chapter 2, Bidartondo and Bruns 2002, Dickie et al. 2005, Simard and Teste 2008).  66 Table 5 Logistic regression testing for the probability of survival of nursery seedlings in response to mesh treatment, seedling provenance (xeric, mesic, hydric), 2006 June-July heat:moisture index (JH:M), and seedling planting date (number of days after May 1st, 2006). The mesh factor was not significant and was therefore removed from the original model. Logistic Regression: c = 0.821 Likelihood ratio P < 0.0001 Effect Odds ratios DF Wald 2 P > 2 JH:M 0.97824 1 59.4599 <.0001 Wet 0.259552 2 10.4372 0.0054 Medium 11.71301 Dry 0.328933 JH:M *Wet 1.001191 2 9.0523 0.0108 JH:M *Medium 0.990238 JH:M *Dry 1.008657 Seedling outplanting date 0.962809 1 11.7912 0.0006   67  Table 6 Logistic regression testing for the probability of survival of field-germinated seedlings in response to mesh treatments, seedling provenance and climate factors. Mesh and provenance factors were not significant and therefore were removed from the original model. Logistic Regression: c = 0.749 Likelihood ratio P < 0.0001 Effect Odds ratios DF Wald 2 P > 2 JH:M 0.983242 1 22.8698 <.0001     68  Table 7 Analysis of covariance testing for response of the natural logarithm of proportion increase in biomass of  nursery seedlings  to mesh treatment, seedling provenance, and summer heat:moisture index (SH:M), after adjustment for total N and its interaction with provenance. NOTE: The coefficient signs are only given for continuous variables. ANCOVA: AIC = 352.3 Effect Coefficient F-value P > F Provenance N/A 3.57 0.0308 Mesh N/A 0.03 0.9749 Provenance*Mesh N/A 1.86 0.1203 SH:M - 5.7 0.0183 SH:M *Provenance N/A 1.01 0.3685 SH:M *Mesh N/A 0.2 0.8205 SH:M *Provenance*Mesh N/A 1.34 0.2566 Covariates: Total N - 4 0.0476 Total N*Provenance N/A 3.76 0.0257  69  Table 8 Analysis of covariance testing for response of the natural logarithm of total biomass of field- germinated seedlings to mesh treatments, seedling provenance, and 2007-8 summer heat:moisture index (SH:M), after adjustment for total N and its interaction with SH:M. NOTE: The coefficient signs are only given for continuous variables. ANCOVA: AIC = 299.4 Effect Coefficient F-value P > F SH:M - 6.57 0.0126 Mesh N/A 0.15 0.8596 SH:M *Mesh N/A 0.2 0.8157 Provenance N/A 0.5 0.6108 SH:M *Provenance N/A 0.8 0.4523 Mesh*Provenance N/A 0.18 0.9472 SH:M *Mesh*Provenance N/A 0.12 0.9762 Covariates: Total N - 7.51 0.0079 SH:M *Total N + 5.87 0.0181     70  Table 9 Analysis of covariance testing for outplanted nursery seedlings natural abundance 13C response to mesh treatments, provenance, and SH:M, after adjustment for the natural logarithm of proportion increase in seedling stem biomass and whether the soil was a Gleysol. NOTE: The coefficient signs are only given for continuous and dummy variables. ANCOVA: AIC = 363.4 Effect Coefficient F-value P > F SH:M + 10.48 0.0016 Mesh N/A 1.5 0.227 SH:M * Mesh N/A 1.17 0.3129 provenance N/A 0.63 0.5349 SH:M * provenance N/A 3.53 0.0327 Mesh * provenance N/A 1.07 0.3742 SH:M * Mesh * provenance N/A 0.71 0.5874 Covariates: Gleysol - 6.91 0.0098 Ln (stem biomass) + 5.42 0.0217        71  Table 10 Analysis of covariance testing for response of field-germinated seedling stem natural abundance 13C to mesh treatments, provenance, and 2007-8 summer heat:moisture index (SH:M). NOTE: The coefficient signs are only given for continuous variables. Multiple Regression: AIC =  307.7 Effect Coefficient F-value P > F SH:M + 1.27 0.2638 Mesh N/A 0.2 0.8202 SH:M *Mesh N/A 0.18 0.8349 Provenance N/A 0.14 0.8727 SH:M *Provenance N/A 0.14 0.868 Mesh*Provenance N/A 1.29 0.2845 SH:M *Mesh*Provenance N/A 0.95 0.4402    72   Figure 5  Location of seedling positions (represented by stars at 2.5 m) around a residual tree (brown circle). Each position represents an experimental unit.  73 Estimated Probability 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 SHM_06 100 120 140 160 180 200 220 240 260 280 300 Provenance Hydric Mesic Xeric Figure 6  Probability of outplanted nursery seedling survival as a function of precipitation within first three months after planting (mm) for the three provenances.   2006 June-July heat:moisture index Probability of survival wet medium dry  74  d13C_adjust -30 -29 -28 -27 -26 -25 -24 SHM_06_07 80 90 100 110 120 130 140 150 160 170 180 190 200 prov_graph TOA + SA TOD Figure 7  Relationship of 2006-7 summer heat:moisture index and stem natural abundance 13C  of outplanted nursery seedlings after adjusting for covariates among provenances. Regression lines for provenance were tested for difference in slope and altitude (P < 0.05), and consequently combined for the dry provenance and wet provenance provenances.    2006-7 summer heat:moisture index   13 Provenance  Wet + dry medium  75 4. Water, but not carbon, translocation through ectomycorrhizal networks affects survival and growth of interior Douglas-fir seedlings  Introduction  The root systems of neighboring woody plants can become interconnected by EM fungal mycelia, thus forming mycorrhizal networks . Under many circumstances, EM networks can modify the outcome of competitive interactions between plants (Perry et al. 1989), and even facilitate seedling establishment in impoverished or hostile soil matrices (Dickie et al. 2005, Nara 2006, McGuire 2007, Teste et al. 2010, chapter 2). It may follow that EM networks also increasingly facilitate seedling establishment under increasing drought, a condition commonly expected in arid temperate forests with climate change (Spittlehouse 2008). These networks, most fundamentally, inoculate the seedling and allow it to tap into an extensive pre-established EM mycelium much more rapidly than would occur via spores. However, it has also been shown that plant-to-plant C transfer is facilitated by EM networks along source-sink gradients, and that EM plants can hydraulically redistribute water obtained via taproots to their EM network; thus, the benefits may go beyond simple increased mycorrhizal colonization and soil nutrient uptake (Simard et al. 1997a, Querejeta et al. 2003b). Yet, it remains to be seen whether plant-to-plant nutrient or water transfer via EM networks actually influences the fitness of establishing plants. Moreover, the prediction that EM network facilitation is more important to seedling establishment under higher nutrient deficiency, in keeping with the  76 stress-gradient hypothesis, has not been tested under changing climatic conditions (Greenlee and Callaway 1996, Callaway et al. 2002, Liancourt et al. 2005). There is a body of empirical and theoretical evidence to suggest that the trade relationship between EM symbionts changes in response to changes in their resource (C, nutrients, water) acquisition ratios, such that the ratios of resources traded between the partners shifts (Hoeksema and Schwartz 2003, Alberton et al. 2005). If multiple plants interconnected by an EM network differ in their resource acquisition ratios, then the potential for source-sink gradients is high, making plant-to-plant net C, nutrient or water transfer plausible. Disparities among plants in their resource acquisition ratios would result from variation in soil nutrients and water spatially, variation in capacity for uptake of specific molecules by the plant and variation in the depth and lateral spread of the root systems. Furthermore, water relations in plants are mediated by availability of CO2, which itself is a resource (Lambers et al. 1998) needed for photosynthetic assimilation. As the rate of assimilation increases in response to increasing photosynthetically active radiation (PAR), intercellular CO2 is consumed more rapidly, and thus the plant must open its stomata more widely to replace this CO2. In contrast, the higher the atmospheric partial pressure of CO2 (pCO2), the less the plant must open its stomata to maintain a particular rate of assimilation. This reduced stomatal conductance also results in a reduced transpiration rate, and as a result soil water is depleted less rapidly. The resource acquisition ratio of C and water to other resources is kept higher for the plant. Thus, pCO2 should affect both C and water transfer between plants through EM networks. The interaction of an establishing plant with an established one can also be viewed in terms of the cost of competition versus the benefit of facilitation when growing  77 at close proximity. Here, the stress-gradient hypothesis would predict a net facilitative benefit when the plant is establishing in a matrix where stress is  reduced when growing near the established plant (Greenlee and Callaway 1996, Callaway et al. 2002, Liancourt et al. 2005). If the limiting stress is water deficiency, then any environmental factor that influences the efficiency of plant acquisition of water and/or C will help to alleviate this stress (Lambers et al. 1998). Plant and regional water budgets are expected to shift not only in response to changing precipitation and temperature regimes, but also to changes