Forestry, Faculty of
DSpace
UBCV
Summers, Breanna
Master of Science - MSc
University of British Columbia
The impacts of overstory tree retention and climatic aridity on plant community assembly were studied in Douglas-fir dominated forests. There were observed declines in species richness, and percent cover of moss and lichens, in the low retention treatments. High overstory retention treatments conserved community composition, while clearcut and low retention treatments triggered distinct changes, with losses of shade tolerant indicator species and increases in invasive herbs. Low retention treatments reduced the presence of certain culturally important food plants. This study also examined the effects of tree retention and climatic aridity gradient on the performance of local and migrated Douglas fir genotypes and understory plant communities. Seedlings from arid climates and those under high overstory retention grew tallest, suggesting potential adaptability to increasingly arid conditions. Conversely, in humid climates, seedlings fared better in more open conditions, except where shrub cover was high. Understory plant communities played a role in sheltering seedlings from drought and frost, thus promoting their survival and growth. However, clearcutting exacerbated drought incidence due to increased plant competition for water. The retention of overstory trees mitigated the competitive effects of understory plant communities on seedlings. This study provides evidence that retention of overstory trees may protect plant species and regenerating seedlings under increasing aridity with climate change.
The Interactive Effects of Harvesting Intensity and Climatic Aridity on Understory Plant Communities and Douglas-fir Seedlings by Breanna Summers B.Sc., The University of Victoria, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2024 © Breanna Summers, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The interactive effects of harvesting intensity and climatic aridity on understory plant communities and Douglas-fir seedlings. submitted by Breanna Summers in partial fulfilment of the requirements for the degree of Master of Science in Forestry Examining Committee: Dr. Suzanne Simard, Professor, Forestry, UBC Supervisor Dr. Tara Martin, Professor, Forestry, UBC Supervisory Committee Member Dr. Elizabeth Wolkovich , Professor, Forestry, UBC Supervisory Committee Member iii Abstract The impacts of overstory tree retention and climatic aridity on plant community assembly were studied in Douglas-fir dominated forests. There were observed declines in species richness, and percent cover of moss and lichens, in the low retention treatments. High overstory retention treatments conserved community composition, while clearcut and low retention treatments triggered distinct changes, with losses of shade tolerant indicator species and increases in invasive herbs. Low retention treatments reduced the presence of certain culturally important food plants. This study also examined the effects of tree retention and climatic aridity gradient on the performance of local and migrated Douglas fir genotypes and understory plant communities. Seedlings from arid climates and those under high overstory retention grew tallest, suggesting potential adaptability to increasingly arid conditions. Conversely, in humid climates, seedlings fared better in more open conditions, except where shrub cover was high. Understory plant communities played a role in sheltering seedlings from drought and frost, thus promoting their survival and growth. However, clearcutting exacerbated drought incidence due to increased plant competition for water. The retention of overstory trees mitigated the competitive effects of understory plant communities on seedlings. This study provides evidence that retention of overstory trees may protect plant species and regenerating seedlings under increasing aridity with climate change. iv Lay Summary This thesis investigates the dynamic interactions between forestry harvesting practices, regional climate conditions, and their impact on plant communities in forest understories of Douglas-fir forests in British Columbia, Canada. The research also considers the implications of climate change for these ecosystems. It aims to understand how strategies like partial tree retention during logging may mitigate the adverse effects of clearcutting, particularly in the context of changing climate conditions. Furthermore, it examines how these practices alter plant community composition and how this change impacts the regeneration process of conifer trees in a warming climate. The study also delves into the differential responses of locally sourced and migrated Douglas-fir seedlings to these fluctuating environmental conditions. The findings will contribute to a more nuanced understanding of forest ecosystems and their resilience to climate change, which is crucial as we devise sustainable forest management strategies for the future. v Preface This thesis is an original, unpublished product of the author, Breanna Summers. Chapter Two is based on a large-scale field experiment (the “Mother Tree Project”), which was designed, developed, and conducted by Suzanne Simard, and in which Brenna Summers examined and analyzed the understory plant communities with intellectual guidance and support from Suzanne W. Simard. Chapter Three is based on a field experiment within the Mother Tree project, where the seedling neighborhood plots were designed and developed by Breanna Summers and Suzanne Simard, and the research was conducted and interpreted by Breanna Summers, with intellectual guidance and support from Suzanne W. Simard. Danica Law and Teah Schacter were indispensable in the field work. Statistical analyses were conducted by Breanna Summers with constant suggestions and support from Dr. Suzanne Simard. vi Table of Contents Table of Contents Abstract ................................................................................................................................ iii Lay Summary ....................................................................................................................... iv Preface ................................................................................................................................... v Table of Contents ................................................................................................................. vi List of Tables ...................................................................................................................... vii List of Figures .................................................................................................................... viii Chapter 1: Introduction ......................................................................................................... 1 1.2 Culturally important food plants ............................................................................... 3 1.3 Research objectives and thesis outline ........................................................................ 4 Chapter 2: Partial retention of overstory trees helps protect forest biodiversity across a climate gradient. .................................................................................................................... 7 2.1 Introduction ................................................................................................................. 7 2.2 Methods ..................................................................................................................... 11 2.3 Results ....................................................................................................................... 19 2.4 Discussion ................................................................................................................. 24 2.5 Conclusion ................................................................................................................ 35 Chapter 3: The interactive effects of harvesting intensity and climatic aridity on understory plant communities and Douglas-fir seedling performance ................................................. 48 3.1 Introduction ............................................................................................................... 48 3.2 Methods ..................................................................................................................... 53 3.3 Results ....................................................................................................................... 59 3.4 Discussion ................................................................................................................. 61 3. 5 Conclusion ............................................................................................................... 66 Chapter 4: Conclusion ......................................................................................................... 86 4.1 Main findings ............................................................................................................ 86 4.2 Contributions to the field of study ............................................................................ 87 4.3 Study limitations ....................................................................................................... 88 4.4 Further research ........................................................................................................ 89 Bibliography ....................................................................................................................... 91 Appendix A ....................................................................................................................... 106 Appendix B ....................................................................................................................... 137 Appendix C ....................................................................................................................... 146 Appendix D ....................................................................................................................... 147 Appendix E ....................................................................................................................... 151 vii List of Tables Table 2.1. Characteristics of the six study sites, including location, history, soil and climate. ................................................................................................................................................. 37 Table 2.2. Principal types of ground surface materials (Ceska & Scagel, 2011) ................... 38 Table 2.3.Indicator species for each harvesting treatment and combination of harvesting treatments using the indispecies package in R. ....................................................................... 38 Table 3.1. Characteristics of the research sites. ..................................................................... 68 Table 3.2. Principal. 1981-2010 Climate averages for B Class Douglas-fir seedlots ........... 69 Table 3.3. Full and recuded models for chapter 3 ................................................................... 69 Table 3.4. ANOVA table total percent cover of the plant neighborhood as affected by harvesting treatment and the aridity (Annual Heat Moisture Index). Type 3 sum of squares. 71 Table 3.5 ANOVA table for species richness of the plant neighborhood as affected by harvesting treatment and the aridity (Annual Heat Moisture Index). Type 3 sum of squares. 71 Table 3.6 ANOVA table for seedling height as affected by neighborhood percent cover. .... 71 Table 3.7 ANOVA table for seedling height. ......................................................................... 72 Table 3.8 ANOVA table for the probability of frost incidence. ............................................. 72 Table 3.9 ANOVA table for the probability of drought incidence. ........................................ 73 viii List of Figures Figure 2. 1. Study locations spanning the distribution of interior Douglas-fir in British Columbia. Shows the five interior and one coastal (Malcolm Knapp) Douglas-fir Forest. ... 39 Figure 2. 2. Interaction plot illustrating the relationship between Annual Heat-Moisture Index (AHM) and Light Index, stratified by harvesting treatment, for the functional groups bryoids and herbs. Light index ranging from 0 to 1, where 0 indicates tolerance to deep shade and 1 indicates a requirement for full sun and an intolerance for shade. Anova results for light index as a function of the interaction between harvesting treatment and AHM for bryoids (p =0.05), for herbs (p=0.001). .................................................................................................... 40 Figure 2. 3. Interaction plot illustrating the relationship between Annual Heat-Moisture Index (AHM) and average soil moisture regime (SMR) for herbs. The colored lines depicting fitted linear regression lines for different harvesting treatments. The mean for each treatment level is indicated by a maroon point. The categories for SMR ranged from 0 (very dry soil) to 8 (very wet soil). ANOVA results of the SMR of the herbs as a function of the interaction between AHM and treatment (p=0.020) ................................................................................. 41 Figure 2.4 Boxplot showing the distribution of species richness across different harvesting treatments for bryoids, herbs and shrubs. The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1. ANOVA results for bryoids (p = 2.29E-05), herbs (p=5.63E-04), shrubs (p = 3.73E -03). ..................................................... 42 Figure 2.5. Percent cover of bryoids across harvesting treatments. According to the ANOVA results, the treatment had a p-value of 2.29E-05 when determining bryoid percent cover. A p-value of 2.29E-05 when determining bryoids percent cover. ................................................. 43 ix Figure 2.6. Effect of harvesting treatment on total species richness associated with a) disturbed habitats (p = 0.007) b). Percent cover of species associated with exposed mineral soil per treatment (p =4.91E-03). ............................................................................................ 44 Figure 2.7. Relationship between Annual Heat-Moisture Index (AHM) and species richness for a) whole community, b) bryoids, c) herbs, d) shrubs, and e) trees. Each point represents the observed species richness within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model Species Richness ~ AHM, whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the covariates pre-richness and harvesting treatment. .......................... 45 Figure 2.8. Relationship between Annual Heat-Moisture Index (AHM) and a) percent cover of vegetation indicative of a) exposed mineral soil b) percent cover of vegetation indicative of moder humus soil. Each point represents the average percent cover within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model percent cover ~ AHM, whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the harvesting treatment. .............. 46 Figure 2.9. Relationship between Annual Heat-Moisture Index (AHM) and species richness of vegetation indicative of a) exposed mineral soil b) moder mor humus, c)mor humus, d) moder humus. Each point represents the average richness within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model percent cover ~ AHM, x whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the harvesting treatment. ........................ 47 Chapter 1: Introduction Trees can be connected below ground via a fungal network of mycorrhizae (Horton, 2015), contributing to the integration of forest ecosystems. The trees can share excess carbon, nutrients and water with seedlings, which has sometimes been associated with changes in survival and growth (Teste et al., 2009). Because of their vast root systems, the oldest and largest trees are the most highly connected to other trees in the forest and are essential components of the regeneration and resiliency of a forest ecosystem (Beiler et al., 2010, 2015). The regenerative capacity of old trees has led to the colloquial naming of these trees as 'Mother Trees' (Simard, 2017). Current silviculture methods, such as clearcutting or high-grading, remove all the large Mother Trees and compromises critical connections to the surrounding seedlings and plants. Cutting these trees also reduces seed sources for the next generation of germinants and changes the environmental growing conditions by altering the amount of sunlight filtering through the canopy and nutrients and moisture in the soil. These alterations may change the plant community's composition, diversity, abundance, and functional roles (Hart & Chen, 2006). However, retaining old trees may mitigate the effects of harvesting and climate change on these plant communities and aid in the recovery of old-growth forest ecosystem conditions (Coates & Burton, 1997). The retention of old trees provides seed, shelter for understory plants, habitat for specific species, and facilitates the re-development of essential forest structure and composition (Craig & Macdonald, 2009). The Mother Tree Project tests the interactive effects of regional climate and partial retention of overstory trees on plant assembly and forest regeneration (Simard et al., 2020; Roach et al., 2021). The project consists of nine research locations covering a range of 2 climates where Douglas-fir forests occur. The nine forests have each been harvested using five treatments, from high tree retention to clearcutting at each location. The project also examines the interactive effects of climate and retention on the success of planted seedlings migrated from warmer and cooler climates. The design of the experiment may contribute to forecasting consequences related to climate change (Simard et al., 2020; Roach et al., 2021), and may provide direction for promoting robust and resilient forests as climate changes. In a pre-harvest analysis of the nine Mother Tree Project forests, Roach et al. (2021) examined how variation in temperature, precipitation and aridity affected ecosystem services. The richness of tree species decreased with increasing aridity, or summer heat moisture index and annual heat moisture. In contrast, the richness of herb species increased with decreasing annual precipitation. Shrub and bryoid species were not linearly correlated with climatic variables, and overall, Shannon's diversity index of understory plants was not correlated with climatic variables (Roach et al., 2021). Tragopogon dubius and Hieracium aurantiacum were the only invasive species present and comprised < 1% cover in < 5% of plots at the Interior Douglas-fir zone sites (Roach et al., 2021). Logging caused substantial changes in the understory plant community. Shannon’s diversity of the whole plant community declined with harvest intensity across the entire aridity gradient, with the most profound impacts following clearcutting (Simard et al. 2020). The richness of trees and bryoids was particularly reduced with intense harvesting treatments, where the loss of tree species was greatest in humid forests. Bryoid losses were greatest in arid forests, whereas herb and shrub species richness was unaffected by climatic aridity and harvesting intensity. At the most productive interior site (Redfish), richness and abundance of bryoids were best protected by the 30% and 60% overstory tree retention 3 levels, and almost eliminated from the clear-cut and seed tree treatments. Simard et al. (2021) also found that forest adapted species, such as Goodyera oblongifolia, Viola glabrum, Aralia nudicaulis and Streptopus amplexifolius, were generally reduced or eliminated with clearcut logging at all sites. Natural regeneration of tree seedlings generally increased with overstory tree retention (Simard et al., 2021). Douglas fir seedling density increased with proximity to Douglas fir seed trees, and the greatest regeneration densities occurred within 10 m of seed trees. 1.2 Culturally important food plants As the impacts of climate change on forest ecosystems increase, it's becoming more crucial to understand the relationship between forestry practices and the growth of culturally important food plants. Forest clearings, or canopy gaps, can benefit these plants. Indigenous forest gardens, usually located in clearings near villages, have been found to have greater functional diversity than plant communities in forests without gaps (Armstrong, 2017). Indigenous Peoples in the Pacific Northwest actively managed their lived landscapes to increase the productivity and proclivity of desired plants, in this case, by managing forest gardens near their homes and camping areas (Armstrong, 2017). These canopy gaps allow more sunlight to reach the forest floor, promoting the growth of food plants that thrive in semi-shade (Feeley et al., 2020). By strategically preserving certain trees, the forest's structure and composition can be adjusted to promote the growth and abundance of these essential foods. Though not specifically involved in this research, the indigenous peoples who have used these culturally important food plants in the interior included the Sinixt, the Ktunaxa, and the Syilx peoples in Nelson, the Dakelh in Fort St James, the Ktunaxa people in 4 Cranbrook, and the Secwepemc in Williams Lake and on the coastal site Katzie First Nation and Kwantlen First Nation. 1.3 Research objectives and thesis outline My thesis more deeply examines the effects of harvesting and climate on the understory plant communities in the Mother Tree Project. The overall objective of my thesis research is to examine the interactive effects of tree retention and regional climate on plant communities and their impact on conifer regeneration. The specific questions addressed were as follows: 1. Does partial overstory tree retention moderate the effects of clearcutting more in arid climates compared to humid regions? (Chapter 2) 2. Does the intensity of harvesting change the plant community, resulting in greater similarity between the clearcut and seedtree treatments, as well as between the 60% retention and control treatments? (Chapter 2) 3. Do traditionally important food plants react differently to retention treatments depending on each species' autecology and climate conditions? (Chapter 2) 4. How does regional climatic aridity, overstory harvest intensity, and planted seedling genotype influence the relationship between seedling performance and understory plant communities? (Chapter 3) 5. How is the growth of locally sourced or migrated seedlings influenced by the abundance and species richness of the neighboring plant community in response to overstory canopy cover and climatic aridity? (Chapter 3) 5 6. Does the understory plant community compete with tree seedlings in clearcuts? Is this negative effect reduced by increased overstory cover and changes in plant composition in partial retention treatments? (Chapter 3) 7. Do seedlings migrated from distant regions need more protection from overstory trees and the plant community than local genotypes? (Chapter 3) 8. Do Douglas-fir genotypes migrated from warmer climates benefit more from frost protection? And do genotypes from colder climates benefit more from drought protection provided by overstory trees and neighboring plant communities? (Chapter 3) Chapter 2 explores the relationship between overstory tree retention, climatic conditions, and plant community assembly during forest harvesting. Focusing on Douglas-fir forests in British Columbia, Canada, the study compares five different overstory retention treatments under various climatic conditions three years post-harvest. The research aims to understand the interactive effect of climate and harvesting on the abundance, species richness, and composition of the plant community, and the presence of indicator and culturally important plants. Chapter 3 explores how regional climate, harvest intensity, and Douglas fir seedling genotype affect the relationship between seedling performance and understory plant communities. It examines how local and migrated seedlings' growth responds to the neighboring plant community in terms of percent cover and species richness, overstory canopy cover, and climatic aridity. This study involved assessing local and migrated planted Douglas fir provenances three years post-harvest. Seedlings height was measured, and the 6 presence or absence of frost or drought damage was evaluated. The abundance and richness of neighboring plant species were measured. The relationships between seedling performance and the neighboring plant community were assessed using linear regression analysis. 7 Chapter 2: Partial retention of overstory trees helps protect forest biodiversity across a climate gradient. 