Forestry, Faculty of
DSpace
UBCV
Comeau, Vanessa
Master of Science - MSc
University of British Columbia
The global rise in temperature and associated changes in climate have led to decline of forests around the globe, across multiple species and ecosystems. This includes yellow-cedar (Callitropsis nootkatensis) decline, which is one of the most severe in North America. I found abundant evidence of tree decline and mortality of yellow-cedar on Haida Gwaii across multiple watersheds and over a range of elevations. The decline on Haida Gwaii parallels the broader yellow-cedar decline in terms of spatial distribution, symptoms, magnitude and timing. Nevertheless, the proposed drivers on the mainland may not adequately explain the decline on Haida Gwaii, due to the more temperate climate and lack of persistent snowpack. I investigated several possible drivers both at the local and regional scale. My results are inconsistent with stand dynamics as a driver of elevated decline and mortality. Neither increased competition, nor aging of a cohort explains the decline. Onset of basal area increment decline and mortality have been accumulating over time, with increased rates since the 1980s. Alternatively, the magnitude and timing of the decline is consistent with well-documented multi-decadal variations and long-term directional trends in regional climate. I found patterns of divergent growth and divergent response to climate among yellow-cedars at the same sites. Yellow-cedars affected by decline were decreasing in growth and negatively associated with warmer drier winter conditions. Whereas, yellow-cedars not affected by decline were increasing in growth and positively associated with warmer growing season temperatures. The limiting factors for declining trees, warm dry winter conditions, are consistent with the hypothesis from the mainland that climate warming has led to root freezing. However, compared to drivers of yellow-cedar decline on the mainland, snowpack plays a less important role on Haida Gwaii. Warming temperatures likely have a more direct effect. I propose warmer winter temperatures have led to a combination of decreased cold-hardening and earlier dehardening, in conjunction with increased frequency of thaw-freeze cycles and freeze events during otherwise milder winters. This exposes yellow-cedar’s fine roots to varying degrees of freezing damage over multiple winter thaw-freeze cycles, causing physiological stress, tree decline and eventual death.
FREEZING TO DEATH IN A WARMING CLIMATE: DRIVERS OF YELLOW-CEDAR DECLINE ON HAIDA GWAII by Vanessa Comeau B.Env.Sc., The University of Manitoba, 2014 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 2019 © Vanessa Comeau, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Freezing to death in a warming climate: Drivers of yellow-cedar decline on Haida Gwaii submitted by Vanessa Comeau in partial fulfillment of the requirements for the degree of Master of Science in Forestry Examining Committee: Dr. Lori Daniels, Forestry Supervisor Stefan Zeglen, British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development Supervisory Committee Member Dr. Richard Hamelin, Forestry Supervisory Committee Member Dr. Sean Michaletz, Botany Non-departmental Examiner iii Abstract The global rise in temperature and associated changes in climate have led to decline of forests around the globe, across multiple species and ecosystems. This includes yellow-cedar (Callitropsis nootkatensis) decline, which is one of the most severe in North America. I found abundant evidence of tree decline and mortality of yellow-cedar on Haida Gwaii across multiple watersheds and over a range of elevations. The decline on Haida Gwaii parallels the broader yellow-cedar decline in terms of spatial distribution, symptoms, magnitude and timing. Nevertheless, the proposed drivers on the mainland may not adequately explain the decline on Haida Gwaii, due to the more temperate climate and lack of persistent snowpack. I investigated several possible drivers both at the local and regional scale. My results are inconsistent with stand dynamics as a driver of elevated decline and mortality. Neither increased competition, nor aging of a cohort explains the decline. Onset of basal area increment decline and mortality have been accumulating over time, with increased rates since the 1980s. Alternatively, the magnitude and timing of the decline is consistent with well-documented multi-decadal variations and long-term directional trends in regional climate. I found patterns of divergent growth and divergent response to climate among yellow-cedars at the same sites. Yellow-cedars affected by decline were decreasing in growth and negatively associated with warmer drier winter conditions. Whereas, yellow-cedars not affected by decline were increasing in growth and positively associated with warmer growing season temperatures. The limiting factors for declining trees, warm dry winter conditions, are consistent with the hypothesis from the mainland that climate warming has led to root freezing. However, compared to drivers of yellow-cedar decline on the mainland, snowpack plays a less important role on Haida Gwaii. Warming temperatures likely iv have a more direct effect. I propose warmer winter temperatures have led to a combination of decreased cold-hardening and earlier dehardening, in conjunction with increased frequency of thaw-freeze cycles and freeze events during otherwise milder winters. This exposes yellow-cedar’s fine roots to varying degrees of freezing damage over multiple winter thaw-freeze cycles, causing physiological stress, tree decline and eventual death. v Lay Summary Yellow-cedar is an incredibly important and valuable tree species found along the north-west coast of North America. Unfortunately, this iconic tree is in decline, with vast areas of yellow-cedar forests that have died as a result of climate change. Yellow-cedar is uniquely susceptible to climate warming due to its natural history and ecological strategies. Warmer winter temperatures have meant less snowpack and more frequent thaw-freeze cycles. The fine roots of yellow-cedar become damaged by frost during late winter and early spring freeze events when they are not protected by an insulating snowpack. Evidence of yellow-cedar decline has also been identified on the island archipelago of Haida Gwaii, where it was previously absent. My research found widespread evidence of yellow-cedar decline on the islands, going back decades. I found that even with the temperate climate of Haida Gwaii, warmer winter temperatures have led to the dieback and death of yellow-cedars. vi Preface This thesis is my original and unpublished work. The field methods and research questions were developed by Dr. Lori Daniels, Stefan Zeglen and I. I collected data and samples with the assistance of Jemina Coutu, Dan Hornsberger and Javier Heredia. I processed samples with the assistance of Jemina Coutu, Anna Weixelman and Spencer Bronson. I conducted the data analysis, interpretation and writing. Dr. Lori Daniels provided guidance and feedback at every stage of the process, including writing and editorial advice. Feedback on the manuscript was provided by Stefan Zeglen and Dr. Richard Hamelin. vii Table of Contents Abstract ................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents ....................................................................................................................vii List of Tables ...........................................................................................................................xii List of Figures........................................................................................................................ xiii List of Abbreviations .............................................................................................................. xiv Acknowledgements .................................................................................................................. xv Chapter 1: Introduction ............................................................................................................ 1 1.1 Introduction .................................................................................................................1 1.2 Forest dieback and decline ...........................................................................................1 1.3 Yellow-cedar decline ...................................................................................................3 1.4 Yellow-cedar decline on Haida Gwaii ..........................................................................6 1.5 Framework for my thesis .............................................................................................8 Chapter 2: Yellow-cedar Decline on the Island Archipelago of Haida Gwaii: A Recent Phenonmenon? ........................................................................................................................ 10 2.1 Introduction ............................................................................................................... 10 2.2 Methods ..................................................................................................................... 13 2.2.1 Study area .......................................................................................................... 13 2.2.2 Site selection ...................................................................................................... 14 2.2.3 Data collection ................................................................................................... 16 viii 2.2.4 Dendrochronological methods ............................................................................ 17 2.2.5 Comparisons among healthy, declining and dead trees ....................................... 19 2.3 Results ....................................................................................................................... 20 2.3.1 Death and decline across all size and age classes ................................................ 20 2.3.2 Attributes of declining yellow-cedar populations ................................................ 21 2.3.3 Little support for stand dynamics driving decline ............................................... 23 2.3.4 Differences in tree-ring versus canopy evidence of decline ................................. 23 2.3.5 Changes in growth during the latter 20th century................................................. 24 2.3.6 Mortality rates increased in the 1980s ................................................................ 26 2.4 Discussion ................................................................................................................. 28 2.4.1 Yellow-cedar decline on Haida Gwaii ................................................................ 28 2.4.2 Stand dynamics do not explain yellow-cedar decline on Haida Gwaii................. 28 2.4.3 Tree-rings provide novel insights on yellow-cedar decline ................................. 30 2.4.4 Climatic drivers of yellow-cedar decline ............................................................ 32 2.5 Conclusion................................................................................................................. 36 Chapter 3: Tree-rings Reveal Accelerated Yellow-cedar Decline with Changes in Winter Climate After 1980 .................................................................................................................. 37 3.1 Introduction ............................................................................................................... 37 3.2 Methods ..................................................................................................................... 39 3.2.1 Study sites .......................................................................................................... 39 3.2.2 Dendrochronological methods ............................................................................ 41 3.2.3 Climate-growth relations .................................................................................... 43 3.2.4 Wavelet transform analysis ................................................................................ 43 ix 3.3 Results ....................................................................................................................... 44 3.3.1 Site chronologies................................................................................................ 44 3.3.2 Regional chronologies ........................................................................................ 46 3.3.3 Climate and yellow-cedar growth ....................................................................... 48 3.3.4 Temporal frequency ........................................................................................... 51 3.4 Discussion ................................................................................................................. 53 3.4.1 Common growth signal present across sites ........................................................ 53 3.4.2 Separating trees by health status ......................................................................... 53 3.4.3 Divergent growth of trees of different health classes .......................................... 55 3.4.4 Climatic drivers of yellow-cedar decline ............................................................ 56 3.4.5 Consistent with Alaska hypothesis ..................................................................... 58 3.5 Conclusion................................................................................................................. 60 Chapter 4: Multiple Divergent Patterns in Yellow-cedar Growth Driven by Anthropogenic Climate Change ....................................................................................................................... 61 4.1 Introduction ............................................................................................................... 61 4.2 Methods ..................................................................................................................... 63 4.2.1 Study sites .......................................................................................................... 63 4.2.2 Data collection ................................................................................................... 64 4.2.3 Dendrochronological methods ............................................................................ 65 4.2.4 Dominant tree growth patterns ........................................................................... 67 4.2.5 Temporal variation in growth patterns ................................................................ 68 4.2.6 Comparison of tree attributes among groups....................................................... 69 4.2.7 Climate-growth relations .................................................................................... 69 x 4.3 Results ....................................................................................................................... 70 4.3.1 Four distinct growth patterns .............................................................................. 70 4.3.2 Distribution of age, size and crown health status ................................................ 75 4.3.3 Climate and yellow-cedar growth ....................................................................... 76 4.4 Discussion ................................................................................................................. 80 4.4.1 Identifying four distinct growth patterns among yellow-cedar ............................ 80 4.4.2 Patterns of growth divergence ............................................................................ 82 4.4.3 Climatic drivers of yellow-cedar decline ............................................................ 83 4.4.4 Fine scale drivers of yellow-cedar decline .......................................................... 87 4.5 Conclusion................................................................................................................. 89 Chapter 5: Conclusion ............................................................................................................ 90 5.1 Summary of main findings ......................................................................................... 90 5.2 Drivers of yellow-cedar decline on Haida Gwaii ........................................................ 93 5.3 Implications for management and conservation .......................................................... 95 5.4 Future research questions ........................................................................................... 97 5.5 Conclusion................................................................................................................. 99 References.............................................................................................................................. 100 Appendices ............................................................................................................................ 114 Appendix A Results of the Kolmogorov-Smirnov tests ....................................................... 114 Appendix B Yellow-cedar tree densities ............................................................................. 115 Appendix C Summary of yellow-cedar mean tree DBH ...................................................... 116 Appendix D Summary of yellow-cedar mean tree basal area............................................... 117 Appendix E Summary of yellow-cedar mean ages .............................................................. 118 xi Appendix F Distribution of yellow-cedar trees by height class ........................................... 119 Appendix G Summary of the ability to determine the outer ring dates ................................ 120 Appendix H Outer ring date estimates for trees .................................................................. 