in the atmospheric partial pressure of pCO2. While an increase in temperature would be expected to increase the market price of water (i.e., an increase in the trade ratio of water to any other resource, between organisms) through increased evapotranspiration, increases in precipitation and/or pCO2 could counter this via increases in soil available water and water use efficiency of C3 plants through decreased stomatal conductance (Lambers et al. 1998, Holtum and Winter 2010). As a consequence, competitive- facilitative interactions among plants at any location should change as global atmospheric pCO2 increases. A better understanding of how EM network facilitation of seedling recruitment may change with time among climatic regions is needed to forecast regeneration patterns among forest types with changing climatic conditions. Results from chapter 2 indicate that EM networks have an effect on seedling water budgets, but the precise nature and mechanisms underlying of these effects are ambiguous. Schoonmaker et al. (2007) found that large trees play a role in redistributing water to interior Douglas-fir seedlings establishing nearby, and that this is influenced by the presence of mycorrhizal networks. Similarly, Querejeta et al. (2003b) found that oak (Quercus agrifolia) seedlings with access to water via their taproots transferred some of  78 that water to their EM fungal symbionts, maintaining the integrity of the EM network during drought; thus, the potential exists for the EM fungi to share  water with other plants through an interconnecting mycelial network. Egerton-Warburton et al. (2007) also found that water was transferred between oak seedlings via EM networks when one seedling was able to access water while the other was maintained in a water- deficient condition; however, the duration of this study was not long enough to assess effects on seedling survival and growth. The main objective of our study was to determine whether pCO2 or temperature modulate the effect of soil moisture on potential EM network facilitation of interior Douglas-fir seedling survival and growth, and whether C and water transfer play a role in this. To test pCO2 and temperature effects on the interaction among EM networks and soil moisture, four growth chambers were utilized to vary pCO2 and temperature, thus simulating predictions for climate change within the range of interior Douglas-fir in British Columbia (Spittlehouse 2008). To test the hypothesis that water deficiency enhances facilitation by EM networks, we made three predictions: when exposed to water deficiency, seedlings associated with an EM network will display (1) greater survival and (2) greater growth where seedlings are growing in the lowest soil moisture, highest temperature, and lowest pCO2 conditions; and (3) C and water should transfer from the large seedling having access to a consistent water supply, to the small seedling unable to access this water, and this transfer should be greatest when the small seedling is the most water-deficient. We tested these predictions using a growth chamber study at the University of British Columbia. Interior Douglas-fir was used as the test species in parallel with field  79 studies conducted at the interface between interior Douglas-fir forests and the sagebrush steppe of North America, where interior Douglas-fir regeneration is expected to become increasingly more difficult as climate shifts (Spittlehouse 2008). The experimental design, which included replication within growth chambers, provides basic information on the physiological mechanisms underlying EM network effects on seedling establishment.  Methods  Experimental design and treatments Four growth chambers at the University of British Columbia were used. Within each growth chamber, the entire platform area was covered with a reservoir we constructed from templast (corrugated plastic) and silicone. The reservoirs were filled with water and refilled to capacity at every watering. The experiment was planned as a nested 3x3x2x2 factorial design, where EM network access (3 levels) and soil moisture regime (3 levels), were nested within temperature regime (2 levels) and CO2 regime (2 levels). Because we had access to only four growth chambers (two retrofitted for pCO2 and the other two not) at a time, one full replicate of the pCO2 x temperature treatment combinations was run in August and September of 2007 (run 1) and the second replicate was run in August and September of 2008 (run 2), treatments shuffled among growth chambers. Within each pCO2 X temperature treatment combination (2 pCO2 regimes x 2 temperature regimes x 2 runs), the soil moisture x temperature treatments were replicated ten times (3 soil moisture regimes x 3 EM network treatments x 10 replications = 90 units within each pCO2 x  80 temperature treatment; 360 units per run; 720 units for the whole experiment). Additional seedlings were planted in each treatment for natural abundance determinations. The basic experimental unit was the root box (Figure 8). In each root box, two stratified interior Douglas-fir seeds were planted, one near each end of the rooting compartment (after Simard et al. 1997b). One was able to access reservoir water via a taproot partition (donor seedling) (after Querejeta et al. 2003b), whereas the other was not (receiver seedling). Access to an EM network was controlled by lining the receiver seedling compartment with a mesh bag made of sturdy plain-weave nylon (Plastok, Birkenhead, UK). There were three ‘mesh’ treatments: (1) no mesh, where receiver seedlings were planted directly into soil and thus could form hyphal and rhizomorph EM networks with donor seedlings, and their roots were free to intermingle; (2) 0.5-µm mesh, where the hyphae and roots of receiver seedlings were restricted from accessing donor seedlings, and (3) 35-µm mesh, where receiver hyphae could access the root systems of donor seedlings, and thus form hyphal EM networks, but roots could not (after Teste and Simard (2008) and Johnson et al. (2001)). All seed originated from the Shuswap-Adams seed planning zone (seedlot # FDI 48507). Donor seedlings were planted one month prior to planting of receiver seedlings, and their taproots were allowed to access reservoir water via the taproot partition to simulate a tree in the field accessing water from depths beyond the rhizosphere of an establishing seedling. To facilitate use of limited space, three root boxes (i.e., three experimental units), each randomly assigned a mesh treatment, were placed in a common container. Each container was randomly assigned one soil moisture treatment (6%, 9%, or  81 12%, see below) for all three experimental units, as the soil moisture treatments were administered based on weight. Two temperature (warm, cool) and two pCO2 regimes (elevated, ambient) were applied to the growth chambers. In the warm temperature regime, the high temperature was set for 19C and the low temperature was set for 16C. In the cool temperature regime, the high temperature was set for 16C and the low temperature for 13C. The CO2-retrofited chambers were set for 800ppm CO2 (13C  -44‰) while the non- retrofitted chambers were vented so as to allow equilibration with ambient levels (average measured [CO2] during the photoperiod for both trials was 420 ppm). Photosynthetically active radiation was measured once per month in each growth chamber using a Sunfleck PAR Ceptometer (Model SF-80, Decagon Devices, Pullman, WA) to check for drifting and adjust light height accordingly.  The target PAR was 440 µmol photons m-2 s-1.  All chambers were set for a 16-h photoperiod (approximation of June Solstice in southern BC) for the duration of the experiment to maximize rates of growth and mortality, as well as to avoid dormancy.  Soil Soil for the root boxes was collected from nine sites across a climatic moisture gradient in southern interior British Columbia, and mixed in equal parts to represent average mineral soil properties and to provide fungal inocula from the full range of locations. Soil at the sites had already been mixed during site preparation, thus organic and mineral components were mixed throughout the cutblocks. Soil was collected from a single representative location at each site. The soil was collected to a depth of 32 cm and placed  82 in coolers for transport. The soil was transferred to UBC within one week of collection, and then it was immediately sieved to 4mm, homogenized, and mixed with perlite (9:1 by volume). The root boxes were filled with soil, but a 1-cm gap was left between the soil at the bottom of a seedling compartment and the surface of the soil within the taproot partition; this was accomplished via a stricture at the hole leading from the seedling compartment to the taproot partition (Figure 8). The gap ensured that reservoir water did not move into the seedling compartment via soil capillary action. Target average volumetric soil water contents for the three soil moisture treatments were 6%, 9% and 12%. Containers were weighed three times per week, and watered to field capacity when their weight dropped to a target level below field capacity, thus simulating naturally occurring wetting and drying cycles that fluctuated about the target soil moisture through time.  