2.1 Introduction Clearcutting, the predominant harvesting method in Canada, has raised concerns due to its detrimental effects on forest ecosystems, particularly structural and species diversity (Bock & Van Rees, 2002; Haeussler et al., 2002; Fedrowitz et al., 2014; Seedre et al., 2018). Clearcutting reduces seed sources for future generations and alters environmental conditions necessary for plant growth (Hart & Chen, 2006). This can result in changes to the composition, diversity, abundance, and functional traits of plant communities (Hart & Chen, 2006). Clearcutting has also been shown to increase invasion of non-native and ruderal species (Haeussler et al., 2002), while negatively affecting specialized plants such as mycoheterotrophs, along with lichens, liverworts, and mosses that rely on specific substrates and humid habitats (Lesica et al., 1991; Smith, 1997; Esseen & Renhorn, 1998; Söderström, 1989). The removal of organic matter and alteration of microclimatic conditions can also lead to losses in soil carbon pools and fungal inoculum (Dean et al. 2020; Simard et al. 2021). How detrimental clearcutting effects are on plant communities is expected to vary with climatic conditions, with greater amplification under arid conditions (Pearson & Dawson, 2003; Simard, 2020), raising concerns that clearcutting effects will be augmented as climate changes. These findings highlight the need to investigate alternative forest management practices to clearcutting in order to protect understory biodiversity with climate change (Lindenmayer & Franklin, 2002; Michel & Winter, 2009; Lindenmayer et al., 2012; Baker et al., 2013; Seedre et al., 2018). 8 Partial retention harvesting involves selectively removing trees while leaving others, offering several benefits for forest ecosystems. Retaining old trees provides a source of seeds , shelter for understory plants, protection of fungal inoculum, and promotes forest recovery (Lindenmayer & Franklin, 2002; Craig & Macdonald, 2009; Michel & Winter, 2009; Gustafsson & Perhans, 2010; Lindenmayer et al., 2012; Baker et al., 2013). The retention of old trees and refuge plants during harvest not only provides mycorrhizal fungal inoculum and protects the soil foodweb, but also increases host plant species diversity and structural heterogeneity, contributing to the resilience and recovery of the forest ecosystem (Franklin et al., 1987; Gustafsson & Perhans, 2010; Simard et al., 2021). Tree retention has been shown to affect the cover, richness, and composition of the understory plant community, but how it does this depends on the forest type and level of tree retention. For example, lowered tree retention was associated with increased vegetation cover in coniferous boreal forests (Craig and MacDonald 2009), but conversely, clearcutting led to persistent declines in plant cover and species richness in eastern mixed hardwood forests (Duffy and Meier (1992). The percentage of trees retained has been a crucial determinant, where low retention (around 20%) has resulted in plant communities similar to clearcuts, while high retention (approximately 75%) has created conditions comparable to unharvested forests (North et al., 1996; Vanha-Majamaa & Jalonen, 2001; Macdonald & Fenniak, 2007; Rosenvald & Lõhmus, 2008; Craig & Macdonald, 2009). Despite the global scale of forestry, little is known about the interactive effects of climate change and overstory tree retention practices on biodiversity. Climate change is expected to have significant impacts on plant communities, potentially shifting their composition and 9 abundance. Changes in temperature and water availability, for example, will affect species availability for plant community assembly (Feeley et al., 2020; Gornish and Tylianakis, 2013). As temperature increases, drought-tolerant and xerophytic species, which are adapted to arid conditions and possess traits to conserve water, may become more abundant (Gornish & Tylianakis, 2013; Feeley et al., 2020). On the other hand, species that depend on higher moisture levels may decline in abundance. Additionally, increasing soil temperature and decreasing moisture levels are expected to lead to a rapid decrease in species richness (Niu et al., 2018). Climate change can also influence the invasion of non-native species, with varying effects depending on the specific context (Bradley et al., 2010). These changes in plant composition can have cascading effects on ecosystem dynamics and resilience. As climate change increasingly impacts forest ecosystems, it's becoming more critical to understand the interplay between forestry practices and the growth of culturally important food plants. Canopy gaps can benefit culturally important food plants. Indigenous forest gardens, which occur in openings created around villages, are more functionally diverse than plant communities in closed forest conditions (Armstrong, 2017). Indigenous Peoples in the Pacific Northwest actively managed their lived landscapes to increase the productivity and proclivity of desired plants, in this case, by managing forest gardens near their homes and camping areas (Armstrong, 2017). The canopy gaps allow more sunlight to reach the forest floor, promoting the growth of food plants that thrive in partial shade (Feeley et al., 2020). By strategically retaining certain trees, the forest structure and composition can be shaped to support the growth and abundance of these important foods. The indigenous peoples who have used these culturally important food plants in the interior included the Sinixt, the Ktunaxa, and the Syilx peoples in Nelson, the Dakelh in Fort St James, the Ktunaxa people in 10 Cranbrook, and the Secwepemc in Williams Lake and on the coastal site Katzie First Nation and Kwantlen First Nation. Clearcut harvesting and increasing aridity from climate change are anticipated to decrease plant diversity (Simard et al., 2021). Therefore, it's essential to identify partial retention forest harvesting practices that could mitigate these losses. Climate change is expected to decrease plant diversity significantly due to rising aridity (Harrison et al., 2020). Areas with warmer and wetter climates support a larger number of species (Hawkins et al., 2003; Kreft & Jetz, 2007; Sommer et al., 2010; Harrison et al., 2020). However, as these regions become more arid due to increased temperatures and changes in precipitation, there is a decrease in taxonomic, functional, and phylogenetic diversity over time (Harrison et al., 2020). This ongoing trend of increasing climatic aridity poses a significant concern for global plant diversity preservation. Clearcutting could exacerbate soil moisture loss due to the increase in surface evaporation rates resulting from the warmer soil temperatures (Childs & Flint, 1987). Here, we investigate the interactive effects of regional climate and overstory tree retention on the abundance, composition and diversity of understory plant communities. Seven study sites, each in distinct regional climates, were located across the range of Douglas-fir in British Columbia, including five sites in the interior plus one on the coast. Three hypotheses were tested. Firstly, partial overstory tree retention will moderate the harsh effects of clearcutting, and this will be more so in arid than humid climatic regions. Secondly, harvesting intensity will affect the plant community, to where it is similar between the clearcut and seedtree treatments, and between the 60% retention and control treatments. Thirdly, the response of culturally important food plants to the retention treatments will vary 11 and depend on the autecology of each species and climatic conditions. The findings of this research will have practical implications for forest management practices and contribute to the conservation and sustainable use of forest ecosystems as climate changes. 2.2 Methods 2.2.1 Study Sites The study took place in five interior and one coastal Douglas-fir forest, covering a 900 km-long climate gradient in British Columbia, Canada (Figure 1 and Table 1). The interior locations are within the current range of interior Douglas-fir (Pseudotuga menziesii var. glauca) in British Columbia (mean annual temperature (MAT) 2.3–7.7 °C, mean annual precipitation (MAP) 532–1059 mm). They occur in the Interior Douglas-fir (IDF), Interior Cedar-Hemlock (ICH), and Sub-boreal Spruce (SBS) biogeoclimatic (BEC) zones, and are located from south of Cranbrook (49.21 °N, 115.37 °W) to north of Fort St James (54.65 °N, 124.43 °W). The single coastal location contains coastal Douglas-fir (P. menziesii var. menziesii) and is in the Coastal Western Hemlock (CWH) zone near Maple Ridge, British Columbia. These were approximately 120-year-old primary forests that had never been logged but had previously been affected by fire. At three out of five interior forests, Douglas-fir made up more than 50% of the basal area (BA), and at the two wet interior forests, it comprised an average of 33% of the basal area. Each interior forest contained other tree species, including: western larch (Larix occidentalis Nutt.) in the IDF zone; hybrid white spruce (Picea glauca x engelmannii) in the SBS; and western larch, western red-cedar (Thuja plicata (Donn ex D Don), western hemlock 12 (Tsuga heterophylla (Raf.) Sarg.), and grand fir (Abies grandis (Dougl. ex D. Don) Lindl.) in the ICH zone. Several other tree species were present in minor amounts, with up to eight tree species at one location. At the coastal location, western hemlock and western red-cedar grew in mixture with coastal Douglas-fir. Prior to logging, the understory was dominated by grasses at the most arid locations, shrubs and herbs at locations with moderate precipitation, and was sparse at the two wettest interior locations and at the coastal site. All sites had a mesic soil moisture regime (relative to their respective BEC zone), warm aspects, gentle slope gradients (< 30 percent) and mid-slope positions. Elevations varied from 540 to 1080 m. The soil texture tended to be coarsest in the ICH and CWH zones and finest in the SBS zone. The dominant soil orders were Podzols in the ICH and CWH zones, and Luvisols in the IDF and SBS, and were representative of their zones (Soil Classification Working Group, 1998). Climate data for each site was obtained from Climate North America, based on their latitude, longitude and elevation, using the 1981–2010 climate normal dataset (Wang et al., 2016). Each climatic location was represented by Annual Heat Moisture Index (AHM)(Wang et al., 2006), a measure of climatic aridity, where AHM is calculated as: AHM = (MAT+10)/(MAP/1000) 2.2.2 Experimental Design Five forest retention treatments were replicated up to three times at each of the six site locations in a hierarchical nested randomized block experimental design. The treatments included clearcut (0% retention), seed tree (about 10% uniform dispersed retention with 25 13 trees/ha retained), small retention (30% aggregated retention, where 30% of the stand was unharvested in 30-40m patches), large patch retention (60% aggregated of the stand retained and thinned from below), and uncut control (100% retention). The harvesting treatments were applied to 4-5 ha areas each in 2017-2019. Harvesting was carried out using feller bunchers, but trees too large for the machine were hand-felled. The high retention blocks were thinned from below by reaching into the uncut patches with the feller-buncher and removing the smaller stems. The five harvesting treatments represented a graduated series of tree retention, from the lowest in the clearcut, with increasing amounts in the single tree, small patch and large patch retention treatments, roughly corresponding with regeneration systems described as seed-tree, group selection and single tree retention with thinning from below. Trees were limbed and topped but not debarked before the boles were skidded to roadside landings, leaving needles, twigs and branches distributed relatively evenly throughout the forest after harvesting. 2.2.3 Measurements The plant community was assessed prior to harvest and three years post-harvest. A single sample was located in the center of each harvesting unit following the methodology of Canada’s National Forest Inventory (NFI) (Canadian National Inventory Committee [CNFI] and Canadian Forest Service [CFS] 2009). The plant community was measured using two plot sizes: (1) a large circular (r = 10 m) plot for estimating the percent cover of each tree and shrub species by height class (>10 m (A layer); 2-10 m (B1 layer); and < 2 m (B2 layer)); 14 and (2) a smaller circular plot (r = 5.64 m) for estimating the percent cover of all herb species, conifer seedlings (< 1-year-old), and bryoids (mosses and lichens). 2.2.4 Functional Traits Species that are important cultural food sources were identified from Turner (1997). The food plants were subdivided into those used for berries and those used for roots. Traits indicating preferred GSM (ground surface substrate) and SMR (Soil Moisture Regime) were determined from Chandler (2014), Green et al. (1989), several studies by Douglas et al. (1998a, 1998b, 1999a, 1999b, 2000, 2001a, 2001b, 2002), Pojar et al. (1994), and Klinkenberg (2021). Species indicative of disturbed soils and native or exotic status were classified using Ceska & Scagel (2011). Ecological indicator traits and habitat descriptions were derived from Douglas et al. (1998a, 1998b, 1999a, 1999b, 2000, 2001a, 2001b, 2002). The understory vegetation in a forest draws water and nutrients from the surface soil layer. Therefore, the material of this layer likely influences the composition of understory vegetation and the potential for plant establishment and growth. We've identified five types of materials that may appear in different combinations and coverages on a forest site to explain the relationships between plants and ground surface materials. Ground surface materials were categorized as mor, moder, mor moder, mull and exposed mineral soil (Krajina et al., 1982) (Table 2). The categories for SMR ranged from 0 (very dry soil) to 8 (very wet soil). The light index for each plant species was drawn from Pojar et al., (1994), Chandler (2014), Humbert et al., (2007), Krajina et al., (1982) and personal communications from W.J. Roach. This information was used to assign a light index ranging from 0 to 1, 15 where 0 indicates tolerance to deep shade and 1 indicates a requirement for full sun and an intolerance for shade. 2.2.5 Data Analysis Biodiversity was estimated as: (i) richness (the number of species occurring in the 10m radius plot for trees and shrubs, and in 5.64m radius plot for herbs and bryoids), and (ii) Shannon’s diversity index (Shannon and Weaver, 1949), calculated as: H’ = 1-Σ[pi(ln(pi)] where H’ is Shannon’s index and pi is the proportion of total cover of species i. All data analysis and graphics were completed in RStudio, version 1.3.1073. The relationships between response variables of the entire plant community (species richness, percent cover, and Shannon's diversity index) and predictors (harvesting treatment, AHM, and initial condition of richness, percent cover, and Shannon's diversity), and the interaction between AHM and harvesting treatment, were analyzed using analysis of variance (ANOVA). To evaluate the significance of the fixed effects in the model, we performed a Type III ANOVA. For the analysis of response of vegetation three years post-logging, the model was specified as follows: Response ~ treatment + AHM + treatment * AHM + pre-response covariate, 16 where the response variable is species richness, total percent cover, or Shannon’s diversity index, treatment is the harvesting treatment, AHM is the annual heat moisture index representing each location, the interaction is the interaction between AHM and treatment, and pre-response covariate is the pre-harvest value of each response variable. Models for each functional group (bryoids, herbs, shrubs, and trees) were also analysed separately. Subsets of each functional group were created using the subset() function, and the model was run for each individual subset. Results of the ANOVA and the individual functional group models were analyzed to determine the significance of predictors and their respective interactions. After the interaction was examined for all of the above models, and where found not significant, the effect of each harvest treatment was analyzed separately from AHM as fixed effects without the interaction. The models assessed the relationships between the response variables (species richness, percent cover, and Shannon’s diversity index) and the predictors (treatment or AHM, and pre-treatment richness). This approach enhanced the interpretability of the model, reduced complexity, and presented a more straightforward understanding of the relationships between harvesting treatments, AHM and the understory vegetation. For the analysis of harvesting effects on response variable three years post-logging, the reduced model was specified as follows: Response ~ treatment + pre-response covariate, Response ~ AHM + pre-response covariate 17 where response represents the response variable of the plant community three years post-logging, treatment represents the harvesting treatment, and pre-response covariate represents pre-treatment conditions of response variable. The effect of treatment on each functional group (bryoids, herbs, shrubs, and trees) was also analysed separately. The separate of effects of AHM were analysed using the same model structure. The model coefficients were estimated using ordinary least squares (OLS) regression. To predict the effect of AHM on species richness, we employed the plot_model() function from the 'sjPlot' package to visualize the model predictions. We specifically focused on the predicted response as a function of AHM. The interactive effects of AHM and harvesting treatment on traits of understory plant species were analyzed separately for each individual trait. The traits tested included light sensitivity, average SMR, percent cover and richness of species indicative of each GSM, percent cover and richness of species indicative of disturbed soils, and percent cover and richness of exotic species. We also analyzed the percent cover and richness of edible root and berry producing plant species. This analysis allowed for the assessment of the main effects of AHM and harvesting treatment, as well as their interaction effect, on individual plant traits. Canonical Correspondence Analysis (CCA) was used to explore the complex relationships between plant community composition and the interaction between harvesting treatment and AHM. The response variable, represented by the matrix of species data, was modeled against harvesting treatment conditions and AHM as variables. The species data matrix served as the predictor variable in the CCA. Prior to analysis, the plant data underwent a Hellinger transformation. Dissimilarity was then measured using Bray-Curtis distance. Following the CCA, we conducted an ANOVA to assess the significance of the 18 observed patterns. If there was no significant interaction, the harvesting treatment and AHM were analyzed separately. This technique was employed to determine the differences in species composition within the treatments across the climatic gradient. The CCA model statement was: Subset of group composition ~ Treatment + AHM + Treatment* AHM Where there was no interaction, the statement was reduced to: Subset of group composition ~ Treatment Subset of group composition ~ AHM This analysis was also run for subsets of groups, equal to the species composition of either bryoids, herbs, shrubs, trees, species indicative of different ground surface material, species characteristic of disturbances, exotic species, or berry or root producing species. Plots were made to visualize CCA ordinations, where colored polygons represented hulls encompassing groups of species corresponding to different treatments, while ellipses depicted the standard deviation of each treatment groups. These graphs were used in combination with indicator species analysis to better understand how the interaction between harvesting treatment and AHM affected species composition within the plots. CCA was also used to determine the interactive effects of AHM and SMR values of berry producing plant species to better understand the moisture requirements of these species across the different locations. The CCA model statement was: 19 Subset of berry producing species ~ AHM*SMR, where the subset of berry producing species are important ecological and traditional berry producing species according to Turner (1997), AHM is annual heat moisture indicator for each location, and SMR is the average soil moisture regime indicated by each species. To assess the association between species and environmental factors, indicator analysis was conducted using the Vegan package in R with the Indicspecies function. A total of 999 permutations were performed to evaluate the significance of species associations with the harvesting treatments using α<1 for each harvesting treatment. We then analyzed the traits associated with these indicator species, particularly their association with substrate disturbance, light index, and moisture levels. By employing the Indicspecies function and conducting permutation analyses, we aimed to identify species that were indicative of specific environmental conditions associated with each harvesting treatment. This process was repeated to determine indicator species associated with each of the locations and thus to assess how the plant community composition shifted with increasing aridity. 2.3 Results Interactive effects of climate and harvesting The interaction terms between Annual Heat Moisture Index (AHM) and harvesting treatment were not significant for species richness, percent cover, or Shannon's diversity three years after harvest across all functional groups. However, the interaction terms were significant for light indicators for whole plant community (p = 0.001), herbs (p= 0.001) and 20 herb Soil Moisture Regime (SMR) indicators (p=0.020). For plants indicating light availability, increasing harvest intensity favoured shade intolerant plants across all locations, but low harvest intensity favoured shade-tolerant plants, particularly in the bryoid and herb functional groups. This was particularly the case at locations with lower AHM (i.e., higher humidity) (Figure 2.2). For plants indicating SMR, there was no interaction between harvesting and AHM for the overall community, but the interaction was significant for the herb functional group (Figure 2.3). The average SMR was higher for herbs in the 30% retention treatment compared to the clearcut and seedtree at the least arid site. In contrast, SMR was lower for herbs in the clearcuts at the most arid site, but greater for herbs in the control and 60 % retention treatment. CCA showed the interaction between harvesting and AHM was significant for bryoids (p=0.007) (Figure A1), herbs (p=0.002) (Figure A2), and shrubs (p=0.029) (Figure A3), but not trees. Species composition was similar among the lower retention treatments, while it was similar among the higher retention treatments. The distinction between these two groups of treatments was most prominent at the less arid sites. Results showed that bryoid species Kindbergia praelonga, Rhizomium glabrescens and Hookeria Lucens were found in the control and species such as Polystepha pilulae and Atrichum selwynii were present in the clearcut treatment at the most humid site Malcolmn Knapp. In the more arid sites, there was less of a distinction between treatments and species such as Pleurozium schreberi and Dicranium scoparium and Rhytidiadelphus triquetrus were observed in all treatments. Shrub species Vaccinum parvifolium and Gaultheria shallon were found in high retentions and control plots in the humid site, whereas Rubus armeniacus and Rubus parviflorous were observed in the lower retention treatments in the more arid sites. 21 For plant species indicating ground surface conditions, non-native plants, or plants indicative of disturbance, none of the interactions between harvesting treatment and AHM were significant. When composition was assessed using CCA, however, the interaction was significant for species indicative of exposed mineral soil (p= 0.046) (Figure A4), disturbed habitats (p=0.026)(Figure A5) and non-native species (0.049) (Figure A6). Species indicative of exposed mineral soil in the clearcut and low retention treatments included Hypochaeris radicata and Atrichum selwynii whereas Pteridium aquilinum was primarily observed in the higher retention and control treatments on the coast. Epilobium ciliatum was observed in all harvesting treatments but not in the controls at all locations. Rubus idaeus was also observed in all harvesting treatments but only in the interior sites. The interaction between AHM and harvesting treatment was significant for richness of edible root producing species (p=0.009), where richness was lower in the 30% retention treatment in more humid regions, but greater in this treatment at the more arid sites. The interaction was not significant, however, for richness of berry producing edible plant species, or percent cover of either edible species groups. CCA showed that edible berry (p=0.004) (Figure A7 ) and root (p=0.001) species composition were also influenced by the interaction between AHM and harvesting treatment. Harvesting Effect Harvesting treatment had no effect on overall species richness. When richness was examined by functional group, however, harvesting treatment affected species richness in the bryoid (p=0.001), herb(p=0.006) and shrub (shrub=0.004), but not tree functional groups. Bryoids in the clearcut treatment were lower in richness than in the 60% retention (p=0.016) 22 or control treatments (p<0.001) but did not differ from the seed tree or 30% retention treatments (Figure ). Herb richness was greater in the clearcut (p=0.020) and seed tree treatments (p=0.030) than the control (Figure 2.4). Shrub richness was greater in the clearcut (p<0.001), seed tree (p=0.030), and 30% retention treatments (p=0.020) than the control (p=0.020) (Figure 2.4) Harvesting treatment affected percent cover of the bryoid functional group (p=0.03), with total bryoid cover lower in the clearcut (p=0.016) and seedtree (p=0.025) treatments than the control (Figure 2.5). Harvesting treatment had no effect on whole plant community, total herb or shrub cover or on Shannon’s diversity for the whole plant community or any of the other functional groups. Harvesting treatment had no effect on percent cover or species richness of non-native species. However, harvesting did influence richness of species associated with disturbed habitats ( p <0.001) (Figure 2.6a ). More species were associated with disturbed habitats in the clearcut (p= 0.002), seed tree (p< 0.001), 30% retention (p<0.001) treatments compared to the control, but there was no significant difference between the control and 60% retention treatment. Percent cover of species associated with exposed mineral soil followed the same pattern, with greater cover in the clearcut than the control (p=0.010) (Figure 2.6b). Harvesting treatment had no effect on percent cover of plants that produce edible berries, where average percent cover of berry-producing plants remained at 2%, Species richness of berry-producing plants was also not affected by harvesting, with approximately five species in each treatment. Harvesting did not affect percent cover of plants with edible roots. 