121 Appendix I Mortality rates of yellow-cedar ........................................................................ 122 Appendix J Characteristics of the site-level ring width chronologies .................................. 123 Appendix K Summary of sample size for site-level and regional chronologies ................... 124 Appendix L Ring width index of standard chronologies ..................................................... 125 Appendix M Correlation between climate variables and ‘healthy’ trees .............................. 126 Appendix N Correlation between climate variables and ‘declining’ trees ............................ 127 Appendix O Correlation between climate variables and ‘dead' trees. .................................. 128 Appendix P Distribution of the four principal components across Graham Island ............... 129 Appendix Q Correlation between climate variables and principal component A ................. 130 Appendix R Correlation between climate variables and principal component B .................. 131 Appendix S Correlation between climate variables and principal component C .................. 132 Appendix T Correlation between climate variables and principal component D .................. 133 xii List of Tables Table 2.1. Summary of site attributes ........................................................................................ 16 Table 2.2. Summary of yellow-cedar statistics by site ............................................................... 22 Table 2.3. Contingency tables comparing final growth patterns ................................................ 26 Table 3.1. Characteristics of the ring width chronologies .......................................................... 47 Table 4.1. Yellow-cedar tree attributes and growth patterns ...................................................... 71 Table 4.2. Characteristics of the basal area increment chronologies .......................................... 73 xiii List of Figures Figure 2.1. Location of sites exhibiting yellow-cedar decline and photos .................................. 15 Figure 2.2. Size and age distributions of yellow-cedars ............................................................. 21 Figure 2.3. Outer-ring dates of living yellow-cedars ................................................................. 24 Figure 2.4. Onset of the most recent growth pattern .................................................................. 25 Figure 2.5. Mortality and radial growth decline of yellow-cedar ............................................... 27 Figure 3.1. Location of sites exhibiting yellow-cedar decline.................................................... 40 Figure 3.2. Correlations among the site-level chronologies ....................................................... 45 Figure 3.3. Summary of negative and positive marker years ..................................................... 46 Figure 3.4. Ring width standard and residual chronologies ....................................................... 47 Figure 3.5. Averaged climatic records from 13 sites ................................................................. 48 Figure 3.6. Correlation between minimum temperature and residual chronologies .................... 50 Figure 3.7. Correlation between precipitation and residual chronologies ................................... 51 Figure 3.8. Morelet continuous wavelet transform .................................................................... 52 Figure 4.1. Location of sites exhibiting yellow-cedar decline.................................................... 64 Figure 4.2. Mean growth patterns of four groups of yellow-cedar trees ..................................... 72 Figure 4.3. Basal area increment standard and residual chronologies ........................................ 74 Figure 4.4. Observed and expected values of healthy, declining and dead trees ......................... 75 Figure 4.5. Size and age distributions of trees in four principal component chronologies .......... 76 Figure 4.6. Correlation between minimum temperature and principal components .................... 77 Figure 4.7. Correlation between monthly precipitation and principal components ..................... 78 Figure 5.1. Spiral of decline model for yellow-cedar decline .................................................... 93 xiv List of Abbreviations ANOVA - Analysis of variance AOS - Aerial overview survey BA - Basal area BAI - Basal area increment BC - British Columbia DBH - Diameter at breast height EPS - Expressed population signal KS test - Kolmogorov-Smirnov test PC - Principal component PCA - Principal component analysis PDO - Pacific Decadal Oscillation xv Acknowledgements First, thank you to my supervisor Dr. Lori Daniels for sharing your knowledge of and passion for BC’s coastal forests. Your guidance and feedback throughout this entire process were instrumental. You have taught me to take pride in a high quality of work, to be passionate about our forests, and how to effectively communicate this important research with others. Thank you for all the time and energy you have invested in this project, it wouldn’t have been possible without you. To the members of my committee, Stefan Zeglen and Dr. Richard Hamelin, thank you both for your help in developing these research questions and methods and for your feedback and insight along the way. I would also like to thank Nick Reynolds for support and guidance in the field and help with choosing field sites, your knowledge and suggestions were so valuable. As well as the team at the Heritage and Natural Resource Department of the Council of the Haida Nation for assisting with field work. Thank you to Jeff Mosher and Janice Stadey at Taan Forest, and to Aline Lachapelle, Sean Muise and others in the Haida Gwaii office of the BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development for supporting fieldwork and keeping us safe. Thank you to Lucy Stefanyk and BC Parks for letting us stay at the field house. To my colleagues; thank you Cameron Naficy for your much-needed help with R. Thank you Raphaël Chavardès for sharing your knowledge of tree-rings and climate, patiently teaching me methods and software, for your helpful suggestions, and your friendship. Thank you, Kelsey xvi Copes-Gerbitz for your feedback, insights and for always being willing to listen and help out. Your friendship and encouragement throughout this process were invaluable. I appreciate the assistance and support of all members of the Tree-Ring Lab. Especially my field crew; Jemina Coutu, Dan Hornsberger and Javier Heredia, who braved flat tires, hikes through dense coastal forests and rainy weather to help collect this data. Your enthusiasm, knowledge and good nature in the face of challenges were much appreciated. Thanks to lab assistants Jemina Coutu, Anna Weixelman and Spencer Bronson for helping me count all those tree-rings (over 120,000 rings!). Lastly, I would like to thank my friends and family for their unending support and for listening to presentations and talk about tree-rings and yellow-cedar. Thank you, Mom, Dad, Keith, Natasha, Hannah, Marina, and most of all Adrián. I am so lucky to have you all in my life. 1 Chapter 1: Introduction 1.1 Introduction Yellow-cedar (Callitropsis nootkatensis (D.Don) Oerst. Ex D.P. Little) decline is one of the most severe forest declines in North America, affecting 300,000 hectares (ha) of forest along the coast of northern British Columbia (BC) and southern Alaska (Westfall & Ebata 2015, Dubois & Wurtz 2017). Evidence of this decline spans back to the late 1800s, with more widespread and severe effects since the 1970s and 1980s (Hennon & Shaw 1994). The proposed driver of this decline is climate warming, rather than biotic factors (Hennon et al. 2006). Warmer winters have reduced insulating snowpacks and led to earlier dehardening, exposing yellow-cedars’ shallow fine roots to freezing damage, which causes moisture stress, dieback and eventual death (Hennon et al. 2012). More recently, yellow-cedar decline has been observed on Haida Gwaii where it was previously absent (CHN & BC 2011). The seeming mismatch between the climate on Haida Gwaii and the proposed decline mechanism led to this research, investigating the drivers of yellow-cedar decline on Haida Gwaii. 1.2 Forest dieback and decline Forest decline has been defined as widespread tree mortality and loss of vigour of a species resulting from a complex set of interacting abiotic and biotic factors (Manion 1991), often occurring over longer periods of time (Anderegg et al. 2013). Conversely, forest die-off or dieback commonly refers to a substantial pulse of tree mortality on a regional scale resulting directly from an abiotic or biotic driver, such as drought/heat stress or an infestation of insects or pathogens (Anderegg et al. 2013). Forest decline can be a normal part of a forest ecosystem, 2 contributing to death of individual trees (Manion 1991). However, forest declines are becoming more common and widespread across diverse tree species and landscapes around the world. As early as the 1960s Hepting (1963) suggested the involvement of significant climate changes in forest declines. Since then, a growing body of evidence suggests that many forests around the globe are increasingly vulnerable to climate-associated tree-mortality events (Allen et al. 2010, Peng et al. 2011, Anderegg et al. 2013). Changes in temperature, precipitation, and insect and pathogen dynamics, as well as more extreme climatic events are expected to lead to increased instances of widespread forest dieback and decline in the future (Adams et al. 2009, Allen et al. 2015). Increased levels of both forest decline and forest dieback have been observed on every forested continent, concurrent with anthropogenic climate change. This includes increases in background mortality rates and regional-scale forest dieback (e.g. van Mantgem et al. 2009, Peng et al. 2011, Zhou et al. 2014), spanning diverse ecosystems and moisture regimes (Allen et al. 2010). Much of this results directly from heat or drought stress, or indirectly through climate-mediated effects on insects and pathogens. Hotter temperatures resulting from anthropogenic climate change have increased the vulnerability of trees to drought and associated pests and pathogens (Adams et al. 2009, Allen et al. 2015). There are examples of this from around the globe, including aspen (Populus tremuloides Michx.) dieback in the western United States and Canada, which is driven by drought (Michaelian et al. 2011, Hanna & Kulakowski 2012). In Patagonia, the declines of both Austrocedrus chilensis (D.Don) Pic. Sern. et Bizarri and Nothofagus pumilio (Poepp. & Endl.) Krasser resulted in part from climatic changes and extreme drought conditions (Amoroso et al. 2015, Rodríguez-Catón et al. 2016). In southern Europe, Pinus spp. have exhibited decline in response to drought (Sánchez-Salguero et al. 2012). There 3 has been a widespread decline in forest greenness in the Congo rainforest likely due to a long-term drying trend (Zhou et al. 2014). Additionally, high elevation and high latitude species across the circumpolar north are experiencing decreased vigour, tree dieback and mortality in response to warmer growing season temperatures (Wilmking et al. 2005, Lloyd & Bunn 2007). Many other examples of tree mortality driven by drought or heat stress associated with climate change exist worldwide, summarized by Allen et al. (2010). In addition to the direct impacts of increased growing season temperatures, indirect impacts of climate warming have also resulted in forest dieback and decline. This includes the increased frequency of late winter and early spring thaw-freeze cycles, which can lead to melting of insulating snowpack and root damage to susceptible trees. This mechanism is the proposed driver of yellow birch (Betula alleghaniensis Britt.) decline in eastern Canada (Bourque et al. 2005) and yellow-cedar decline in western Canada and Alaska (Hennon et al. 2012). As forest declines and diebacks have become more pervasive, this has emerged as an important global issue in recent decades. In 1994 the Food and Agriculture Organization of the United Nations developed an overview of forest declines and diebacks worldwide, demonstrating how widespread this phenomenon is and how varied the causes are (Ciesla & Donaubauer 1994). 1.3 Yellow-cedar decline In the Pacific Northwest, the decline of yellow-cedar is one particularly severe example of climate-induced mortality. Yellow-cedar is an important part of the ecosystem, culturally significant and economically valuable. Its wood and bark have been used by the region’s Indigenous peoples for everything from shelter and clothing to canoe paddles and masks (Pojar & MacKinnon 1994). Yellow-cedar is found along the Pacific coast from the California-Oregon 4 border to Alaska (Harris 1990). It is one of the slowest growing conifers in the Pacific Northwest, has few natural disease or insect agents, and can live more than 1000 years (Harris 1990). Throughout most of its range, yellow-cedar is restricted to high elevations, however from the midcoast of BC northward, yellow-cedar is found from sea-level to near timberline (Harris 1990). It is in these mid to northern regions that yellow-cedar has experienced widespread mortality and decline over the past century (Hennon et al. 2012). Dieback of yellow-cedar forests was first observed in Alaska and has more recently been found on the northern coast of BC, extending the area of known decline 150 km south of the Alaska border (Hennon et al. 2005, Buma 2016). More than 240,000 ha of decline have been mapped in southeastern Alaska since the late 1980s (Dubois & Wurtz 2017) and another 40,000 ha have been mapped in BC (Westfall & Ebata 2015), making this one of the most severe forest declines in North America. Most mortality has occurred in open canopy forests on poorly drained sites, at low elevations in more northerly latitudes and slightly higher elevations in more southerly latitudes (Hennon et al. 1990a; Hennon & Shaw 1997). Symptoms of decline appear first in the root systems followed by crown thinning, bole lesions, and eventual death (Hennon et al. 1990b). Stand age studies suggest that declining yellow-cedar populations established during the Little Ice Age (Beier et al. 2008; Hennon et al. 1990c; Stan et al. 2011), a cooler period, lasting from c. 1400 until around 1850, when most glaciers in the region reached their last maximum extension (Mann 2002). The onset of yellow-cedar decline occurred between 1880 and 1900, roughly coinciding with the end of the Little Ice Age (Hennon et al. 1990c). Yellow-cedar mortality continued through the 1900s, intensifying in the 1970s to 1980s (Hennon & Shaw 1994). 5 The driving factor of this regional decline is thought to be climatic warming since the end of the Little Ice Age, rather than biotic factors (Hennon et al. 1990a; Hennon et al. 1990b). Increasing late winter temperatures are leading to reduced snowpack and earlier dehardening. Cold hardening is the physiological process by which plants protect against cold weather, dehardening is the reverse of this process (Klimov 2003). Combined with severe late winter and early spring freeze events, this causes fine root injury, moisture stress, decline and eventual tree death (Hennon et al. 2006; Hennon et al. 2010). Yellow-cedar roots are both less cold tolerant and more concentrated in the upper soil horizons than co-occurring species (Schaberg et al. 2011). Hennon et al. (2012) suggests that the risk of tree mortality can be defined by snow depth and soil drainage; snow cover acts to insulate the soil and buffer temperature fluctuations (Schaberg et al. 2008), while poorly drained soils limit nutrient cycling and rooting depth. This, in turn, inhibits growth rates and reduces canopy cover; forests with less canopy cover experience more extreme daily temperature ranges and earlier snow melt (Hennon et al. 2012). Yellow-cedar also dehardens earlier than other species, in response to temperature rather than photoperiod (day length; Schaberg et al. 2011). These physiological factors make yellow-cedar uniquely susceptible to fine root damage and decline, particularly with warmer winter temperatures and reduced snowpacks in recent decades driven by global warming and multi-decadal climate variation. One example of influential climatic variation is the Pacific Decadal Oscillation (PDO), an ocean-atmospheric climate regime that may be exacerbating yellow-cedar decline in its positive (warm) phase. Pulses of mortality occurring in the 1970s and 1980s (Hennon & Shaw 1994) correspond with a shift in the PDO from negative to positive, which occurred in 1977 (Mantua & Hare 2002). This is consistent with the present hypothesis of warming climate as the driver of 6 yellow-cedar decline. Beier et al. (2008) found that five of the top 10 most extreme thaw-freeze events of the 20th century have occurred since 1987 and that the most extreme example of a thaw-freeze event coupled with low snowcover occurred in 1987. Similarly, a pilot study on Haida Gwaii, found a change in growth between live, declining, and dead trees, as well as, an increase in mean sensitivity in the 1970s for trees which were dead at the time of sampling (Daniels et al. 2016; Knochenmus 2016). 