Pulse-labeling Prior to labeling, a subset of the extra seedlings was randomly selected for isotope analysis to determine natural abundance. Donor seedlings were randomly selected for pulse-labeling with 13CO2. Donor seedlings were sealed in 0.5-L Tedlar® gas sampling bags (Cel Scientific Corp., Santa Fe Springs, CA, USA) with septa and valves. Gas labeling bags were inflated with 100mL of ambient air using a 0.5-L gas-tight super syringe (Hamilton, Reno, Nevada, USA). One milliliter of 13CO2 (99.9%; Cambridge Isotope Labs, Andover, MA, USA) was then injected into the bag using a 1-mL gas-tight syringe (Hamilton, Reno, Nevada, USA), and the donor seedling allowed to photosynthesize within the bag for 1-7 hours, due to variation in uptake rates and  83 limitations of worker speed. Carbon dioxide concentrations within the bags were measured at the beginning and end of the pulse using a LI-6251 CO2 analyzer (LI-COR Biosciences, Lincoln, NB, USA). In most cases, the seedlings had consumed all of the CO2. At the end of the pulse, each root box was moved to the outside of the growth chamber and the labeling bag was then removed to minimize cross-contamination. Root boxes were then placed back in their respective growth chambers for a 10-13 day chase period to maintain the treatment conditions prior to harvest. Unlabeled donor seedlings associated with dead receiver seedlings and growing in close proximity to labeled donors were selected for isotope analysis to determine potential uptake of 13CO2 respired by labeled seedlings (aerial contamination). Just prior to the commencement of labeling seedlings with 13CO2, all growth chamber reservoirs were emptied and refilled with water enriched with deuterium at a level of 16,810‰ D (Spectra Stable Isotopes, Columbia, MD, USA). Donor seedlings were continuously labeled with deuterated water (D2O) for 10-18 days prior to being harvested in each trial. Within 10 days of reservoir enrichment, all seedling xylem water D should have equilibrated with reservoir water D. Reservoirs were refilled to capacity daily with water of the same enrichment level. A total of 350mL per donor seedling was added to the reservoirs.  Seedling measurements Donor and receiver seedlings were growing under the same conditions throughout the experiment, except that donor seedling taproots had access to the reservoir. After receiver seedlings were established, all treatment factors were applied and each seedling was  84 monitored for survival three times per week until harvest (February 2008 for run 1 and February 2009 for run 2). Growth of surviving seedlings was measured by oven-dry weight (65°C for 48 hours) of root and shoot biomass. Prior to drying, weighing, and milling, the xylem of pulse-labeled donor seedlings and their associated receiver seedlings was removed at harvest and frozen until the water was extracted following Schoonmaker et al. (2007). After water extraction, the xylem of each seedling was reunited with the remainder of the shoot, and both shoots and roots were milled. Milled biomass samples were analyzed for 13C by mass spectrometry using a PDZ Europa (Sercon Ltd., Cheshire, UK) and xylem water was analyzed for D and 18O by tunable diode laser using a LGR DLT-100 (Los Gatos Research, Inc., Mountain View, CA, USA) at the University of California Davis Stable Isotope Facility (UCDSIF). This was done to assess transfer of C and H2O from the donor to the receiver seedling, as well as evaporative enrichment of the water. Isotope abundance was expressed as: 10001 tan          R RIδ dards sampleH in ‰ units, where R = HI/LI, HI is the heavy isotope of interest, and LI is the predominant isotope of I, the element in question. Standard R used here is Vienna Pee Dee Belemnite for 13C (0.0112372) and Vienna Standard Mean Ocean Water for D (0.00015575) and 18O (0.0020052). Delta deuterium was converted into percent of xylem water originating from reservoir following procedures outlined in Schoonmaker et al. (2007). The xylem water extracted for each treatment combination was of insufficient quantity for UCDSIF measurement protocol; thus, we pooled samples across soil moisture and temperature  85 treatments because the EM network and CO2 concentration factors were of greater interest in this study.  Data analysis Receiver seedlings that died of causes other than water deficiency, or that were growing with a dead donor, were excluded from the analysis. Effects of the treatments on receiver seedling survival, growth, 13C and percent of xylem water originating from the reservoir were analyzed using the SAS System for Windows, V9.2 (2009). Logistic regression analysis was used to determine whether seedling survival was associated with mesh treatment, soil moisture regime, temperature regime, CO2 regime, or PAR (SAS PROC LOGISTIC) (Tabachnick and Fidell 2001). The general form of the model was: [5] p(Y) = exp(β0 + β1x1 + β2x2+ …+ βkxk)/1 + exp(β0 + β1x1 + β2x2+ ….…+ βkxk), where p(Y) is the probability of survival, β0 is the intercept, β1 ….βk are estimated coefficients, and x1…xk are independent treatment, climatic, site or planting date variables. The predictive factors were allowed to enter the model if they improved the overall fit, but were ultimately removed from the model if they did not meet the criteria of P  0.05, with the stipulation that the treatment factors and interactions had to be retained until all remaining covariates were significant. All growth and isotope analyses were performed as an analysis of covariance (ANCOVA) for a factorial set of treatments using PAR, 18O of donor seedling xylem water, 18O of receiver seedling xylem water, percent reservoir water taken up by donor seedlings, percent reservoir water taken up by receiver seedlings, post-labeling chase period (for 13C), labeling period (for D) and run as covariates in a completely  86 randomized design using SAS PROC MIXED (Milliken and Johnson 2002). The general form of the model was: [6] Yijk  = μ + δi + τj + k + (δτ)ij + (δ)ik + (τ)jk + (δτ)ijk + (δτµ)ijl + (δµ)ikl + (τµ)jkl + (δ τ µ)ijkl + β 1(X 1ijk – 1X ..) +… β n(X nijk – nX ..) + εijk, where Yijk is the dependent variable (growth or isotope variable); μ is a general mean; δi, τj, k, µl, (δτ)ij, (δ)ik, (δµ)il, (τ)jk, (τµ)jl, (µ)kl, (δτ)ijk, (δτµ)ijl, (δµ)ikl, (τµ)jkl, and (δτµ)ijkl are the fixed effects parameters for the treatment factors mesh treatment, soil moisture regime, temperature regime, and CO2 regime, and their interactions; β 1 ….β n are estimated coefficients; X 1…X k are PAR, 18O of donor seedling xylem water, 18O of receiver seedling xylem water, percent reservoir water taken up by donor seedlings, percent reservoir water taken up by receiver seedlings, post-labeling chase period (for 13C), labeling period (for D), or run are covariates; and εijk is the residual (Steel et al. 1980). The procedure for entry and retention of the covariates was the same as that of the survival analysis, except that the requirement for retention of covariates was P  0.1, and treatment factors and interactions were retained regardless of P-value. Growth and 13C were logarithmically transformed to conform to the assumptions of ANCOVA.  Results Of the 624 receiver seedlings that were retained in the analysis, a total of 360 survived to harvest. The logistic model predicting receiver seedling survival included CO2 concentration, soil moisture regime, mesh treatment, and the interaction of CO2 concentration with soil moisture regime as significant predictors (Wald 2 = 150.7, df = 8, P < 0.0001, c = 0.842) (Table 11). PAR also entered the model as a covariate. All other  87 variables and interactions tested did not enter the model at P<0.05. The probability of survival was lowest among receiver seedlings planted in 0.5-µm mesh, and greatest among seedlings growing in no mesh (Figure 9a). Contrary to expectations, receiver seedling survival decreased under CO2 enrichment. Among the soil moisture treatments, the probability of survival was lowest in the xeric and highest in the hygric treatment (Figure 9b). As expected, the receiver seedlings growing in xeric soil performed best when growing at 800ppm CO2. Unexpectedly, the receiver seedlings growing in hygric soil had the poorest performance when growing at 800ppm CO2. Additionally, the probability of survival decreased by 2% for every 1-µmol photon/m2/second increase in PAR. Growth of receiver seedlings was affected by CO2 concentration (P=0.0094), temperature (P=0.0017), soil moisture (P<.0001), and the interaction between CO2 concentration and temperature regime (P=0.0448), as well as the interaction between soil moisture regime and mesh treatment (P=0.0475), when PAR was included as a covariate (Table 12). Growth differences between the warm and