23 Indicator species analysis showed that the clearcut had the most unique species, with 33 species indicatives of clearcuts. The control and 30% retention treatment also had a high number of unique indicator species. When analyzing combinations of harvesting, any harvesting activity led to a shared number of 24 indicator species between the treatments. The low retention treatments (clearcut and seed tree) shared nine indicator species whereas the high retention treatments (60% retention and control) were less unique in composition (Table 2.3). Appendix B.1. shows indicator species for each treatment and combination of treatments. Climate Effect Overall, species richness tended to increase as aridity levels rise (p <0.001). However, when we considered the effect of AHM (Annual Heat-Moisture Index) separately, higher levels of aridity are associated with lower species richness, as indicated by the negative coefficient (-0.159) (Figure 2.6 a). Species richness of all functional groups was also affected by AHM three years after harvest. The richness of bryoids (p=0.007), shrubs (p=0.005) and trees (p< 0.001) declined with AHM, whereas richness and cover of herbs increased (p=0.005) (Figure 2.7 (b, c, d, e)). Shannon’s diversity index for trees was negatively affected by AHM (p <0.001), whereas that for byroids, herbs or shrubs was not affected. Aridity had no impact on percent cover, but it increased the species richness of non-native species, with richness especially in more arid sites (p=0.011). Richness and percent cover of species associated with exposed mineral soil (p=0.009, p= 0.004) (Figure 2.8a, 2.9a) or moder humus form (p <0.001, p<0.001) (Figure 2.9b, 2.9d) were greater at sites with high 24 aridity, as was richness of species associated with mor (p<0.001)(Figure 2.9 c) and mor moder humus forms (p <0.001)(Figure 2.9 d). Indicator species analysis showed that sites closer in AHM shared more indicator species in common (Table B2). The interaction of AHM and SMR for CCA of berry producing species showed that moisture demanding species are grouped together at the most humid coastal site (p=0.041), and include Rubus spectabilis and Vaccinium alaskaense. At the more arid sites, such as John Prince, Jaffray and Alex Fraser, species with lower SMR are grouped together and include Amelanchier alnifolia, Fragaria virginiana, and Shepherdia canadensis (Figure A8). 2.4 Discussion Interactive effects of climate and harvesting The effects of harvesting treatment on the richness, diversity, and total cover of each plant functional group did not vary with regional climate, indicating that harvesting treatments generally had similar impacts on the plant community regardless of climatic aridity. In other words, regional climate did not affect the general responses of the plant community to harvesting intensity. However, indicator species analysis and multivariate ordinations revealed nuanced changes among individual species within functional groups. Abundance of specific moss species was more sensitive to harvesting at sites with greater humidity, such as Malcolm Knapp and Narrows, than at the arid sites. The moss species that decreased at the Malcolm Knapp site with harvesting were shade tolerant, and included Hylocomium splendens, Kindbergia praelonga, and Rhizomnium glabrescens. At more arid sites such as Alex Fraser, composition of mosses was similar among treatments, 25 containing Pleurozium schreberi, Dicranum scoparium, and Rhytidiadelphus triquetrus, all sun and shade tolerant species. It was surprising that neither the abundance, richness, nor diversity of the bryoid functional group declined with harvesting intensity at the most arid site, Jaffray. One possible reason for this outcome is that some mosses such as Ceratodon purpureus, Polytrichum piliferum, Funaria hygrometrica, and Leptobryum pyriforme, which frequently establish after fire, can also quickly colonize disturbed clearcut areas. These mosses may have traits that enable them to withstand increasing disturbance intensity, even under higher aridity conditions. Herb and shrub species that declined with harvesting at the coastal site included Listera cordata and Gaultheria shallon. At the wettest interior location, Narrows, there was a decrease in the shade tolerant wintergreen species, Pyrola asarifolia and Moneses uniflora, with harvesting. Shade-intolerant herb species, Achillea millefolium and Cirsium arvense, were present in almost all harvesting treatments at the two most the arid locations, Alex Fraser and Jaffray. It is possible that in the more arid sites, the control treatments still receive more sunlight than those at the humid locations, which results in less of a species composition shift in terms of light requirement. Average soil moisture regimen (SMR) of indicator herbs generally decreased as AHM increased, as expected. In the controls, SMR of the indictor plants varied predictably as AHM increased. The interaction between harvesting treatment and average SMR of herb species revealed that average SMR of the indicator plants decreased with increasing site aridity especially in the clearcut and low retention treatments. In the control treatment, however, the SMR remained relatively stable as AHM increased, providing further support of our first hypothesis. The clearcut at our arid site, Jaffray, contained moist to wet indicator 26 species such as Calamagrostis canadensis and Viola renifolia. At this arid site, removal of the overstory would have severely reduced canopy evapotranspiration, and thus increased soil water content, which would have been retained in the fine textured Luvisol soils. Nevertheless, at Jaffray, average SMR of indicator plants was still higher in the control than in the clearcut. At our most arid site, Alex Fraser, the clearcut contained xeric indicator species such as Festuca campestris and Cerastium arvense, whereas average SMR of plants in the control treatment was greater. Thus, our indicator species analysis appears to align with our first hypothesis that overstory trees can protect more moisture-dependent species in arid sites. Removing the overstory at Alex Fraser would have severely reduced the canopy trees that provide shade to the vegetation below, resulting in reduced direct sunlight impact. This shade helps lower temperatures and decreases the rate of evaporation from the soil and transpiration from the plants beneath the canopy. At the coastal site, the average SMR value of the plants was higher in the lower retention treatments than the control and 60% retention. This particular finding appears to align with our hypothesis that in more humid locations, overstory trees may be less necessary to moderate the harsh effects. Although overall SMR value was higher in the low retention treatments at Malcolm Knapp, there were also species present with lower, drier SMR values. Species such as Cirsium brevistylum, Hypochaeris radicata, and Ribes sanguineum for example, increased in the clearcuts compared with the controls. This suggests that clearcutting on the coast can result in increased moisture deficiency, where the coarse-textured Podzolic soils were unable to retain the excess soil water from reduced canopy evapotranspiration. This could have exacerbated coastal climatic drought conditions, driving a shift in the composition towards species adapted to a lower SMR. In combination, these results partially support our first hypothesis, where overstory 27 trees appear to moderate the harsh conditions of clearcutting, but in this analysis, they contradict the second part of the hypothesis in that this moderation by the overstory appears just as important on humid as arid sites. Interactive harvesting and climate effect on food plants Harvesting treatments had an impact on the abundance of individual plant species that produce edible berries important for traditional use. The interaction between harvesting treatment and AHM was also significant in affecting the composition of berry-producing plant species. These results support our third hypothesis that the response of traditionally important food plants to the retention treatments will vary with climate and depend on the autecology of each species. At the coastal site, Malcolm Knapp Research Forest, the control had the highest percentage of berry-producing plant species, including Gaultheria shallon, Vaccinium parvifolium, and Vaccinium alaskaense. Canopy openings, by contrast, increased the abundance of Vaccinium ovatum, with cover greatest in the 30% and 60% retention treatments. Rubus ursinus was also greatest in the 30% retention treatment at Malcolm Knapp Research Forest. The presence of edible berry-producing species in the 30% and 60% retention treatments aligns with the idea that moderate canopy openings can promote the growth of certain culturally important food plant species. In contrast, at sites with high AHM, including Alex Fraser Research Forest, the clearcut had a higher percentage of berry-producing plant species than the control, and included species such as Rubus parviflorus and Vaccinium membranaceum. The seed tree and clearcut treatments also had a high presence of Fragaria virginiana. 28 Together, these results show that it may be beneficial to maintain canopy cover in areas of lower AHM to preserve shade-tolerant berry shrubs such as Vaccinium alaskaense, whereas creating canopy gaps will favour shade intolerant berry shrubs and herbs in more arid regions. Indigenous groups have acknowledged and implemented the importance of open and moderate canopy structures in their forest gardens, where localized areas have been cleared of large conifer trees for gardening (tending, weeding, pruning)(Armstrong et al., 2021). These ideas should be considered when restoring forest biodiversity to coniferous forests. Harvesting treatment Species richness was consistent across harvesting treatments, contradicting our second hypothesis that treatments would diverge. Our results differ from findings elsewhere that there is higher species richness, diversity, and cover in clearcuts due to greater light availability from the reduced canopy (Battles et al., 2001; Heithecker & Halpern, 2007). The stability of species richness and abundance despite the effects of harvesting, however, could be due to shifts in species composition. Harvesting can initiate ecological succession; as succession progresses, certain species may become dominant while others decline or are replaced by new colonizers without impacting overall species richness and abundance (K. J. Anderson, 2007). We found that the bryoid functional group declined with decreasing overstory retention, aligning with the findings of Halperb et al. (2012). In contrast, herb and shrub richness increased with decreasing tree retention, likely because many species were early-successional pioneers that respond positively to reduced overstory cover and increased soil disturbance (Halpbert et al., 2012). However, we also found that total species loss in 29 harvesting treatments was greater than anticipated, particularly due to the loss of moss and lichen species. The decline in bryoid species coupled with the increase in shrub and herb richness with harvesting intensity masked any harvesting effects on total richness. Community composition converged with the control at high overstory retention levels, and contrasted with composition in the clearcut and low retention treatments, supporting our second hypothesis. Even small-scale harvesting in the seed tree and 30% patch retention led to distinct changes in species composition compared to the unharvested control. This outcome was verified by the multiple (23) indicator species shared across all partial retention treatments. However, it is important to note that clearcutting had the highest number of unique indicator species (26), with the control the second highest number (24). These findings show that any harvesting will shift the composition, but clearcutting results in the most unique composition compared with all other treatments. This highlights the importance of keeping some large overstory trees, even in small numbers, to provide conducive conditions for plant community recovery. Responses of key indicator species to overstory tree removal provide deeper insight into the effects of harvesting on the plant community. Epilobium ciliatum was notably present in all harvesting treatments except the control at each location, indicating that any amount of harvesting can create an environment suitable for this early successional species. The preference of Epilobium ciliatum for disturbed areas, rapid growth, and ability to thrive in nutrient-rich soils contribute to its prevalence in the harvested sites (Douglas, G.W., et al., 1998). For the low to high retention levels (seed tree, 30%, 60%, and control), we observed eight indicator species shared between treatments, including Peltigera aphthosa. The scarcity of lichens and mosses in the clearcut treatments three years after harvesting, however, along 30 with their limited presence in the seed tree treatment, highlights the sensitivity of lichens to severe disturbances and specific environmental conditions. This finding emphasizes the importance of retaining overstory trees when harvesting to maintain specific habitats and support sensitive species like lichens. The loss of old-growth trees, which support lichen species, also poses a threat to endangered species like the southern mountain caribou (Goward et al., 2022). These overstory retention trees can act as refuges for species, including providing propagules and appropriate habitat, contributing to biodiversity in harvested areas (Sayer et al., 2004). Harvesting treatment affected species indicative of disturbed habitats. The clearcut and seed tree treatments had more species associated with disturbed environments compared to the 60% retention and control treatments. Invasive grasses like Agropyron repens in Alex Fraser research forest and the perennial Lolium perenne in the 30% retention treatment at the Narrows and Redfish sites, are harmful agricultural pests in the northern hemisphere and negatively impact native diversity in habitat restoration (Choi & Pavlovic, 1994; Holm et al., 1997; Choi & Pavlovic, 1998). The presence of Agropyron repens can change ecosystem dynamics and significantly impact native diversity in northern sites like the Alex Fraser Research Forest, reducing biodiversity and causing ecological disruption. Invasive species alter the composition and structure of plant communities when they dominate and outcompete native plants (Choi & Pavlovic, 1994; Holm et al., 1997; Choi & Pavlovic, 1998). Species such as Agropyron repens may also alter succession and hinder Douglas fir forest regeneration. Solidago canadensis is a significant invader in the John Prince Research Forest. Its presence can lead to soil degradation (Baranová et al., 2017) and it competes strongly with native vegetation, dominating plant communities (Sun et al., 2006; Abhilasha 31 et al., 2008; Yuan et al., 2013). The species releases secondary metabolites through roots and rhizomes, potentially contributing to its success as an invader (Sun et al. 2006; Abhilasha et al. 2008; Yuan et al. 2013). However, it benefits insects like honeybees during its flowering period (Jachuła et al., 2019). The low retention treatments at the Malcolm Knapp Research Forest were invaded by Hypochaeris radicata and Rubus laciniatus. Hypochaeris radicata in particular displaces native species, especially annual plants in open areas, with its dense rosette of long leaves. Its deep taproot and water-drawing ability give it a competitive advantage in arid or semi-arid habitats (Son et al., 2023), which were enhanced with clearcutting at the coastal location. Invasion by these disturbed habitat species can alter ecosystem dynamics and impact the composition and structure of plant communities. Harvesting treatments affected composition of plants indicating ground surface materials (i.e, exposed mineral soil, moder and mull humus form). In clearcuts, there was a higher abundance of species associated with exposed mineral soil and the mull humus form. The indicator species analysis revealed that Calamagrostis canadensis, Hieracium scouleri, Carex mertensii, and Alnus crispa, all associated with exposed mineral soil, increased in the clearcuts. The John Prince Research Forest experienced a significant increase in Equisetum arvense and Rubus idaeus with clearcutting, both indicators of disturbed and exposed mineral soil, as well as wet and mesic habitats. The loss of moss species and plant species indicative of moder humus ground surface material with soil disturbance provides an opportunity for these other invasive species to thrive. Exposed mineral soil, along with overstory removal, may also enhance water absorption in areas such as the John Prince Research Forest, and promote the growth of wet species. Such species, like Rubus idaeus, may hinder the regeneration of shade-intolerant conifers and alter the composition 32 and structure of the forest ecosystem. The clearcut and seedtree treatments at Redfish resulted in a significant increase in species indicative of exposed mineral soil, such as Fragaria virginiana and Hieracium albiflorum. Fragaria virginiana indicates water-shedding sites, where water tends to run off the surface instead of being absorbed into the soil. Harvesting activities greatly reduced the percent cover of bryoids at Redfish. Studies have shown that shrub and herb biomass are highest in areas with shallow moss, where soil moisture levels appeared to have been the highest (Gornall et al., 2011). Moss can enhance soil moisture availability during drought periods and contribute to higher nutrient levels. The removal of moss with logging may therefore result in drier, warmer and more aerated soils (Sohlberg & Bliss, 1987) that are prone to invasion by herbs. Preserving an intact forest floor to prevent water loss from the soil may also be crucial for regenerating forests, especially as the climate warms and drought becomes more prevalent. Increasing harvesting intensity was associated with a shift toward indicator plant species with higher light requirements. The clearcut, seed tree, and 30% aggregated retention treatments had more shade-intolerant plants such as Agropyron spicatum, Ceanothus sanguineus, Hierochloe odorata, and Rubus armeniacus compared to the control and the 60% retention treatment. These shade-intolerant species are fast-growing and can alter ecosystems. Retaining 60% of overstory trees met the threshold for maintaining the most shade-tolerant plants compared with the control, but leaving only 30% of trees shifted plant community composition toward more shade-intolerant species. Maintaining a 60% overstory threshold could be essential in preserving biodiversity. The 60% retention treatment and control shared the shade tolerant wintergreen indicator species, Pyrola chlorantha and Pyrola asarifolia. In the coastal sites, light-intolerant mosses such as Kindbergia praelonga and 33 Rhizomnium glabrescens were also conserved in the 60% retention and the control treatments. Research has shown that mosses and lichens, despite their small size, have functional impacts disproportionate to their biomass. They are responsible for sequestering one-third of the world's terrestrial carbon as they regulate water tables, cool soils, and inhibit microbial decomposition (Smith et al., 2015). Climate Species richness, particularly of bryoids, and except for herbs, decreased with climatic aridity. However, Shannon’s diversity index was unaffected by annual heat moisture index. We found the total percent cover remained relatively consistent throughout the aridity gradient. There was a three-year post-harvest trend, however, indicating that as annual heat moisture increased, the percent cover of herbs also increased. These findings suggest that while aridity might have some influence on percent cover, harvesting is a stronger factor affecting the distribution and abundance of vegetation. At the more humid locations such as Malcolm Knapp Research Forest, due to treatment the harvested plots had higher species richness than the control. As the aridity of the site locations increased, however, there was less of a difference in species richness, particularly among herbs and shrubs, between the harvesting treatments. Plant community composition changed with increasing aridity, where drought-tolerant species were favoured over moisture-dependent species at our arid sites. At locations with higher AHM, such as Jaffray, plants with lower soil moisture regime (SMR) were more abundant than at than more humid sites including Malcolm Knapp and Narrows. The arid sites, Jaffray and Alex Fraser, shared drought tolerant species, Achillea millefolium, Antennaria racemosa and Tragopogon 34 dubius, whereas the coastal site at Malcolm Knapp Resarch Forest had more moisture dependent species such as Rubus spectabilis. These findings suggest moisture-dependent species will become increasingly stressed as arid conditions increase with high intensity harvesting and climate change, potentially reducing their abundance. Early seral environments of drought tolerant species, which are prone to high-severity fire, can hinder the return of mature composition and increase fire frequency and severity (Cochrane & Laurance, 2008; Thompson et al., 2007; Taylor et al., 2014; Bowd et al., 2018). In contrast, drought-tolerant species should increase in areas with higher AHM values due to their ability to grow under water limited conditions. Given the ongoing changes in climate, these findings have important implications for plant community dynamics. As aridity levels continue to rise due to climate change, we can anticipate further shifts in plant species composition, potentially leading to alterations in ecosystem structure and function. Effect of climate on culturally important food plants Culturally and ecologically important berry-producing coastal plant species, such as Vaccinium ovalifolium and Rubus spectabilis, which occur on fresh to moist soils, may be impacted by an increase in aridity with climate change. Rubus spectabilis benefited from the canopy openings and was found in the highest percentage in the 30% retention treatment. However, it is possible that as aridity increases, this local coastal species will not be able to adapt to drier soils that may occur in these openings. Other coastal berry-producing species, such as Rubus ursinus, may be able to adapt to more arid environments due to their lower moisture requirements. Interior moisture-dependent berry-producing species, such as Ribes hudsonianum, may also be impacted by an increase in aridity. This species was of lowest abundance in the clearcut at Alex Fraser, one of the most arid sites. However, it occurred in 35 highest abundance in the clearcut at the more humid site, John Price Research Forest, where clearcutting increased soil moisture availability. It is possible that the exacerbation of aridity with harvesting at some sites will cause these species to decline. Conversely, an increase in aridity may increase the abundance of the cherry species, Prunus pensylvanica, which occurred in the low retention treatments at Alex Fraser and Redfish. Amelanchier alnifolia is another interior berry-producing species that can tolerate an increase in aridity. It was observed in all treatments at the four most arid sites (except the control and 60% retention). While species may be able to persist in more arid conditions, their productivity, particularly in terms of berry production, can be impacted by soil moisture (Krüger et al., 2000; Glass et al., 2005). 