1.4 Yellow-cedar decline on Haida Gwaii Yellow-cedars on Haida Gwaii have exhibited canopy decline and tree mortality in more recent years, becoming visually obvious in several stands in the 2000s (CHN & BC 2011) and spanning 30,000 ha of forest as of 2017 (S. Zeglen, personal communication, March 25, 2019). Visually, the patterns of decline observed on Haida Gwaii are similar to those seen in Alaska and along the north coast of BC. In both cases, stands of yellow-cedar decline are typically composed of long-dead, recently dead, dying, and some surviving trees, suggesting that mortality is long-term and continuing (Hennon et al. 2012). Declining stands on Haida Gwaii also appear to be concentrated on wetter sites, mirroring patterns seen in Alaska and the north coast of BC. Although mortality exhibits similar patterns and timing on Haida Gwaii compared to the rest of the species’ range, the proposed mechanisms of climate-driven decline for coastal BC and Alaska do not fit for Haida Gwaii due to the temperate climate and the ephemeral snowpack. Haida Gwaii does not typically experience persistent snowpacks or extreme freeze events like those seen on the mainland (Banner et al. 2014). The climate of Haida Gwaii has been described as wet – hypermaritime. The climate is heavily regulated by the Pacific Ocean and winter temperatures are affected by proximity to marine areas. Snowpack along the coast is usually 7 ephemeral, with more snow falling at higher elevations (Banner et al. 2014), however declining stands of yellow-cedar are present in Haida Gwaii from sea-level upwards. Mean annual temperature is 7-8°C at low elevations and slightly higher than 6°C for montane areas, with narrow daily, seasonal and annual ranges (Banner et al. 2014). This re-introduces the possibility of a pathogen as the cause of decline on Haida Gwaii. On the coast, biotic agents were systematically ruled out as the cause of decline through a series of experiments, summarized by Hennon et al. (2006). However, as the drivers of decline on the coast do not seem to fit for Haida Gwaii climate may not be the main cause of decline in these forests. It is also possible that stand level competition is responsible for the dead and declining yellow-cedars. Moreover, yellow-cedar is slow to decay and dead trees can remain standing for over 200 years (Kellner et al. 2000, Stan et al. 2011), therefore trees which have been long dead may be still standing in these forests creating the appearance of an elevated level of death. These potential explanations for the seemingly high level of dead and declining yellow-cedar will be investigated through multiple lines of evidence. In addition to the decline of canopy and sub-canopy yellow-cedar trees, there is a unique threat to seedlings and saplings on Haida Gwaii. Yellow-cedar is severely browsed by introduced, invasive Sitka black-tailed deer on Haida Gwaii (Martin & Daufrense 1999). The deer prefer western redcedar (Thuja plicata Donn ex D. Don) and yellow-cedar, and as a result there are fewer of these trees in the younger age class. A study on the vascular plant communities of 10 islands in Haida Gwaii found that yellow-cedar was only present on larger islands and had been eliminated from smaller islands where deer were present (Gaston et al. 2006). Deer now play a major role in structuring the plant community on Haida Gwaii. It has been suggested that a 8 large unmanaged deer population would greatly reduce or possibly eliminate yellow-cedar on Haida Gwaii in the long-term (Salzl et al. 1999). The widespread decline present along the coasts of Alaska and BC creates concern for the future of yellow-cedar on Haida Gwaii, especially when combined with the browsing effects of deer on the islands. Land managers, locals and members of the Haida Nation have all expressed concern. As a result of this and the seeming mismatch between proposed drivers on the mainland and climate conditions on Haida Gwaii, my research focuses on investigating possible drivers of yellow-cedar decline on Haida Gwaii. 1.5 Framework for my thesis I used population demography and dendrochronology to determine the timing and severity of decline within stands exhibiting visual symptoms of yellow-cedar decline. I then tested the hypotheses that (1) stand dynamics or (2) climate drove increased rates of tree decline and death. If competition and self-thinning were responsible for the observed tree decline and mortality of yellow-cedar, smaller and younger trees would be more severely affected. Alternatively, if this decline and mortality was due to aging of a cohort, the oldest and largest trees would be expected to show higher proportions of affected trees. I hypothesize that the decline and mortality of yellow-cedars observed was not due to either competition or aging of a cohort. The second hypothesis is that climate is the driver of the observed yellow-cedar decline on Haida Gwaii, in line with the mechanism developed in Alaska (Hennon et al. 2012). Meaning warmer winters lead to melting of snow, increased frequency of thaw-freeze cycles, and freezing events that cause damage to yellow-cedars’ fine roots. Under this scenario warmer winter temperatures and lower winter precipitation (as snow) would be associated with lower growth 9 rates and onset of decline or death of affected trees. I hypothesize that yellow-cedar decline on Haida Gwaii is driven by climate, through a similar mechanism to the yellow-cedar decline on the mainland. The three data chapters of my thesis explore these questions. Chapter 2 quantifies the severity and timing of decline and mortality within visually declining stands of yellow-cedar. I tested for when decline began and intensified, the presence of external and internal symptoms of decline, and tested the hypothesis that stand dynamics drove decline. Chapter 3 tested for common patterns of growth among trees and response to climate over time. Chapter 3 also tested the hypothesis that climate is driving yellow-cedar decline, by investigating whether response to climate differed by the health status of trees and over different phases of the PDO. Chapter 4 tested for distinct patterns of growth present within stands of declining yellow-cedar. Chapter 4 then tested for evidence of climate as the driver of decline, by investigating how trees exhibiting divergent patterns of growth responded to climate over time as climate warmed. Finally, Chapter 5 summarized my findings and implications for management. Each chapter builds on those preceding, working to uncover the complex drivers of yellow-cedar decline on Haida Gwaii. 10 Chapter 2: Yellow-cedar Decline on the Island Archipelago of Haida Gwaii: A Recent Phenonmenon? 2.1 Introduction Changes in climate, including warming temperatures and droughts, have led to forest declines around the world (van Mantgem et al. 2009, Allen et al. 2010, Allen et al. 2015, Cohen et al. 2016). One of the most severe examples in North America is yellow-cedar (Callitropsis nootkatensis) which has experienced widespread tree mortality and canopy decline in the middle of its latitudinal range for over a century (Hennon et al. 2012). More than 240,000 ha of declining forests have been mapped in southeastern Alaska since the late 1980s (Dubois & Wurtz 2017) and another 40,000 ha have been mapped along the north and mid-coast of British Columbia (BC) (Westfall & Ebata 2015), collectively referred to as yellow-cedar decline. Most mortality has occurred in open canopy forests with poorly drained sites on warm (south and southwest) aspects, at low elevations in more northerly latitudes and slightly higher elevations in more southerly latitudes (Hennon et al. 1990a, Hennon & Shaw 1997, Hennon et al. 2006). There is strong evidence that the driving factor of yellow-cedar decline is regional climatic warming, rather than local biotic factors (Hennon et al. 1990a, 1990b). At the tree level, symptoms of yellow-cedar decline appear first in the root systems, followed by crown thinning, bole lesions, and eventual death (Hennon et al. 1990b). Extensive research in Alaska has eliminated root pathogens as the primary cause (Hennon et al. 1990b, Hennon & Shaw 1994). Rather, yellow-cedar’s physiology makes it uniquely susceptible to fine root damage and tree decline, particularly with warmer winter temperatures and reduced snowpacks in recent decades 11 (Hennon et al. 2012). Increasing late winter temperatures are leading to earlier dehardening of yellow-cedar, which takes place earlier than co-occurring species in response to warming temperature (Schaberg et al. 2011). Snowpack acts as an insulating layer protecting yellow-cedar’s fine roots, which are abundant in the shallow horizons of wet soils (Schaberg et al. 2008, 2011, D’Amore et al. 2009). Reduced snowpacks make the shallow roots of yellow-cedar more vulnerable to periodic severe late winter and early spring freeze events that cause fine root injury, that subsequently results in moisture stress, crown symptoms and eventual tree death (Hennon et al. 2006, 2010). Consistent with this mechanism, Beier et al. (2008) found that in southern Alaska, nine of the top ten years with most extreme thaw-freeze impact of the second half of the 20th century occurred since 1983 and that the most extreme example of a thaw-freeze event coupled with low snowcover occurred in 1987. Though Beier et al. did not look at dead trees, all surviving trees contained a marker year of exceptionally low growth in 1987, and chronologies from declining sites increased after this year, possibly due to competitive release of surviving trees. Mortality in recent decades may reflect background decadal to multi-decadal climate variation, acting synergistically with regional warming. An important influence on the climate of coastal Alaska and BC is the Pacific Decadal Oscillation (PDO), an ocean-atmospheric climate regime that may be exacerbating yellow-cedar decline in its positive (warm) phase. Pulses of tree mortality in the 1970s and 1980s (Hennon & Shaw 1994) correspond with the 1977 shift in the PDO from negative to positive phase (Mantua & Hare 2002). During the positive phase, regional climate is warmer, and the snow-rain threshold is elevated so that lower-elevation forests have shallower and less persistent snowpacks making yellow-cedars more vulnerable to thaw-freeze 12 events. Mortality following this climatic shift is consistent with the hypothesis that warming climate is a primary driver of yellow-cedar decline. The elevated levels of yellow-cedar decline and mortality recently observed on the Haida Gwaii archipelago along the north coast of BC are of great concern (CHN & BC 2011). Although yellow-cedar mortality exhibits similar spatial patterns compared to the adjacent mainland of BC and Alaska, the proposed mechanisms of climate-driven decline may not adequately explain the decline on Haida Gwaii. The wet–hypermaritime climate of Haida Gwaii is heavily regulated by the Pacific Ocean, with cool moist summers and wet winters with little snowfall (Banner et al. 2014). Much of Haida Gwaii exists within the snow-rain threshold, where winter temperatures typically range between −2 and 2°C (Buma et al. 2016), which is not cold enough to freeze yellow-cedar fine roots that are cold hardy to -5°C (Schaberg et al. 2008, 2011). Snow pack along the coast is usually ephemeral, with more snow falling at higher elevations (Banner et al. 2014). However, yellow-cedar decline has affected 29,688 ha of forest on Haida Gwaii (tallied from 2006-2017, S. Zeglen, personal communication, March 25, 2019), from the mountains of Graham and Moresby Islands, down to sea-level in the north. This research addresses the fundamental question: What is causing yellow-cedar decline on Haida Gwaii? Before addressing the primary hypothesis that climate is driving yellow-cedar decline, I tested the alternative that stand dynamics are responsible for the observed increase in declining and dead yellow-cedars. High rates of competition and self-thinning associated with stand development disproportionately affects young or small statured trees. Alternatively, senescence of a cohort of trees can lead to increased mortality of old and large trees (Franklin et al. 2002). Therefore, at sites exhibiting crown symptoms and tree death, I quantified the 13 magnitude of decline and compared the relative abundance, age and size of healthy, declining and dead yellow-cedars to assess the potential effects of stand dynamics. I then tested the primary hypothesis that climatic change is responsible for the yellow-cedar decline on Haida Gwaii due to low snowpack that makes yellow-cedars vulnerable to thaw-freeze weather events. To differentiate potential climatic influences at a range of temporal scales, I used dendrochronology to uncover the timing of mortality and the onset of tree decline in basal area increment (BAI) of individual trees. Episodic mortality or onset of tree decline over discrete periods of one to a few years would be consistent with periodic thaw-freeze events driving tree decline. Alternatively, increased rates of mortality and onset of tree decline since the 1977 shift to the warm phase of the PDO would indicate the importance of multi-decadal variation on regional temperatures and snowpack. Finally, long-term directional climate change may have caused physiological stress to trees and resulted in widespread decreases in growth and increasing rates of tree decline and mortality over the 20th century and to the present. 2.2 Methods 2.2.1 Study area This research was conducted on Graham Island, Haida Gwaii, which is the largest of over 150 islands that make up the archipelago off the north coast of British Columbia. The climate is wet–hypermaritime, with cool moist summers and wet winters, with little snowfall. The mean annual temperature is 8.6 ± 1.2°C (± standard deviation) at Sandspit on Haida Gwaii (53°15’N 131°48’W, 6.4 m above sea level), with narrow daily, seasonal and annual ranges (Environment Canada Weather Archives 2018). On Haida Gwaii yellow-cedar is commonly found in wetter boggy sites, with a more open canopy, but can also be found at lower densities on more well- 14 drained sites. Yellow-cedar does well in deep, slightly acidic, moist soils (Parish & Thomson 1994) and is commonly associated with western redcedar (Thuja plicata), Sitka spruce (Picea sitchensis), western hemlock (Tsuga heterophylla), or mountain hemlock (Tsuga mertensiana) at higher elevations. 2.2.2 Site selection To address questions about the drivers of yellow-cedar decline, I used a purposive sampling approach to identify sample sites with declining and dead yellow-cedar. This study was not intended to quantify or represent the frequency or degree of yellow-cedar decline in the broader landscape. Potential study sites were identified using the most recent aerial overview survey (AOS) and aerial photographs taken in 2015, combined with knowledge gained from local experts. From aerial observation and ground-truthing, I confirmed that numerous patches of forest exhibited symptoms of yellow-cedar decline and eliminated sites dominated by dead western redcedar and western hemlock. Limited road access prevented some sites from being accessed for reasons of safety and efficiency. I identified and sampled 13 suitable sites in the southern portion of Graham Island and two additional sites near the north coast of the Graham Island (Figure 2.1). These sites were distributed across multiple watersheds and over a range of elevations where decline has been documented (Table 2.1). Each site was located adjacent to a water feature such as a stream or wetland. The stands sampled within sites contained declining yellow-cedar as a codominant species, mixed with Sitka spruce, western hemlock and western redcedar. None of the sites exhibited evidence that wind, fire, insects or pathogens were driving mortality at the stand level. 15 Figure 2.1. Location of (a) 15 sites exhibiting yellow-cedar decline on Graham Island, Haida Gwaii. Photos of (b) healthy, (c) declining, (d) dead yellow-cedar, and (e) a stand of yellow-cedar exhibiting decline. 16 Table 2.1. Summary of site attributes. Site ID Lat and Long (dec degrees) Elev. (masl) Slope† Angle (°) Aspect AWU1 53.58, -132.55 370 6/5 NW BRE1 53.31, -132.22 437 14/4 NE CHI1 53.28, -132.09 410 13/12 S DAT1 53.54, -132.59 356 23/15 NE DAT2 53.54, -132.57 253 19/27 NE DEL1 54.00, -132.08 87 N/A‡ N/A‡ DIN1 53.64, -132.72 243 12/10 NE FLO1 53.53, -132.26 321 13/2 NE/W GHO1 53.40, -132.37 365 15 NW IRA1 53.60, -132.61 278 12/11 NE PHA1 53.37, -132.35 311 12 E PHA2 53.37, -132.34 198 6/14 NW REN1 53.35, -132.34 207 14 N REN2 53.34, -132.36 310 11/10 E SKO1 54.02, -131.97 45 N/A‡ N/A‡ †two values reported if the two transects differed ‡no slope data for two sites in the north 2.2.3 Data collection I collected biophysical information at each site including location (GPS coordinates), elevation, slope angle and aspect. To assess population dynamics and drivers of decline, I conducted a census of yellow-cedars in two 50m x 20m transects per site oriented along contour lines to ensure the sampled areas were as homogeneous as possible with respect to the biophysical traits and included representative areas exhibiting yellow-cedar decline. To represent yellow-cedar population structure, I conducted a census of yellow-cedars with diameter at breast height (DBH, 1.3 m) ≥10cm within each transect and I estimated site-level population density and relative density of yellow-cedars in each of three health status classes. Standing trees were classified as “healthy” if they had full, green crowns, “declining” if they had abundant discoloured foliage or were partly defoliated, or “dead” if they lacked foliage 17 or had only brown foliage (Figure 2.1); logs were not sampled. I recorded the DBH and height class (dominant, co-dominant, intermediate or suppressed) of each tree. I took increment cores from 25-30 trees at each site to enable tree-ring analyses to detect internal evidence and to quantify the timing, duration and severity of basal area increment (BAI) decline. To objectively select trees, I cored the first 15 healthy yellow-cedars (except two sites where only 10 and 14 healthy trees were available to sample), and the first 15 declining or dead yellow-cedars along the transects at each site. One core, sampled c. 115cm above the ground, was taken from each tree and diameter at core height was measured. I aimed to intercept the pith and, where possible, I included the bark or outer cambium. Trees with decayed outer rings or heartwood that obscured rings were replaced with the next tree along the transect in the same health status class. For transects with fewer than 15 healthy or 15 declining and dead yellow-cedars that were suitable to core, additional trees were sampled and cored from outside of the transect. These supplemental trees were not included in density calculations or other representations of population structure. 