cool temperature regimes were greater under the high than under the low CO2 concentration (Figure 10a). Growth was highest under warm temperatures at both CO2 levels and lowest under cool temperatures at 800ppm CO2. While growth increased with soil moisture, this effect was mediated by the mesh treatments, with growth exhibiting a general decrease with pore size under hygric conditions (i.e., greatest in the 0.5-µm mesh and lowest in no mesh) and no effect of pore size under mesic or xeric conditions (Figure 10b). Growth decreased with PAR, and this response differed in magnitude by run.  88 The δ13C of labeled donor seedlings averaged 8.64‰, while natural abundance, receiver and chase period-control seedlings were statistically indistinguishable, with an average 13C of -32.8‰ (Figure 11). The δ13C of receiver seedlings was affected by CO2 concentration (P=<0.0001) (Table 13), and was higher under ambient CO2 than 800ppm CO2. The same was true for natural abundance and control seedlings. The D of receiver seedlings was affected by mesh treatment (P=0.0298), CO2 concentration (P=0.0735), and the interaction of mesh treatment and CO2 concentration (P=0.0596), when 18O of donor seedling xylem water, 18O of receiver seedling xylem water, percent reservoir water taken up by donor seedlings, labeling period, and trial year were included as covariates (Table 14). The 18O values not only relate to variation in D through fractionation, but also through increased uptake of reservoir water when soil moisture is depleted and seedlings are transpiring more at higher temperatures. Therefore, using it as a covariate captures more of the variation in D, and helps to capture some of the variation in D originating from the temperature and soil moisture treatments, both of which had to be removed as factors for the pooling of xylem water. D of receiver seedlings was highest when they were growing with no mesh under ambient CO2 and lowest in 35-µm mesh at 800ppm CO2 concentration (Figure 12). The D of receiver seedlings decreased with 18O and D of donor seedlings, and increased with 18O of receiver seedling xylem water and post-labeling chase period.  Discussion  Regardless of environmental treatment, survival of receiver seedlings increased as the potential to form EM networks with donor seedlings increased; it was highest where  89 there was no mesh and lowest in 0.5-µm mesh. Survival was also affected by an interaction of [CO2] and soil moisture regime, such that it was maximized under elevated [CO2] in xeric soil conditions and minimized under elevated [CO2] in hygric soil conditions. These results are consistent with our first hypothesis that EM networks would facilitate seedling establishment and our expectation that increased [CO2] would alleviate water deficiency in the driest soils. However, the EM network factor did not interact with water- deficiency related factors as hypothesized; regardless of soil moisture regime, seedling survival was reduced when unable to form an EM network. We are confident that seedlings were limited by water availability in the driest treatment. By contrast, seedlings were likely limited by N in the wettest treatment because seedlings were clearly chlorotic, and we did not fertilize because N amendment is known to reduce EM mycelial growth (Arnebrant 1994). If seedlings in the wettest environments were severely N- limited, they might benefit from a larger mycelium through uptake from a greater soil volume or plant-to-plant N-transfer (He et al. 2003).  Mycorrhizal networks acted independently of abiotic factors to affect survival, whereas they interacted with soil moisture regime to affect growth. The general pattern, that mean total biomass did not change with access to EM networks or roots under xeric conditions, while declining with increasing network access in the highest soil moisture treatment, was consistent with our  second hypothesis that EM networks would benefit seedling growth when they were the most water-deficient; however, the trend was weak (P<0.5). We posit that this may even involve network extraction of N from receiver seedlings or soil inside the 35-µm bag and mesh-less treatments under high soil moisture, and subsequent allocation to the donor seedling, which would have been a more lucrative  90 source of C for the fungi than the receiver seedlings (Hoeksema and Schwartz 2003). The biological market model of mutualism would predict that networking fungi access N more readily than do plants, and the fungi will trade that N with the plants for C. Moreover, the fungi will provide more N to plants that provide the fungi with the most C, which in this case would be the larger donor seedlings. Growth was lower in the 35-µm mesh of the mesic soil moisture treatment than in the 0.5 or 35-µm mesh treatments of the hydric treatment. Here, it is possible that hyphae but not roots were able to explore beyond the bag, allowing the networking hyphae to trade N for C from the donor. Delta carbon-13 did not increase with network forming potential, leading us to reject part of our third hypothesis that plant-to-plant C transfer was one mechanism by which EM networks affected seedling survival and growth. Although 13C enrichment was unaffected by EM network potential, it was affected by [CO2], where receiver seedling 13C in ambient [CO2] was close to 10‰ higher that in elevated [CO2]. Carbon dioxide respired by donor roots should comprise a greater proportion of available CO2 in the chamber under the ambient [CO2] treatment than it does under the 800ppm [CO2] treatment, however, the difference observed in natural abundance, labeled donor, receiver, and control seedlings in 13C between ambient and 800ppm [CO2] treatments (Figure 12) is easily accountable for by the difference in 13C of ambient  CO2 (averaged approximately -8.2‰ during the experiment) and fossil fuel derived CO2 (approximately -44‰) used for enrichment.  While this effect is not surprising, we also expected 13C to differ by mesh treatment, with more transfer with increasing network potential. Severe N deficiency in this system may have masked network effects because most C is likely transferred through EM networks with N in amino acids (e.g., glutamine) (He et al. 2003).  91 In contrast to C, transfer of water was facilitated in the no mesh treatment when seedlings were growing under ambient [CO2]. Here, the xylem D of receiver seedlings was affected by the interaction between mesh and [CO2] factors. The xylem D tended to be greatest for receiver seedlings growing in no mesh under ambient [CO2]. This is easily explained by the ability of the receiver seedling roots to grow closer to the donor seedlings, and thus capture more labeled water directly from the donor’s soil, and plausibly even through shorter and more numerous hyphal network connections with the donor. At 800ppm [CO2], however,  D of receiver seedlings tended to be lowest in the 35-µm mesh, suggesting that water movement to receivers was occurring mainly via the soil matrix, and that EM networks were unimportant in transferring water. We were unable to test whether water transfer alleviates water deficiency of receiver seedlings because we had to pool xylem water samples across soil moisture and temperature treatments to meet analytical requirements. We expect, however, that EM networks may facilitate water transfer in low soil moisture/high temperature treatments, as was found among oak seedlings by Egerton-Warburton et al. (2007) in a similar study design. While the patterns in the  [CO2] treatments partially support the hypothesis that EM networks facilitate water transfer between plants, the reduced precision that resulted from the pooling of samples meant we could not adequately test whether EM network facilitation increases under water-deficient conditions. The lack of a clear network effect on water deficiency may, therefore, simply be due to low resolution. However, seedlings were clearly N- deficient, and the difference in survival among mesh treatments may have been related to alleviation of N deficiency. Furthermore, differences in water deficiency among seedlings may have been greater during the first two months of the  92 treatments, when root systems were less well-develop and mortality was occurring at the greatest rate. Our study showed that interior Douglas-fir seedling regeneration parameters were affected by temperature regime, [CO2], and the interaction of soil moisture regime with EM networks, but the EM network effects did not appear to be related to C transfer and were only weakly related to water transfer. We conclude, where water-deficient seedlings were growing near well-hydrated seedlings, that: (1) survival of receiver seedlings was affected by EM networks, as well as the interaction of [CO2] and soil moisture regime, with EM networks improving seedling survival under elevated [CO2]  and soil moisture deficiency ; (2) growth of receiver seedlings was affected by the interaction of EM networks with soil moisture regime, where growth increased with EM network access under xeric but not hygric conditions; (3) growth of receivers also declined, however, when temperature was low, but [CO2] was high; (4) C transfer to receiver seedlings was not affected by EM network access, but it did decrease under elevated [CO2]; and (5) EM networks and [CO2] interacted to affect water transfer and water deficiency of receiver seedlings, but the effects were complex and the data were not sufficient to resolve the precise mechanism of facilitation.  