2.5 Conclusion The effects of harvesting treatment on the total cover and species richness of the plant communities were consistent across regional climates, indicating that climatic aridity did not significantly impact the generalized response of the plant community to harvesting intensity. Surprisingly, total abundance, richness, and diversity of the bryoid functional group also did not decline with increasing harvesting intensity at the most arid locations, potentially due to the ability of certain moss species to quickly colonize disturbed areas. In spite of these generalities, specific plant species showed greater sensitivity to harvesting at the humid sites, while composition remained more similar among harvesting treatments at arid sites. Clearcutting resulted in plant communities with the most unique composition and the most widely divergent from the uncut control forests. The 60% retention treatment, by contrast, resulted in species compositions similar to the controls, potentially representing a 36 threshold overstory cover for protecting sensitive plant communities. The presence of certain indicator species, particularly lichens, mosses and wintergreen herbs, highlighted the importance of retaining overstory trees to support sensitive species and maintain biodiversity. These findings suggest that while aridity influences plant community composition, harvesting also plays a significant role, and clearcutting combined with increasing aridity with climate change will result in large shifts toward greater dominance by invasive herbs and diminishment of important traditional berry producing food plants. The study underscores the importance of retaining overstory trees to maintain original structure and composition of the understory plant community, through preserving vital ecological processes and creating microhabitats that support native plant species. Additionally, it highlights the role that increasing climatic aridity with climate change will play in shaping species composition and diversity. As aridity intensifies due to climate change, we can anticipate further shifts in plant communities, emphasizing the need for strategic conservation efforts, such as a shift away from clearcutting to high retention harvesting systems, to protect biodiversity in changing environments. These plots need to remeasured every five to ten years to see if the trends have shifted or held, and this study provides valuable baseline data for future measurements. Overall, this research contributes to our understanding of the complex dynamics of plant communities under the influence of overstory tree retention and climate change, providing valuable information for sustainable forest management practices. 37 Table 2.1. Characteristics of the six study sites, including location, history, soil and climate. b Climate data was derived from ClimateWNA (1981-2010 averages). c Heat: moisture indices indicate the aridity of a site. A higher number means the climate is warmer and drier. Site name Jaffray Redfish Creek Narrows Creek Alex Fraser John Prince Malcolm Knapp No. of replicate sites 3 4 1 2 + 1 burned 3 3 No. of NFI plots 15 18 5 15 15 15 Geographic Variables Nearby town Cranbrook Nelson Nelson Williams Lake Fort St James Maple Ridge Longitude (degrees) 115.37 117.03 116.98 121.75 124.43 122.54 Latitude (degrees) 49.21 49.63 49.58 52.45 54.65 49.32 Elevation (m) 1080 900 1080 950 890 540 Site Variables Logging date June 2017 June 2017 Oct 2018 Nov 2018 Jan-Mar 2018 Jan 2018 Pre-logging NFI assessment (meas 0) 2017 2017 2018 2018 2017 2017 1st post-logging NFI assessment (meas 1) Aug 2018 July 2018 July 2019 July 2019 Sep 2018 Sep 2018 2nd post-logging NFI assessment (meas 2) “year 3” = 3 years after logging 2020 (year 3) 2020 (year 3) 2021 (year 3) 2021 (year 3) 2021 (year 3) 2021 (year 3) Biogeoclimatic variant IDFdm2 ICHdw1 ICHdw1 IDFdk3 ICHmk3 SBSdw1 SBSdw3 CWHvm1 Dominant site series 01 101/104 101/104 01 01 01/03 Dominant soil order Luvisol Podzol Podzol Luvisol Luvisol Podzol Mean annual precipitation (mm)b 618 874 1059 539 593 2701 Mean annual temperature 5.3 6.8 5.1 4.4 2.3 8.0 Summer heat: moisture indexc 68.2 65.7 51.4 60.0 57.7 24.5 Annual heat: moisture index 24.7 19.2 14.3 26.8 20.8 6.6 38 Table 2.2. Principal types of ground surface materials (Ceska & Scagel, 2011) Ground surface material Differentiating characteristic Mor humus forms (MOR) A layer approximately 5 cm (or more) of compacted (matted by fungal mycelia) organic materials (of more than 17% organic carbon), overlying mineral soil Moder and Mull humus forms A layer less than 5 cm of friable (partly fragmented or comminuted by soil fauna)organic materials (of more than 17%organic carbon), overlying mineral soil (anAh horizon in Mulls) Mineral soil (MS) Unconsolidated mineral materials with less than 1 7% organic carbon and particle size less than 2 mm in diameter; often temporarily covered by litter Table 2.3.Indicator species for each harvesting treatment and combination of harvesting treatments using the indispecies package in R. Treatment Count Clearcut 33 Control 28 30% Retention 25 All harvesting treatments (cc, seed, 30, 60) 24 Seed tree 21 60% Retention 17 Clearcut and Seed tree 9 60% Retention and Control 6 Clearcut and Seed tree and 30% 5 39 Figure 2. 1. Study locations spanning the distribution of interior Douglas-fir in British Columbia. Shows the five interior and one coastal (Malcolm Knapp) Douglas-fir Forest. 40 Figure 2. 2. Interaction plot illustrating the relationship between Annual Heat-Moisture Index (AHM) and Light Index, stratified by harvesting treatment, for the functional groups bryoids and herbs. Light index ranging from 0 to 1, where 0 indicates tolerance to deep shade and 1 indicates a requirement for full sun and an intolerance for shade. Anova results for light index as a function of the interaction between harvesting treatment and AHM for bryoids (p =0.05), for herbs (p=0.001). BRYOID HERB10 15 20 25 10 15 20 250.250.500.751.00AHMLight indexTreatment clearcut seed tree 30% retention 60% retention control 41 Figure 2. 3. Interaction plot illustrating the relationship between Annual Heat-Moisture Index (AHM) and average soil moisture regime (SMR) for herbs. The colored lines depicting fitted linear regression lines for different harvesting treatments. The mean for each treatment level is indicated by a maroon point. The categories for SMR ranged from 0 (very dry soil) to 8 (very wet soil). ANOVA results of the SMR of the herbs as a function of the interaction between AHM and treatment (p=0.020) 23456710 15 20 25AHMAverage SMRTreatment clearcut seed tree 30% retention 60% retention control 42 Figure 2.4 Boxplot showing the distribution of species richness across different harvesting treatments for bryoids, herbs and shrubs. The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1. ANOVA results for bryoids (p = 2.29E-05), herbs (p=5.63E-04), shrubs (p = 3.73E -03). 43 Figure 2.5. Percent cover of bryoids across harvesting treatments. According to the ANOVA results, the treatment had a p-value of 2.29E-05 when determining bryoid percent cover. A p-value of 2.29E-05 when determining bryoids percent cover. ****020406080Clearcut Seed tree 30% Retention 60% Retention ControlPercent Cover % 44 Figure 2.6. Effect of harvesting treatment on total species richness associated with a) disturbed habitats (p = 0.007) b). Percent cover of species associated with exposed mineral soil per treatment (p =4.91E-03). **********Kruskal−Wallis, p = 5.7e−05010203040clearcut seed tree 30% retention 60% retention controlTreatmentSpecies Richness 45 Figure 2.7. Relationship between Annual Heat-Moisture Index (AHM) and species richness for a) whole community, b) bryoids, c) herbs, d) shrubs, and e) trees. Each point represents the observed species richness within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model Species Richness ~ AHM, whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the covariates pre-richness and harvesting treatment. Predicted Slope: −0.164Slope 0.162010203010 15 20 25AHMSpecies Richnessa)Predicted Slope: −0.223Slope −0.04905101510 15 20 25AHMSpecies Richnessb)Predicted Slope: 0.471Slope 0.75510203010 15 20 25AHMSpecies Richnessc)Predicted Slope: −0.16Slope 0.0185101510 15 20 25AHMSpecies Richnessd)Predicted Slope: −0.067Slope −0.088024610 15 20 25AHMSpecies Richnesse) 46 Figure 2.8. Relationship between Annual Heat-Moisture Index (AHM) and a) percent cover of vegetation indicative of a) exposed mineral soil b) percent cover of vegetation indicative of moder humus soil. Each point represents the average percent cover within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model percent cover ~ AHM, whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the harvesting treatment. Predicted Slope: 0.011010203010 15 20 25AHMPercent Covera)Predicted Slope: 0.058020406010 15 20 25AHMPercent Coverb)LocationMKNARRFJPJAFAF 47 Figure 2.9. Relationship between Annual Heat-Moisture Index (AHM) and species richness of vegetation indicative of a) exposed mineral soil b) moder mor humus, c)mor humus, d) moder humus. Each point represents the average richness within a site (Malcolm Knapp, Narrows, Redfish, John Prince, Alex Fraser), with error bars indicating standard error. The blue line depicts the fitted regression line from the linear model percent cover ~ AHM, whereas the red line depicts the predicted relationship between Annual Heat-Moisture Index (AHM) and species richness while controlling for the harvesting treatment. Predicted Slope: 0.0882.55.07.510.010 15 20 25AHMSpecies Richnessa)Predicted Slope: 0.469510152010 15 20 25AHMSpecies Richnessb)Predicted Slope: −0.0952.55.07.510 15 20 25AHMPercent Coverc)Predicted Slope: 0.52401020304010 15 20 25AHMSpecies Richnessd)LocationMKNARRFJPJAFAF 48 Chapter 3: The interactive effects of harvesting intensity and climatic aridity on understory plant communities and Douglas-fir seedling performance 3.1 Introduction Forest regeneration is facing significant challenges worldwide, raising concerns about the future composition of forest ecosystems (Austin et al., 2020; Felipe-Lucia et al., 2018; Mo et al., 2023). Climate projections indicate dramatic changes, with northern latitudes experiencing the greatest impacts (Lee et al., 2023; Drever et al., 2021; Zhou et al., 2001; Hansen et al., 1999). The acceleration of climate change, coupled with intensive forest practices, poses a compounding effect on regeneration, particularly in clearcut areas (Moomaw et al., 2019; Moomaw & Law, 2023; Tian et al., 2023; Ripple et al., 2024). The loss of overstory trees, which act as buffers against climatic extremes, can exacerbate the vulnerability of new seedlings to frost or drought (Simard et al., 2021). To improve the success of regeneration efforts in the face of these challenges, innovative approaches to forest harvesting, such as partial retention cutting, may be designed on a local, ecosystem basis. The rate of climate change is outpacing the speed at which populations can naturally adapt or migrate, raising concerns about their ability to cope with new conditions, while also maintaining adaptation to other local environmental factors (Aitken & Bemmels, 2016). Extreme weather events, such as heat domes and unexpected late spring frost spells (Benomar et al., 2022), are becoming more frequent, potentially intensifying the challenges faced by tree species (Daniels et al., 2011). This can significantly impact the productivity and composition of forests through its effects on particular tree species and genotypes. While tree species and genotypes have adaptations to their local climates (Gougherty et al., 2021), some are expected to expand their range as climate changes, while others will experience increased 49 stress and mortality (Coops & Waring, 2011; Gray & Hamann, 2013; Rehfeldt et al., 2014; Mathys et al., 2018). For instance, populations from colder regions are often more frost-resistant but have lower growth potential (Bannister & Neuner, 2001). Conversely, those from warmer areas are more drought-resistant and have extended growing seasons, but may be less frost-resistant (Aitken & Bemmels, 2016; Sáenz-Romero et al., 2021). Climate change can result in maladaptation, where local populations become ill-suited to their changing environments (Aitken & Bemmels, 2016). Understanding these influences is vital for effective forest management and conservation as climate changes. To mitigate the effect of the decoupling of locally adapted tree species and genotypes from the changing environment, human-assisted migration has been proposed for forest regeneration (Sáenz-Romero et al., 2021). Assisted migration involves planting tree seeds or seedlings from sources with slightly warmer climates than the local site, considering projected future climates (Rehfeldt, 1988; Sáenz-Romero et al., 2021). This approach aims to reduce maladaptation and ensure the productivity and health of future forests. However, the effectiveness of this approach could depend on the harvesting methods (Simard, 2021). Forestry practices can interact with climate change, leading to novel conditions for seedlings. For example, intense harvesting practices such as clearcutting can exacerbate the impacts of climatic extremes, as regenerating seedlings face adverse conditions without sufficient shelter (Daniels et al., 2011). Reducing seedling vulnerability will be critical in mitigating the impacts of climate change on forests. Compared to adult trees, seedlings are more susceptible to small changes in precipitation and temperature. High temperatures, in particular, are detrimental to their growth and survival (Moran et al., 2019). Decreased soil moisture and direct tissue damage or needle drop can also significantly impact their 50 development (Kolb & Robberecht, 1996; Andrus et al., 2018). Therefore, understanding and addressing seedling vulnerabilities to forest harvesting will be crucial for the persistence of tree populations. The understory plant community plays a significant role in either competing for resources or facilitating seedling establishment and growth under stressful conditions or may have no influence at all. Climate variations can intensify resource limitations and competition on seedlings, making it more challenging for them to establish and survive. As aridity increases, however, according to the stress gradient hypothesis, facilitation ought to become more important than competition (Maestre et al. 2009). Nurse plants can positively influence tree regeneration by enhancing air humidity, preventing extreme temperature fluctuations, improving soil properties, and reducing mechanical or herbivory damage (Franco and Nobel 1988, 1989; Barton 1993). Nurse plants also offer shade, which can increase seedling survival and improve plant water relations in water-stressed environments. In arid environments, seedlings often grow under the canopy of larger plants, benefiting from reduced soil temperatures, thermal stress and competition (Turner et al., 1966; Franco & Nobel, 1989). The role of nurse plants ought to become even more crucial for seedling survival when climate change leads to higher temperatures and aridity. However, nurse plants can also have negative effects on seedlings. They can hinder seedling emergence through litter accumulation and restrict the growth potential of newly established plants by reducing light and soil water availability (Barton, 1993; Franco & Nobel, 1988, 1988). Some plants may even release allelopathic substances that inhibit the growth of other plants (Muller, 1953). Additionally, in certain situations, strong competition for soil moisture with 51 established grasses may outweigh the positive effects of the nurse plant canopy (Aguiar & Sala, 1994). Plant communities play a crucial role in enhancing water retention in the soil (Ryan et al., 2010). Plants extract water from the soil through their root systems, contributing to the redistribution of water within the soil profile (Prieto et al., 2012). This helps prevent excessive water loss through runoff and promotes water retention. This ability of vegetation to retain water is particularly beneficial for tree seedlings during drought conditions. Native plants with dense cover effectively reduce soil erosion and stabilize soil structure, facilitating water infiltration and maintaining soil moisture (Ryan et al., 2010). By promoting a healthy soil-water balance and preventing water loss, neighboring plants can help mitigate drought stress and provide essential moisture for the survival and growth of tree seedlings. Overstory trees greatly shape the characteristics and competitive dynamics of the understory plant community by affecting light, water, and nutrient availability. The density of the canopy can alter soil water availability through changes in water interception, water absorption by tree roots, and water distribution at the trunk and crown (Leuschner et al., 2001). Reduced light under trees can decrease the competitive effects of shade-intolerant grasses and promote the growth of more shade-tolerant tree seedlings (Huston, 1982; Holmgren et al., 1997). The canopy can also affect the depth of litter and the quantity of nitrogen and calcium in the humus layer (Beatty, 1984). The vitality and composition of species competing under a tree canopy significantly differ from those in open field conditions (Lüpke, 1987; Hannerz & Hånell, 1997; Wagner et al., 2011). In high-light environments, graminoids, in particular, are strong competitors, with growth rates and root density increasing markedly under better light conditions (Kull & Aan, 1997). Light-demanding or 52 early successional species benefit from gaps, edges, or openings in the canopy (Anderson et al., 1969; Fischer et al., 2002; Harrington, 2006). The overall objective of this study was to examine how regional climatic aridity, overstory harvest intensity and planted seedling genotype influence the relationships between seedling performance and understory plant communities. More specifically, we examined how growth of locally sourced or migrated seedlings ranging from the Northern location of Fort St. James and the southern location of Jaffray, were affected by the abundance and species richness of the neighboring plant community in response to overstory canopy cover and climatic aridity. Four hypotheses were tested. Firstly, we hypothesize that harvesting treatment and aridity interactively affect seedling neighborhood percent cover and species richness. Secondly, we hypothesized that seedling height growth would benefit from overstory cover in arid climates, particularly where seedlings were migrated from less arid climates. Thirdly, the understory plant community would compete with tree seedlings in clearcuts, but this negative effect would be mitigated through increased overstory cover and changes in plant composition in the partial retention treatments. Fourthly, seedlings migrated from distant regions would require more protection than local genotypes from overstory trees and the plant community. Lastly, Douglas-fir genotypes migrated from warmer climates would benefit from frost protection while genotypes from colder climates will benefit from drought protection provided by overstory trees and the neighboring plant communities. By testing these hypotheses, this study seeks to provide insights into the relationships between understory plant communities resulting from different harvesting treatments, and the performance of Douglas fir seedling genotypes, thus informing the design of management practices for promoting forest regeneration in the face of a changing climate. 53 3.2 Methods 3.2.1 Study sites The study was conducted at three interior Douglas fir (Pseudotuga menziesii var. glauca) forests in interior British Columbia: Jaffray, Redfish, and the John Prince Research Forest. The three forests were 116-129 years old at the time the study was initiated, and all had originated from stand replacing wildfires. They covered a climatic drought stress gradient, with mean annual temperature (MAT) ranging from 2.3 to 6.8°C and mean annual precipitation (MAP) ranging from 532 to 683 mm (Table 3.1 and Figure 3.1). The sites occurred in the Interior Douglas-fir (IDF), Interior Cedar-Hemlock (ICH), and Sub-boreal Spruce (SBS) biogeoclimatic (BEC) zones, and were located from south of Cranbrook (49.21 °N, 115.37 °W) to north of Fort St James (54.65 °N, 124.43 °W). Douglas-fir made up 61-83% of the pre-harvest basal area (BA). Each forest contained other tree species, including: ponderosa pine (Pinus ponderosa) in the IDF zone; hybrid white spruce (Picea glauca x engelmannii) in the SBS; and western larch (Larix occidentalis), western red-cedar (Thuja plicata (Donn ex D Don), western hemlock (Tsuga heterophylla (Raf.) Sarg.), and grand fir (Abies grandis (Dougl. ex D. Don) Lindl.) in the ICH zone. Prior to logging, the understory was dominated by grasses at the most arid location and shrubs/herbs at the other two locations. All sites had a mesic soil moisture regime (relative to their respective BEC zone), warm aspect, gentle slope gradient (< 30 percent) and mid-slope position. Elevations varied from 870 to 1100 m. The soil texture tended to be coarsest in the ICH and finest in the SBS zone. The dominant soil order was Podzol in the ICH and Luvisol in the IDF and SBS zones (Soil Classification Working Group, 1998). Climate data for each site was obtained from 54 Climate North America, based on their latitude, longitude and elevation, using the 1981–2010 climate normal dataset (Wang et al., 2016). Each climatic location was represented by Annual Heat Moisture Index (AHM), a measure of climatic aridity, where AHM is calculated as: (1) AHM = (MAT+10)/(MAP/1000) Harvesting treatments and experimental design Five forest retention treatments were replicated three times at each site location (however, there were only two replicates of the 60% retention at the John prince site), in a hierarchical nested randomized block experimental design, where the harvesting treatments represented a gradient of overstory tree retention and were nested inside the site locations that together represented a climatic aridity gradient. The individual harvesting treatments were applied to 4-5 ha areas at each location in 2017-2019, and these included clearcut (0% retention), seed tree (about 10% uniform dispersed retention with 25 trees/ha retained), small patch (30% aggregated retention, where 30% of the stand was unharvested in 30-40m patches), large patch (60% aggregated of the stand retained and thinned from below), and uncut control (100% retention). Only the clearcut, seed tree and 60% retention treatments were assessed in this study. These treatments were selected to enable a comparison between the treatment with the highest retention and the one with the lowest retention (seed tree). The clearcut was included to represent the most intense harvesting practice. The control was excluded since it did not contain any planted seedlings. Harvesting was carried out using feller bunchers, but trees too large for the machine were hand-felled. The 60% retention 55 blocks were thinned from below by reaching into the uncut patches with the feller-buncher and removing the smaller stems. Trees were limbed and topped but not debarked before the boles were skidded to roadside landings, leaving needles, twigs and branches distributed relatively evenly throughout the forest after harvesting. Provenances In this study, we evaluated the growth and health of five Douglas-fir seedling provenances, these included seedlings from the survey sites: Redfish, John Prince, and Jaffray, as well as two additional provenances from Kamloops and Alex Fraser. The latter were selected due to their geographical positions between the southern Jaffray site and the northern John Prince site. As they originate from arid regions, they also provided a useful comparison to assess how genotypes adapted to dry conditions may be more resilient than the local genotype. We assessed all five provenances at each location and harvesting treatment (Table 3.2 and Figure 3.2). B class seed, collected from wild stands as defined by the BC Forest Service (1995), was used for all provenances. Planted seedlings Local and migrated provenances of interior Douglas-fir seedlings were planted in the central 1-ha measurement plot in each harvesting unit (Table 3.2). Planting was completed one year after harvest (Table 3.1). A planting crew randomly planted the seedlings, which were marked with a stake and flagging tape to indicate provenance. The five Douglas-fir provenances were assessed 4-5 years post-harvest (Table 3.2). 56 Seedling and neighbourhood measurements A systematic 10 x10 grid (100 points) was established within the central 1-ha plot of each harvesting unit, and from these, five grid points, at least 10 meters apart, were randomly chosen for measurements (Appendix 1). A 1.78 m circular (10 m2) neighborhood plot was centered on each of the five grid points. At each grid point, the height of the nearest seedling of each of the five genotypes was measured (to the nearest cm). The presence or absence of frost or drought damage was recorded as a binary yes or no variable. If a certain genotype was not within the 10 m2 plot area chosen plot, another grid point was randomly selected to identify a seedling for measurement. With the 10 m2 neighborhood plot, the abundance of each neighboring plant species was recorded, and total species richness was calculated. 