2.2.4 Dendrochronological methods The cores were air dried, glued onto wooden supports, sanded with progressively finer sand paper to 600 grit, and scanned at a resolution of 2400 or 3200 dpi. Ring widths were measured (to 0.001mm) using the program CooRecorder (version 7.8, Larsson 2014a). Cores were cross-checked under a microscope, particularly for areas with very suppressed or difficult to distinguish rings. Using the program CDendro (version 7.8, Larsson 2014b), I initially crossdated each ring-width series against existing yellow-cedar chronologies for three sites on Haida Gwaii (Daniels et al. 2016). Ring-width chronologies were assembled for each site and crossdating was confirmed using the programs CDendro and COFECHA (Holmes 1983). 18 Crossdating improved the quality of age estimates by accounting for missing or false rings. For cores that did not intercept the pith, the number of missed rings was estimated based on the dimensions of the rings closest to the pith (Duncan 1989). If the number of rings added exceeded one third of the total number of rings, the sample was excluded from age estimations. If the radius of a tree exceeded the length of the increment borer or the tree had abundant heartwood decay, age could not be accurately estimated and the sample was excluded from age estimates. Crossdating also allowed me to assign a calendar year to the outermost ring, indicating the year of death of snags and cessation of radial growth of some living trees. I took a conservative approach and assessed the quality of each outer-ring date, as follows. Dates assigned to trees with clearly distinguishable outer rings that were crossdated with their site chronology were considered “accurate”. Dates assigned to outer rings that were crossdated, but extremely narrow or showed evidence of decay were considered “best estimates”. I eliminated trees for which the outer rings did not crossdate with the site chronology. For analyses of growth trends over time, I calculated BAIs from ring widths. This approach removes the age- and size-related trends in ring widths that result from adding the same amount of wood to an increasing diameter, while maintaining year-to-year and longer-term variation. BAIs were calculated by subtracting the cross-sectional area in year t–1 from that in year t (LeBlanc 1990, Phipps 2005), starting from the pith to the bark. For cores that missed the pith, the distance to the pith was calculated from geometric measures of arced rings (Duncan 1989). For cores that included neither pith nor arcs, the length of the core was subtracted from half of the diameter at coring height (minus average bark thickness) to estimate the missing distance to pith. I applied a correction factor to the raw ring-widths to account for asymmetric growth around the diameter of the tree (correction = [diameter at coring height/2 – average bark 19 thickness] / [length of radius measured + distance to pith]). BAIs were calculated from the corrected ring-width series using the inside-out method on the dplR package in R (Bunn 2008, 2010, Bunn et al. 2017, R Core Team 2014). 2.2.5 Comparisons among healthy, declining and dead trees I undertook three tests to determine if tree decline and death could be attributed to stand development. First, to test for significant differences among the healthy, declining and dead populations of trees, I compared the frequency distributions of age, DBH, and basal area using two-sample Kolmogorov-Smirnov tests (KS test) for all pairwise combinations (Kolmogorov 1933; Smirnov 1939). Second, I used analysis of variance (ANOVA) to determine if the mean diameters of declining or dead trees were significantly different from healthy trees. Third, the number of healthy, declining and dead trees in the overstory (canopy dominant and co-dominant layers) versus understory (intermediate and suppressed layers) were compared in contingency tables using the chi-squared goodness of fit statistic. For these three tests, evidence that tree decline and death affected small or young trees in the understory more acutely than large or old trees in the overstory, would indicate competitive exclusion of small-statured trees. Alternatively, disproportionate effects on old trees would indicate senescence. I tested for internal symptoms of yellow-cedar decline in two ways. First, by assessing the outer-ring date of each tree, I identified living trees, including trees classified in the field as healthy or declining, that were no longer forming annual rings. Second, to determine the timing of the onset of BAI decline in individual trees, I identified breakpoints in the BAI series of individual trees that differentiated periods of increasing and decreasing growth during the 20th century. Breakpoints were identified using the segmented package in R, which fits a piecewise regression model to each raw ring-width series using an iterative approach (Muggeo 2008, R 20 Core Team 2014). I tested for breakpoints from 1901−2015; however, some trees established after 1901 or stopped ring formation before 2015. Therefore, I tested for (a) 3 breakpoints in samples that included ≥80 years between 1901 and 2015, (b) 2 breakpoints in samples with 40-79 years, and (c) one breakpoint in samples with ≤39 years. The pattern of growth during each segment was characterized as decreasing or increasing based on the slope of the line and the start and end dates of each segment indicated the duration in years that the growth pattern persisted. The last pattern of growth ended when rings stopped forming or the year of sampling. Annual mortality rates were calculated as the number of trees that died in each calendar year (based on outer-ring dates) divided by the sample depth or the number of living trees that could have died each year. Decadal mortality rates were calculated by averaging the annual mortality rates in each decade. Mortality rates were calculated using (a) the most accurate outer-ring dates only, and (b) both the most accurate and best estimates of outer-ring dates. 2.3 Results 2.3.1 Death and decline across all size and age classes Yellow-cedar decline, including canopy dieback and death, was present at varying levels across the landscape. Death and decline affected trees of all ages and sizes, but there were also healthy live trees across the range of ages and sizes (Figure 2.2). The distributions of DBH, basal area and age did not differ significantly among the three health status categories (Figures 2.2a-c; Appendix A; p > 0.05 for all pairwise tests). Although differences were not significant, there was a higher proportion of healthy trees in the 0-99 and 100-199 year categories compared to the declining and dead trees (Figure 2.2c). 21 Figure 2.2. Size and age distributions of yellow-cedar trees by health status class (healthy=green, declining=grey, dead=black). Class sizes for each frequency distribution are (a) 10cm for diameter at breast height (DBH) (n = 1016), (b) 0.10 m2 for basal area (n = 1016), and (c) 100 years for age estimates (n = 377). (d) Trees are stratified by canopy position (n = 1011). 2.3.2 Attributes of declining yellow-cedar populations A total of 1016 yellow-cedars were sampled, 26% of which were healthy, while 74% were declining (27%) or dead (47%) (Table 2.2; Appendix B). At the site level, the proportion of declining and dead trees ranged from 59 to 86%, with healthy trees making up 14 to 41% (Appendix B). The site REN1 showed the highest proportion of dead and declining versus healthy yellow-cedar, while the site DIN1 showed the highest proportion of healthy yellow- 22 cedar. The density of yellow-cedar ranged from 125 to 740 trees/ha, with an average of 339 ± 192.2 (± standard deviation) trees/ha (Table 2.2; Appendix B). Table 2.2. Summary of yellow-cedar statistics by site. Site ID Sample size (trees) Density (trees/ha) Proportion of Trees Mean ± Standard Deviation Proportion of Trees Healthy Declining Dead DBH (cm) BA (m2) Age (years) Over-story Under-story AWU1 132 660 0.26 0.49 0.25 24.2±12.4 0.058±0.065 367±246 0.39 0.61 BRE1 127 635 0.28 0.25 0.46 28.0±12.8 0.075±0.067 230±147 0.51 0.49 CHI1 30 150 0.33 0.17 0.50 45.9±19.3 0.201±0.138 297±146 0.73 0.27 DAT1 46 230 0.37 0.22 0.41 52.8±27.1 0.256±0.261 389±214 0.59 0.41 DAT2 89 445 0.27 0.27 0.46 33.5±18.3 0.114±0.143 235±128 0.57 0.43 DEL1 52 260 0.19 0.27 0.54 39.3±16.3 0.142±0.116 339±86 0.69 0.31 DIN1 51 255 0.41 0.25 0.33 36.6±19.5 0.135±0.153 207±90 0.47 0.53 FLO1 37 185 0.24 0.19 0.57 38.7±19.1 0.155±0.130 226±133 0.76 0.24 GHO1 25 125 0.36 0.40 0.24 35.6±16.0 0.100±0.088 315±225 0.60 0.40 IRA1 148 740 0.18 0.29 0.53 22.3±10.5 0.048±0.055 253±159 0.32 0.68 PHA1 40 200 0.35 0.03 0.63 43.7±25.5 0.177±0.267 347±174 0.63 0.38 PHA2 71 355 0.25 0.30 0.45 38.2±18.0 0.140±0.171 324±120 0.69 0.31 REN1 36 180 0.14 0.19 0.67 41.8±18.3 0.133±0.139 329±126 0.56 0.44 REN2 84 420 0.20 0.24 0.56 28.7±14.9 0.082±0.090 288±179 0.61 0.39 SKO1 48 240 0.31 0.15 0.54 29.5±11.3 0.078±0.058 216±94 0.60 0.40 The average DBH of the sampled yellow-cedars was 31.9 ± 17.9 cm (n = 1016); the largest DBH was 140.0 cm (Table 2.2; Appendix C). The corresponding average basal area was 0.105 m2 ± 0.136 m2; the largest basal area was 1.539 m2 (Table 2.2; Appendix D). Among sites, yellow-cedars had the greatest average DBH and basal area at the site DAT1 (52.8 cm and 0.256 m2), and the smallest values at the site IRA1 (22.3 cm and 0.048 m2). The estimated yellow-cedar ages ranged from 29 to 1004 years (n = 377 trees), with an average of 288 ± 159 years (Table 2.2, Appendix E). Age could not be estimated for 67 trees, as the cores did not reach pith or arcing rings near the pith that enabled age corrections to be applied. All sites included trees of a broad range of estimated ages. Site AWU1 included the 23 oldest tree (1004 years) and site DAT1 had the greatest average age of 389 ± 214 years; site DIN1 included the youngest trees, on average (207 ± 90 years). The height classes were determined for 1011 trees; the height class could not be determined for 5 severely decayed snags. Most yellow-cedars were in the co-dominant (115 ± 66 trees ha-1) or intermediate (99 ± 93 trees ha-1) height classes, with fewer dominant (54 ± 31 trees ha-1) or suppressed (51 ± 43 trees ha-1) trees (Appendix F). 2.3.3 Little support for stand dynamics driving decline The declining and dead trees had no clear relation to size, age or canopy position (Figure 2.2). The mean DBH values of declining trees (29.4±8.1 cm) was significantly smaller than healthy (33.1±10.1 cm) and dead (32.8±8.7 cm) trees (F=4.00, p=0.020). The average DBH of healthy and dead trees did not differ significantly. The number of healthy, declining and dead trees differed significantly between the understory and overstory (chi-squared=20.27, p<0.001, Figure 2.2d). Healthy and declining trees were more abundant in the understory and dead trees were more abundant in the overstory than expected by chance. 2.3.4 Differences in tree-ring versus canopy evidence of decline I determined the outer-ring date on 306 of 325 living (healthy or declining) trees; 19 trees were excluded from analyses due to suppressed outer rings or inability to crossdate (Appendix G). Many living trees in the healthy and declining categories had not produced a ring (at the location the core was taken) for the past 1 to 99 years (Figure 2.3). Of 213 live trees with crowns that appeared healthy, 193 (90.6%) produced a ring the year of sampling, but 20 (9.4%) did not. Of the 93 declining trees, 41 (44.1%) produced a ring the year of sampling and 52 (55.9%) did not. Of the living trees no longer producing rings, the last ring formed between 2014 and 2000 for 48 trees and between 1999 and 1916 for 24 trees. 24 Figure 2.3. Outer-ring dates of living yellow-cedars (healthy=green, declining=grey) which stopped forming rings between 1975 and 2014, n = 64 trees with accurate outer ring dates. Eight trees stopped forming rings before 1975; 234 living trees formed rings the year of sampling, as expected. 2.3.5 Changes in growth during the latter 20th century One to three breakpoints were detected in the growth of 403 trees that were alive during the 20th century. The majority of trees switched to a decreasing pattern of growth during the second half of the 20th century (Figure 2.4). These switches were common in the 1940s, 1960s and 1980s, then relatively continuous after the 1990s. Fewer trees exhibited an increasing final pattern of growth. Switches to increasing growth occurred in 1970s, 1980s and 1990s. The final pattern of growth was decreasing in 232 (57.6%) trees, increasing in 171 (42.4%) trees, and differed significantly among health status classes (chi-squared=9.75, p=0.008; Table 2.3). More declining trees had decreasing growth and more healthy trees had increasing growth than expected by chance. The duration of the final growth pattern also differed 25 significantly among health status classes, especially for growth patterns that persisted ≥15 years (chi-squared=14.98, p=0.020; Table 2.3). For persistent growth patterns, more healthy trees increased in growth and more declining trees decreased in growth than expected by chance. The numbers of dead trees with increasing or decreasing growth prior to death, regardless of duration, were similar to those expected by chance. Figure 2.4. Onset of the most recent change in growth pattern of 403 yellow-cedars alive during the period from 1900 to 2015. Frequencies above (below) 0 indicate the year that tree growth switched to an increasing (decreasing) pattern. Growth patterns continued until ring formation stopped or the tree was sampled in 2015. Bars indicate annual values, lines indicate 10-year averages from 1901-2010. 26 Table 2.3. Contingency tables comparing final growth patterns among trees stratified by health status. Growth Pattern Number of Trees Observed (Expected) Healthy Declining Dead X2 (p-value) Decreasing 111 (121) 69 (56) 52 (55) 9.75 (0.008) Increasing 100 (90) 28 (41) 43 (40) Decreasing ≥15 years 68 (77) 50 (36) 30 (35) 14.98 (0.020) Decreasing <15 years 43 (44) 19 (20) 22 (20) Increasing ≥15 years 78 (71) 21 (32) 36 (32) Increasing <15 years 22 (19) 7 (9) 7 (9) 2.3.6 Mortality rates increased in the 1980s I accurately determined the outer-ring date of 70 snags, with years of death between 1860 and 2015 (i.e. up to 155 years before sampling in 2015). I estimated another 46 snags died between 1724 and 1998 (Appendix H). Calculated annual and decadal mortality rates increased starting in the mid-1900s, with an abrupt increase after 1980 (Figure 2.5). Based on the most accurate outer-ring dates only, the background decadal mortality rate was 0.03% (range = 0 to 0.08%) from 1860-1950, increased to 0.14% from 1960-1980 and to 0.58% from 1990-2010. It peaked at 0.82% in the 2000s. When the best estimates of outer-ring dates were included, the decadal mortality rates were higher prior to 1980 (0.07% from 1860-1940 and 0.24% from 1950-1980) but similar after 1980 (0.60% from 1990-2010 and 0.80% in the 2000s, Appendix I). 27 Figure 2.5. Mortality and radial growth decline of yellow-cedar. Annual (bars) and decadal (line) mortality rates of 70 yellow-cedar for which the year of death could be accurately determined (top). Changes in sample depth (blue) due to cumulative mortality of dead trees (black) and cessation of ring formation in healthy (green) and declining (grey) trees (bottom). n = 376 trees for which accurate outer ring dates between 1860 and 2015 could be determined. 28 2.4 Discussion 2.4.1 Yellow-cedar decline on Haida Gwaii Yellow-cedars are declining in c. 280,000 ha of forests along the coast of Alaska and BC (Westfall & Ebata 2015, Dubois & Wurtz 2017), including the forests of Haida Gwaii. Yellow-cedar decline was not expected on Haida Gwaii given the temperate climate and lack of persistent snowpack, which is inconsistent with the hypothesized winter climate drivers on the mainland coast of Alaska and BC (Hennon et al. 2005, 2012). However, the decline on Haida Gwaii near wetlands and in riparian forests parallels that seen on the mainland in terms of tree- and stand-level symptoms, magnitude, and timing. Observed symptoms of decline are similar, including discolouration of needles, crown thinning and partial cambial mortality, followed by eventual death (Hennon et al. 1990b, Stan et al. 2011). Comparable to Alaska, declining stands contain dead, dying, as well as live, healthy yellow-cedars (Hennon et al. 1990a, Hennon & Shaw 1997). The severity of decline on Haida Gwaii mirrors declining forests in Alaska, which have experienced up to 70% mortality in some locations (Hennon et al. 1990a, D’Amore & Hennon 2006). My results indicate evidence of severe decline across multiple geographically diverse watersheds, including crown symptoms or death in 74% of trees, and decreasing BAI in an additional 53% of trees with asymptomatic crowns. 