Thus, the results of this experiment support our hypothesis that EM networks are important to interior Douglas-fir seedling establishment and growth under water-deficient conditions. Moreover, the data also partially support the hypothesis that water transfer plays a role in this facilitation, but that C transfer appears to be unimportant.   93 Table 11 Logistic regression testing for the probability of seedling survival in response to, CO2 concentration (ppm), mesh treatments, soil moisture regime, temperature regime, and photosynthetically active radiation (µmol/m2/s) (PAR). Temperature was removed from the original model. Logistic Regression: c = 0.842 Likelihood ratio P < 0.0001 Effect Odds ratios DF Wald 2 P > 2 800 ppm CO2 0.970516 1 5.7755 0.0163Ambient CO2 1.03038 xeric 0.479438 2 103.6506 <.0001 mesic 0.901919 hygric 1.908867 0.5-µm mesh 0.6 2 89.5458 <.0001 35-µm mesh 0.791281 No mesh 1.891892 800ppm *xeric 0.585091 2 5.9129 0.052 800ppm *mesic 0.917258 800ppm *hygric 1.447368 Ambient *xeric 0.48221 Ambient *mesic 0.921053 Ambient *hygric 1.747814 PAR 0.983635 1 7.4978 0.0062      94 Table 12 Analysis of covariance testing for response of the natural logarithm of total biomass of seedlings to CO2 concentration (ppm), temperature regime, soil moisture regime and mesh treatment after adjustment for photosynthetically active radiation (µmol/m2/s) (PAR) nested within run. NOTE: The coefficient signs are only given for continuous variables. ANCOVA: AIC = 238.4 Effect Coefficient F-value P > F CO2 concentration N/A 6.9 0.0094 Temperature N/A 10.16 0.0017 CO2* Temperature N/A 4.09 0.0448 Soil moisture N/A 22.97 <.0001 CO2*Soil moisture N/A 0.19 0.8286 Temperature*Soil moisture N/A 0.13 0.8762 CO2* Temperature *Soil moisture N/A 0.85 0.4279 Mesh N/A 0.16 0.8537 CO2*Mesh N/A 0.69 0.5019 Temperature*Mesh N/A 0.53 0.5921 CO2* Temperature *Mesh N/A 0.75 0.4736 Soil moisture*Mesh N/A 2.46 0.0475 CO2*Soil moisture *Mesh N/A 0.21 0.9353 Temperature*Soil moisture *Mesh N/A 0.12 0.9765 CO2* Temperature *Soil moisture *Mesh N/A 1.13 0.3397 Covariates: PAR(2007-8) (µmol/m2/s) - -3.61 0.0004 PAR(2008-9) (µmol/m2/s) - -3.71 0.0003  95  Table 13 Analysis of covariance testing for response of the natural logarithm of 13C of seedlings to CO2 concentration (ppm), soil moisture regime, temperature regime and mesh treatment, after adjustment for growth chamber nested within run. NOTE: an adjustment for growth chamber nested within run was made to minimize variation intrinsic to individual growth chambers, thus P-values are conservative. No growth chamber by trial combinations were significant, and thus are not shown. ANCOVA: AIC = 635.4 Effect F-value P > F CO2 concentration 19.21 <.0001 Temperature 0.7 0.4044 CO2* Temperature 1.06 0.3063 Soil moisture 1.26 0.287 CO2*Soil moisture 1.05 0.3521 Temperature*Soil moisture 0.36 0.7003 CO2* Temperature *Soil moisture 0.46 0.6322 Mesh 0.79 0.4573 CO2*Mesh 0.94 0.3921 Temperature*Mesh 0.9 0.4087 CO2* Temperature *Mesh 1.05 0.3531 Soil moisture*Mesh 0.79 0.5333 CO2*Soil moisture *Mesh 0.71 0.5887 Temperature*Soil moisture *Mesh 1.24 0.2989 CO2* Temperature *Soil moisture *Mesh 1.49 0.221   96  Table 14 Analysis of covariance testing for response of the percent reservoir D2O taken up by receiver seedlings to mesh treatments and CO2 concentration (ppm), after adjustment for 18O of donor seedling xylem water, 18O of receiver seedling xylem water, percent reservoir water taken up by donor seedlings, labeling period, and run. NOTE: The coefficient signs are only given for continuous variables. Run was analyzed as a random effect, but not shown due to lack of significance. ANCOVA: AIC = 11.8 Effect Coefficient F-value P > F Mesh N/A 563.8 0.0298 CO2 N/A 74.33 0.0735 Mesh* CO2 N/A 140.37 0.0596 Covariates: Donor 18O (‰) - 208.91 0.044 Donor % reservoir uptake - 76.73 0.0724 Receiver 18O (‰) + 1143.67 0.0188 Labeling period (days) + 149.42 0.052     97  Reservoir Taproot 1-cm gap Septum 35-µm mesh Hyphae Donor seedling Receiver seedling  Figure 8  The general arrangement for donor and receiver seedlings in each root box. In this example, the 35-µm mesh treatment has been applied to the receiver seedling.  98 0.1 1 10 0.5 -µm  m es h 35 -µm  m es h No  m es h O dd s ra tio  (l og  s ca le )  0.1 1 10 xe ric me sic hy dri c O dd s ra tio  (l og  s ca le ) 800 ppm ambient  Figure 9  Odds ratio values (logarithmic y-axis scale) for (a) mesh treatment and (b) the interaction of CO2 concentration and soil moisture in the logistic regression model predicting survival of receiver seedlings. The odds ratio for a category is the odds of survival of a seedling in that particular category relative to seedlings not in that category, after adjusting for covariates. a b  99   0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 co ol wa rm B io m as s (g ) ambient 800 ppm   0 0.05 0.1 0.15 0.2 0.25 xe ric me sic hy dri c B io m as s (g ) 0.5-µm 35-µm no mesh  Figure 10  Unadjusted means for total receiver seedling biomass under (a) various combinations of temperature regime and CO2 concentration and (b) various combinations of soil moisture regime and mesh treatments. Bars with different letters are significantly different at P ≤ 0.05. Error bars are 1 s.e. b a b a a a a bab ab a a b aa  100  -50 -40 -30 -20 -10 0 10 20 30 na tur al ab un da nc e lab ele d d on or rec eiv er co ntr ol13 C  ambient 800 ppm  Figure 11  Means for 13C of seedlings among natural abundance controls, labeled donors, receivers and post-label controls. Error bars are 1 s.e. a a a d b c b b  101  0 0.5 1 1.5 2 2.5 3 3.5 am bie nt 80 0 p pm %  re se rv oi r w at er 0.5-µm 35-µm no mesh  Figure 12  Unadjusted means for the percent reservoir water taken up by receiver seedlings (calculated from D) under various combinations of CO2 concentration and mesh treatments. Bars with different letters are significantly different at P ≤ 0.1. Error bars are 1 s.e.     ab b abab a ab  102 5. Conclusions  Summary  Facilitation of plants by EM networks  has been shown to occur in a number of biomes (van der Heijden and Horton 2009), but this facilitation appears to be particularly important for establishment of conifer seedlings in soils that are depauperate of  EM fungi (Dickie et al. 2002, Dickie et al. 2005, Dickie et al. 2007, McGuire 2007). The importance of EM networks is of particular concern near the forest-grassland interface, where AM commonly dominate the fungal communities and where we expect increasing growing-season water deficiency, and thus an increased reliance of seedlings on EMs for establishment (Hamann and Wang 2005). Mycorrhizal networks may be central to establishment in these areas, where a lack of wood and organic layer refugia for EM fungi, combined with an understory dominated by AM plants, may preclude seedling establishment during dry years when all trees are removed (Callaway 1995). But the question remains as to whether EM networks transfer C and/or water from trees to seedlings, or whether they just facilitate seedlings through colonization and thus increased capacity to  acquire water and other nutrients from a larger volume of soil (Warren et al. 2008). In this thesis, I attempted to answer whether plant-to-plant facilitation occurs via EM networks in interior Douglas-fir ecosystems, how facilitation is mediated by climatic drought (i.e. climatic moisture gradients), and whether plant-to- plant transfer of C and/or water play a role in facilitation.   103 The main objectives were:  (1) To determine the effects of regional climate (represented by precipitation and temperature) on EM network facilitation of Douglas-fir seedling establishment; (2) To separate genotypic effects from climatic effects; (3) To compare the importance of EM networks to 3-year-old outplanted nursery seedlings versus 1st year seedlings germinated in the field ; (4) To parse the competitive from facilitative effects of residual Douglas-fir trees on small seedlings by determining the effect of Douglas-fir residual tree distance on seedling establishment, and how this changes with climate and connection through an EM network; and (5) To determine the interaction between soil water and the partial pressure of carbon dioxide (pCO2) in their effects on EM network-facilitated seedling establishment and C- and water transfer between different sized Douglas-fir seedlings.  