3.2.2 Statistical Analysis All data analysis and graphics were completed in RStudio, version 1.3.1073. Neighborhood approach The interactions between seedling performance and the neighborhood plant community were evaluated using linear regression analysis. The slope of the regression was used to indicate the strength and direction of the interaction. If the slope was not significantly different from 0, it suggested no interactions were present. On the other hand, slopes greater than 0 indicated beneficial effects of the neighbours on the seedling, while slopes less than 0 signify competitive effects (Goldberg & Werner, 1983). 57 (2) P(T) = Y + XTN(AN) P(T) represents the performance of the seedling, such as height or damage indicator. AN denotes the characteristic abundance of the plant neighborhood, either in terms of percent cover or richness. Y is the intercept of the regression line, representing the performance of target individuals (P(T)) when the richness/cover of neighbors (AN) is zero. X is the slope of the regression line, showing the impact of the independent variable (richness/cover of neighbors, AN) on the dependent variable (performance of target individuals, P(T)) (Goldberg & Werner, 1983). We constructed predictive models to visualize the relationships between seedling height and climatic aridity (AHM) under different harvesting treatment conditions, and the relationships between seedling height and total percent cover or species richness. A coefficient plot was generated to visualize the estimated slope coefficients, including their confidence intervals, derived from the linear regression models. Here, the coefficients were standardized and centered for easier interpretation. The 60% retention treatment was used as the reference level for comparison against other treatment levels. Harvesting and aridity effects on the plant community and seedling performance The interactive effects of harvesting treatment and aridity (AHM) on the richness and cover the neighborhood plant community were evaluated first (Model 1). Next, we analyzed how harvesting treatment and aridity, as well as their interactions, affected Douglas fir seedling height, using neighborhood plant cover and richness as covariates (Model 2). Then, we incorporated genotype into the model to assess how harvesting treatment and aridity affected different genotypes, as identified by AHM of seedling source location. Here, the 58 genotype was analyzed as a continuous variable, represented by the AHM of the seedling source location (Model 3). For models 2-3, we used a full model initially, followed by a backward stepwise regression procedure to eliminate non-significant variables iteratively, thereby enhancing the model fit, as evaluated using the Akaike Information Criterion (AIC) (Appendix D). For all models we analyzed significance using ANOVA tests using the 'Anova' function with type 3 sums of squares (Table 3.3). It should be noted that Backward selection in modeling can result in various problems. It may increase bias for weak predictors, misrepresenting their actual relationship with the outcome. Frequently, it leads to a higher Mean Squared Error (MSE), signifying subpar model performance. Additionally, the uncertainty in the selection decision adds randomness, further increasing the MSE and reducing the model's reliability (Pearce & Ferrier, 2000) Generalized Linear Models (GLMs) were then used to evaluate the interactive effects of harvesting treatment, aridity, genotype, and plant neighborhood on the incidence of drought or frost on the Douglas fir seedlings (Model 4). Because incidence was a binary variable, it was modeled using a binomial distribution. The covariates included plot vegetation percent cover and species richness (Table 3.3). Seedlings that had died, where the cause of death was indeterminable, were omitted from the analysis. 59 3.3 Results Interactive effects of aridity and harvesting treatment on seedling neighborhood plant community The interaction between harvesting treatment and aridity was not significant for predicting understory neighborhood cover. Cover increased significantly with aridity (Figure 3.3 and Table 3.4) but was unaffected by harvesting treatment. For predicting plant species richness, there was a significant interaction between treatment and aridity (Table 3.5). In the clearcuts, species richness increased with aridity, but was unaffected in the seed tree treatment, and declined in the 60% retention treatment (Figure 3.4). Overstory cover benefits seedling growth in arid climates for seedlings migrated from less arid regions. In predicting seedling height with all genotypes grouped together, there was a significant interaction between aridity and harvesting treatment (Table 3.6). Seedling height increased with aridity in the 60% retention treatment, but it declined with aridity in the clearcut and seed tree treatments (Figure 3.5). In the full model the interactive effects of Douglas fir genotype (indicated by AHM of seedling source), site aridity, and harvesting treatment on seedling height were not significant (Table E1). However, when the model was reduced using best fit AIC values, the interactions between aridity of seedling source and harvesting treatment, along with the aridity of the site, were removed (Table E1). This reduction revealed a positive relationship between aridity of seedling source and seedling height (p<0.01). This suggests that seedlings sourced from more arid regions were taller (Table 3.7 and Figure 3.6). 60 Competition between understory vegetation and Douglas fir provenances seedlings mitigated with overstory tree retention. The main effects of harvesting treatment, the covariate of neighborhood species richness, the interaction between aridity and treatment and the interactive effects of plant neighborhood cover, harvesting treatment and aridity on seedling height were significant (Table 3.6), indicating that the plant neighborhood influenced the seedlings in different ways depending on the harvesting treatment and aridity of the site. Seedling height declined with increasing neighborhood cover in the clearcut treatments at the more humid forests, Redfish and John Prince, whereas it benefited from increasing neighborhood cover in the clearcuts at the most arid forest, Jaffray (Figure 3.7). Seedling height tended to decline with plant species richness across all harvesting and aridity treatments (Table 3.6, Figure 3.8). Douglas fir provenances from distant locations require more shelter to prevent frost and drought damage from both overstory and understory vegetation. Frost incidence was affected by the interaction between harvesting treatment, aridity, and neighborhood cover (Table 3.8, Figure 3.9). In spite of these interactions, the overall likelihood of frost incidence generally decreased with neighborhood cover, but increased with species richness (Table 3.8, Figure 3.9, Figure 3.10). At the arid site, Jaffray, seedlings were more likely to experience greater frost in the 60% treatment where neighborhood plant cover was low, than in the clearcut. The opposite was true at the more humid sites. Frost incidence varied by the aridity of the genotype source, with seedlings from more arid regions experiencing lower incidence of frost (Table 3.8 and Figure 3.11). 61 The interaction between harvesting treatment, aridity and neighborhood cover significantly affected incidence of drought (Table 3.9). Seedlings at Redfish and John Prince experienced more drought where there was low understory plant cover in clearcuts than in the higher retention treatments (Figure 3.12). Increasing neighborhood cover at these humid sites, however, reduced drought incidence. In contrast, seedlings at the most arid site, Jaffray, experienced more drought stress in clearcuts, where neighborhood plant cover was greater, than in the 60% retention treatment. The interaction between harvesting treatment, aridity and species richness was also significant in determining seedling drought incidence (Table 3.9). At the less arid Redfish and John Prince forests, seedlings in clearcuts were more likely to show signs of drought as species richness increased, whereas seedlings at the most arid Jaffray forest were more prone to drought where species richness was lower (Figure 3.13). Seedling genotype or seed source aridity did not influence drought incidence. 3.4 Discussion Plant neighborhood community and seedling growth. For the first hypothesis, 'Harvesting treatment and aridity interactively affect seedling neighborhood percent cover and species richness', we found an increase in species richness with clearcutting, but a decline in the 60% retention treatment in the most arid forest. The species that thrived were likely adapted to harsh, dry conditions (Anderson, 2007). In contrast, the plant species in the overstory retention treatments grew slower, were more shade-tolerant, and less competitive than those in the clearcuts (Macdonald & Fenniak, 2007). This indicates that the interplay between climatic aridity and harvesting treatment 62 significantly affects species richness, potentially enhancing the diversity of drought-resistant plants as the climate changes. We also found that percent cover understory plants increased as aridity increased, this could be due to increase in grasses and smaller forbs at the Jaffray site. An increase in percent cover at arid sites might lead to an increase in competition for growing seedlings. Our second hypothesis suggested that 'Seedling growth would benefit from overstory cover in arid climates, especially when seedlings were migrated from less arid climates'. In our most arid forest, we found seedlings grew taller under the 60% retention treatment compared to the clearcut or seed tree treatments. This supports the hypothesis that regeneration in hotter, drier climates benefits from overstory cover protection. We also found that genotypes originating from arid climates grew taller than those migrated from humid climates, and this height advantage was particularly pronounced at the most arid site. This finding supports our hypothesis that genotypes from less arid regions may benefit more from overstory cover and understory plant shelter. This finding indicates genotypes migrated from more arid climates may be more adaptable than those moved from colder climates to increasingly drier conditions with climate change, agreeing with Rehfeldt (1988) and Sáenz-Romero et al. (2021). Our third hypothesis suggested that 'The understory plant community would compete with tree seedlings in clearcuts, but this negative effect would be mitigated through increased overstory cover and changes in plant composition in the partial retention treatments'. As expected, seedling height growth declined where understory plant cover increased in the clearcuts in humid regions. At John Prince, for instance, seedling growth was most negatively affected in clearcuts where tall shrubby vegetation grew. Although tall plants can 63 provide seedlings with protection from harsh environmental conditions, they can also complete for light and hinder seedling growth (Craine & Dybzinski, 2013). In contrast, seedling growth benefited from increased plant cover with clearcutting in the arid regions. At Jaffray, the most arid site, the plant community in clearcuts was dominated by grasses and low-stature herbs, which would have been less competitive with seedlings for light. These findings support our hypothesis, that increased understory plant competition with clearcutting in productive environments may negatively affect seedling growth. On the other hand, the understory plant community was beneficial to seedling height growth in both the seed tree and 60% retention treatments, also agreeing with our second hypothesis that retention of trees could mitigate the effects of understory competition. In the overstory retention treatments, the plant species were slower-growing, more shade-tolerant and less competitive than those in the clearcuts (Macdonald & Fenniak, 2007). It appears that retention of even a small number of overstory trees can dampen increases in the understory plant community in productive environments, benefiting seedling growth. Frost incidence The reduction in seedling frost incidence with increasing understory plant cover and overstory retention supports our third hypothesis and suggests that understory plants can provide an insulating effect by creating slightly warmer or more humid microclimates for seedlings (Stickley & Fraterrigo, 2021). This aligns with Jordan & Smith (1995), who found that plants sheltered by a forest canopy experienced frost only 10-40% as often as those in the center of a meadow (Jordan & Smith, 1995). We found frost risk was greater in clearcuts, 64 where the protective effects of overstory trees were completely removed, than in the 60% retention treatment, particularly in less arid climates. The results of our study also partially support our fourth hypothesis that the plant community can act as a buffer against frost for seedlings that have migrated from further distances and adapted to different climates. Genotypes from more arid regions showed a reduction in frost incidence. Therefore, genotypes from more humid locations such as Redfish may benefit from the increased protection of surrounding vegetation. Drought incidence The relationship between cover of the plant community and drought incidence on seedlings fluctuated depending on climatic aridity and harvesting treatment, partially supporting our fourth hypothesis. At the humid sites, seedlings in clearcuts were more desiccated in open conditions, but less affected by drought where plant cover was greater, possibly because the plants provided protection against the sun (Turner et al., 1966; Franco & Nobel, 1989). At Jaffray, the most arid site, drought incidence was high in all harvesting treatments with low vegetation cover. However, it decreased with increased plant cover, except in the clearcut, where there was an increase in drought incidence among the abundant grasses and herbs. These plants were competitive with the seedlings for soil water (Elliott & White, 1987), and reductions in overstory tree cover that amplified understory grass cover (Hedwall et al., 2013) reduced seedling performance. These findings show that changes in the understory plant community, through manipulations of the overstory, influences the incidence of drought among seedlings. 65 Understory plant species richness was also positively associated with greater drought symptoms in more humid forests, but the reverse was true at the most arid site. A diverse plant community may compete more intensively for soil water (Casper & Jackson, 1997), especially where diversity is enhanced by clearcutting. However, this effect seems to be moderated by overstory retention. Notably, at the most arid site, greater species richness was associated with reduced incidence of drought in the clearcut, possibly because the diverse plant community shaded seedlings from the harsh sun. A more diverse plant community could potentially lead to fewer dominant grass species that compete with seedlings for water (Elliott & White, 1987). Considering the changing climate, clearcuts are expected to become more arid, leading to poorer conditions for seedling regeneration (Moomaw et al., 2019; Moomaw & Law, 2023; Tian et al., 2023; Ripple et al., 2024). Our findings emphasize the importance of maintaining overstory trees to provide shelter for plant communities and regenerating seedlings, protecting them from drought stress. Caveats In this study, we examined various interactions, including those between plant neighborhood cover/richness, harvesting treatment, and the aridity of the survey location. Gelman et al., (2020) noted that to achieve the desired statistical power for an interaction, we need 16 times the sample size required for the main effect. However, due to time and resource constraints, as well as the scale of the project, we only had a limited number of replicates for each genotype at each harvesting treatment and survey location. If we were only considering the main effects our sample size would be sufficient however, this limitation made it difficult to confirm the significance of interactive effects. Therefore, more research with a greater number of replicates is needed to verify these results. 66 Because of the complexity of our models backward selection was used in order to reduce and simplify the models(Ambler et al., 2002). However as noted, backward selection with interactions may result increased bias for weak predictors and higher Mean Squared Error (MSE), indicating poor performance. Uncertainty in the selection can add randomness, reducing the model's reliability. However, backward selection is a useful technique in data analysis, particularly when studying a new or poorly understood outcome (Hosmer and Lemeshow, 1989). This method evaluates individual covariates whose significance may be unknown. It also helps establish associations with the outcome that may not be well-defined. Therefore, it balances between minimizing confounding variables and avoiding the inclusion of variables that could increase bias (Pearce, J., & Ferrier, S. 2000; Hosmer and Lemeshow, 1989; Sauer). Backward selection procedure also accurately discards covariates that are independent of the response. It can also eliminate covariates that may influence treatment but are unrelated to the outcome (Vander Weele & Shpitser, 2011). For these reasons we thought it was an appropriate method. However, in order to reduce the bias for weak predictor of the covariates we recommend that further research should consider increasing sample size. 3. 5 Conclusion This study revealed several key findings. Seedlings were taller in the 60% retention treatment than the clearcut and seed tree treatments in the most arid climate, indicating an advantage to retaining overstory trees in arid forests. Conversely, seedlings performed better in the low overstory retention treatments in more humid climates, where protection from drought was less necessary. Genotypes from arid climates also grew taller than those from humid climates, indicating their superior adaptability to the progressively drier conditions brought 67 about by climate change. The relationship between plant community cover and drought incidence on seedlings varied depending on climate aridity and harvesting treatment. At the most arid site, drought incidence was generally high, but it decreased with increased understory plant cover, except in the clearcuts. In these areas, drought incidence increased due to potential competition by the plant community for water. Lastly, frost incidence decreased with increasing understory plant cover and overstory retention, especially in more humid climates. This suggests understory plants may create warmer microclimates for seedlings. The findings of our study underscore the importance of considering the interaction between climatic aridity, harvesting treatments and understory plant cover in forest management strategies, especially considering climate change. Retaining overstory trees during harvesting can mitigate understory plant competition and drought or frost incidence, reducing their harmful effects on seedling growth. As global climate shifts, clearcutting will increase the aridity of ecosystems, but overstory trees and understory plants can be managed in such a way to enhance soil water retention and reduce drought stress. By prioritizing retention of overstory trees and promoting healthy understory plant communities using climate-responsive strategies, forest managers can help enhance forest regeneration and foster more resilient forests for the future. 68 Table 3.1. Characteristics of the research sites. Redfish John Prince Jaffray Location details Partner Kalesnikoff Lumber John Prince Research Forest Canfor Logging date June 2017 Jan-Mar 2018 June 2017 Planting date June 2018 June 2018 May 2018 Survey date July 2022 Aug 2022 July 2022 No. of replicates 3 3 3 No. of plots 9 9 9 Geographic variables Nearby town Nelson Ft St James Cranbrook Latitude (oN) 49.63 54.65 49.21 Longitude (oW) 117.03 124.43 115.37 Elevation (m) 890-940 870-920 1060-1100 Site variables Soil texturea SL CL SiL, SiCL Soil orderb Podzol/Brunisol Luvisol Luvisol Biogeoclimatic variantc ICHdw1 SBSdw3 IDFdm2 Site series 101/104 01 01 Stand characteristics Age (years) 116 129 123 Disturbance history Wildfire Wildfire Wildfire Site index Fdd (m) 20.8 (0.5) 21.9 (2.3) 15.0 (0.9) Live basal area (m2 ha-1) 50.0 (4.3) 53.8 (9.7) 35.6 (0.7) Live volume (m3 ha-1) 504 (10) 637 (50) 248 (17) Percent Fd by basal area 61.4 83.4 78.4 Secondary tree speciesd Cw,Lw,Hw, Pw,Py,Bg,Ep Sx,Ep,Bl,Pw, At,Pl Lw,Pl,Py Climatic variablese MAT (oC) 6.8 2.3 5.3 SMMT (oC) 22.9 19.3 23.6 MAP (mm) 868 593 618 MSP (mm) 268 240 249 AHM 19.4 20.8 24.7 SHM 66.1 57.7 68.2 a CL = clay loam; L = loam; SL = sandy loam; SCL = sandy clay loam;, SiL = silt loam, SiCL = silt clay loam; gr = gravelly b Soil Classification Working Group (1998) c IDFdk3 = Fraser Dry Cool Interior Douglas-fir; IDFdm2 = Kootenay Dry Mild Interior Douglas-fir; ICHdw1 = West Kootenay Dry Warm Interior Cedar Hemlock; ICHmk3 = Horsefly Moist Cool Interior Cedar Hemlock; SBSdw1 = Horsefly Dry Warm Sub- 69 Boreal Spruce; SBSdw3 = Stuart Dry Warm Sub-Boreal Spruce; (Lloyd et al., 1990; Braumandl and Curran, 1992; Delong et al., 1993; Steen and Coupe, 1997) d At = trembling aspen (Populus tremuloides); Bg = grand fir (Abies grandis), Bl = subalpine fir (Abies lasiocarpa); Cw = western redcedar (Thuja plicata); Fd = Douglas-fir (Pseudotsuga menziesii); Ep = paper birch (Betula papyrifera); Hw = western hemlock (Tsuga heterophylla); Lw = western larch (Larix occidentalis); Pl = lodgepole pine (Pinus contorta); Pw = white pine (Pinus monticola); Py = ponderosa pine (Pinus ponderosa); Sx = hybrid spruce (Picea engelmanni x glauca) e Climate data are 1981-2010 averages obtained from ClimateWNA v5.50 (Wang et al., 2016). MAT = Mean annual temperature; SMMT = Summer mean maximum temperature; MAP = Mean annual precipitation; MSP = May–September precipitation; AHM = Annual heat: moisture index (AHM) = (MAT+10)/(MAP/1000); SHM = Summer heat: moisture index = (Mean warmest month temperature)/(MSP/1000) Table 3.2. Principal. 1981-2010 Climate averages for B Class Douglas-fir seedlots Seedlot Name MAP (mm) MAT (oC) AHM SHM John Prince B Class 575 2.4 21.6 59 Alex Fraser B Class 542 4.5 26.8 60 Jaffray B Class 588 5 25.5 75.8 Redfish Creek B Class 927 6.9 18.2 64.2 Kamloops B Class 398 15.1 36.0 80.7 70 Table 3. 