2.4.2 Stand dynamics do not explain yellow-cedar decline on Haida Gwaii I explored several lines of evidence to investigate the possible drivers of yellow-cedar decline on Haida Gwaii, both at the local and regional scale. Considering the effects of stand dynamics is critical to understand whether elevated levels of mortality can be attributed to stand development processes (e.g. van Mantgem et al. 2009, Amoroso et al. 2015). High levels of mortality among young and small-statured trees is often attributed to crowding at high tree 29 densities, inter-tree competition and self-thinning (Franklin et al. 1987, 2002). However, I found no support for greater proportions of decline and death among young, small, understory trees. The distributions of tree age and size did not differ significantly among healthy, declining and dead trees. Trees <200 years old tended to be healthy, and living trees were more abundant than expected in the understory, although some had declining crowns. These patterns are opposite to the distributions expected if competition and self-thinning were driving the decline and death of yellow-cedars. Also associated with stand dynamics, elevated mortality rates can be attributed to the aging of a cohort of trees (Franklin et al. 1987, 2002). The relative abundance of dead trees in the overstory is consistent with senescence of large, and presumably old, trees. However, trees crossed a range of age classes at all sites and I found no evidence that the yellow-cedars formed even-aged cohorts that were senescing simultaneously. Years of death derived from tree-rings indicated that recent high rates of mortality were not limited to the old or large overstory trees. Additionally, yellow-cedar is slow to decay and dead trees can remain standing for over 200 years (Kellner et al. 2000, Stan et al. 2011); therefore, the overstory included persistent snags that had accumulated over many decades. Combined, the evidence is inconsistent with stand dynamics as a driver of elevated decline and mortality of yellow-cedar. However, this pattern does mirror the increased tree mortality across the western United States and Canada that spans age, size and species and has no clear relation to stand dynamics, for which changes in climate are considered the most likely driver (van Mantgem et al. 2009). 30 2.4.3 Tree-rings provide novel insights on yellow-cedar decline This research was the first to use tree-ring analyses to complement crown health to evaluate the decline of yellow-cedar. A switch towards decreasing growth rates from which a tree does not recover is a commonly used internal indicator of tree decline (Cherubini et al. 2002, Bigler et al. 2004, Amoroso et al. 2012, Rodríguez-Catón et al. 2016, Cailleret et al. 2017). I identified the year of onset of tree decline based on sustained decreases in the BAI and found that health status assigned in the field based on external crown symptoms contrasted with the internal radial growth symptoms for many yellow-cedars. Specifically, 111 trees with asymptomatic crowns had decreasing growth patterns, including 68 trees with decreasing growth that persisted ≥15 years. Another 20 trees with asymptomatic crowns had stopped producing rings. These discrepancies represent early signs of decline in radial growth before crown symptoms develop or mortality occurs (Cailleret et al. 2017). Reduction in radial growth before mortality is nearly universal in trees and is especially common in gymnosperms (Cailleret et al. 2017). Partial cambial mortality leading to cessation of ring formation has been found in yellow-cedar as the tree dies (Hennon 1990b, Stan et al. 2011) and species in the Cupressacea family, such as Thuja plicata (Daniels et al. 1997) and Austrocedrus chilensis (Amoroso & Daniels 2010). Scaling up from individual trees to stands, the pattern of decreasing radial growth has been observed in declining A. chilensis forests (Amoroso et al. 2012) and in Abies alba and Pinus sylvestris forests in Spain (Camarero et al. 2015). The onset of tree decline and rates of mortality have amplified in recent decades, from the 1980s on. Yellow-cedar BAI patterns switched throughout the 20th century; however, growth of most of my study trees began to decrease in recent decades, with 50% after 1980. This timing parallels observed yellow-cedar decline in Alaska, which spanned the 20th century with peaks in 31 tree decline and mortality the 1970s and 1980s (Hennon & Shaw 1994). Although crown symptoms have been observed recently on Haida Gwaii, I have shown that internal decline in BAI goes back much further, including peaks in the onset of decreasing growth patterns in the 1940s and 1960s. This tree-ring evidence is earlier than expected, as concern around yellow-cedar decline on Haida Gwaii began in the 2000s when crown symptoms became obvious (CHN & BC 2011). I propose that decreasing radial growth has potential as an early indicator of tree decline in yellow-cedar forests that do not currently exhibit crown symptoms. Paralleling recent onset of tree decline, I detected a sharp increase in mortality rates from <0.30% per year prior 1980 to ≥0.80% per year in the 2000s. This is comparable to the mortality rate estimated for declining forests in Alaska ranging which ranged from 0.50 – 0.80%, since decline began in 1880 (Hennon & Shaw 1994). Determining years of tree death and estimating absolute rates of mortality have greater degrees of uncertainty than detecting onset of decline. I have greatest confidence in calculated mortality rates in recent decades, although the outer-ring dates obtained from decay-resistant trees with persistent snags provide long records of mortality going back through time (Daniels et al. 1997, Hennon et al. 1990c, Stan et al. 2011). On Haida Gwaii, the yellow-cedar snags that I crossdated with the greatest confidence had outer rings indicating years of death from 1860-2015. Mortality rates can be estimated from outer rings, with the assumption that there is an equal chance of sampling a tree and accurately dating its rings, whether that tree died recently or in the distant past. In reality, mortality records are incomplete and less reliable through time due to loss of small trees and progressively older snags as outer ring decay or the snags fall (Cline et al. 1980, Angers et al. 2010). This form of bias influenced my calculations of background mortality rates prior to 1950. For example, by including only trees for which an outer-ring date could accurately be determined, the average decadal mortality 32 rate was 0.03% for 1860-1950. When I included the 27 snags for which best estimates of the outer-ring dates were determined, the average decadal mortality rate during the same period was 0.07%, which is comparable to the background rate of 0.08% estimated in healthy yellow-cedar forests in Alaska (Hennon & Shaw 1994). The discrepancy in my calculated mortality rates indicates the sample size of snags with outer-ring dates deemed “accurate” did not adequately represent the population background rate of mortality prior to 1950, despite the long-term persistence of yellow-cedar snags. Although the decadal rate of 0.07% was more realistic, it may still under-represent the actual background mortality rate. I recommend larger sample sizes that include downed logs, as well as snags, would improve accuracy of past mortality rates. Regardless of absolute mortality rates, the abrupt changes in relative mortality rates after 1980 are corroborated by multiple lines of evidence that provide strong evidence of amplified yellow-cedar decline. 2.4.4 Climatic drivers of yellow-cedar decline With the similarities between yellow-cedar decline on Haida Gwaii and the broader decline on the mainland, the drivers may be the same as well. However, on Haida Gwaii, tree decline and mortality appears to be a cumulative process rather than episodic. The proposed mechanism on the mainland is late winter or spring thaw-freeze events under low snowpack conditions, which can damage the shallow fine roots of yellow-cedar, leading to crown dieback and eventual death (Hennon et al. 2006, 2012). Additionally, there has been an increase in the frequency and severity of thaw-freeze events in southern Alaska explaining the high rates of decline since the 1980s (Beier et al. 2008). I had hypothesized that discreet episodes of mortality and onset of decline would indicate periodic thaw-freeze events were driving decline. As an example of mortality in response to acute events, Amoroso et al. (2015) found peak years in A. 33 chilensis mortality were associated with late spring and summer drought. However, both the onset of BAI decline and mortality at sites on Haida Gwaii appeared more spread out, accumulating over time, with pulses of increased rates occurring over multiple years during the 1940s and 1960s, and continuous amplified decline and mortality from the 1980s on. Given the relatively warm temperatures and ephemeral snowpacks on Haida Gwaii, especially at low elevation, the roots of yellow-cedars may be perpetually exposed to freezing damage. Rather than warm temperatures reducing snowpack to make trees more vulnerable to freezing events, warmer temperature effects may be more direct. In absence of moderation by snowpack warm temperatures may induce dehardening regularly. Alternatively, temperatures may not be low enough for cold hardening of roots and they may remain active and vulnerable to freezing all winter. Under these conditions weather events causing below-freezing temperatures may not need to be as cold or long in duration to cause root damage. The magnitude of the effects may also vary, with rare extreme effects causing acute damage, and more frequent lower-severity impacts explaining the long-term accumulation of BAI decline and mortality that I observed. Although neither outer ring dates nor onset of decline appear to support the acute weather event theory, lagged effects may confound interpretations of the relative importance of acute events. Response to acute weather events may be delayed, making it challenging to detect disturbance effects using tree-rings. Weather events can incite stresses that impact tree physiology that predispose trees to decline and death. For example, the loss of fine root biomass can lead to reduction in water and nutrient supply, and increase the rate of foliage turnover (Pederson 1998a). Lagged responses to drought have been documented in a variety of different species, where significant increases in mortality rates followed the inciting drought stress by 34 years to decades (Pederson 1998b, Bigler et al. 2007, Suarez et al. 2010). Further testing of the anatomical and physiological effects of thaw-freeze events and their manifestation in tree-rings would help elucidate the importance of acute events on the decline of yellow-cedar on Haida Gwaii versus the mainland of BC and Alaska. My data strongly indicate multi-decadal variation superimposed on long-term directional changes in climate drive yellow-cedar decline. Regionally, declining stands of yellow-cedar are typically composed of snags of a broad range of decay classes, trees with crown dieback, and surviving trees with healthy crowns, suggesting that mortality is long-term and continuing (Hennon et al. 2012, Stan et al. 2011). Demographic studies suggest that mainland yellow-cedar populations prone to decline established during the Little Ice Age, a relatively cool period from c.1400 until c.1850 (Mann et al. 1999, Mann 2002), when most glaciers in the region reached their maximum advance (Beier et al. 2008, Hennon et al. 1990c). The onset of yellow-cedar decline coincided with the end of the Little Ice Age (Hennon et al. 1990c), with tree mortality continuing through the 1900s, with peaks in the 1970s and 1980s (Hennon & Shaw 1994, Stan et al. 2011). On Haida Gwaii, most yellow-cedars at my sites (88%) established prior to 1880 and the oldest trees predated the Little Ice Age. Decline of trees of all ages has been accumulating throughout the 20th century, during which global temperatures warmed by 0.85 °C over the period 1880-2012 (IPCC 2014). The most dramatic increases in yellow-cedar decline and mortality between 1980 and 2015 corresponds to the period of the greatest warming, with the period 1983 to 2012 likely being the warmest 30-year period of the last 1400 years in the Northern Hemisphere (IPCC 2014). This period also corresponds to a switch in the Pacific 35 Decadal Oscillation (PDO) to warmer and wetter conditions with lower snowpacks, which occurred in 1976/1977 (Mantua & Hare 2002). The global rise in temperature has been hypothesized as a driver affecting forests across a wide variety of species, forest types and moisture regimes (van Mantgem et al. 2009, Allen et al. 2010, 2015, Cohen et al. 2016), including examples from BC (Daniels et al. 2011). The timing of yellow-cedar decline on Haida Gwaii, mainland BC and Alaska parallels widespread increases in tree mortality and forest decline worldwide. Examples of recent forest declines are particularly well-documented in semi-arid regions in southern Europe and western North America. Importantly, Allen et al. (2010) showed that drought-induced tree mortality was not restricted to semi-arid regions, but also found in mesic forests that are not normally considered water-limited. In the case of yellow-cedar, climate-induced tree mortality is complex and somewhat counterintuitive. Although snowpack modulates temperature effects on the mainland, I propose it is less important on Haida Gwaii where snow is more ephemeral. Similar to the mainland, warming winter temperatures induce dehardening and photosynthesis, increasing fine root vulnerability to freezing injuries that ultimately cause physiological drought and nutrient stress. The effects are especially severe on sites with wet soils and open canopy conditions, where roots are more shallowly rooted and the soil is exposed to greater temperature extremes. Warmer winter temperatures in recent decades have increased the frequency and magnitude of thaw-freeze cycles (Beier et al. 2008). As a result, freezing damage to dehardened fine roots has led to drought-induced decline and death of yellow-cedars, concentrated on wet sites in coastal temperate rainforests of the Haida Gwaii archipelago. 36 2.5 Conclusion I found abundant evidence of tree decline and mortality of yellow-cedar on Haida Gwaii across multiple watersheds and over a range of elevations. This decline on Haida Gwaii parallels the broader yellow-cedar decline in terms of spatial distribution, symptoms, magnitude and timing. Nevertheless, the proposed drivers on the mainland may not adequately explain the decline on Haida Gwaii, due to the more temperate climate and lack of persistent snowpack. I investigated several possible drivers both at the local and regional scale. My results are inconsistent with stand dynamics as a driver of elevated decline and mortality. Neither increased competition, nor aging of a cohort explains the decline. Onset of basal area increment decline and mortality have been accumulating over time, with increased rates since the 1980s. The timing of this decline does not appear to be consistent with isolated disturbance events, such as acute freezing, though problems with lagged effects can make detecting responses to episodic disturbances challenging. Alternatively, the magnitude and timing of the decline is consistent with well-documented multi-decadal variations and long-term directional trends in regional climate. Compared to drivers of yellow-cedar decline on the mainland, snowpack plays a less important role on Haida Gwaii. I propose warming temperatures have led to more direct effects. Warmer temperatures mean less cold-hardening throughout the winter which exposes yellow-cedar’s fine roots to varying degrees of freezing damage over multiple winter thaw-freeze cycles, causing physiological stress, tree decline and eventual death. 