The most important findings were:  Ectomycorrhizal networks increased survival of planted seedlings  of the intermediate moisture provenance, regardless of where they were established or their distance from the conspecific tree. Networks also increased growth of seedlings when they were growing close to the tree at drier sites (Chapter 2). With the covariate adjustments, seedlings were more than seven times as likely to survive when growing in the 35-µm mesh treatment relative to the other mesh treatments combined, while those  104 growing in the 0.5-µm mesh treatment were only 20% as likely to survive. These results provide strong evidence that EM networks facilitated seedling establishment. This facilitation effect, however, declined with planting date. Moreover, the distance from mature trees at which EM networks were facilitative depended on the climate. When seedlings grew in 35-µm mesh, their growth increased with proximity to the tree in the driest climate, but the reverse was true in the wettest climate. Plant-to-plant facilitation  has been shown to be most important when environmental stress is high, as predicted by the stress-gradient hypothesis ( Castro et al. 2004, Liancourt et al. 2005, Cavieres et al. 2006). Ectomycorrhizal networks may play a role in facilitation across this gradient through either interplant transfer of nutrients or water, or through mycorrhizal fungal colonization of establishing plants (Dickie et al. 2002, Querejeta et al. 2002, Querejeta et al. 2003a, Querejeta et al. 2003b, Dickie and Reich 2005, Dickie et al. 2005; Nara 2006).  Current management of interior Douglas-fir is based on the assumption that interactions between Douglas-fir seedlings and trees are the same regardless of regional climatic differences. However, inter-tree competitive and facilitative interactions have been shown to be highly variable across precipitation gradients of interior Douglas-fir forests, and that EM networks can mediate them (Simard 2009). Carbon and N transfer between EM seedlings is known to occur, but the magnitude and direction of  transfer depends on tree species composition, mycorrhizal type, inter-tree competition for resources (especially light), tree phenology, tree size, growth rate, and soil disturbance level (Arnebrant et al. 1993, Simard et al. 1997b, Teste et al. 2010, Philip et al. 2010). There is also evidence that both seedlings and mycorrhizal mycelia receive hydraulically redistributed water from mature trees in a number of  105 ecosystems, that this transfer is facilitated by EM networks, and that this facilitation is likely to occur in interior Douglas-fir forests (Querejeta et al. 2002, Brooks et al. 2002, Querejeta et al. 2003a, Querejeta et al. 2003b, Brooks et al. 2006, Schoonmaker et al. 2007). In chapter 2, I provide the first evidence that EM network facilitation of interior Douglas-fir seedling establishment varies along a climatic drought gradient, with greater facilitation in drier than wetter climates. Thus, I provide a previously unknown mechanism for regeneration facilitation of interior Douglas-fir where it is growing at the periphery of its fundamental niche (Perry et al. 1989). Our group has previously shown that genets of Rhizopogon spp. can link mature interior Douglas-fir trees with regenerating seedlings in the understory or uneven-aged forests, and that access to both roots and mycelial networks of mature trees facilitates long-distance transfer of C and N to seedlings. However, the climatic conditions under which EM network-mediated facilitation of seedling establishment was most likely to occur had not been established (Beiler et al. 2009, Teste et al. 2009, Teste et al. 2010). My results suggest that the drier the climate, the more likely EM network-mediated facilitation of seedling establishment is to occur. Thus, my data add to the mounting body of evidence that traditional models of plant community dynamics (e.g. Tilman 1988) need modification to allow for a multiplicity of interactions among plants, rather than simply competition determined by resource ratios (Simard et al. 1997a, Simard et al. 1997b, Read 2002, Brooker and Callaway 2009, Heijden & Horton 2009).   106 Seedling origin, provenance and climate, but not EM networks, affected survival, growth and stemwood 13C of outplanted nursery seedlings. Only climate affected survival and growth of field-germinated seedlings (Chapter 3). Survival of  seedlings originating from the medium moisture provenance declined the most sharply with increasing drought, but they also had the highest average survival. As expected, seedlings from the driest provenance performed the best of the three populations on the most drought-prone sites. Stemwood 13C increased with drought, but seedlings from the medium moisture provenance were the least responsive, after adjusting for seedling stem biomass and whether the soil order was a Gleysol.  The geographic mosaic theory of coevolution predicts that EM facilitation should vary among populations as a result of coevolutionary specialization between fungal and plant partners, where plants should be most adapted to trading with their local fungal populations (Thompson 2005; Hoeksema 2010; Johnson et al. 2010). Previous research by our group in interior temperate forests containing a large component of interior Douglas-fir indicates that EM networks mediate interactions among plants (Simard 2009), but how seedling genotype and age interact with EM networks has remained unknown. Teste et al. (2009 and 2010) has already shown that C transfer from established interior Douglas-fir trees is greater to new seedlings naturally established from seed than to those that are planted as nursery stock, but what this means for subsequent survival and growth is poorly understood. Evidence also suggests that naturally regenerated Douglas-fir seedlings develop root systems that can rapidly exploit EM networks via rhizomorphs, resulting in improved height growth (Halter and Chanway 1993, Teste et al. 2010). In  107 chapter 3, I found no evidence that EM networks interact with seedling genotype or life history to affect survival, growth or stemwood 13C when seedlings were growing near the dripline of mature trees. My results from this experiment thus contrast with the life history effect reported by Teste et al. (2009 and 2010). My data also do not support the hypothesis that coevolution between the trees and their EM fungi affected variation in EM fungal facilitation of interior Douglas-fir  through EM networks (Thompson 2005, Hoeksema 2010). Previous provenance experiments have found genotypic variation in Pinaceae seedling responses to EM fungi, but they were conducted in controlled environments and did not include tests of EM network effects (Rosado et al. 1994, Karst et al. 2009). My results suggest that gene flow in interior Douglas-fir and its fungal symbionts is of great enough magnitude to counter plant-fungus specialization, at least at the scale of hundreds of kilometers. Thus, my data do not support the geographic mosaic theory of coevolution as it pertains to gymnosperms and their EM symbionts (Thompson 2005; Hoeksema 2010; Johnson et al. 2010). That said, my experiment was limited in testing this theory because it examined general provenance effects rather than kin effects on network facilitation.  Ectomycorrhizal networks increased survival of interior Douglas-fir seedlings and appeared to increase water transfer under ambient [CO2], but not at 800ppm [CO2]. Transfer of C between seedlings was unaffected by EM networks (Chapter 4). Overall, the probability of survival was lowest among receiver seedlings planted in 0.5- µm mesh, and greatest among seedlings growing in no mesh. Under ambient CO2, there was a trend toward increased uptake of reservoir water by receiver seedlings where they  108 were growing with no mesh, while at 800ppm CO2 concentration, there was a trend toward reduced uptake of reservoir water in 35-µm mesh. Uptake of reservoir water by receiver seedlings increased with receiver seedling xylem water 18O, a proxy for transpiration.  