3. Full and reduced models, where the neighborhood response variable was either percent cover or species richness of the plant neighborhood, treatment corresponded to the harvesting treatment, aridity referred to the annual heat moisture index (AHM) of the three survey locations, seedling height referred to the height of the Douglas fir seedlings and aridity of seedling source refers to the AHM of the seed source of the Douglas fir genotype. In model (4), "likelihood of a seedling showing signs of damage" is the predicted outcome of a seedling showing incidence of either frost or drought. Model # Model Statement Reduction Model 1 Neighborhood response variable ~ aridity * harvesting treatment Full Model 2 (Height without genotype) Seedling height ~ neighborhood species richness: harvesting treatment: aridity + neighborhood percent cover : Treatment: aridity Full Seedling height ~ neighborhood species richness+ harvesting treatment+ aridity + neighborhood species richness: aridity+ harvesting treatment: aridity +neighborhood percent cover+ neighborhood percent cover: harvesting treatment+ neighborhood percent cover: aridity: harvesting treatment Reduced Model 3 (Height with genotype) Seedling height ~ neighborhood percent cover + neighborhood species richness+ harvesting treatment: aridity: aridity of seedling source Full Seedling height ~ neighborhood percent cover + species richness + harvesting treatment + aridity + harvesting treatment: aridity+ aridity of seedling source. Reduced Model 4 (Signs of Frost) Likelihood of a seedling showing signs of frost~ neighborhood species richness: harvesting treatment: aridity + neighborhood percent cover: harvesting treatment: aridity + aridity of seedling source Full Likelihood of frost~ neighborhood species richness + aridity + harvesting treatment + neighborhood percent cover + aridity of seedling source + aridity * harvesting treatment + harvesting treatment *neighborhood percent cover + aridity*neighborhood percent cover + aridity* harvesting treatment * neighborhood percent cover. Reduced Model 4 (Signs of Drought) Likelihood of a seedling showing signs of drought~ neighborhood pecies richness: harvesting treatment: aridity + neighborhood percent cover: harvesting treatment: aridity+ aridity of seedling source Full Likelihood of a seedling showing signs of drought~ neighborhood species richness + aridity + harvesting treatment + neighborhood percent cover + neighborhood species richness* aridity + neighborhood species richness* harvesting treatment + aridity* harvesting treatment + harvesting treatment* neighborhood percent cover + aridity* neighborhood percent cover + neighborhood species richness*aridity*harvesting treatment + aridity *harvesting treatment *neighborhood percent cover Reduced 71 Table 3.4. ANOVA table total percent cover of the plant neighborhood as affected by harvesting treatment and the aridity (Annual Heat Moisture Index). Type 3 sum of squares. term sumsq df F-statistic p.value (Intercept) 5405.9 1 25.088 0.000 Harvesting treatment 154.2 2 0.358 0.699 Aridity 9350.2 1 43.394 0.000 Harvesting treatment: Aridity 114.8 2 0.266 0.766 Residuals 130577.456 606 NA NA Table 3.5 ANOVA table for species richness of the plant neighborhood as affected by harvesting treatment and the aridity (Annual Heat Moisture Index). Type 3 sum of squares. term sumsq df F-statistic p.value (Intercept) 1227.088 1 48.153 0.000 Harvesting treatment 552.414 2 10.839 0.000 Aridity 158.149 1 6.206 0.013 Harvesting treatment: Aridity 509.550 2 9.998 0.000 Residuals 15442.7184 606 Table 3.6 ANOVA table for seedling height as affected by neighborhood percent cover, neighborhood species richness, harvesting treatment, aridity, and interaction between neighborhood percent cover, harvesting treatment, aridity and neighborhood species richness and aridity. Type 3 sum of squares. term sumsq df F statistic p.value (Intercept) 742.752 1 0.913 0.340 Neighborhood species richness 3477.776 1 4.274 0.039 Harvesting treatment 44521.124 2 27.360 0.000 Aridity 45.195 1 0.056 0.814 Neighborhood percent cover 926.448 1 1.139 0.286 Neighborhood species richness: Aridity 2921.994 1 3.591 0.059 Harvesting treatment: Aridity 39671.102 2 24.379 0.000 Harvesting treatment: Neighborhood percent cover 13510.654 2 8.303 0.000 Aridity: Neighborhood percent cover 821.032 1 1.009 0.316 Harvesting treatment: Aridity: Neighborhood percent cover 13491.559 2 8.291 0.000 Residuals 486546.976 598 72 Table 3.7 ANOVA table for seedling height as affected by neighborhood percent cover, neighborhood species richness, aridity, aridity of seedling source, harvesting treatment, and the interaction between aridity and harvesting treatment. Type 3 sum of squares. term sumsq df F statistic p.value (Intercept) 1521.2 1 1.892 0.169 Neighborhood percent cover 634.5 1 0.789 0.375 Neighborhood species richness 5217.7 1 6.490 0.011 Harvesting treatment 40770.1 2 25.354 0.000 Aridity 3249.7 1 4.042 0.045 Aridity of seedling source 18792.1 1 23.373 0.000 Harvesting treatment: Aridity 35884.2 2 22.316 0.000 Residuals 484821.089 603 Table 3.8 ANOVA table for the probability of frost incidence as a function of neighborhood percent cover, neighborhood species richness, their interaction between harvesting treatment and aridity and aridity of seedling source. Type 3 sum of squares. term L.R Chisq statistic df p.value Neighborhood species richness 21.185 1 0.000 Aridity 20.555 1 0.000 Harvesting treatment 12.698 2 0.002 Neighborhood percent cover 0.462 1 0.497 Aridity of seedling source 7.105 1 0.008 Aridity: Harvesting treatment 12.138 2 0.002 Harvesting treatment: Neighborhood percent cover 6.384 2 0.041 Aridity: Neighborhood percent cover 0.767 1 0.381 Aridity: Harvesting treatment: Neighborhood percent cover 6.336 2 0.042 73 Table 3.9 ANOVA table for the probability of drought incidence as a function of neighborhood percent cover, neighborhood species richness their interaction between harvesting treatment and aridity. Type 3 sum of squares. term L.R Chisq statistic df p.value Neighborhood species richness 12.082 1 0.001 Aridity 6.830 1 0.009 Harvesting treatment 3.809 2 0.149 Neighbourhood percent cover 8.116 1 0.004 Neighborhood species richness: aridity 11.249 1 0.001 Neighborhood species richness: harvesting treatment 8.552 2 0.014 Aridity: harvesting treatment 3.672 2 0.159 Harvesting treatment: neighbourhood percent cover 9.425 2 0.009 Aridity: Neighbourhood percent cover 8.026 1 0.005 Neighborhood species richness: aridity: harvesting treatment 7.777 2 0.020 aridity: harvesting treatment: neighbourhood percent cover 10.031 2 0.007 Neighborhood species richness 12.082 1 0.001 74 Figure 3. 1Map of study site locations and genotype seed sources within the range of Interior Douglas-fir in British Columbia 75 Figure 3.2. Experimental design shows the survey locations as well as the three harvesting treatments and the Douglas- fir provenances planted at each location. 76 Figure 3.3 Predicted neighborhood percent cover as a function of aridity (Annual Heat Moisture index (AHM)) visualized using the model predictions. Red line is the slope after controlling for covariates, whereas blue line slope covariates are not included in the model. Refer to Table 3, ANOVA results show that aridity is significant in determining neighborhood percent cover (p <0.01). Predicted Slope: 3.033Slope 2.783025507510019 20 21 22 23 24 25Annual Heat Moisture IndexPercent Cover % 77 Figure 3.4 Predicted neighborhood species richness as a function of site aridity (AHM) and harvesting treatment visualized using the model predictions. Refer to table 4, ANOVA results show that interaction between treatment and aridity was significant (p<0.01). 10203019 20 21 22 23 24Annual Heat Moisture IndexSpecies RichnessTreatment Clearcut Seed tree 60% Retention 78 Figure 3.5 Predicted seedling height as a function of site aridity (AHM), and harvesting treatment, visualized using the model predictions. Refer to Table 5, ANOVA results for the interaction between harvesting treatment and aridity (p<0.01). 05010015019 20 21 22 23 24Annual Heat Moisture IndexHeight (cm)Treatment Clearcut Seed tree 60% Retention 79 Figure 3.6 Predicted seedling height as a function of aridity of seedling source, visualized using the model predictions. (Refer to Table 3.7, ANOVA results for aridity of seedling source (p <0.01). Predicted Slope: 0.93405010015020 25 30 35Genotype (AHM)Height (cm) 80 Figure 3.7 Predicted seedling height as a function of aridity, harvesting treatment, neighborhood percent cover, and their interactions, visualized using the model predictions. Refer to Table 5, ANOVA results for the interaction between harvesting treatment, neighborhood cover and aridity (p<0.01) aridity = 19.2 aridity = 20.8 aridity = 24.70 25 50 75 100 0 25 50 75 100 0 25 50 75 100050100150Percent Cover %Height (cm)Treatment Clearcut Seed tree 60% Retention 81 Figure 3.8 Predicted seedling height as a function neighborhood species richness, visualized using the model predictions. Predicted slope is coefficient of neighborhood species richness. Refer to Table 5, ANOVA results for neighborhood species richness (p <0.01). 82 Figure 3.9 Predicted probability of a seedling showing signs of frost as a function of neighborhood percent cover for each harvesting treatment at each of the survey locations (AHM 19.2= Redfish, AHM 20.8 = John Prince, AHM 24.7 = Jaffray). (Refer to Table 3.8, ANOVA results for the interaction between harvesting treatment, aridity, and neighborhood percent cover (p = 0.04)). aridity = 19.2 aridity = 20.8 aridity = 24.70 25 50 75 100 0 25 50 75 100 0 25 50 75 1000%25%50%75%Percent Cover %Probablity %Treatment Clearcut Seed tree 60% Retention 83 Figure 3.10 Predicted probability of a seedling showing signs of frost as a function of neighborhood species richness (Refer to Table 3.8, ANOVA results for neighborhood species richness (p <0.01)). Figure 3.11 Probability of frost incidence as a function of the aridity of the seedling genotype source (Refer to Table 3.8, ANOVA results for genotype (p =0.01)). 25%50%75%10 20 30Species RichnessProbablity %20%30%40%50%60%20 25 30 35AHM of Douglas fir provenanceProbablity % 84 Figure 3.12 Predicted probability of a seedling showing signs of drought as a function of neighborhood percent cover for each harvesting treatment at each of the survey locations (AHM 19.2= Redfish, AHM 20.8 = John Prince, AHM 24.7 = Jaffray). (Refer to Table 3.9, ANOVA results for the interaction between harvesting treatment, aridity, and neighborhood percent cover (p = 0.007)). aridity = 19.2 aridity = 20.8 aridity = 24.70 25 50 75 100 0 25 50 75 100 0 25 50 75 1000%25%50%75%100%Percent Cover %Probablity %Treatment Clearcut Seed tree 60% Retention 85 Figure 3.13 Predicted probability of a seedling showing signs of drought as a function of neighborhood species richness for each harvesting treatment at each of the survey locations (AHM 19.2= Redfish, AHM 20.8 = John Prince, AHM 24.7 = Jaffray). (Refer to Table 3.9, ANOVA results for the interaction between harvesting treatment, aridity, and neighborhood percent cover (p = 0.02)). aridity = 19.2 aridity = 20.8 aridity = 24.710 20 30 10 20 30 10 20 300%25%50%75%100%Species RichnessProbablity %Treatment Clearcut Seed tree 60% Retention 86 Chapter 4: Conclusion 4.1 Main findings We found that retaining some overstory trees can mitigate the severe effects of clearcutting, especially in arid regions, as they can protect moisture-dependent plant species. Yet, canopy trees also play a crucial role in moderating harsh conditions in humid regions and protecting sun-intolerant bryoids and herbs. The intensity of harvesting significantly influenced the understory conditions, with clearcut and seed tree treatments fostering similar plant communities and the high overstory retention treatments resembling the uncut controls. We found that retaining overstory trees was especially vital for supporting sensitive species, particularly lichen and mosses. Additionally, the study confirmed that the abundance and composition of culturally important food plants vary based on each species' autecology and the prevailing climatic conditions. Notably, moderate canopy openings in the 30% and 60% retention treatments promoted growth of certain species like Vaccinium ovatum in coastal sites, whereas clearcut treatments in arid sites had a higher percentage of certain berry plants, indicating low retention treatments can increase growth of shade-intolerant berry shrubs and herbs in such regions. Our results supported the hypothesis that seedling height growth benefits from overstory cover in arid climates. In these hot, dry climates, the presence of overstory trees had a positive impact on seedling height growth. We also found that increased overstory cover and changes in plant community composition with partial tree retention reduced the competitive effect of the understory community on planted seedlings . This was confirmed by the significant interaction between neighborhood percent cover, treatment, and aridity, 87 suggesting that the impacts of harvesting treatments on seedling performance and understory plant communities vary with the climatic aridity level of the study areas. In clearcut treatments at more humid sites, plant community competition seemed to have the most negative impact on seedling growth. Clearcut treatments in arid sites with high vegetation cover also resulted in higher drought incidence among seedlings, likely due to increased competition for water. We also hypothesized that Douglas-fir genotypes from warmer climates would benefit from frost protection, while those from colder climates would benefit from drought protection provided by overstory trees and neighboring plant communities. However, this was not supported by the results. Instead, we found that all genotypes benefited from neighboring plant communities for frost protection, and the presence of overstory trees generally decreased the occurrence of drought. 4.2 Contributions to the field of study This research provides a comprehensive understanding of the relationship between climatic aridity, harvesting treatments, and the understory plant community, which are crucial for designing forest management strategies for our changing climate. It underscores the need to retain overstory trees during harvesting to protect the original structure and composition of the understory plant community and mitigate climate change impacts. It emphasizes the significant role increasing climatic aridity will have in determining plant species composition and diversity. These results highlight the need for strategic conservation efforts, such as shifting from clearcutting to high retention harvesting systems, to safeguard biodiversity in the changing climate. The results also highlight the importance of retaining overstory trees during harvesting to mitigate the effects of understory plant competition, drought, and frost incidence on planted seedlings. These findings are significant as they suggest ways to design 88 forest management practices so they better promote seedling growth under varying climatic conditions, thus promoting healthier, more resilient forests for the future. To that end, this research provides crucial information that will assist in developing and implementing sustainable forest management practices, playing a key role in efforts to cultivate more resilient and healthy forests amidst climate change. Additionally, the study provides a useful set of baseline data for future research. This contribution is significant as it sets the foundation for continuous research. 4.3 Study limitations A key limitation of this study is the short time frame. The research records the impact of climatic aridity, harvesting treatments, and understory plant cover during a three-year period immediately after logging. Consequently, the findings may not correctly represent long-term trends or account for potential successional changes that could significantly influence plant communities (Connell & Slatyer, 1977). Additionally, the complex and ongoing effects of climate change may cause shifts in forest ecosystems that were not observable or predictable within the study's time frame (Hamann & Wang, 2006). The time frame is crucial when evaluating seedling growth; longer-term studies may produce different results. Therefore, extrapolating these results to older trees must be done with great caution. The study's findings may be specific to the studied regions and forest types, and caution should be taken when applying these results to different ecosystems or geographical locations. The study focused on Douglas-fir dominated forests in British Columbia, Canada, across a range of climatic conditions. The unique characteristics of these forests, including 89 species composition, soil types, topography, and historical land use, may influence the responses of plant communities to overstory tree retention and climate change. The study recognizes that plant species' response to harvesting treatments can differ depending on their autecology and under varying climatic conditions (Roberts & Gilliam, 2003). This complexity can make it challenging to predict overall ecosystem responses. The species-specific variability in response to harvesting and climate introduces difficulties in accurate predictions and complicates the forecasting of long-term effects of forestry practices on biodiversity and ecosystem functioning. Another limitation of this study is that it doesn't consider planted seedlings that have entirely died as a result of drought or frost. This could potentially distort our understanding of the real impact of these conditions on seedling performance. The incidences of frost and drought were recorded as a binary variable, either yes or no, not on a scale reflecting the extent of seedling damage. The measurement of height also did not account for other factors potentially impacting tree seedling height, like browsing. 4.4 Further research Given the limited timescale of this study, future research should incorporate long-term monitoring methods. This should span over an extended period, with plot measurements taken at regular intervals, ideally every 5-10 years. Adopting this long-term approach would allow for a thorough analysis of successional stages of forest regeneration. By monitoring these changes over the long term, researchers can gain a better understanding of the characteristics and timing of each successional stage and weather harvesting alters the trajectory of the regenerating forests (Povak et al., 2020). This includes identifying which 90 species dominate at different stages, how species composition changes over time, and how the effect harvesting, and climate change influence these successional patterns. It would also be beneficial to extend the study sites to include the southernmost range of interior Douglas fir forests. This expansion could offer further insights into how the combined effects of harvesting and climate change may influence plant composition and seedling regeneration in harsher environments. Climate change will most significantly affect species at the extreme ends of their range (Griesbauer & Green, 2010). By studying Douglas-fir at its climatic extremes, we can gain insights into how climate change may influence the species' entire range in the future. Future research could expand the scope beyond examining the percent cover and species richness of neighboring vegetation of the planted seedlings to including species composition. By considering the species composition of neighboring plant communities, we might gain a deeper understanding of the relationship between seedlings and understory vegetation, including the influence of harvesting and climate changes. Factoring in the height of surrounding vegetation could help us understand its potential to inhibit growth, as well as provide shelter to seedlings from harsh environmental conditions. 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This table presents the results of a multiple linear analysis of variance (ANOVA) tests, with species richness as the response variable. The analysis explores the fixed effects, including the Annual Heat Moisture Index (AMH) and the harvesting treatment, and their interaction with pre-conditions as a covariate. The analysis is subsetted by vegetation group of bryoids, herbs, shrubs, or trees. term sumsq df statistic p.value All Species Combined (Intercept) 222.302 1 7.911 0.005 AHM 131.749 1 4.688 0.031 Treatment 46.383 4 0.413 0.799 Pre- richness 8070.752 1 287.210 0.000 AHM: Treatment 22.835 4 0.203 0.937 Residuals 75.312 69 NA NA Herbs (Intercept) 61.849 1 2.142 0.148 AHM 116.346 1 4.029 0.049 Treatment 114.970 4 0.995 0.416 Pre-richness 211.845 1 7.336 0.009 AHM: Treatment 21.416 4 0.185 0.945 Residuals 1963.771 68 NA NA Bryoids (Intercept) 45.160 1 2.352 0.130 AHM 86.121 1 4.486 0.038 Treatment 24.684 4 0.321 0.863 Pre-richness 336.267 1 17.516 0.000 AHM: Treatment 34.360 4 0.447 0.774 Residuals 1324.663 69 NA NA Shrubs AHM 33.166 1 3.518 0.065 Treatment 62.591 4 1.660 0.170 Pre-richness 679.661 1 72.093 0.000 AHM: Treatment 19.021 4 0.504 0.733 Residuals 1324.663 69 NA NA Trees (Intercept) 6.630 1 6.074 0.016 AHM 5.209 1 4.772 0.032 Treatment 0.610 4 0.140 0.967 Pre-richness 30.283 1 27.745 0.000 AHM: Treatment 0.316 4 0.072 0.990 Residuals 75.312 69 NA NA 107 Table A 2. Results of multiple linear analysis of variance (ANOVA) for fixed effects such as Annual Heat Moisture Index (AMH) harvesting Treatment and their interaction with pre-conditions as a covariate and percent cover as a response. The analysis is subsetted by vegetation group of bryoids, herbs, shrubs, or trees. Significance ( p <0.005) is indicated by bold. Percent Cover term sumsq df F-statistic p.value All species (Intercept) 456.06 1 1.135 0.288 AHM 1547.339 1 3.85 0.051 Treatment 2698.945 4 1.679 0.155 Pre-cover 22358.86 1 55.628 0 AHM: Treatment 638.648 4 0.397 0.811 Residuals 125805.841 313 NA NA Bryoids (Intercept) 564.397 1 1.992 0.163 Pre-cover 2236.265 1 7.893 0.006 AHM 162.944 1 0.575 0.451 Treatment 8182.989 4 7.221 0 AHM: Treatment 1425.899 4 1.258 0.295 Herbs (Intercept) 132.171 1 0.38 0.54 Pre-cover 1570.557 1 4.515 0.037 AHM 1902.243 1 5.468 0.022 Treatment 157.806 4 0.113 0.977 AHM: Treatment 347.478 4 0.25 0.909 Residuals 24351.85 70 NA NA Shrubs (Intercept) 4.457 1 0.02 0.889 Pre-cover 11333.19 1 50.19 0 AHM 386.97 1 1.714 0.195 Treatment 105.265 4 0.117 0.976 AHM: Treatment 133.737 4 0.148 0.963 Residuals 15580.701 69 NA NA Trees (Intercept) 564.397 1 1.992 0.163 Pre-cover 2236.265 1 7.893 0.006 AHM 162.944 1 0.575 0.451 Treatment 8182.989 4 7.221 0 AHM: Treatment 1425.899 4 1.258 0.295 Residuals 19831.454 70 NA NA 108 Table A 3. Results of multiple linear regression analysis of variance (ANOVA) for fixed effects such as Annual Heat Moisture Index (AMH) harvesting Treatment and their interaction with pre-conditions as a covariate and Shannon’s diversity as a response. The analysis was subsetted by vegetation group of bryoids, herbs, shrubs. Shannon's diversity term sumsq df statistic p.value All Species (Intercept) 4.552 1 8.964 0.003 AHM 0.466 1 0.917 0.339 Treatment 1.352 4 0.666 0.616 Pre-Shannon's diversity 26.424 1 52.037 0 AHM: Treatment 0.817 4 0.402 0.807 Bryoids (Intercept) 3.811 1 5.915 0.018 Pre-Shannon's diversity 0.001 1 0.001 0.977 AHM 1.371 1 2.127 0.149 Treatment 0.836 4 0.325 0.861 AHM: Treatment 2.343 4 0.909 0.463 Residuals 45.75 71 NA NA Herbs (Intercept) 2.315 1 8.155 0.006 Pre-Shannon's diversity 1.807 1 6.365 0.014 AHM 0.032 1 0.111 0.74 Treatment 0.929 4 0.818 0.518 AHM: Treatment 0.587 4 0.517 0.724 Residuals 19.875 70 NA NA Shrubs (Intercept) 2.255 1 4.441 0.039 Pre-Shannon's diversity 7.332 1 14.441 0 AHM 0.153 1 0.302 0.584 Treatment 0.085 4 0.042 0.997 AHM: Treatment 0.185 4 0.091 0.985 Residuals 35.032 69 NA NA 109 Table A 4. The results of a linear regression analysis of variance (ANOVA) conducted to assess the relationship between light index of understory vegetation and Annual Heat Moisture Index (AHM), harvesting treatment and their interactions for each functional group of bryoids, herbs, shrubs, and trees. term sumsq df statistic p.value All species (Intercept) 16.183 1 447.377 0 Treatment 1.518 4 10.488 0 AHM 0.11 1 3.031 0.082 Treatment: AHM 0.68 4 4.702 0.001 Residuals 100.561 2780 NA NA Bryoids (Intercept) 2.118 1 70.54 0 Treatment 0.448 4 3.726 0.006 AHM 0.014 1 0.469 0.494 Treatment: AHM 0.281 4 2.337 0.056 Residuals 8.317 277 NA NA Herbs (Intercept) 8.377 1 202.032 0 Treatment 1.56 4 9.406 0 AHM 0.019 1 0.448 0.503 Treatment: AHM 0.767 4 4.625 0.001 Residuals 55.979 1350 NA NA Shrubs (Intercept) 5.179 1 207.812 0 Treatment 0.236 4 2.366 0.051 AHM 0.013 1 0.54 0.463 Treatment: AHM 0.184 4 1.846 0.118 Residuals 19.464 781 NA NA Trees (Intercept) 1.627 1 49.582 0 Treatment 0.074 4 0.565 0.688 AHM 0.021 1 0.649 0.421 Treatment: AHM 0.13 4 0.989 0.413 Residuals 11.221 342 NA NA 110 Table A 5. The results of multiple linear regression analysis of variance (ANOVA) conducted to assess the relationship between Soil Moisture Regime (SMR) and factors such as Annual Heat Moisture Index (AHM), Treatment as well as their interactions. The analysis is stratified by vegetation type. term sumsq df statistic p.value Bryoids (Intercept) 105.136 1 70.426 0 Treatment 8.729 4 1.462 0.214 AHM 0.02 1 0.014 0.907 Treatment: AHM 6.723 4 1.126 0.345 Residuals 412.03 276 NA NA Herbs (Intercept) 402.549 1 587.408 0 Treatment 6.67 4 2.433 0.046 AHM 29.036 1 42.369 0 Treatment: AHM 8.044 4 2.935 0.02 Residuals 877.866 1281 NA NA Shrubs (Intercept) 247.615 1 723.171 0 Treatment 0.86 4 0.628 0.643 AHM 1.576 1 4.603 0.032 Treatment: AHM 1.071 4 0.782 0.537 Residuals 263.992 771 NA NA Trees (Intercept) 215.896 1 980.002 0 Treatment 0.362 4 0.411 0.801 AHM 12.631 1 57.334 0 Treatment: AHM 0.412 4 0.468 0.759 Residuals 70.937 322 NA NA 111 Table A 6. ANOVA results for Canonical Correspondence Analysis (CCA) examining the influence of the interaction between Treatment and AHM on the composition of different vegetation groups. The vegetation groups analyzed include bryoids, herbs, shrubs, and trees. Significant p-values (p < 0.05) indicate that the interaction between Treatment and AHM has a significant impact on the composition of the respective vegetation groups. Df ChiSquare F- statistic Pr(>F) Bryoids TREATMENT: AHM 4 0.683 1.473 0.007 Herbs TREATMENT: AHM 4 0.766 1.498 0.002 Shrubs TREATMENT: AHM 4 0.364 1.338 0.029 Trees TREATMENT: AHM 4 0.240 1.380 0.072 Table A 7. Analysis of Variance (ANOVA) results for Canonical Correspondence Analysis (CCA) of species composition. The table presents statistics for three ecological groups: non-native species, disturbed habitat indicators, and exposed mineral soil Indicators. The model includes the interaction between Treatment and AHM (Average Heat Moisture) as predictors for species composition. Significant p-values (p < 0.05) suggest that the interaction between Treatment and AHM influences the composition of the respective functional groups. Group Term Df ChiSquare F- statistic Pr(>F) Disturbed Treatment: AHM 4 0.490 1.411 0.018 Residual 72 6.255 NA NA Non-native Treatment: AHM 4 0.491 1.412 0.018 Residual 72 6.255 NA NA Mineral Soil Treatment 4 0.393 1.008 0.465 AHM 1 0.629 6.460 0.001 Treatment: AHM 4 0.536 1.376 0.053 Residual 68 6.619 NA NA 112 Table A 8. The results of a linear regression analysis of variance (ANOVA) conducted to examine the relationship between Treatment and Annual Heat Moisture Index (AHM), as well as their interactions on the percent cover of traditional and ecologically important edible berry and root producing plant species. Table A 9. Analysis of Variance (ANOVA) results for Canonical Correspondence Analysis (CCA) of species composition for edible berries and edible roots looking at the interaction between Annual Heat Moisture index and harvesting Treatment Group Term Df