37 Chapter 3: Tree-rings Reveal Accelerated Yellow-cedar Decline with Winter Climate After 1980 3.1 Introduction Climatic warming over the 20th century has led to widespread forest dieback around the globe (Allen et al. 2010, Peng et al. 2011). Many of these diebacks are driven by drought, though forests have also exhibited dieback in direct response to heat stress, indirect temperature influences on insects and pathogens, or more complex drivers mediated by climate change (Anderegg et al. 2013). Forest dieback now affects all forested continents worldwide (Allen et al. 2010) and these events have far reaching effects on ecosystem dynamics. Drought exacerbated by climate warming has had major impacts on forests in semiarid regions at low latitude, for example Pinus spp. in southern Europe (Sánchez-Salguero et al. 2012) and southwestern United States (e.g. Pinus edulis in Meddens et al. 2015). However, climate warming also poses a serious threat to forests of higher latitudes, such as the Canadian interior where the dieback of Populus tremuloides is of great concern (Michaelian et al. 2011) and the circumpolar boreal forests, where a diverse range of genera have exhibited dieback (Lloyd & Bunn 2007). In the Pacific Northwest, climate warming has driven yellow-cedar (Callitropsis nootkatensis) decline, which is one of the largest forest declines in North America and includes nearly 300,000 ha of forest in the mid to northern range of the species’ distribution (Westfall & Ebata 2015, Dubois & Wurtz 2017). However, rather than direct drought limitation, declining yellow-cedars are limited by physiological drought during the growing season, caused by freezing damage to fine roots through a complex pathway identified by research in Alaska 38 (Hennon et al. 2012). Warming temperatures during the 20th century have reduced snowpacks, exposing shallow fine roots to freezing damage (Hennon et al. 2006, Hennon et al. 2010). Local microsite conditions predispose trees to freezing injury during years when a low snowpack coincides with thaw-freeze events in the late winter and early spring (Schaberg et al. 2008, Hennon et al. 2012). While large areas have experienced high levels of mortality, a subpopulation of surviving, apparently healthy trees coexist with dying and dead trees in the same stands (D’Amore & Hennon 2006, Stan et al. 2011). On the island archipelago of Haida Gwaii, yellow-cedar decline was unexpected due to the milder temperatures, moderated by the Pacific Ocean, and the lack of persistent snowpack (Banner et al. 2014). However, many stands of yellow-cedar on Haida Gwaii now exhibit moderate- to high-severity decline, in which an average of 70% of trees are dying or dead (Chapter 2). All declining stands studied had trees of varying health status, including trees that appeared healthy, those with crown dieback, and trees recently to long dead. In addition to crown dieback, many declining trees exhibited decreasing radial growth rates or cessation of ring formation (Chapter 2). Dendrochronology can be used to pinpoint the timing of dieback and death of individual trees (Cailleret et al. 2017) and quantify rates and magnitude of decline across populations at stand and landscape scales (Amoroso et al. 2012). Tree-rings can also reveal relationships between climate and growth, including limiting factors (e.g. Chavardès et al. 2012). I hypothesized that yellow-cedars in declining stands on Haida Gwaii were limited by and responding to climate variables, with common growth patterns across sites. Within sites, I hypothesized that yellow-cedars of different health classes were responding differently to climate variables, given that the proposed driver of decline is associated with climate warming (Hennon 39 et al. 2012) and that the timing of decline on Haida Gwaii parallels the greatest warming (IPCC 2014). Following this hypothesis, trees that were declining or had died, would be expected to respond negatively to warmer temperatures, whereas healthy trees would be expected to respond positively to warmer temperatures. I tested for evidence of growth response to climate indicated by common year to year growth patterns across the region. I differentiated growth patterns and responses to climate among trees that were healthy, had crown and/or tree-ring symptoms of decline, and trees that had died. Finally, I determined whether yellow-cedars of different health classes responded differently to climate over time. Concurrent with yellow-cedar decline, the climate of Haida Gwaii has been affected by the multi-decadal climate regime known as the Pacific Decadal Oscillation (PDO) and anthropogenic warming (Mantua & Hare 2002, IPCC 2014). If changing climate is leading to decline, I expect changing climate-growth relations over time and among yellow-cedar subpopulations, both as climate warms and following the most recent switch of the PDO to the warm phase. This phenomenon has been observed in declining Pinus albicaulis forests in the Rocky Mountains, where trees were limited by different factors during the warm and cool phases of the PDO (Wong & Daniels 2016). Determining if this is true in yellow-cedar will help uncover the drivers of decline on Haida Gwaii and determine whether they are the same as those on the mainland. Identifying the mechanism leading to decline is crucial for the management and conservation of this important tree species. 3.2 Methods 3.2.1 Study sites To address the causes of yellow-cedar decline, I selected stands exhibiting crown symptoms. Fifteen study sites were located on Graham Island, Haida Gwaii, with 2 sites on the 40 northeastern coast and 13 further south (Figure 3.1). The sites are distributed across multiple watersheds and over a range of elevations where decline has been documented. The sites were wet, with boggy soils, often adjacent to a water feature such as a stream or wetland. Within sites, the sampled stands contained declining yellow-cedar as a codominant species, with Sitka spruce (Picea sitchensis), western hemlock (Tsuga heterophylla) and western redcedar (Thuja plicata). The climate of Haida Gwaii is wet hypermaritime, strongly influenced by proximity to the Pacific Ocean. Figure 3.1. Location of 15 sites exhibiting yellow-cedar decline on Graham Island, Haida Gwaii. 41 Yellow-cedar were sampled in 100m x 20m transects bisecting the forest most heavily affected by decline. Increment cores from 30 trees at each site enabled tree-ring analyses to detect internal evidence of decline, growth patterns, and relationships to climate. Tree diameter at breast height (DBH, 1.3 m) and health status were recorded. Trees were classified as healthy if they had full, green crowns, declining if they had discoloured foliage or were partly defoliated, or dead if they lacked foliage or had only brown foliage. To objectively select trees, I cored the first 15 healthy yellow-cedars (except two sites where only 10 and 14 healthy trees were available to sample), and the first 15 declining yellow-cedars or snags; downed trees were not sampled. One core, sampled at standing height (c. 115cm above the ground), was taken from each tree. I aimed to intercept the pith and where possible I included the bark or outer cambium. Trees with decayed outer rings or abundant heartwood rot were replaced with the next tree along the transect in the same health status class. For transects with fewer than 15 healthy or 15 declining or dead yellow-cedars that were suitable to core, additional trees were sampled and cored from outside of the transect. 3.2.2 Dendrochronological methods Cores were mounted on to wooden supports, sanded with progressively finer grains (120-600 grit), and scanned at a high resolution (2400 or 3200 dpi). I measured ring widths to 0.001mm using the program CooRecorder (version 7.8, Larsson 2014a). Cores with particularly suppressed or difficult to distinguish rings were cross-checked under a microscope. I crossdated each ring-width series using the program CDendro (version 7.8, Larsson 2014b), and created ring-with chronologies for each site. I confirmed crossdating accuracy using the program COFECHA (Holmes 1983). Crossdating allowed me to account for missing or false rings and to 42 assign a calendar year to each ring. The year of the outermost ring represents cessation of growth of living trees and year of death of snags. Site level chronologies were created using trees that were highly correlated. A cross correlation matrix of these chronologies was calculated for the 15 sites, to test for similarity in growth patterns. Positive and negative marker years were also identified for each site-level chronology, using the pointer function of dplR in R (Bunn 2008, 2010, Bunn et al. 2017, R Core Team 2014). A marker year was defined as years when >75% of ring-width series have greater than 10% absolute relative radial growth variation. Marker rings were reported beginning in 1781, when a minimum of 10 chronologies were made up of 10 or more samples. I built three regional ring-width chronologies based on health class (healthy, declining and dead) by grouping highly correlated trees from the 13 sites in the southern portion of Graham Island. Samples from the two northern sites were not included as their chronologies did not correlate well with those from the other 13 sites. Trees in the ‘healthy’ category that exhibited internal tree-ring symptoms of decline, including suppressed outer rings or cessation of ring formation, were moved to the ‘declining’ category. Trees with low correlations with the regional ‘health status’ chronologies were removed. The program CRUST was used to detrend the ring-with series (Melvin & Briffa 2013). I chose a 100-year spline, which leaves 99% of variance at 31.69 years (50% variance at 100 years, 1% of variance at 315.43 years). This detrending method removes longer term variation and retains variation at the ~30 year level. The Pacific Decadal Oscillation (PDO) has also had approximately 30-year periodicity during the 20th century. This will effectively retain the PDO signal in the rings and remove the longer-term variability at the multi-century level. For climate-growth analysis I used the residual chronologies, which were created by applying an autoregressive time series model. 43 3.2.3 Climate-growth relations I compared climate-growth relations among the trees of the three health statuses (using the three residual chronologies) for 1901-2015, the beginning of the climate record to year of sampling. I obtained monthly total precipitation and mean/minimum/maximum monthly temperature records for each of the 13 southern sites using the software ClimateNA (Wang et al. 2016) and averaged the records for each climate attribute to represent the dominant regional climate of the study area. ClimateNA uses bilinear interpolation and elevation adjustments to downscale historical climate data to scale-free point estimates of climate values (Wang et al. 2016). To assess relationships between the annual index values of the three residual ring-width chronologies and the regional monthly precipitation and temperature records, I conducted a bootstrap correlation function analysis (Blasing et al. 1984) using the R package treeclim (Zang & Biondi 2015, R Core Team 2014). Twelve months from the October prior to ring formation through September of the year of ring formation were analyzed. The period of analysis, 1901-2015, was split in to three segments based on the two major switches in the PDO. The PDO switched from a warm-dry to a cool-wet phase in 1945-1946 and back to a warm-dry phase in 1976-1977 (Mantua & Hare 2002). Therefore, the three segments were 1902-1945, 1946-1976, and 1977-2015. I compared monthly temperatures and total monthly precipitation in the three time-periods using a Kruskal-Wallis one-way analysis of variance (ANOVA) on ranks with a Dunn’s post-hoc test. 3.2.4 Wavelet transform analysis I conducted a wavelet transform analysis on the standard chronologies for the three health status groups using the morelet function of dplR in R (Bunn 2008, 2010, Bunn et al. 2017, R Core Team 2014). This is a useful tool for analyzing time series with many different 44 timescales or changes in variance (Torrence & Compo 1998). I performed a continuous Morlet wavelet transform on the time series, enabling the detection of predominant periodicities, and the relationship between short- to long-term climatic variations and tree-ring series. By applying wavelet analysis to a tree-ring series, I was able to determine both the dominant frequency modes of the variability, from 2 to 512 years, and how the modes varied through time. 3.3 Results 3.3.1 Site chronologies The 15 site-level chronologies contained 317 highly correlated trees, with 16 to 28 trees in each chronology (Appendix J). Trees which had low correlations (n=106) or could not be crossdated (n=21) were not included in these chronologies. The chronologies were 440 to 903 years in length reaching as far back as the calendar year 1112 AD. Mean series intercorrelations ranged from 0.342 to 0.461, indicating robust crossdating among trees whose ring-width series exhibited both complacent periods and suppressions with locally absent rings over time. In general, during periods when there are sufficient resources and environmental conditions to support optimum growth, the ring pattern of trees is complacent and there is little variation in ring-width year to year (Stokes & Smiley 1996). Conversely, in years of extremely little growth, when trees are limited by some factor, rings are suppressed and a ring may not appear at every point around the circumference of the tree, leading to a locally absent ring (Stokes & Smiley 1996). Given this temporal variation within and among sites, these intercorrelation values are lower than those reported for other yellow-cedar chronologies (r = 0.46-0.51, Stan et al. 2011, r = 0.46-0.50, Wiles et al. 2012, r = 0.50, Kellner et al. 2000), but are comparable to those associated with yellow-cedar trees found in more temperate locations, such as Vancouver Island, 45 BC (r = 0.30-0.47, Laroque & Smith 1999). Mean sensitivity was 0.236 to 0.308, indicating modest and variable changes in ring-width from one year to the next. The chronologies for the 13 sites from the southern portion of Graham Island were highly correlated (r = 0.08 to 0.86, Figure 3.2). The two sites from the north, DEL1 and SKO1 did not correlate well with the southern sites, (r = -0.47 to 0.25), but were correlated with each other (r = 0.42). Figure 3.2. Correlations (r) among the 15 site-level chronologies organized from north to south. Darker shades represent higher positive correlations, bolded font represents significance. Trees from different sites often exhibited the same positive and negative marker years between 1781 and 2015 when at least 10 chronologies (sites) included sufficient trees (Figure 3.3). Five to 11 of the 15 chronologies exhibited positive marker years in 1989 (5 chronologies), 1988 (6), 1929 (12), 1927 (11), 1890 (6), 1876 (5), 1873 (5), 1857 (9), and 1830 (6). Chronologies exhibited negative marker years in 1986 (11 chronologies), 1970 (5), 1936 (5), 1934 (5), 1926 (8), and 1846 (7). 46 Figure 3.3. Summary of negative and positive marker years (i.e. years when >75% of ring-width series had greater than 10% absolute radial growth variation) at the 15 sites from 1781 to 2015 (top) beginning when sample depth reached 10 to year of sampling. Sample depth over time defined by the number of sites with 10 or more samples contributing to the chronology (bottom). 3.3.2 Regional chronologies Grouping trees from the 13 southern sites by health status yielded three regional chronologies made up of trees that were healthy (n=99), declining (n=64) or dead (n=57) when sampled in 2016 (Figure 3.4). Trees from the two northern sites were excluded as correlations were low (n=44, Figure 3.2). The remaining 53 trees from the site chronologies were excluded because they had low correlations at the regional level (Appendix K). Mean series intercorrelations ranged from 0.377 to 0.381 and mean sensitivity was 0.260 to 0.273, similar to the site-level chronologies (Table 3.1). All three chronologies followed similar patterns of 47 growth from the 1500s to the late 1900s, with the same multi-decadal variation (Figure 3.4, Appendix L). After a narrow growth ring in 1986, the declining chronology exhibited decreasing growth, diverging from the other two chronologies (Figure 3.4, Appendix L). Figure 3.4. Ring width standard (top) and residual (middle) chronologies for healthy, declining and dead groups from 1524 to 2015, beginning when all three chronologies had ≥10 samples. The bottom panels depict sample depth or the number trees included in each chronology over time (n = 99, 64 and 57 for the healthy, declining and dead groups respectively). Table 3.1. Characteristics of the ring width chronologies for the healthy, declining and dead groups. Mean series intercorrelation and sensitivity are for the full length of the chronologies. Group n Full length of n ≥ 10 Mean series Mean EPS ≥ 0.85 chronology (year) intercorrelation sensitivity (year) Healthy 99 1324−2015 1521 0.381 0.260 1759 Declining 64 1282−2015 1500 0.377 0.273 1741 Dead 57 1297−2008 1524 0.377 0.260 1759 48 3.3.3 Climate and yellow-cedar growth Among the three periods associated with PDO phases, monthly minimum, average and maximum temperatures differed significantly although monthly total precipitation values were similar (Figure 3.5). Growth was significantly associated with temperature and precipitation although these relations varied among chronologies and through time (Figures 3.6-3.7). Results for minimum temperature are presented, as it had the strongest and most consistent outcomes of the three temperature variables. Results for average and maximum temperature are shown in Appendices M, N and O. Statistically significant (α= 0.05) outcomes are summarized below. Figure 3.5. Averaged climatic records from the 13 sites on the southern portion of Graham Island, including monthly total precipitation (top) and minimum temperature (bottom). Bars represent mean values for the three time periods; 1901 to 1945 (left), 1946 to 1976 (middle) and 1977 to 2015 (right), split based on the two major switches in the Pacific Decadal Oscillation (PDO). Whiskers are standard deviations. Different letters denote significant differences among means (α=0.05). 49 Overall, 1946-1976 was the coolest of the three periods and 1977-2015 was the warmest (Figure 3.5). Consequently, 1946-1976 had more precipitation as snow, while 1977-2015 had the least precipitation as snow. The 1977-2015 period was warmer than the two earlier periods during the growing season, with significant increases of 0.65 to 0.76 °C from May through August. Additionally, January, March and April were warmer on average during the 1977-2015 period than 1946-1976, but not different from 1902-1945. February and September were warmer on average during 1977-2015 than 1902-1945 but not different from 1946-1976. The greatest warming in average monthly minimum temperature over the three time periods occurred in January and February, with a change of over 1.3°C. The three chronologies exhibit similar climate-growth responses in the two earlier periods (1902-1945, 1946-1976), followed by divergent climate-growth responses in the most recent period (1977-2015, Figure 3.6). There were fewer significant climate-growth relations in the two earlier periods. In the 1902-1945 period, warm October and dry May were positively associated with growth of trees in all three chronologies (Figures 3.6-3.7). In the 1946-1976 period, lower May precipitation continued to be positively associated with growth. Additionally, wet warm November was negatively associated with growth and wet October was positively associated with growth. There were many more significant relationships in the most recent period from 1977-2015 (Figures 3.6-3.7). Warm January temperatures became negatively associated with growth for all three chronologies (Figure 3.6). However, other climate-growth relationships diverged between the three chronologies. Growth of trees in the healthy and dead chronologies was positively associated with spring and summer temperatures, however this relationship was only significant for the healthy chronology, in April, June and July. Growth of trees in the declining 50 chronology was not associated with spring and summer temperatures. Higher November and December precipitation was positively associated with growth of trees in the healthy and declining chronologies (Figure 3.7). Conversely, growth was negatively associated with wet springs for the healthy and dead chronologies, significant in April (healthy and dead) and May (healthy). Figure 3.6. Correlation between monthly minimum temperature and the residual chronology for healthy, declining and dead groups over the time periods 1902-1945 (left), 1946-1976 (middle) and 1977-2015 (right). Bars are the correlation coefficients, a dot indicates significance (α=0.05). The final period for the dead chronology is 1977-2008. 