While an increase in temperature would be expected to increase the market price of water (i.e., an increase in the trade ratio of water to any other resource, between organisms) through increased evapotranspiration, increases in precipitation and pCO2 could counter this via increases in soil available water and water use efficiency of C3 plants through decreased stomatal conductance (Lambers et al. 1998). Results from chapter 2 and 3 indicate that EM networks have an effect on seedling water budgets, but the precise nature and mechanisms underlying these effects are ambiguous. Teste et al. (2010) has already shown that reciprocal C transfer occurs between interior Douglas-fir seedlings through EM pathways, but how this is mediated by soil moisture, temperature, and pCO2 has remained unknown. Schoonmaker et al. (2007) has also shown that large trees play a role in redistributing water to nearby interior Douglas-fir seedlings establishing nearby, and that this is influenced by the presence of EM networks. Similarly, Querejeta et al. (2003b) found that oak (Quercus agrifolia) seedlings with access to water via their taproots transferred some of that water to their EM fungal symbionts, maintaining the integrity of the EM network during drought; thus, the potential exists for the EM fungi to share water with other plants through an interconnecting mycelial network. While I did not find C transfer in chapter 4, I did provide the first evidence that EM network facilitation of seedling survival is related to water transfer under conditions  109 of water deficiency. Thus, I provide a previously unknown mechanism for EM network facilitation under the stress-gradient hypothesis, which predicts that plant stress should be reduced when growing near an established plant (Greenlee and Callaway 1996, Callaway et al. 2002, Liancourt et al. 2005). Egerton-Warburton et al. (2007) also found that water was transferred between oak seedlings via EM networks when one seedling was able to access water while the other was maintained in a water-deficient condition; however, the duration of this study was not long enough to assess effects on seedling survival and growth. My results suggest that EM networks connecting Douglas-fir seedlings with taproot water access to seedlings unable to access this same water facilitates survival and water transfer when the receiver seedling is water-deficient. Thus, my data add to the mounting body of empirical and theoretical evidence that the trade relationship between EM symbionts changes in response to changes in their resource (C, nutrients, water) acquisition ratios, such that the ratios of resources traded between the partners shifts (Hoeksema and Schwartz 2003, Alberton et al. 2005).  As a whole, the data from these three experiments suggest that the potential for water-deficient seedlings to form EM networks with established conspecifics facilitates their establishment, and that transfer of water may play a role in this, but that these benefits vary as a function of abiotic and biotic factors.  110 Strengths and limitations  A major strength of my dissertation is my attempt to take a holistic approach through the integration of multiple factors within each experiment, the integration of the multi-factor experiments, the variety of variables measured, and the incorporation of network, stress- gradient, ecological economic, and geographic mosaic coevolution theories into the hypothesis testing framework of the project. All of the results combined help to form a larger picture, overcoming some of the limitations inherent in individual experiments. For instance, the larger seedlings in the growth chamber trials able to access reservoir water simulated trees in the field having access to groundwater at depths beyond the reach of the mycorrhizosphere of establishing seedlings. This allowed me to label the donor seedlings with isotopes at a much lower cost than it would have been to label a tree. I was thus able to test for C and water transfer at replication in the growth chambers that would have been prohibitive in the field, then relate it back to survival and mortality results from the field. Based on this, I would infer that the improved growth of seedlings at 0.5m from the tree at the dry sites when able to form a mycorrhizal network resulted from EM network-facilitated transfer of hydraulically redistributed water from the tree.  A major limitation of the field experiments was the high loss of replication resulting from lack of germination, bear activity, herbivory, human activity and tree death. This reduced my ability to detect differences among treatments, and may account for the lack of mesh effects in chapter 3, and the lack of an interaction among mesh treatment, proximity and drought in their effect on survival in chapter 2. However, the two field experiments  111 required planting 1,890 seedlings in precise locations, most inside mesh bags and with minimal soil disturbance. Thus, the time and money available had to be balanced among field planting, field measurements, field harvest, cost of supplies, lab processing and administration of the growth chamber trials. There is always a tradeoff between the quality of information, and the variety of information, given finite resources. Field ecologists pursuing these questions further would be advised to assemble large field crews and increase their replication, with a focus on distance experiments across climatic gradients, and an extension of the length of transects within the distance experiments. Foliar nutrient analysis was also dropped due to high costs. These data would have helped to interpret mechanisms.  A major limitation specific to the provenance test was that it was conducted at a genetic scale that may have been too large to truly test this effect. My provenances, even where they matched the climate, were not adapted to the local fungi of my sites. Instead, they were mixes of seeds collected from an envelope of common climate and gene flow. To truly test the geographic mosaic theory of coevolution, one should collect seed from the very sites where the experiment is being conducted, and compare them with off-site seed (home versus away seed). Furthermore, the provenance study did not adequately test plant-to-plant facilitation, because all seedlings were planted at the same distance from the dominant conspecific.  A major limitation of the growth chamber trial was the reduced replication in the xylem water D measurements stemming from the low xylem water volumes, leading to the  112 decision to pool samples. More pools could have been generated had we extracted more seedlings, but many treatment combinations had three or fewer surviving seedlings; thus this was not a viable option. Moreover, extracting water from xylem for isotope analysis is a very time-intensive process.  A major limitation with the EM community assessment was the difficulty of morphotyping fungal taxa precisely, and successful amplification and sequencing of the species present. Morphotyping is a notoriously unreliable method for identifying fungi without sporocarps. Moreover, some genera that were thought to be present based on visual identification and past research by our lab group, did not amplify and sequence successfully. Compounding the lack of molecular verification of species present is the relatively small number of known fungal species in the public genetic databases, thus limiting our ability to match many sequences with known taxa.  113 Future directions  Results from research on EM networks are highly variable in showing a benefit to the establishing plant. This highly variable role of EM networks in ecosystems is not well understood. Their presence is inferred through the use of isotope tracers, dye tracers, quantum dot tracers, molecular methods and effects of physical barriers on plant relations, but the confirmation of unbroken mycelia between the root systems of two plants in the field has not been established. Studies indicate that C is transferred between plants through EM networks, but it still has not been shown that this affects survival or growth. In my view, further research should examine not only the role of EM networks along stress gradients, but also their role in forest succession. This suggestion is based on my finding in chapter 2 that EM networks facilitate growth of interior Douglas-fir seedlings where interior Douglas-fir is the climax species; thus EM networks appear to play a role in the self-regeneration of climax forests. It is also based on the work of Philip et al. (2010), who found that C-transfer between early successional paper birch (Betula papyrifera) and longer-lived interior Douglas-fir shifted direction with phenology. The net direction of translocation to interior Douglas-fir during the most productive part of the growing season suggests that EM networks may facilitation succession toward later seres. Furthermore, there would be a selective advantage for K-selected EM fungi to invest more resources, potentially including C derived from earlier successional plants, into later successional plants, as they are a long-term source of large amounts of C for the fungus. Use of isotope and quantum dot tracers of mineral and organic C, water, N and other elements applied directly to rhizomorph-forming fungi and plants of various locations  114 within the sere, could be used to  elucidate what role EM networks play in the allocation of resources within forest ecosystems, and how this allocation relates to succession. Ectomycorrhizal network facilitation may partially explain why EM climax species are able to persist in the understory of early seral species for decades, and why they tend to dominate throughout stand development toward the dry periphery of their biogeoclimatic envelope.  