ChiSquare F Pr(>F) Roots Treatment 4 0.5194 2.084 0.001 AHM 1 0.757 12.162 0.001 Treatment: AHM 4 1.315 5.278 0.001 Residual 39 2.429 NA NA Berries Treatment 4 0.480 1.351 0.041 AHM 1 0.768 8.646 0.001 Treatment: AHM 4 0.627 1.764 0.004 Residual 72 6.399 NA NA Response Group term sumsq df statistic p.value Percent- cover Roots (Intercept) 0.184 1 3.510 0.068 Treatment 0.055 4 0.263 0.899 AHM 0.002 1 0.055 0.814 Treatment: AHM 0.041 4 0.199 0.937 Residuals 2.30 44 NA NA Berries (Intercept) 13.20 1 0.410 0.522 Treatment 94.126 4 0.731 0.570 AHM 54.478 1 1.694 0.193 Treatment: AHM 73.216 4 0.569 0.685 Residuals 14568.207 453 NA NA 113 Table A 10. ANOVA table showing the individual effects and interaction between Annual heat moisture index (AHM) and harvesting Treatment on percent cover and species richness of indicator species of disturbed habitats and non-native species. Group Response term sumsq df statistic p.value Disturbed Species richness (Intercept) 66.250 1 4.741 0.032 Treatment 77.793 4 1.391 0.245 AHM 26.967 1 1.929 0.169 Treatment: AHM 6.7926 4 0.121 0.974 Residuals 1006.110 72 NA NA Percent Cover (Intercept) 1.650 1 0.185 0.666 Treatment 15.930 4 0.448 0.773 AHM 6.550 1 0.737 0.390 Treatment: AHM 20.655 4 0.580 0.676 Residuals 21855.62 2459 NA NA Non-native Species Richness (Intercept) 21.133 1 3.230 0.078 Treatment 49.797 4 1.903 0.124 AHM 0.0381 1 0.005 0.939 Treatment: AHM 46.513 4 1.777 0.147 Residuals 333.596 51 NA NA Percent Cover (Intercept) 20.841 1 5.176 0.023 Treatment 18.428 4 1.144 0.336 AHM 7.772 1 1.930 0.166 Treatment: AHM 16.230 4 1.007 0.404 114 Table A 11. An analysis of variance (ANOVA) conducted to examine the relationship between species richness and harvesting treatment for bryoids, herbs, shrubs, and trees. Group term sumsq df F- statistic p.value All (Intercept) 198.864 1 6.894 0.009 Treatment 161.370 4 1.398 0.234 Pre-richness 8150.092 1 282.521 0.000 Residuals 8971.644 311 Bryoids (Intercept) 0.298 1 0.015 0.904 Treatment 379.322 4 4.666 0.002 Pre-richness 206.499 1 10.160 0.002 Residuals 1504.043 74 Herbs (Intercept) 1120.364 1 36.839 0.000 Treatment 526.127 4 4.325 0.003 Pre-richness 2148.946 1 70.659 0.000 Residuals 2220.137 73 Shrub (Intercept) 478.559 1 47.342 0.000 Treatment 161.157 4 3.986 0.006 Pre-richness 608.320 1 60.179 0.000 Residuals 727.818 72 Tree (Intercept) 0.380 1 0.305 0.582 Treatment 6.312 4 1.267 0.291 Pre-richness 44.027 1 35.336 0.000 Residuals 92.199 74 115 Table A 12. Multiple analysis of variance (ANOVA) conducted to examine the relationship between Shannon’s diversity and harvesting Treatment for bryoids, herbs, shrubs, and trees. Group term sumsq df statistic p.value All (Intercept) 14.887 1 28.749 0.000 Treatment 1.400 4 0.676 0.609 Pre - Shannon's Diversity 22.958 1 44.336 0.000 Residuals 164.666 318 Bryoids (Intercept) 3.938 1 6.011 0.017 Treatment 5.425 4 2.070 0.093 Pre - Shannon's Diversity 0.017 1 0.026 0.872 Residuals 49.793 76 Herbs (Intercept) 16.802 1 61.346 0.000 Treatment 1.663 4 1.518 0.206 Pre - Shannon's Diversity 2.257 1 8.240 0.005 Residuals 20.542 75 Shrubs (Intercept) 7.503 1 15.597 0.000 Treatment 0.895 4 0.465 0.761 Pre - Shannon's Diversity 6.977 1 14.503 0.000 Residuals 35.598 74 Tree (Intercept) 0.984 1 6.871 0.011 Treatment 0.163 4 0.285 0.887 Pre - Shannon's Diversity 3.315 1 23.139 0.000 Residuals 10.745 75 116 Table A 13. Multiple analysis of variance (ANOVA) conducted to examine the relationship between percent cover and harvesting Treatment for bryoids, herbs, shrubs, and trees. Group term sumsq df statistic p.value All (Intercept) 1513.565 1 3.608 0.058 Treatment 7622.304 4 4.543 0.001 Pre- % cover 23897.005 1 56.967 0.000 Residuals 133396.368 318 Bryoids (Intercept) 386.203 1 3.554 0.063 Treatment 1710.808 4 3.936 0.006 Pre- % cover 2670.697 1 24.575 0.000 Residuals 8259.398 76 Herbs (Intercept) 3896.960 1 9.658 0.003 Treatment 1853.357 4 1.148 0.341 Pre- % cover 11620.632 1 28.801 0.000 Residuals 30261.020 75 Shrubs (Intercept) 3018.453 1 13.544 0.000 Treatment 744.062 4 0.835 0.507 Pre- % cover 12515.605 1 56.159 0.000 Residuals 16491.760 74 117 Table A 14. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between species richness before and after treatment, harvesting treatment, and AHM on bryoids richness. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree -3.304 1.589 73 -2.080 0.240 Clearcut - 30% Retention -3.813 1.534 73 -2.486 0.105 Clearcut - 60% Retention -4.788 1.485 73 -3.225 0.016 Clearcut - Control -7.038 1.567 73 -4.491 0.000 Seed tree - 30% Retention -0.509 1.563 73 -0.326 0.998 Seed tree - 60% Retention -1.485 1.531 73 -0.969 0.868 Seed tree - Control -3.734 1.580 73 -2.363 0.137 30% Retention - 60% Retention -0.975 1.485 73 -0.657 0.965 30% Retention - Control -3.225 1.552 73 -2.077 0.241 60% Retention - Control -2.250 1.515 73 -1.485 0.576 Table A 15. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between species richness before and after treatment, harvesting treatment on bryoids richness. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree -3.304 1.589 73 -2.080 0.240 Clearcut - 30% Retention -3.813 1.534 73 -2.486 0.105 Clearcut - 60% Retention -4.788 1.485 73 -3.225 0.016 Clearcut - Control -7.038 1.567 73 -4.491 0.000 Seed tree - 30% Retention -0.509 1.563 73 -0.326 0.998 Seed tree - 60% Retention -1.485 1.531 73 -0.969 0.868 Seed tree - Control -3.734 1.580 73 -2.363 0.137 30% Retention - 60% Retention -0.975 1.485 73 -0.657 0.965 30% Retention - Control -3.225 1.552 73 -2.077 0.241 60% Retention - Control -2.250 1.515 73 -1.485 0.576 118 Table A 16. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between species richness before and after treatment, harvesting treatment on herb species richness. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree 0.379 1.857 72 0.204 1.000 Clearcut - 30% Retention -0.177 1.858 72 -0.095 1.000 Clearcut - 60% Retention 2.963 1.838 72 1.612 0.495 Clearcut - Control 6.252 1.948 72 3.210 0.016 Seed tree - 30% Retention -0.556 1.859 72 -0.299 0.998 Seed tree - 60% Retention 2.584 1.836 72 1.407 0.625 Seed tree - Control 5.873 1.951 72 3.010 0.029 30% Retention - 60% Retention 3.140 1.847 72 1.700 0.441 30% Retention - Control 6.429 1.937 72 3.319 0.012 60% Retention - Control 3.289 1.961 72 1.678 0.454 Table A 17. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between species richness before and after treatment, harvesting treatment on shrub species richness. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree 0.839 1.107 71 0.758 0.942 Clearcut - 30% Retention 0.635 1.073 71 0.592 0.976 Clearcut - 60% Retention 2.013 1.074 71 1.875 0.340 Clearcut - Control 4.165 1.126 71 3.700 0.004 Seed tree - 30% Retention -0.204 1.073 71 -0.190 1.000 Seed tree - 60% Retention 1.175 1.073 71 1.095 0.808 Seed tree - Control 3.326 1.128 71 2.949 0.034 30% Retention - 60% Retention 1.378 1.039 71 1.327 0.676 30% Retention - Control 3.530 1.093 71 3.229 0.016 60% Retention - Control 2.152 1.095 71 1.964 0.294 = 119 Table A 18. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between percent cover before and after treatment, harvesting treatment on whole plant community percent cover. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree -0.571 3.711 75 -0.154 1.000 Clearcut - 30% Retention -4.839 3.658 75 -1.323 0.678 Clearcut - 60% Retention -8.764 3.636 75 -2.411 0.124 Clearcut - Control -12.134 3.777 75 -3.213 0.016 Seed tree - 30% Retention -4.268 3.656 75 -1.167 0.770 Seed tree - 60% Retention -8.193 3.627 75 -2.259 0.170 Seed tree - Control -11.563 3.773 75 -3.064 0.025 30% Retention - 60% Retention -3.925 3.564 75 -1.101 0.805 30% Retention - Control -7.295 3.718 75 -1.962 0.294 60% Retention - Control -3.370 3.678 75 -0.916 0.890 Table A 19. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between percent cover before and after treatment, harvesting treatment on bryoids percent cover. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value Clearcut - Seed tree -0.571 3.711 75 -0.154 1.000 Clearcut - 30% Retention -4.839 3.658 75 -1.323 0.678 Clearcut - 60% Retention -8.764 3.636 75 -2.411 0.124 Clearcut - Control -12.134 3.777 75 -3.213 0.016 Seed tree - 30% Retention -4.268 3.656 75 -1.167 0.770 Seed tree - 60% Retention -8.193 3.627 75 -2.259 0.170 Seed tree - Control -11.563 3.773 75 -3.064 0.025 30% Retention - 60% Retention -3.925 3.564 75 -1.101 0.805 30% Retention - Control -7.295 3.718 75 -1.962 0.294 60% Retention - Control -3.370 3.678 75 -0.916 0.890 120 Table A 20. Multiple analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover and the effect of harvesting Treatment for species indicative of various ground surface materials. Response Variable Group term sumsq df statistic p.value Percent Cover Exposed Mineral (Intercept) 10.526 1 5.311 0.021 Treatment 18.026 4 2.274 0.049 Residuals 5916.067 2985 Mor (Intercept) 15.640 1 9.082 0.003 Treatment 5.778 4 0.839 0.500 Residuals 5140.370 2985 Moder (Intercept) 1082.112 1 63.299 0.000 Treatment 126.382 4 1.848 0.117 Residuals 51029.199 2985 Mull (Intercept) 26.161 1 13.289 0.000 Treatment 5.499 4 0.698 0.593 Residuals 5876.238 2985 Mor Moder (Intercept) 116.844 1 34.020 0.000 Treatment 34.235 4 2.492 0.041 Residuals 10252.166 2985 Species Richness Exposed Mineral (Intercept) 296.529 1 66.689 0.000 Treatment 36.397 4 2.046 0.097 Residuals 324.590 73 Moder (Intercept) 16836.765 1 207.700 0.000 Treatment 385.974 4 1.190 0.322 Residuals 6322.893 78 Mor Moder (Intercept) 2057.000 1 93.611 0.000 Treatment 89.496 4 1.018 0.403 Residuals 1670.010 76 Mull (Intercept) 445.235 1 54.786 0.000 Treatment 1.043 4 0.032 0.998 Residuals 568.877 70 Mor (Intercept) 264.063 1 51.137 0.000 Treatment 16.106 4 0.780 0.542 Residuals 397.614 77 121 Table A 21. Multiple analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover and the effect of harvesting treatment for species indicative of disturbed habitats and non native species. Group Response variable term sumsq df statistic p.value Disturbed Percent Cover (Intercept) 144.852 1 16.255 0.000 Treatment 49.422 4 1.387 0.236 Residuals 21956.963 2464 Species richness (Intercept) 1562.882 1 104.390 0.000 Treatment 371.200 4 6.198 0.000 Residuals 1152.812 77 Non-native Percent Cover (Intercept) 50.074 1 12.465 0.001 Treatment 15.313 4 0.953 0.434 Residuals 843.640 210 Species richness (Intercept) 200.642 1 26.223 000 Treatment 20.767 4 0.6785 0.609 Residuals 1152.812 77 Table A 22. The table presents pairwise treatment contrasts derived from a linear regression model examining the relationship between harvesting treatment on species indicative of disturbed habitats species richness. Pairwise treatment contrasts were calculated using estimated marginal means (EMMs). The contrasts table summarizes the comparisons between different treatment levels. contrast estimate SE df t.ratio p.value 30% retention - 60% retention 2.235 1.110 72.039 2.014 0.478 30% retention - clearcut 0.408 1.128 72.060 0.361 1.000 30% retention - control 5.564 1.151 72.317 4.834 0.000 30% retention - seed tree -0.471 1.110 72.039 -0.424 1.000 60% retention - clearcut -1.828 1.128 72.060 -1.620 1.000 60% retention - control 3.328 1.151 72.317 2.892 0.051 60% retention - seed tree -2.706 1.110 72.039 -2.438 0.173 clearcut - control 5.156 1.167 72.317 4.417 0.000 clearcut - seed tree -0.878 1.128 72.060 -0.779 1.000 control - seed tree -6.034 1.151 72.317 -5.243 0.000 122 Table A 23. Multiple analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover and the effect of harvesting treatment for edible food plants. Group Response Variable term Sum Sq Df F value Pr(>F) Berries Percent Cover (Intercept) 764.446 1 24.021 0.000 Treatment 92.868 4 0.730 0.572 Residuals 14352.617 451 Species Richness (Intercept) 564.063 1 107.579 0.000 Treatment 33.295 4 1.588 0.186 Residuals 403.730 77 Roots Percent Cover (Intercept) 0.801 1 16.650 0.000 Treatment 0.160 4 0.830 0.513 Residuals 2.357 49 123 Table A 24. Combined analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover or Shannon’s diversity, and harvesting and AHM, for bryoids, herbs, shrubs, and trees. Response Variable Group term sumsq df F- statistic p.value Species richness All (Intercept) 912.720 1 32.603 0.000 AHM 342.599 1 12.238 0.001 Pre-richness 8044.640 1 287.360 0.000 Residuals 8790.415 314 Bryoids (Intercept) 375.423 1 16.394 0.000 AHM 120.080 1 5.244 0.025 Pre-richness 275.656 1 12.037 0.001 Residuals 1763.286 77 Herbs (Intercept) 132.121 1 4.140 0.045 AHM 320.872 1 10.055 0.002 Pre-richness 183.218 1 5.741 0.019 Residuals 2425.391 76 Shrubs (Intercept) 534.778 1 49.681 0.000 AHM 81.656 1 7.586 0.007 Pre-richness 670.960 1 62.332 0.000 Residuals 807.319 75 Trees (Intercept) 19.287 1 18.115 0.000 AHM 16.529 1 15.524 0.000 Pre-richness 31.906 1 29.967 0.000 Residuals 81.982 77 124 Table A 25. Combined analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover or Shannon’s diversity, and harvesting and AHM, for bryoids, herbs, shrubs, and trees. Response Variable Group term sumsq df F- statistic p.value Percent Cover All (Intercept) 308.212 1 0.738 0.391 AHM 6944.058 1 16.625 0.000 Pre- % cover 24393.241 1 58.402 0.000 Residuals 134074.614 321 Bryoids (Intercept) 1.918 1 0.015 0.902 AHM 0.816 1 0.006 0.936 Pre- % cover 2305.182 1 18.267 0.000 Residuals 9969.390 79 Herbs (Intercept) 831.274 1 2.464 0.121 AHM 5796.587 1 17.180 0.000 Pre- % cover 1991.559 1 5.903 0.017 Residuals 26317.790 78 Shrubs (Intercept) 126.363 1 0.592 0.444 AHM 798.802 1 3.742 0.057 Pre- % cover 10982.665 1 51.449 0.000 Residuals 16437.021 77 Trees (Intercept) 274.538 1 0.381 0.539 AHM 2087.381 1 2.897 0.093 Pre- % cover 3090.873 1 4.289 0.042 Residuals 56207.329 78 125 Table A 26. Combined analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover or Shannon’s diversity, and harvesting and AHM, for bryoids, herbs, shrubs, and trees. Response Variable Group term sumsq df F- statistic p.value Shannon's Diversity All (Intercept) 35.848 1 71.378 0.000 AHM 4.852 1 9.660 0.002 Pre-Shannon's diversity 26.404 1 52.574 0.000 Residuals 161.215 321 Bryoid (Intercept) 15.976 1 23.517 0.000 AHM 1.549 1 2.280 0.135 Pre-Shannon's diversity 0.014 1 0.020 0.887 Residuals 53.670 79 Herb (Intercept) 19.921 1 70.166 0.000 AHM 0.060 1 0.210 0.648 Pre-Shannon's diversity 1.616 1 5.694 0.019 Residuals 22.145 78 Shrub (Intercept) 9.569 1 20.406 0.000 AHM 0.386 1 0.822 0.367 Pre-Shannon's diversity 7.225 1 15.407 0.000 Residuals 36.108 77 (Intercept) 3.742 1 31.675 0.000 Tree AHM 1.693 1 14.333 0.000 Pre-Shannon's diversity 1.565 1 13.244 0.000 Residuals 9.215 78 126 Table A 27. Combined analysis of variance (ANOVA) conducted to examine the relationship between species richness/ percent cover and the effect of harvesting AHM for species indicative of various ground surface materials. Response Variable Group term sumsq df statistic p.value Percent Cover Exposed mineral (Intercept) 1.476 1 0.745 0.388 AHM 14.590 1 7.365 0.007 Residuals 5919.503 2988 Moder (Intercept) 21.132 1 1.246 0.264 AHM 475.205 1 28.017 0.000 Residuals 50680.375 2988 Mull (Intercept) 1.903 1 0.968 0.325 AHM 5.700 1 2.899 0.089 Residuals 5876.037 2988 Mor Moder (Intercept) 9.185 1 2.705 0.100 AHM 139.012 1 40.934 0.000 Residuals 10147.389 2988 Mor (Intercept) 10.579 1 6.143 0.013 AHM 0.009 1 0.005 0.944 Residuals 5146.139 2988 Species Richness Exposed mineral (Intercept) 47.708 1.000 10.941 0.001 AHM 29.581 1 6.784 0.011 Residuals 331.406 76 Moder (Intercept) 3171.637 1 45.481 0.000 AHM 1060.346 1 15.205 0.000 Residuals 5648.521 81 Mor Moder (Intercept) 9.711 1 0.795 0.375 AHM 794.536 1 65.047 0.000 Residuals 964.970 79 Mull (Intercept) 128.819 1 16.671 0.000 AHM 5.852 1 0.757 0.387 Residuals 564.068 73 Mor (Intercept) 47.708 1 10.941 0.001 AHM 29.581 1 6.784 0.011 Residuals 331.406 76 127 Table A 28. Multiple analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover and the effect of AHM for species indicative of disturbed habitats and non-native species. Group Response Variable term sumsq df statistic p.value Disturbed Percent Cover (Intercept) 0.054 1 0.006 0.938 AHM 69.696 1 7.838 0.005 Residuals 21936.688 2467 Species Richness (Intercept) 185.910 1 10.686 0.002 AHM 132.175 1 7.597 0.007 Residuals 1391.837 80 Non-native Percent Cover (Intercept) 5.871 1 1.456 0.229 AHM 0.309 1 0.077 0.782 Residuals 858.643 213 Species Richness (Intercept) 9.256 1 1.346 0.251 AHM 43.407 1 6.310 0.015 Residuals 405.839 59 Table A 29. Multiple analysis of variance (ANOVA) conducted to examine the relationship between species richness, percent cover and the effect of AHM for edible food plants. Group Response Variable term Sum Sq Df F value Pr(>F) Berries Percent Cover (Intercept) 140.028 1 4.424 0.036 AHM 74.907 1 2.366 0.125 Residuals 14370.579 454 Species Richness (Intercept) 322.365 1 59.486 0.000 AHM 3.488 1 0.644 0.425 Residuals 433.536 80 Roots Percent Cover (Intercept) 0.266 1 5.519 0.023 AHM 0.009 1 0.181 0.672 Residuals 2.508 52 128 Table A 30. Analysis of Variance (ANOVA) results for the canonical correspondence analysis (CCA) model examining the relationship between vegetation composition of edible berry producing species and Annual Heat-Moisture Index (AHM), and Average SMR value of species (SPP), using Bray-Curtis dissimilarity. Statistical significance was assessed using ANOVA on the CCA model. Term Df Chisq Pr(>Chisq) AHM 1 0.0150 0.902 Average SMR value 1 1.07E+30 0 AHM: Average SMR value 1 4.168 0.0411 129 Figure A1 . Canonical Correspondence Analysis (CCA) ordination of vegetation composition of bryoids species response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. AMH gradient shows species found at most arid sites to the right. Interaction between AHM and harvesting treatment significant (p <0.05 −4 −2 0 2−4−3−2−10123CCA1CCA2Pleurozium schreberiDicranum scopariumCeratodon purpureusPolytrichum juniperinumRhytidiadelphus triquetrusKindbergia oreganaHylocomium splendensPlagiomnium undulatumclad_spDicranum polysetumScapania bolanderiDicranum fuscescensHypnum circinaleRhizomnium glabrescensRhytidiadelphus loreusPseudisothecium myosuroidesAmphidium lapponicumScorpidium revolvensKindbergia praelongaBryum pseudotriquetrum Hookeria lucensDichodontium pellucidumAtrichum selwyniiOligotrichum parallelumCeratina perforatrix Radula complanataHomalothecium fulgescensAcomitrium canescensPolystepha pilulaeMarchantia polymorphaLycopodium annotinumLeptarrhena pyrolifoliaTreatmentclearcutseed tree30% retention60% retentioncontrol 130 Figure A2. Canonical Correspondence Analysis (CCA) ordination of vegetation composition herbs in response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. AMH gradient shows species found at most arid sites to the left. Interaction between AHM and harvesting treatment significant (p=0.05) −2 −1 0 1 2 3 4−4−2024air = 2CCA1CCA2Listera cordataPolystichum munitumHeuchera glabraDigitalis purpureaPteridium aquilinumHierochloe ordataPyrola asarifoliaTrillium ovatumDicentra formosaViola sempervirensSenecio macroglossusBlechnum spicantAsarum caudatumEquisetum scirpoidesTrifolium repensPyrola chloranthaAntennaria umbrinellaOrthilia secundaGoodyera oblongifoliaLolium perenneViola aduncaAntennaria racemosaCrepis atribarbaErigeron canadensisFragaria vescaTreatmentClearcutSeed_treeRetention_30Retention_60Control 131 Figure A3. Canonical Correspondence Analysis (CCA) ordination of vegetation composition of shrub species response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. AMH gradient shows species found at most arid sites to the right. −4 −2 0 2−4−202air = 2CCA1CCA2Vaccinium parvifoliumRubus laciniatusGaultheria shallonSalix commutataSorbus sitchensisRubus ursinusPrunus emarginataLonicera ciliosaVaccinium ovalifoliumVaccinium alask aenseRubus spectabilisRhus glabraOplopanax horridusAlnus viridisPrunus pensylvanicaRosa woodsiiRibes hudsonianumPrunus virginianaSalix bebbianaTreatmentClearcutSeed_treeRetention_30Retention_60Control 132 Figure A4. Canonical Correspondence Analysis (CCA) ordination of vegetation composition of plant species indicative of exposed mineral soils in response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. AMH gradient shows species found at most arid sites to the left. Interaction between AHM and harvesting treatment significant (p=0.05) −1 0 1 2 3−3−2−10123CCA1CCA2Hieracium albiflorumEpilobium ciliatumRubus idaeusAchillea millefoliumAnaphalis margaritaceaSambucus racemosaHieracium umbellatumPteridium aquilinumLupinus arcticusLuzula parvifloraPhleum pratenseLeucanthemum vulgareSalix scoulerianaPrunus pensylvanicaHieracium gracileEquisetum arvenseAtrichum selwyniiTrifolium hybridumPetasites palmatusHypochaeris radicataHieracium scouleriTreatmentClearcutSeed_treeRetention_30Retention_60Control 133 Figure A5. Canonical Correspondence Analysis (CCA) ordination of vegetation composition of plant species indicative of disturbed habitats in response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. AMH gradient shows species found at most arid sites to the left. Interaction between AHM and harvesting treatment significant (p=0.01) −2 −1 0 1 2 3−2−10123CCA1CCA2ooooooooooooooooooooooooooooooooooooooooooooooooooooooo ooo o ooFRAGVIRLACTSERCIRSVULCREPTECAMELALNROSANUTFRAGVESAGROGIGPOACOMCERAPURCORYCOREPILANGAGROREPHIERALBADENBICPHLEPRAAQUIFORAGROSCAEPILCILEPILBRARUBUIDACIRSARVHORDJUBBROMVULLATHNEVLEUCVULTRAGDUBTARAOFFLILICOLSYMPALBASTRMISTRIFHYBTRIFPRATRIFREPPLANMAJSAMBRACANAPMARCHRYLEULUPIARCELYMCINPSEUSTRVERBTHAPOTEGRAMELIALBCASTMINEQUIARVSOLICANRUBUURSLACTMURDICEFORPTERAQUDIGIPURCICHINTCAREMERHYPORADSONCASPSENEMACRUBULACSENESILLOLIPERAPOCANDHOLODISARCTLAPCEANSANSENEVULERIGCANCHENALBSALIBEBRUBUARMTreatmentclearcutseed tree30% retention60% retentioncontrol 134 Figure A6. Canonical Correspondence Analysis (CCA) ordination of vegetation composition of non-native plant species in response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group −2 0 2 4−4−202air = 2CCA1CCA2Phragmites australisSonchus asperDigitalis purpureaLuzula multifloraSenecio silphioidesMycelis muralisAgropyron repensPoa compressaCrepis tectorumLeucanthemum vulgareAgrostis giganteaChrysanthemum leucanthemumTrifolium hybridumLolium perenneTrifolium repensVerbascum thapsusChenopodium albumRubus armeniacusHieracium aurantiacumTreatmentClearcutSeed_treeRetention_30Retention_60Control 135 Figure A7. Canonical Correspondence Analysis (CCA) ordination of vegetation composition of berry producing species in response to different harvesting treatments and AHM using Bray-Curtis dissimilarity and Hellinger transformation. Blue labels represent individual species. Colored polygons represent hulls encompassing groups of species corresponding to different treatments, while ellipses depict the standard deviation of each treatment group. −2 −1 0 1 2 3−2−101234air = 2CCA1CCA2Vaccinium parvifoliumGaultheria shallonRubus leucodermisRubus ursinusVaccinium ovalifoliumRubus spectabilisSambucus caeruleaPrunus pensylvanicaFragaria vescaSambucus racemosaRubus idaeusViburnum edule Ribes hudsonianumAmelanchier alnifoliaPrunus virginianaRubus armeniacusTreatmentClearcutSeed_treeRetention_30Retention_60Control 136 Figure A8. A Canonical Correspondence Analysis (CCA) ordination plot capturing the variation in species composition across different locations and AHM gradient. Each label denotes an edible berry producing plant. The plot includes 95% confidence ellipses, shading the regions where we have high confidence in the spatial distribution of species for each location group. Polygons further identify distinct location groups. Species increase in moisture requirements to the right, more arid species to the left. Average Soil moisture regime (SMR) values denoted in black. Model used: Berries composition ~ SMR*AHM (p-value=0.001. −3 −2 −1 0 1 2 3−2−10123air = 2CCA1CCA22345Rubus ursinusRubus leucodermisRibes hudsonianumArctostaphylos uva−ursiPrunus virginianaRubus armeniacusVaccinium membranaceumVaccinium ovalifoliumRubus idaeusVaccinium scopariumVaccinium caespitosumCornus canadensisShepherdia canadensisFragaria virginianaSambucus racemosaRubus parviflorusLocationsMKNARRFJPJAFAF 137 Appendix B Indicator species Tables Table B. 1. Indicator species table that shows species and traits of each species associated with each treatment and combination of treatments. Also shows locations at which this species was observed. Species Name GROUP Status Disturbed Ave SMR Treatment GSM Location Aulacomnium palustre BRYOID native 0 7 Clearcut Surface water JP Funaria hygrometrica BRYOID native 0 NA Clearcut Exposed JP Leptobryum pyriforme BRYOID native 0 5 Clearcut Mor Humus/Moder/Exposed/Surface water NAR, JP Plagiomnium venustum BRYOID native 0 NA Clearcut NA MK Polystepha pilulae BRYOID native 0 NA Clearcut NA MK Agropyron repens HERB exotic 1 4 Clearcut Mor/Moder/Mull AF Calamagrostis canadensis HERB native 0 5 Clearcut Exposed/Moder JAF Carex mertensii HERB native 1 5 Clearcut Moder/Surface water MK Chrysanthemum leucanthemum HERB exotic 1 4 Clearcut Mor Humus/Moder JAF Cirsium brevistylum HERB native 0 2 Clearcut Mor Humus/Moder MK Cirsium edule HERB native 0 4 Clearcut Mor/Moder/Mull AF, MK Festuca campestris HERB native 0 2 Clearcut Mor Humus/Moder AF Festuca saximontana HERB native 0 2 Clearcut Mor Humus/Moder JP Hieracium scouleri HERB native 0 5 Clearcut Exposed AF Hypochaeris radicata HERB exotic 1 2 Clearcut Exposed MK Lilium philadelphicum HERB native 0 3 Clearcut Mor Humus/Moder AF Melilotus albus HERB exotic 1 3 Clearcut Mor/Moder/Mull JP