1902-1945 1946-1976 1977-2015TaveSummer(prior)Winter (prior)Summer(current)Summer(prior)Winter (prior)Summer(current)PPTO N D J F M A M J J A SCorrelation Coefficient-0.4-0.20.00.20.4-0.4-0.20.00.20.4O N D J F M A M J J A S O N D J F M A M J J A SO N D J F M A M J J A SO N D J F M A M J J A S-0.4-0.20.00.20.4O N D J F M A M J J A SHealthyDecliningDeadMonth and season relative to ring formationSummer(current)Winter (prior)Summer(current)Winter (prior)Summer(current)Winter (prior) 51 Figure 3.7. Correlation between monthly precipitation and the residual chronology for healthy, declining and dead groups over the time periods 1902-1945 (left), 1946-1976 (middle) and 1977-2015 (right). Bars are the correlation coefficients, a dot indicates significance (α=0.05). The final period for the dead chronology is 1977-2008. 3.3.4 Temporal frequency Variation in tree growth was concentrated at similar temporal frequencies among the three chronologies. Variation was concentrated at low frequencies and was most stable in the 60- to 250-year range (Figure 3.8). The wavelet analysis also highlighted modes of variability in the 15- to 30-year frequency, though they were less stable through time. Variation at c. 30-years was 1902-1945 1946-1976 1977-2015TaveSummer(prior)Winter (prior)Summer(current)Summer(prior)Winter (prior)Summer(current)PPTO N D J F M A M J J A SCorrelation Coefficient-0.4-0.20.00.20.4-0.4-0.20.00.20.4O N D J F M A M J J A S O N D J F M A M J J A SO N D J F M A M J J A SO N D J F M A M J J A S-0.4-0.20.00.20.4O N D J F M A M J J A SHealthyDecliningDeadMonth and season relative to ring formationSummer(current)Winter (prior)Summer(current)Winter (prior)Summer(current)Winter (prior) 52 present in all three chronologies during the 20th century, most stable in the healthy and dead chronologies, during the mid 20th century. Figure 3.8. Morelet continuous wavelet transform demonstrating the dominant frequency modes of variability for the chronologies comprised of yellow-cedar trees that were healthy (top), declining (middle), and dead (bottom) when sampled. The black contour line encloses regions of greater than 95% confidence. Hatched regions indicate the “cone of influence”, where edge effects become important (Torrence & Compo 1998). 53 3.4 Discussion 3.4.1 Common growth signal present across sites Yellow-cedar is one of the longest-lived tree species in British Columbia. It is important culturally to the Indigenous peoples of the region and an integral part of the ecosystem. On Haida Gwaii, I sampled yellow-cedar trees up to 1004 years old (Chapter 2) and all 15 site-level chronologies equaled or exceeded 440 years. Over the long-term yellow-cedar exhibited common growth patterns consistent with meso- and regional climate. Thirteen of the fifteen site-level chronologies were highly correlated. The two exceptions were geographically isolated, located near the north coast of Graham Island, and may have different micro-climate or site-level factors. Marker rings, common across the site-level chronologies on Haida Gwaii, were also shared at a broader regional scale – spanning nine degrees of latitude (50°-59° N) from southern Alaska to Vancouver Island (see Stan et al. 2011). This included 22 years with common negative marker rings between one to eleven sites on Haida Gwaii and one to three other yellow-cedar studies (Stan et al. 2011). The three years with the greatest number of sites with negative marker rings on Haida Gwaii that corresponded to other studies (1986, 1936 and 1926) had common negative marker rings with the declining chronologies (declining site from Beier et al. 2008 and Stan et al. 2011), but not with the healthy stands (Laroque & Smith 1999, Parish 2005, healthy site from Beier et al. 2008). Suggesting that climate conditions had anomalous impacts on trees at declining sites during these three years. 3.4.2 Separating trees by health status I tested the hypothesis that climate is the driving mechanism behind yellow-cedar decline on Haida Gwaii. If this is the case I would expect trees of different health status to have responded differently to climate variables over time. I stratified all trees by health-class and 54 grouped them in to ‘healthy’, ‘declining’ and ‘dead’ chronologies to shed light on the drivers of decline. This strategy has been applied successfully across a diversity of species. Including broadleaved deciduous Nothofagus pumilio (Rodríguez-Catón et al. 2015) and coniferous Austrocedrus chilensis (Amoroso et al. 2012) in Patagonia, which both exhibited multiple growth patterns based on whether trees were affected by decline. Two examples of Pinus in the northern hemisphere, in which climate interacted with biotic disturbance include; Pinus albicaulis trees in the Rocky Mountains, which exhibited different growth patterns based on whether they were healthy or affected by mountain pine beetle, Ips species and/or white pine blister rust (Wong & Daniels 2016), and Pinus mugo in the Swiss Alps, which exhibited different growth patterns when affected by two different pathogens (Cherubini et al. 2002). Combining all trees within a stand can mask the multiple growth patterns that can exist based of the health status of trees. Whereas, separating trees by health status can uncover divergent growth patterns of trees affected by pathogens, disease or decline (Cailleret et al. 2017). To classify the health status of trees, I used a two-pass system, assessing crown dieback (external symptoms of decline) and radial growth decline (internal symptoms), to be as robust as possible. Tree-ring analysis revealed that over half of trees with external crown symptoms, had internal tree-ring symptoms of decline, including narrow suppressed outer rings and cessation of ring formation (Chapter 2). Additionally, a proportion of trees with no crown symptoms had tree-ring symptoms. This suggests that radial growth decline can develop before external symptoms become apparent. In many gymnosperm species decreasing growth is a common indicator of decline and often precedes mortality (Cailleret et al. 2017). Yellow-cedars exhibited periods of suppression through their lifespan, including many rings that were locally absent or discontinuous, which is common in extremely slow growing trees in harsh environments (e.g. 55 Kelly et al. 1992). However, yellow-cedars affected by decline entered decreasing growth, exhibiting extremely narrow and suppressed rings, and did not recover, or stopped forming rings all together. Furthermore, species in the Cupressaceae family are known to exhibit partial cambial mortality, documented in Thuja plicata (Daniels et al. 1997), Austrocedrus chilensis (Amoroso & Daniels 2010) and yellow-cedar (Hennon 1990b, Stan et al. 2011). Cessation of ring formation was common in yellow-cedars with crown symptoms of decline (56%), and appeared in an additional 9% of yellow-cedars lacking crown symptoms (Chapter 2). This can also be used as an early warning signal of tree decline. 3.4.3 Divergent growth of trees of different health classes The divergence of growth among trees of different health status is a recent phenomenon, which emerged after a strong negative marker year in 1986. All three chronologies had similar patterns from the early 1500s to 1900s, responding to climate in the same way, with multi-decadal variability and common narrow marker years. This includes very narrow growth in 1986, after which growth of trees in the declining chronology decreased sharply and mortality rate of trees also increased (Chapter 2). Declining trees survived to 2016 with decreased growth rates, whereas, trees that died after 1986 did not exhibit a slower growth rate and had weaker associations with climate prior to death. Beier et al. (2008) found that 1986 was the fourth highest year of high thaw-freeze magnitude and impact in south-eastern Alaska from 1950 to 2004. This 1986 marker ring on Haida Gwaii is consistent with the Alaska hypothesis, that thaw-freeze events during years of low snowpack lead to fine root damage, moisture stress, tree dieback and death (Hennon et al. 2012). 56 3.4.4 Climatic drivers of yellow-cedar decline Regional warming due to climate change, enhanced by multi-decadal variation resulting from the PDO, is the most likely driver of yellow-cedar decline on Haida Gwaii. Increases in decline and mortality of yellow-cedar beginning in the late 20th century coincide with the greatest increases in temperature (IPCC 2014). The wavelet analysis highlighted multi-decadal periodicity in the 30-60 range, evident in all three chronologies over time, including the 20th century. This suggests that the trees are responding to multi-decadal climatic regimes. Regionally, the most important ocean-atmospheric climate regime is the PDO (Mantua & Hare 2002). The PDO operated at the ~30-year scale over the 20th century and played a significant role in the regional climate of Haida Gwaii. Climatic shifts related to the PDO have been shown to influence the growth and limiting factors of conifer species in northwestern North America (Chavardès et al. 2012, Wong & Daniels 2016). Additionally, the most dramatic warming has occurred after a switch in the PDO to a positive (warm) phase in 1976/1977. The rates of both mortality and onset of decline of yellow-cedars on Haida Gwaii have increased since then. Given this evidence and the ~30-year periodicity observed in yellow-cedar growth on Haida Gwaii, I analysed climate growth relations over three time-periods divided based on the major switches in the PDO. Importantly, I detected a change in climate-growth responses around the 1976/1977 switch in the PDO. From 1902-1945, all chronologies responded to climate in a similar way. Longer growing seasons facilitated increased growth, evidenced by a positive relationship with warm October and dry May. This is consistent with observations from many other high latitude forests, which are typically thought to be temperature limited (e.g. Nemani et al. 2003). Many tree species have exhibited increasing growth over the 20th century in response to warmer and longer growing 57 seasons, with earlier snowmelt and warmer fall temperatures (Wilmking & Juday 2005, Driscoll et al. 2005, Lloyd et al. 2011). Similarly, in the 1946-1976 period, which was cooler overall, there was a positive relationship between growing season temperatures and growth of all chronologies, though it was not significant. This relationship was significant during the most recent period (1977-2015), which was the warmest of the three periods, with warmer growing season temperatures facilitating growth of trees in the healthy chronology. This follows the trend of many northern plant species that have experienced increased growth rates since the 1980s (Myneni et al. 1997) in response to warming temperatures. Additionally, many high latitude and high elevation tree species include subpopulations which have responded positively to warmer growing season temperatures over the late 20th century (Lloyd & Bunn 2007, Lloyd et al. 2011, Wang et al. 2017). One striking example of this is the increased growth rate of Pinus longaeva in upper elevations near treeline in response to warming temperatures, with trees exhibiting greater ring growth during the second half of the 20th century than any 50-year period over than past 3700 years (Salzer et al. 2009). Conversely, trees in the declining chronology have not responded positively to increasing growing season temperatures. This parallels trends of species across the north, where a subpopulation of trees have exhibited a weakened or negative response to warming growing season temperatures (D’Arrigo et al. 2004, Wilmking & Juday 2005, Lloyd & Bunn 2007, D’Arrigo et al. 2008, Schneider et al. 2014). In most species, this is due to moisture limitation or heat stress in association with warmer growing season temperatures (Wilmking et al. 2004, Lloyd & Bunn 2007). However, yellow-cedars at my study sites were not water-limited in any of the three time periods, and did not benefit from increased precipitation in the spring or summer. 58 Moreover, growth was limited by high May precipitation from 1902-1976 and high March/April precipitation from 1977-2015. This is not surprising as all stands were located on wetter sites and were not moisture limited, therefore increased spring precipitation would not be beneficial. Instead, the limiting factors of yellow-cedar on Haida Gwaii are unique. Growth was negatively associated with low winter precipitation, consistent with the root-freezing hypothesis from Alaska. 3.4.5 Consistent with Alaska hypothesis Rather than direct drought limitation due to warmer or drier growing seasons, yellow-cedars are limited by physiological drought stemming from damaged roots. When yellow-cedar’s shallow fine roots are not protected by an insulating snowpack, thaw-freeze cycles can cause damage to fine roots (Schaberg et al. 2008, 2011). This leads to moisture stress during the growing season, and eventual dieback and death of the tree (Hennon et al. 2012). Following this hypothesis, winter conditions are more biologically relevant than growing season conditions. Warmer winter conditions lead to more precipitation as rain than as snow (Buma et al. 2016), earlier melting of snowpack (Stewart et al. 2004), and more frequent thaw-freeze cycles (Beier et al. 2008). Low winter precipitation leads to less snow and therefore less snowpack. The limiting factors to yellow-cedar growth on Haida Gwaii during the two most recent periods are consistent with the root-freezing hypothesis. In Alaska, growth of yellow-cedars at declining sites was limited by warm and dry winter conditions (Beier et al. 2008, Wiles et al. 2012). Similarly, growth on Haida Gwaii was limited by warm dry Novembers as early as 1946-1976 and mortality rates began increasing in the 1960s (Chapter 2). After 1977, significant relationships shifted to later in the winter, with growth limited by warm January and dry November and December. At the same time, the correlations between the chronology derived 59 from dead trees and precipitation were not significant, suggesting a decoupling of the climate-growth relationships prior to tree death. The wavelet analysis highlighted evidence that yellow-cedars in declining stands on Haida Gwaii are responding to a PDO-length signal, however the decline does not appear to be driven primarily by the PDO. The latter two periods analyzed had similar climate-growth relations, spanning both the negative and positive phases of the PDO. Instead, the likely primary driver of decline is climate warming over the 20th century. Support for the root-freezing hypothesis is evident in the second half of the 20th century, becoming apparent in the middle period and stronger in the most recent period, not the two periods that correspond to the positive phases of the PDO. Evidence of yellow-cedar decline from decreasing radial growth (Chapter 4) and increased mortality (Chapter 2) begins during the negative phase of the PDO, in the 1960s. Following the 1976/1977 switch to positive PDO phase, yellow-cedar decline was exacerbated, as both onset of decline and mortality rates increased after this time (Chapter 2). I suggest that decline was driven by increasing temperatures over the 20th century, and intensified after the switch to positive PDO phase in the late 1970s. Given multiple lines of corroborating evidence, it is clear that winter climatic conditions drive yellow-cedar decline on Haida Gwaii, consistent with the proposed mechanism of Hennon et al. 2012. While snowpack largely modulates temperature effects on the mainland, on Haida Gwaii snow is more ephemeral. Additionally, given the milder climate on Haida Gwaii, fine roots are likely active throughout the winter and therefore are continuously exposed to freezing damage. As winter temperatures have increased, confounded by the PDO and climate change, yellow-cedar’s fine roots are exposed to varying degrees of freezing damage over time. These periodic thaw-freeze events may cause damage that accumulates over time, in addition to more 60 acute events. The effects are especially severe on sites with wet soils and open canopy conditions, where roots are more shallowly rooted and the soil is exposed to greater temperature extremes (D’Amore & Hennon 2006, Hennon et al. 2010). This climate-induced decline is complex and somewhat counterintuitive. Ultimately, warming winter temperatures increase susceptibility to freezing damage of yellow-cedar’s fine roots, leading to drought-induced dieback and death, concentrated on wet sites in the coastal temperate rainforests of Haida Gwaii. 3.5 Conclusion Multiple lines of evidence corroborate the hypothesis that freezing damage to roots during years of warm winter temperatures and low winter precipitation have driven yellow-cedar decline on Haida Gwaii. This decline is driven by climate change, exacerbated by the PDO, rather than being associated with the positive phases of the PDO. Identifying this mechanism of decline allows for greater understanding of which stands may be a risk, though tree-level factors that protect trees from or predispose trees to decline remain unknown. Ultimately, warming winter temperatures, accelerated by anthropogenic climate change, have had led to dieback and death of yellow-cedars on wet sites, even with the temperate ocean-moderated climate of the Haida Gwaii archipelago. 