In chapter 3, I tested how EM networks, climate, seed origin within a moisture gradient, and life history affected interior Douglas-fir seedling establishment when growing near the dripline of a conspecific dominants. I found no main effect of EM networks, nor an interaction of EM networks with seed origin. Brendan Twieg, a PhD student with Dr. Suzanne Simard, is examining the role EM fungi play in limiting tree migrations at a larger scale than my experiment was conducted. While fungal species composition will likely be an important driver of regeneration patterns, a finding that seedlings are being colonized by the same fungal species regardless of establishment success, after accounting for climatic differences, would give support for selectivity between plants and fungi at the scale that he is conducting his research.  Probably the most interesting result of my thesis is that EM networks, with roots excluded, interacted with tree proximity and climate to affect growth of seedlings adapted to mesic conditions. To my knowledge, this interaction has not been tested elsewhere. The pattern was weak, so further research is desirable to assess whether the pattern has any long-term consequences for forest regeneration patterns. In chapter 2, I discuss other  115 studies that have shown seedling facilitation effects emanating from EM networks with dominant conspecifics. None of them utilized a climatic gradient for experimentation, but they did plant seedlings at further distances from dominant conspecifics than I did, and the shorter maximum distance is a weakness of my study. Future research should combine climatic gradients with transects extending to greater distances from dominant conspecifics, and should use tracers, especially water isotopes, in the field to directly assess how EM networks are benefiting seedlings. Precisely controlled amounts of deuterated water could be injected into the soil directly under the dominant conspecific, at a depth beyond the reach of seedling roots. This may require designing and manufacturing one’s own tool for this purpose.  It is predicted that seres defined by their dominant climax species and structure will shift in latitude, longitude and altitude in response to pCO2 forcing of temperature in BC, and over much of the globe (Hamman and Wang 2005). Results from chapter 4 suggest that EM networks, translocation of hydraulically redistributed water, and increased pCO2 may alleviate some of the water deficiency incurred upon seedlings by climate change via increases in temperature and subsequent decreased moisture in the top soil layers. This may serve to reduce regeneration failure and diebacks and thus moderate tree species shifts with climate change. Questions that follow include: What role will EM networks play in future plant assemblages? How will EM networks interact with pCO2 in this process, since both factors have variable effects on plants independent of each other, temperature and soil moisture? Observing plant-fungus-plant interactions dispersed over the entire range and plant community assemblages of each species studied would help to  116 parse out some of these interactions, since there already exists substantial independent variation in temperature, precipitation, pCO2 and species assemblages across the range of most temperate species (McGill et al. 2006).  The knowledge that increased pCO2 and water translocation through EM networks may moderate water deficiency in establishing seedlings should be incorporated into ecologist’s and land manager’s models on stand dynamics. Historically, logging has removed the largest trees in interior Douglas-fir forests (and elsewhere world-wide), thus destroying mycorrhizal hubs and the primary nodes for C and deep water redistribution within the network. This harvesting practice should be greatly scaled back to conserve the ecological and genetic complexity of these stands. Moreover, assisted migration of seedlings might be less successful than anticipated if the competitive ability of preexisting seed source populations declines less than forecast. A positive feedback may even develop whereby increased C-fixation resulting from increased photosynthetic rates and increased water use efficiency among plants generates a higher demand for N, thus feeding more C into medium and long-distance exploration type EM fungi (Agerer 2001, Parrent and Vilgalys 2007), which in turn generate a more robust node-to-node network, feeding back into the plant community with more N and water. This, of course, would have implications for C sequestration, timber production, understory plant production, sporocarp production and heterotroph production. This increase in robustness of the EM network may also reduce encroachment of AM plant communities. 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Morphotype Closest BLAST matches Database10 Accession number Total base pairs aligned NCBI % similarity Morphotype host Agaricus albolutescens Agaricus albolutescens NCBI AY484675.1 713 99.3 seedling Amphinema byssoides Amphinema byssoides NCBI AY838271.1 571 98.6 both CDE8 nd nd nd nd nd seedling Cenococcum geophilum Cenococcum geophilum NCBI DQ179119.1 915 97.05 both  Phialocephala fortinii NCBI AY394915.1 593 99.83 Cortinarius sp. Cortinarius sp. NCBI EU326169.1 805 97.52 both  Rhizoctonia sp. NCBI AJ419931.1 599 97.83  10 NCBI = National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/). nd = not determined.  134 Supplementary table 1 Ectomycorrhizal (EM) morphotypes on interior Douglas-fir (Pseudotsuga menziesii var. glauca) trees and seedlings (outplanted nursery and field-germinated) at the nine site located across a climatic moisture gradient (biogeoclimatic zones PP, IDF, and ICH) in southern interior British Columbia, Canada. Sampling was conducted in September 2007 (nursery seedlings) and 2008 (germinants). Morphotype Closest BLAST matches Database10 Accession number Total base pairs aligned NCBI % similarity Morphotype host DSE Tomentella sp. NCBI GQ398250.1 641 97.04 seedling Lactarius rubrilacteus nd nd nd nd nd both Russula acrifolia Russula acrifolia NCBI DQ421998.1 638 98.75 tree Russula brevipes Russula brevipes NCBI AF349714.1 593 99.33 both Rhizopogon/Suillus Boletales NCBI FJ554146.1 660 99.55 both  Inocybe sororia NCBI EU525947.1 672 99.4  Meliniomyces variabilis NCBI EF093172.1 562 99.47  Rhizopogon vinicolor NCBI AF263931 91 100  Tomentella sp. NCBI EF655697.1 601 98.84  135 Supplementary table 1 Ectomycorrhizal (EM) morphotypes on interior Douglas-fir (Pseudotsuga menziesii var. glauca) trees and seedlings (outplanted nursery and field-germinated) at the nine site located across a climatic moisture gradient (biogeoclimatic zones PP, IDF, and ICH) in southern interior British Columbia, Canada. Sampling was conducted in September 2007 (nursery seedlings) and 2008 (germinants). Morphotype Closest BLAST matches Database10 Accession number Total base pairs aligned NCBI % similarity Morphotype host  Tricholoma myomyces NCBI FJ845443.1 656 99.7 Russula sp. Russula sp. NCBI EF218808.1 637 100 both Russula xerampelina Russula xerampelina NCBI AY061734.1 706 99.86 seedling Sebacina sp. Sebacina sp. NCBI EU668272.1 594 98.99 both Thelephora terrestris Thelephora terrestris NCBI GQ267490.1 684 99.42 both UNKNOWN nd nd nd nd Nd both Wilcoxina rehmii Geopora sp. NCBI EU668289.1 600 98.33 both  Thelephora terrestris NCBI GQ267490.1 687 99.27  Wilcoxina rehmii NCBI AF266708.1 611 99.35    136  Supplementary figure 1  Map showing the locations of seed trees (colored by site) and seed sources (labeled with seedlot number) with respect to the biogeoclimatic unit (colored by area) and seed planning zones (outlined and labeled in white) in which they occur.  137 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 Rhizopogon/Suillus Wilcoxina rehmii Cenococcum geophilum Amphinema byssoides Lactarius rubrilacteus UNKNOWN Russula sp. Cortinarius sp. Russula brevipes Russula acrifolia Sebacina sp. Thelephora terrestris Russula xerampelina DSE CDE8 Agaricus albolutescens Trees Nursery seedlings  0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 Rhizopogon/Suillus Wilcoxina rehmii Cenococcum geophilum Amphinema byssoides Lactarius rubrilacteus UNKNOWN Russula sp. Cortinarius sp. Russula brevipes Russula acrifolia Sebacina sp. Thelephora terrestris Agaricus albolutescens CDE8 DSE Russula xerampelina Trees Germinants  Supplementary figure 2  Relative abundance of EM morphotype colonization of trees and seedlings for outplanted nursery seedlings (a) and field-germinated seedlings (b). a b  138 0 0 0 0 distance dis tan ce dist anc e distance dro ugh t drou ght drought drought Ln (g ro w th ) Ln (g ro w th ) Ln (g ro w th ) Ln (g ro w th ) 0.5-µm mesh (no network) 35-µm mesh (network – roots) no mesh (network + roots) P=0.0375 * *   139 Supplementary figure 3  (previous page) Four perspectives of a three-dimensional graph of the relationship of drought (summer heat:moisture index) and ln(growth) (natural logarithm of proportional biomass increase) after adjusting for covariates at different distances from the established tree under the no mesh treatment (blue), the 0.5-µm mesh treatment (red) and the 35-µm mesh treatment (green).  

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