Senecio silphioides HERB exotic 1 4 Clearcut NA MK Solidago canadensis HERB exotic 1 4 Clearcut Mor/Moder/Mull JP Tiarella trifoliata HERB native 0 4 Clearcut Moder NAR, MK, RF Lysimachia latifolia HERB native 0 4 Clearcut Mor Humus/Moder JAF, AF Urtica dioica HERB exotic 0 5 Clearcut Mor Humus/Moder JP Viola renifolia HERB native 0 4 Clearcut Mor Humus/Moder JAF Alnus crispa SHRUB native 0 4 Clearcut NA RF Ribes sanguineum SHRUB native 0 3 Clearcut Mor Humus MK Salix commutata SHRUB native 0 5 Clearcut Mor Humus/Moder MK 138 Bryum pseudotriquetrum BRYOID native 0 NA Clearcut_Seed NA MK Ceratina perforatrix BRYOID native 0 NA Clearcut_Seed NA JP Oligotrichum parallelum BRYOID native 0 NA Clearcut_Seed NA MK Agrostis scabra HERB native 1 4 Clearcut_Seed Exposed AF, JAF Cerastium arvense HERB native 0 2 Clearcut_Seed Mor Humus/Moder AF Dicentra formosa HERB native 1 4 Clearcut_Seed Exposed/Moder MK Eleymus cinereus HERB native 1 2 Clearcut_Seed Mor/Moder/Mull JAF Maianthemum dilatatum HERB native 0 4 Clearcut_Seed Moder MK Larix occidentalis SHRUB native 0 3 Clearcut_Seed Mor Humus/Moder JAF, JP, NAR Polytrichum commune BRYOID native 0 NA Seed_tree NA MK Woolly Fringe-Moss BRYOID native 0 NA Seed_tree NA MK Cichorium intybus HERB exotic 1 2 Seed_tree Mor Humus/Moder MK Elymus elymoides HERB native 0 2 Seed_tree Mor Humus JAF Hordeum jubatum HERB native 1 5 Seed_tree Mor/Moder/Mull AF Moneses uniflora HERB native 0 4 Seed_tree Mor Humus AF, JP, NAR Oryzopsis asperifolia HERB native 0 3 Seed_tree Moder AF, RF Poa compressa HERB exotic 1 3 Seed_tree Mor Humus/Moder AF Polystichum lonchitis HERB native 0 3 Seed_tree Mor Humus/Moder RF Potentilla pensylvanica HERB native 0 3 Seed_tree Mor Humus/Moder JAF Sonchus asper HERB exotic 1 4 Seed_tree Mor/Moder/Mull MK Trillium ovatum HERB native 0 4 Seed_tree Moder MK Juniperus communis SHRUB native 0 2 Seed_tree Mor Humus/Moder JAF Rubus laciniatus SHRUB exotic 1 5 Seed_tree Mor Humus/Moder MK Salix bebbiana SHRUB native 1 4 Seed_tree Exposed/Moder RF Agropyron spicatum HERB native 0 2 Clear_Seed_30 Moder JAF Hierochloe ordata HERB native 0 NA Clear_Seed_30 Exposed MK Ceanothus sanguineus SHRUB native 1 2 Clear_Seed_30 Mor Humus RF Rubus armeniacus SHRUB exotic 1 5 Clear_Seed_30 Moder/Mull RF Aulacomnium androgynum BRYOID native 0 NA Retention_30 NA MK 139 Pogonatum urnigerum BRYOID native 0 NA Retention_30 NA MK Pseudognaphalium obtusifolium BRYOID native 0 2 Retention_30 Mor Humus RF Acomitrium canescens BRYOID native 0 NA Retention_30 NA MK Tortula ruralis BRYOID native 0 NA Retention_30 NA JP, JAF, RF Antennaria umbrinella HERB native 0 2 Retention_30 Mor Humus/Moder AF Arctium lappa HERB exotic 1 5 Retention_30 Mor/Moder/Mull RF Artemisia frigida HERB native 0 2 Retention_30 Mor Humus/Moder JAF Aruncus dioicus HERB native 0 5 Retention_30 Moder MK Balsamorhiza sagittata HERB native 0 2 Retention_30 NA JAF Carex sitchensis HERB native 0 6 Retention_30 Surface water MK Crepis atribarba HERB native 0 3 Retention_30 Mor Humus/Moder RF Erythronium grandiflorum HERB native 0 3 Retention_30 Mor Humus/Moder MK Heuchera glabra HERB native 0 4 Retention_30 Mor/Moder/Mull MK Lolium perenne HERB exotic 1 3 Retention_30 Mor/Moder/Mull RF, NAR Pedicularis bracteosa HERB native 0 4 Retention_30 Moder RF Phragmites australis HERB exotic 0 7 Retention_30 Moder/Mull MK Poa secunda HERB native 0 2 Retention_30 Mor Humus/Moder JAF Potentilla gracilis HERB native 1 3 Retention_30 Mor/Moder/Mull JAF Pseudognaphalium stramineum HERB native 1 5 Retention_30 NA JAF Roella maculata HERB unk 0 NA Retention_30 NA AF Sorbus sitchensis SHRUB native 0 3 Retention_30 Moder AF, MK Brachythecium frigidum BRYOID native 0 NA Retention_60 NA MK Hypnum subchrysogaster BRYOID native 0 NA Retention_60 NA MK Juniperus scopulorum BRYOID native 0 2 Retention_60 Very Shallow RF, JAF Marchantia polymorpha BRYOID native 0 6 Retention_60 Exposed RF Aquilegia formosa HERB native 1 4 Retention_60 Moder AF, JP Castilleja miniata HERB native 1 3 Retention_60 Exposed JP 140 Ranunculus occidentalis HERB native 0 3 Retention_60 Exposed/Moder JP Senecio macroglossus HERB native 1 4 Retention_60 Mor/Moder/Mull MK Viola orbiculata HERB native 0 3 Retention_60 Moder RF, AF, NAR Vaccinium scoparium SHRUB native 0 3 Retention_60 Mor Humus JAF, AF, JP Hookeria lucens BRYOID native 0 6 Retntion_60_Control Mor Humus MK Kindbergia praelonga BRYOID native 0 NA Retntion_60_Control NA MK, RF Rhizomnium glabrescens BRYOID native 0 NA Retntion_60_Control Mor Humus/Moder MK Heuchera cylindrica HERB native 0 2 Retntion_60_Control Mor Humus/Moder JAF, RF, JP Pyrola asarifolia HERB native 0 4 Retntion_60_Control Moder AF, NAR, JP, RF, MK Pyrola chlorantha HERB native 0 3 Retntion_60_Control Mor Humus JP, AF Blindia acuta BRYOID native 0 5 Control NA MK Cladonia coniocraea BRYOID native 0 NA Control NA JP Cladonia rangiferina BRYOID native 0 3 Control Very Shallow AF, JAF Scorpidium revolvens BRYOID native 0 NA Control NA JP, JAF Peltigera neopolydactyla BRYOID native 0 NA Control NA AF, RF, JP, JAF Timmia austriaca BRYOID native 0 NA Control NA JP Asarum caudatum HERB native 0 4 Control Moder RF Calochortus apiculatus HERB native 0 2 Control Mor Humus/Moder JAF Corallorhiza maculata HERB native 0 3 Control Mor Humus RF, AF Equisetum scirpoides HERB native 0 4 Control Surface water JP Listera cordata HERB native 0 5 Control Moder RF, MK Lycopodium annotinum HERB native 0 4 Control Exposed/Moder MK Platanthera unalascensis HERB native 0 4 Control NA RF Pyrola minor HERB native 0 4 Control Mor Humus AF Orthilia secunda HERB native 0 6 Control Mor Humus/Moder AF Moneses uniflora HERB native 0 4 Control Mor Humus/Moder NAR Trifolium hybridum HERB exotic 1 4 Control Exposed/Mor Humus AF 141 Lonicera ciliosa SHRUB native 0 3 Control Mor Humus/Moder RF Oplopanax horridus SHRUB native 0 4 Control Moder JP, AF Philadelphus lewisii SHRUB native 0 2 Control Mor Humus/Moder RF Campylium stellatum BRYOID native 0 6 No_clearcut NA AF, JP, RF Dicranum fuscescens BRYOID native 0 2 No_clearcut Mor Humus MK, AF, RF Hylocomium splendens BRYOID native 0 5 No_clearcut Mor Humus RF, AF, MK, JP, JAF Pseudisothecium myosuroides BRYOID native 0 NA No_clearcut NA MK Peltigera aphthosa BRYOID native 0 2 No_clearcut Mor Humus RF, JAF, JP, AF Rhytidiopsis robust BRYOID native 0 6 No_clearcut Mor Humus RF, AF, MK, JAF, NAR Scapania bolanderi BRYOID native 0 NA No_clearcut Mor Humus MK Orthilia secunda HERB native 0 6 No_clearcut Mor Humus NAR, AF, RF, JP Allium cernuum HERB native 0 2 harvesting_trt Mor Humus/Moder JAF, AF, RF Anaphalis margaritacea HERB native 1 3 harvesting_trt Exposed JAF, NAR, RF, MK Athyrium filix-femina HERB native 0 5 harvesting_trt Moder MK Bromus vulgaris HERB native 1 4 harvesting_trt Moder JP, AF Epilobium brachycarpum HERB native 1 2 harvesting_trt Mor Humus/Moder JAF, RF, NAR, AF Epilobium ciliatum HERB native 1 5 harvesting_trt Exposed AF, MK, RF, NAR, JP, JAF 142 Mycelis muralis HERB exotic 1 4 harvesting_trt Moder MK, NAR, RF Lactuca serriola HERB exotic 1 3 harvesting_trt Mor/Moder/Mull JAF, RF, AF Lithospermum ruderale HERB native 0 3 harvesting_trt Mor Humus/Moder JAF Luzula parviflora HERB native 0 4 harvesting_trt Exposed/Moder MK Trifolium pratense HERB exotic 1 4 harvesting_trt Mor Humus/Moder JP, RF, AF, JAF Verbascum thapsus HERB exotic 1 2 harvesting_trt Mor Humus RF, JAF, MK Alnus viridis SHRUB native 0 4 harvesting_trt NA RF, AF, MK Menziesia ferruginea SHRUB native 0 4 harvesting_trt Mor Humus MK, AF Prunus emarginata SHRUB native 0 3 harvesting_trt Mor Humus/Moder MK Rosa nutkana SHRUB native 1 3 harvesting_trt Mor Humus/Moder JP, AF, RF Rubus leucodermis SHRUB native 0 3 harvesting_trt Mor Humus MK, NAR Sambucus racemosa SHRUB native 1 4 harvesting_trt Exposed JP, MK, RF, AF Vaccinium ovalifolium SHRUB native 0 4 harvesting_trt Moder MK Pinus contorta TREE native 0 3 harvesting_trt NA JAF, JP, AF, NAR Pinus monticola TREE native 0 3 harvesting_trt NA MK Pinus ponderosa TREE native 0 2 harvesting_trt Mor Humus/Moder AF, JAF, JP, NAR Populus trichocarpa TREE native 0 4 harvesting_trt Moder AF, MK, JP, RF 143 Table B. 2. Indicator species analysis of each location and combination of locations. Also shows associated average SMR value for each species and functional group. Species name GROUP Average SMR Location of indicator species Antennaria neglecta HERB 2 AF Crepis tectorum HERB 3 AF Fragaria vesca HERB 3 AF Rosa acicularis SHRUB 3 AF Taraxacum officinale HERB 3 AF Vicia americana HERB 3 AF Viola adunca HERB 3 AF Agrostis gigantea HERB 4 AF Alnus viridis SHRUB 4 AF Cirsium vulgare HERB 4 AF Dicranum polysetum BRYOID NA AF Antennaria racemosa HERB 2 AF+JAF Tragopogon dubius HERB 2 AF+JAF Achillea millefolium HERB 3 AF+JAF Arnica cordifolia HERB 3 AF+JAF Ceratodon purpureus BRYOID 3 AF+JAF Aster conspicuus HERB 3 AF+JAF+JP Festuca occidentalis HERB 3 AF+JAF+JP Galium boreale HERB 3 AF+JAF+JP Lathyrus ochroleucus HERB 3 AF+JAF+JP Dicranum scoparium BRYOID NA AF+JAF+JP+MK+RF Peltigera canina BRYOID 2 AF+JAF+JP+RF Spiraea betulifolia SHRUB 3 AF+JAF+JP+RF Symphoricarpos albus SHRUB 3 AF+JAF+JP+RF Dicranella crispa BRYOID NA AF+JAF+JP+RF Calamagrostis rubescens HERB 3 AF+JAF+RF Fragaria virginiana HERB 3 AF+JAF+RF Hieracium albiflorum HERB 3 AF+JAF+RF Lactuca serriola HERB 3 AF+JAF+RF Mahonia aquifolium SHRUB 3 AF+JAF+RF Cirsium arvense HERB 4 AF+JAF+RF Pinus ponderosa TREE 2 AF+JP Lathyrus nevadensis HERB 3 AF+JP Mnium spinulosum BRYOID 3 AF+JP Populus tremuloides TREE 3 AF+JP Rosa woodsii SHRUB 3 AF+JP Shepherdia canadensis SHRUB 3 AF+JP Aster ciliolatus HERB 4 AF+JP Bromus vulgaris HERB 4 AF+JP Elymus glaucus HERB 4 AF+JP Lonicera involucrata SHRUB 4 AF+JP Ribes lacustre SHRUB 4 AF+JP Thalictrum occidentale HERB 4 AF+JP Pleurozium schreberi BRYOID NA AF+JP Ptilium crista-castrensis BRYOID NA AF+JP Rhytidiadelphus triquetrus BRYOID NA AF+JP Cornus canadensis HERB 4 AF+JP+MK Hylocomium splendens BRYOID 5 AF+JP+MK Rubus parviflorus SHRUB 4 AF+JP+MK+NAR+RF 144 Pinus contorta TREE NA AF+JP+NAR Amelanchier alnifolia SHRUB 3 AF+JP+RF Chimaphila umbellata HERB 3 AF+JP+RF Linnaea borealis HERB 3 AF+JP+RF Aralia nudicaulis HERB 4 AF+JP+RF Prosartes hooker HERB 4 AF+JP+RF Prosartes hooker HERB 4 AF+JP+RF Osmorhiza berteroi HERB 4 AF+JP+RF Rubus idaeus SHRUB 4 AF+JP+RF Maianthemum racemosum HERB 4 AF+JP+RF Galium triflorum HERB 5 AF+JP+RF Corylus cornuta SHRUB 3 AF+MK+RF Paxistima myrsinites SHRUB 3 AF+NAR+RF Hieracium umbellatum HERB 3 AF+RF Rosa gymnocarpa SHRUB 3 AF+RF Adenocaulon bicolor HERB 4 AF+RF Agropyron spicatum HERB 2 JAF Allium cernuum HERB 2 JAF Arctostaphylos uva-ursi HERB 2 JAF Campanula rotundifolia HERB 2 JAF Festuca idahoensis HERB 2 JAF Penstemon confertus HERB 2 JAF Hedysarum sulphurescens HERB 3 JAF Lithospermum ruderale HERB 3 JAF Lupinus arcticus HERB 3 JAF Symphyotrichum foliaceum HERB 4 JAF Verbascum thapsus HERB 2 JAF+RF Koeleria macrantha HERB 2 JP Peltigera aphthosa BRYOID 2 JP Abies lasiocarpa TREE 3 JP Picea engelmannii TREE 3 JP Picea engelmannii x glauca TREE 3 JP Rosa nutkana SHRUB 3 JP Sorbus scopulina SHRUB 3 JP Cornus stolonifera SHRUB 4 JP Rubus pubescens SHRUB 4 JP Viburnum edule SHRUB 4 JP Viola canadensis HERB 4 JP Campylium stellatum BRYOID 6 JP Orthilia secunda HERB 6 JP Populus trichocarpa TREE 4 JP+MK+RF Sambucus racemosa SHRUB 4 JP+MK+RF Betula papyrifera TREE 3 JP+RF Clintonia uniflora HERB 3 JP+RF Acer glabrum var. douglasii SHRUB 4 JP+RF Dicranum fuscescens BRYOID 2 MK Luzula multiflora HERB 2 MK Prunus emarginata SHRUB 3 MK Pteridium aquilinum HERB 3 MK Rubus leucodermis SHRUB 3 MK Rubus ursinus SHRUB 3 MK Viola sempervirens HERB 3 MK Acer circinatum SHRUB 4 MK 145 Blechnum spicant HERB 4 MK Gaultheria shallon SHRUB 4 MK Kindbergia oregana BRYOID 4 MK Luzula parviflora HERB 4 MK Menziesia ferruginea SHRUB 4 MK Polystichum munitum HERB 4 MK Douglas fir TREE 4 MK Rubus spectabilis SHRUB 4 MK Vaccinium alask aense SHRUB 4 MK Vaccinium ovalifolium SHRUB 4 MK Vaccinium ovalifolium SHRUB 4 MK Vaccinium parvifolium SHRUB 4 MK Athyrium filix-femina HERB 5 MK Amphidium lapponicum BRYOID NA MK Heterocladium procurrens BRYOID NA MK Hierochloe ordata HERB NA MK Hypnum circinale BRYOID NA MK Pseudisothecium myosuroides BRYOID NA MK Plagiomnium undulatum BRYOID NA MK Rhytidiadelphus loreus BRYOID NA MK Rhytidiopsis robust BRYOID NA MK Scapania bolanderi BRYOID NA MK Thuja plicata TREE 4 MK+RF Tsuga heterophylla TREE 4 MK+RF Ceanothus sanguineus SHRUB 2 RF Holodiscus discolor SHRUB 2 RF Ribes viscosissimum SHRUB 2 RF Lonicera utahensis SHRUB 3 RF Abies grandis TREE 4 RF Senecio vulgaris HERB 4 RF Taxus brevifolia SHRUB 4 RF Rubus armeniacus SHRUB 5 RF Veronica americana HERB 6 RF 146 Appendix C Chapter 3 Experimental Design Figure C a) Example of grid map with randomly selected grid points. b) Example of how seedlings were selected around each grid point. c) Diagram of a seedling vegetation plot. a) b) c) 147 Appendix D Table D 1 Presents the results of the stepwise backward selection process applied to the regression models. The table shows the AIC values, or changes in model fit criteria. The final selected model, denoted in the last step. The reduced models are the models that have been analyzed within the analysis. Model # Model Statement Reduction term AIC ∆ AIC Model 2 (Height without genotype) height ~ species richness: harvesting treatment: aridity + percent cover Treatment: aridity Null 4117.2 0 height ~ species richness+ harvesting treatment +aridity + species richness: harvesting treatment+ species richness: aridity+ harvesting treatment: aridity+ percent cover+ percent cover: harvesting treatment+ percent cover: aridity - species richness: harvesting treatment: aridity 4116.4 -0.81 height ~ species richness+ harvesting treatment+ aridity + species richness: aridity+ harvesting treatment: aridity +percent cover+ percent cover: harvesting treatment+ percent cover: aridity: harvesting treatment - species richness: harvesting treatment 4115.1 -2.06 Model 3 (Height with genotype) height ~ percent cover + species richness+ harvesting treatment: aridity: aridity of seedling source Null 4110.1 0 height ~ percent cover + species richness + harvesting treatment + aridity + aridity of seedling source+ harvesting treatment: aridity + harvesting treatment: aridity of seedling source+ aridity: aridity of seedling source - Harvesting treatment: aridity: aridity of seedling source 4107.1 -3 height ~ percent cover + species richness + harvesting treatment + aridity + aridity of seedling source+ harvesting treatment: aridity + aridity: aridity of seedling source - Harvesting treatment: aridity of seedling source 4104.8 -5.3 height ~ percent cover + species richness + harvesting treatment + aridity + harvesting treatment: aridity+ aridity of seedling source. -aridity: aridity of seedling source 4103 -7.1 Model 4 (Signs of Frost) Likelihood of a seedling showing signs of frost~ species richness: harvesting treatment: aridity + percent cover: harvesting treatment: aridity + aridity of seedling source Null 650.01 0 Likelihood of frost ~ species richness + species richness: harvesting treatment + species richness: aridity + harvesting treatment: aridity + percent cover + harvesting treatment: percent cover + aridity: percent cover + aridity: harvesting treatment: percent cover - species richness: aridity: harvesting treatment 646.8 -3.21 Likelihood of frost ~ species richness + species richness: aridity + harvesting treatment: aridity + percent cover + harvesting treatment: percent cover + aridity: percent cover +aridity: harvesting treatment: percent cover - species richness: harvesting treatment 643.37 -6.64 148 Likelihood of frost ~ species richness + harvesting treatment: aridity + percent cover + harvesting treatment: percent cover + aridity: percent cover + aridity: harvesting treatment: percent cover. - species richness: aridity 641.69 -8.32 Model 4 (Signs of Drought) Likelihood of a seedling showing signs of drought~ species richness: harvesting treatment: aridity + percent cover: harvesting treatment: aridity+ aridity of seedling source Null 532.75 0 Likelihood of a seedling showing signs of drought~ species richness: harvesting treatment: aridity+ percent cover: treatment: aridity - aridity of seedling source 531.48 -1.27 Table D 2. Full model 2: ANOVA table for seedling height as affected by neighborhood percent cover, neighborhood species richness, harvesting treatment, aridity and interaction between neighborhood percent cover, harvesting treatment, aridity (AHM) and neighborhood species richness and aridity. Type 3 sum of squares. term sumsq df F- statistic p-value (Intercept) 361.5 1 0.446 0.505 Neighborhood species richness 50.1 1 0.062 0.804 Harvesting treatment 12520.6 2 7.717 0.000 Aridity 977.3 1 1.205 0.273 Neighborhood percent cover 120.6 1 0.149 0.700 Neighborhood species richness: harvesting treatment 2768.9 2 1.707 0.182 Neighborhood species richness: Aridity 78.5 1 0.097 0.756 Harvesting treatment: Aridity 11080.4 2 6.830 0.001 Harvesting treatment: Neighborhood percent cover 14357.0 2 8.849 0.000 Aridity: Neighborhood percent cover 84.1 1 0.104 0.748 Neighborhood species richness: harvesting treatment: Aridity 2486.8 2 1.533 0.217 Neighborhood percent cover: harvesting treatment: Aridity 14410.0 2 8.882 0.000 149 Table D 3. ANOVA table for seedling height as affected by neighborhood percent cover, neighborhood species richness, aridity (AHM), aridity of seedling location, harvesting treatment, and the interaction between aridity and harvesting treatment. Type 3 sum of squares. term sumsq df F- statistic p.value (Intercept) 1248.209 1 1.547 0.214 Neighborhood percent cover 923.297 1 1.144 0.285 Neighborhood species richness 5207.819 1 6.454 0.011 Harvesting treatment 4785.437 2 2.965 0.052 Aridity 1703.192 1 2.111 0.147 Aridity of seedling source 1151.145 1 1.427 0.233 Harvesting treatment: Aridity 4850.731 2 3.006 0.050 Harvesting treatment: Aridity of seedling source 653.411 2 0.405 0.667 Aridity: Aridity of seedling source 808.371 1 1.002 0.317 Harvesting treatment: Aridity: Aridity of seedling source 825.963 2 0.512 0.600 Table D 4. Full Model 4a: ANOVA table for the probability of frost incidence as a function of neighborhood percent cover, neighborhood species richness, their interaction between harvesting treatment and aridity (AHM) and aridity of seedling source. Type 3 sum of squares. term L.R Chisq df p.value Species richness 0.233 1 0.629 Aridity 1.145 1 0.285 Harvesting treatment 3.266 2 0.195 Neighborhood percent cover 0.856 1 0.355 Aridity of seedling source 6.765 1 0.009 Neighborhood species richness: Aridity 0.494 1 0.482 Neighborhood species richness: harvesting treatment 0.698 2 0.705 Aridity: harvesting treatment 3.112 2 0.211 Harvesting treatment: neighborhood percent cover 5.640 2 0.060 Aridity: neighborhood percent cover 1.225 1 0.268 Neighborhood species richness: aridity: harvesting treatment 0.792 2 0.673 Aridity: harvesting treatment: neighborhood percent cover 5.712 2 0.058 150 Table D 5. Full Model 4b: ANOVA table for the probability of drought incidence as a function of neighborhood percent cover, neighborhood species richness, their interaction between harvesting treatment and aridity (AHM) and aridity of seedling source. Type 3 sum of squares. term L.R Chisq df p.value Species richness 0.305 1 0.581 Aridity 0.213 1 0.644 Harvesting treatment 3.953 2 0.139 Neighborhood percent cover 1.847 1 0.174 Aridity of seedling source 0.730 1 0.393 Neighborhood species richness: Aridity 0.267 1 0.605 Neighborhood species richness: harvesting treatment 8.704 2 0.013 Aridity: harvesting treatment 3.818 2 0.148 Harvesting treatment: neighborhood percent cover 9.604 2 0.008 Aridity: neighborhood percent cover 2.404 1 0.121 Neighborhood species richness: harvesting treatment: Aridity 7.924 2 0.019 Neighborhood percent cover: harvesting treatment: aridity 10.208 2 0.006 151 Appendix E Model 2: Visualization of the effects of predictors on tree height based on the linear regression model. Treatment groups are displayed with 60% retention treatment serving as the reference category. The plot illustrates the magnitude and direction of the effects of predictors, including neighborhood percent cover, harvesting treatment, neighborhood species richness and their interaction with aridity (AHM), on tree height. Red indicates a negative slope; blue indicates a positive slope. "The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1, respectively, indicating the level of statistical significance for each coefficient estimate in the model. −5.30 *69.53 307.16 ***−0.47 1.58 0.23 −2.54 −13.26 ***0.42 −9.49 ***−0.06 −0.02 0.40 ***(harvesting treatment [cc] × aridity) × neighbourhood percent cover(harvesting treatment [seed] × aridity) × neighbourhood percent coveraridity × neighbourhood percent coverharvesting treatment [cc] × neighbourhood percent coverharvesting treatment [seed] × neighbourhood percent coverharvesting treatment [cc] × aridityharvesting treatment [seed] × aridityneighborhood species richness × aridityneighbourhood percent coveraridityharvesting treatment [cc]harvesting treatment [seed]neighborhood species richness−100 0 100 200 300 400 500Estimates 152 Model 3: Visualization of the effects of predictors on tree height based on the linear regression model. Treatment groups are displayed with 60% retention treatment serving as the reference category. The plot illustrates the magnitude and direction of the effects of predictors, including covariates neighborhood percent cover and neighborhood species richness and the main effects of harvesting treatment and aridity (AHM) and their interaction and the effect of aridity of seedling source. Red indicates a negative slope; blue indicates a positive slope. "The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1, respectively, indicating the level of statistical significance for each coefficient estimate in the model. −0.55 *75.58 **195.65 ***2.13 *0.94 ***−2.83 *−8.34 ***harvesting treatment [cc] × aridityharvesting treatment [seed] × aridityaridity of seedling sourcearidityharvesting treatment [cc]harvesting treatment [seed]neighborhood species richness−50 0 50 100 150 200 250 300Estimates 153 Model 4a (Frost) : Visualization of the effects of predictors on probability of a seedling showing frost based on the generalized linear regression model. Harvesting treatment groups are displayed with 60% retention treatment serving as the reference category. The plot illustrates the magnitude and direction of the effects of predictors, including neighborhood percent cover, harvesting treatment and their interaction with aridity (AHM), on signs of frost. Red indicates a negative slope; blue indicates a positive slope. The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1, respectively, indicating the level of statistical significance for each coefficient estimate in the model. 1.10 ***1.61 ***245.84 530403.69 ***1.08 0.96 **0.77 0.55 ***0.94 0.54 *1.00 1.00 1.03 *(aridity × harvesting treatment [cc]) × neighbourhood percent cover(aridity × harvesting treatment [seed]) × neighbourhood percent coveraridity × neighbourhood percent coverharvesting treatment [cc] × neighbourhood percent coverharvesting treatment [seed] × neighbourhood percent coveraridity × harvesting treatment [cc]aridity × harvesting treatment [seed]aridity of seedling sourceneighbourhood percent coverharvesting treatment [cc]harvesting treatment [seed]aridityneighborhood species richness0.1 10 1000 1e+05 1e+07 1e+09Odds Ratios 154 Model 4b (Drought): Visualization of the effects of predictors on probability of a seedling showing frost based on the generalized linear regression model. Harvest treatment groups are displayed with 60% retention treatment serving as the reference category. The plot illustrates the magnitude and direction of the effects of predictors, including neighborhood percent cover, species richness, harvesting treatment and their interaction with aridity (AHM), on drought incidence. Red indicates a negative slope; blue indicates a positive slope. The significance codes (*, **, ***, ., and ) correspond to p-values less than 0.05, 0.01, 0.001, 0.1, and 1, respectively, indicating the level of statistical significance for each coefficient estimate in the model. 0.71 1.17 5.51 0.00 1.52 1.01 1.63 7.16 *0.94 2.13 0.65 0.29 **0.98 0.98 0.92 *1.02 1.06 **(aridity × harvesting treatment [cc]) × neighbourhood percent cover(aridity × harvesting treatment [seed]) × neighbourhood percent cover(neighborhood species richness × aridity) × harvesting treatment [cc](neighborhood species richness × aridity) × harvesting treatment [seed]aridity × neighbourhood percent coverharvesting treatment [cc] × neighbourhood percent coverharvesting treatment [seed] × neighbourhood percent coveraridity × harvesting treatment [cc]aridity × harvesting treatment [seed]neighborhood species richness × harvesting treatment [cc]neighborhood species richness × harvesting treatment [seed]neighborhood species richness × aridityneighbourhood percent coverharvesting treatment [cc]harvesting treatment [seed]aridityneighborhood species richness1e−19 1e−14 1e−09 1e−04 10 1e+06Odds Ratios
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Simard, S. (Suzanne)
The interactive effects of harvesting intensity and climatic aridity on understory plant communities and Douglas-fir seedlings
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