61 Chapter 4: Multiple Divergent Patterns in Yellow-cedar Growth Driven by Anthropogenic Climate Change 4.1 Introduction Forest decline is a global phenomenon that has intensified and become more widespread in the late 20th century, largely driven by anthropogenic warming (van Mantgem et al. 2009, Allen et al. 2010, Peng et al. 2011). Forests are declining across a broad range of ecosystems and climatic conditions. Examples include coniferous Austrocedrus chilensis (Amoroso et al. 2015) and broadleaved Nothofagus pumilio (Rodríguez-Catón et al. 2015) in northern Patagonia; Pinus spp. in southern Europe (Sánchez-Salguero et al. 2012) and southwestern United States (e.g. Pinus edulis in Meddens et al. 2015); and a diverse range of genera in western North America undergoing increased mortality rates (Breshears et al. 2005, van Mantgem et al. 2009, Peng et al. 2011). While warming climate is a common thread among these forest declines, the exact mechanisms leading to tree dieback and death can be more complex. In addition to climate, there are often multiple interacting factors at play, including pathogens (e.g. Cherubini et al. 2002, Amoroso et al. 2015), insects (e.g. Wong & Daniels 2016), pollution (e.g. Lawrence et al. 2005), microsite conditions (e.g. Hennon et al. 2010), and genetic variation (e.g. Helama et al. 2008). The dieback and death of yellow-cedar (Callitropsis nootkatensis) in the mid to northern range of its distribution is a particularly severe example of forest decline (Hennon et al. 2012). Nearly 300,000 ha of forest along the coast of southern Alaska and northern British Columbia have been affected by the decline (Westfall & Ebata 2015, Dubois & Wurtz 2017). Evidence of yellow-cedar decline spans back to the 1880s (Hennon et al. 1990c), but has intensified since the 62 1970s (Hennon & Shaw 1994). Research in Alaska has identified a sequence of extreme weather and climatic drivers of yellow-cedar decline. Warming temperatures over the 20th century have reduced snowpacks, exposing shallow fine roots to freezing damage (Hennon et al. 2006, 2010). Local microsite conditions predispose trees to freezing injury during years when a low snowpack coincides with thaw-freeze events in the late winter and early spring (Schaberg et al. 2008, Hennon et al. 2012). While large areas have experienced high levels of mortality, a subpopulation of surviving, apparently healthy trees coexist with dying and dead trees in the same stands (D’Amore & Hennon 2006, Stan et al. 2011). On the island archipelago of Haida Gwaii, located 70+ km west of the mainland, yellow-cedar decline was unexpected due to the mild winter temperatures, moderated by the Pacific Ocean, and the lack of persistent snowpack (Banner et al. 2014). However, many stands of yellow-cedar on Haida Gwaii now exhibit moderate- to high-severity decline, in which an average of 70% of trees are dying or dead (Chapter 2). Given the relatively mild climate on Haida Gwaii, I hypothesize the mechanism driving yellow-cedar decline may differ from that characterized in forests on the mainland. As with other forest declines, there are likely multiple interacting factors that have led to the decline and may influence trees in different locations in subtly different ways (Cherubini et al. 2002, Amoroso et al. 2015). Dendrochronology can be used to pinpoint the timing of dieback and death of individual trees (Cailleret et al. 2017) and quantify rates and magnitude of decline across populations at stand and landscape scales (Amoroso et al. 2012). Tree-rings can also reveal divergent growth patterns within a species, which can be an indicator of forest decline (Rodríguez-Catón et al. 2016). For example, many species in the boreal forest have exhibited divergent growth responses to warming temperatures, with a subset of trees responding weakly or negatively to increasing 63 growing season temperatures (D’Arrigo et al. 2008, Lloyd & Bunn 2007). Divergent growth is one explanation for differences among symptoms or health conditions of trees within the same stand. In previous work within stands with visual evidence of yellow-cedar decline on Haida Gwaii (Chapters 2 & 3), I have shown that yellow-cedars that have died or are visually declining grow alongside those with healthy crowns, but there are discrepancies between internal tree-ring and external crown symptoms in many trees (Chapter 2). As well, yellow-cedars have experienced a range of growth responses to climatic variation and change (Chapters 2 & 3). This study tests the hypothesis that yellow-cedars within declining stands on Haida Gwaii exhibit divergent growth responses to changing climate. I used a principal component analysis (PCA), which is an effective way to identify divergent growth patterns and onset of decline, and can also aid in uncovering mechanisms of decline. It has been used to uncover multiple growth patterns within declining Nothofagus pumilio stands in Patagonia (Rodríguez-Catón et al. 2015), and to illuminate drivers of N. pumilio decline (Rodríguez-Catón et al. 2016). Given the climate driven decline mechanism on the mainland and the fact that the timing of decline on Haida Gwaii parallels the period of greatest warming (Hennon et al. 2012, IPCC 2014), I tested whether yellow-cedars with different growth patterns responded differently to climate over time. 4.2 Methods 4.2.1 Study sites To address the causes of yellow-cedar decline, I selected stands exhibiting crown symptoms. Fifteen study sites were located on Graham Island, Haida Gwaii, with 2 sites on the northeastern coast and 13 in the central and southern portion (Figure 4.1). The sites are 64 distributed across multiple watersheds and over a range of elevations where decline has been documented. The sites were wet, with boggy soils, often adjacent to a water feature such as a stream or wetland. Within sites, the sampled stands contained declining yellow-cedar as a codominant species, with Sitka spruce (Picea sitchensis), western hemlock (Tsuga heterophylla) and western redcedar (Thuja plicata). Figure 4.1. Location of 15 sites exhibiting yellow-cedar decline on Graham Island, Haida Gwaii. 4.2.2 Data collection Yellow-cedar were sampled in 100m x 20m transects bisecting the forest most heavily affected by decline. Increment cores taken from 30 trees at each site enabled tree-ring analyses to 65 detect internal evidence of decline, growth patterns, and relationships to climate. Tree diameter at breast height (DBH, 1.3 m) and health status were recorded. Trees were classified as healthy if they had full, green crowns, declining if they had discoloured foliage or were partly defoliated, or dead if they lacked foliage or had only brown foliage. To objectively select trees, I chose the first 15 healthy yellow-cedars (except two sites where only 10 and 14 healthy trees were available to sample), and the first 15 declining or dead yellow-cedar trees or snags for coring. Downed trees were not sampled. One core, sampled at standing height (c. 115cm above the ground), was taken from each tree. I aimed to intercept the pith and, where possible, included the bark or outer cambium. Trees with decayed outer rings or abundant heartwood rot were replaced with the next tree along the transect in the same health status class. For transects with fewer than 15 healthy and/or 15 declining or dead yellow-cedars that were suitable to core, additional trees were sampled and cored from outside of the transect. 4.2.3 Dendrochronological methods The cores were air dried, glued onto wooden supports, sanded with progressively finer sand paper to 600 grit, and scanned at a resolution of 2400 or 3200 dpi. Ring widths were measured (to 0.001mm) using the program CooRecorder (version 7.8, Larsson 2014a). Cores were cross-checked under a microscope, particularly for areas with very suppressed or difficult to distinguish rings. Using the program CDendro (version 7.8, Larsson 2014b), I crossdated each ring-width series and assembled ring-width chronologies for each site. Crossdating was confirmed using the program COFECHA (Holmes 1983). Crossdating improved the quality of age estimates by accounting for missing or false rings. For cores that did not intercept the pith, the number of missed rings was estimated based on the dimensions of the rings closest to the pith (Duncan 1989). If the number of rings added exceeded one third of the total number of rings the 66 sample was excluded from age estimations. If the radius of a tree exceeded the length of the increment borer or the tree had abundant heartwood decay, pith could not be reached and an accurate age estimate could not be calculated. Crossdating also allowed me to assign a calendar year to the outermost ring, indicating the cessation of growth of living trees and year of death of snags. For subsequent analyses, I used basal area increments (BAI) as they provide a better estimate of forest decline than ring-widths (Johnson & Abrams 2009, Rodríguez-Catón et al. 2015). Using BAI accounted for the age- and size-related growth trends inherent to ring widths, while maintaining both year-to-year and long-term variation. BAI in year t is calculated by summing the cumulative ring-widths from pith to latewood of year t (i.e. radius), converting to cross-sectional or basal area (BA = pi (radius2)), and subtracting BA in year t – 1 from the BA in year t (LeBlanc 1990, Phipps 2005). For the subset of cores that did not intercept the pith the distance to pith was estimated in one of two ways, depending the presence or absence of arced rings. Either the distance was estimated using the dimensions of the arced rings closest to the pith (Duncan 1989), or the cumulative ring-widths were subtracted from the average radius (i.e. half of the tree diameter inside the bark at coring height). For the four cores that lacked arced rings and were longer than half the diameter, distance to pith could not be estimated and was assumed to be negligible. To account for asymmetrical growth, I applied a correction factor to the raw ring widths to reflect the average radius around the circumference: Correction factor = (average radius) / (cumulative ring widths + distance to pith). BAI was calculated from the corrected ring widths using the inside-out method on the dplR package in R (Bunn et al. 2017, R Core Team 2014). 67 4.2.4 Dominant tree growth patterns To identify dominant patterns in the growth of yellow-cedar, I conducted a principal component analysis (PCA) on raw BAI series using SAS® 9.4 software (SAS 2016). PCA transforms a set of correlated variables in to a set of linearly uncorrelated variables called principal components (PCs). The first PC has the largest possible variance, and therefore accounts for the largest amount of variability in the data. Successive PCs account for decreasing amounts of variance. PCA has been used extensively in tree-ring research (Peters et al. 1981), because the PCs emphasize the dominant forms of co-variation in the ring-width series (Enright 1984). Originally PCA was used to uncover the dominant growth pattern of the stand, in order to reconstruct climate variables (e.g. Jacoby & Cook 1981, Enright 1984, Andreu et al. 2007). More recently, a novel application of PCA was to uncover multiple distinct growth patterns within declining forests where trees were responding to multiple limiting factors (Rodríguez-Catón et al. 2015, 2016). PCA requires a common period, so that all variables have a value in each year. I used a common period of 1959-2015 (57 years) to include as many trees as possible (n = 366). Trees that could not be crossdated (n = 21), died or stopped forming rings before 1959 (n = 54) or were <50 years old (n = 3) were excluded. Following Rodríguez-Catón et al. (2015), I retained the first two PCs that cumulatively accounted for at least 45% of the total variance in BAIs. Rather than using the original PC scores, individual BAI series (trees) were grouped according to the PC with which they had the highest absolute correlation, with a minimum r of |0.45|, and whether their correlation was positive or negative (Rodríguez-Catón et al. 2015). This yielded four groups, labelled A−D, which covered a longer period than the 57 years used to estimate the PCs. For 68 each group, the raw BAI series from individual trees were averaged, resulting in four time-series that provided an extended perspective of long-term changes in tree growth. 4.2.5 Temporal variation in growth patterns I developed standard and residual BAI chronologies for groups A−D using the R package dplR (Bunn 2008, 2010, Bunn et al. 2017, R Core Team 2014). Within each group, the individual BAI series were detrended using a horizontal line through the mean followed by autoregressive time series modelling. The first detrending transformed raw BAI measurements for individual trees to dimensionless indices that were averaged into the four standard chronologies, one for each group (A-D). The autoregressive modelling removed long-term trends, while maintaining the high-frequency variation in tree growth, yielding the residual chronology. The quality of the standard chronologies over the time span 1674−2015, when all four chronologies included ≥10 samples, was assessed using three metrics. The mean series intercorrelation (r) is the coefficient derived from a correlation matrix of all BAI series in the chronology (Fritts 1976). High values indicate that trees respond similarly to coarse-scale climate and environmental variation. Mean sensitivity measures ring-width variability between successive years (Fritts 1976). High values indicate a high degree of inter-annual variation in tree growth. The expressed population signal (EPS) estimates how closely a mean chronology based on a finite number of trees expresses its hypothetically perfect chronology based on an infinite number of trees (Wigley et al. 1984). EPS values ≥ 0.85 indicate a coherent stand level signal. I tested for breakpoints in the four standard BAI chronologies to identify periods of increasing and decreasing growth between 1674 and 2015. I identified breakpoints, defined as abrupt changes in growth, using the segmented package in R, which fits a piecewise regression model to each standard BAI chronology using an iterative approach (Muggeo 2008, R Core 69 Team 2014). Given a linear regression model, segmented estimates a new model with broken-line relationships. A broken-line relationship is defined by the slope parameters and the breakpoints where the linear relationship changes (Muggeo 2008). The number of breakpoints is specified. I identified three breakpoints in each of the standard BAI chronologies as three distinct changes in slope magnitude or direction were apparent in the chronologies. 4.2.6 Comparison of tree attributes among groups I examined whether the health status, size, or age differed among the four groups exhibiting differing growth patterns. To determine whether the observed distribution of healthy, declining and dead trees differed among groups A−D, I used a contingency table and chi-squared goodness of fit test. To test whether the frequency distributions of age (100-year classes) and DBH (10-cm classes) differed among the four groups, I conducted a two-sample Kolmogorov-Smirnov test (KS test) for all pairwise combinations (Kolmogorov 1933, Smirnov 1939). For all tests, α was 0.05. 4.2.7 Climate-growth relations I compared climate-growth relations among the four PC groups for 1901-2015, the beginning of the climate record to year of sampling. I obtained monthly total precipitation and mean/minimum/maximum monthly temperature records for the 13 southern sites using the software ClimateNA (Wang et al. 2016) and averaged them to represent the dominant regional climate of the study area. ClimateNA uses bilinear interpolation and elevation adjustments to downscale historical climate data to scale-free point estimates of climate values (Wang et al. 2016). To assess relationships between the annual index values of the four residual BAI chronologies and the regional monthly precipitation and temperature records, I conducted 70 bootstrap correlation function analysis (Blasing et al. 1984) using the R package treeclim (Zang & Biondi 2015, R Core Team 2014). The twelve months from the October prior to annual ring formation through September of the year of ring formation were analyzed. The period of analysis, 1901-2015, was split into four segments based on the breakpoints of each chronology. As I was examining 12 variables (months), segments of less than 12 years were excluded. 4.3 Results 4.3.1 Four distinct growth patterns The PCA revealed four distinct growth patterns in the sampled population of yellow-cedar trees between 1959-2015 (A-D; Table 4.1, Figure 4.2). PCs 1 and 2 accounted for 34.65% and 14.89% of the variance in the population, respectively. The majority (62%) of trees were most strongly correlated with PC1. They formed groups A (n = 119) and B (n = 107) based on their positive or negative correlations with PC1 (Table 4.1). Another 21% of trees were most strongly correlated with PC2. Trees with positive correlations formed group C (n = 26) and those with negative correlations formed group D (n = 51) (r ≤ -0.45). For the remaining 17% of trees, correlations with PCs 1 and 2 were <0.45 and they did not exhibit consistent growth patterns, so they were excluded from further analyses. Groups A, B and D included trees from all 15 sites, whereas group C included trees from 11 sites. All four patterns were present across sites on Graham Island, with no clear relation to space (Appendix P). The oldest trees were 853, 908, 904 and 729 years in groups A−D, respectively (Table 4.1). 71 Table 4.1. Yellow-cedar tree attributes and growth patterns for groups A−D. Trees were stratified according to positive or negative correlations with principal components (PC) 1 or 2. Correlation coefficients (r) are the means of individual trees in each group. Segmented regression was used to detect three breakpoints separating periods with significantly different slopes, indicating changes in growth rates, from 1674−2015 (α= 0.05). Group PC r n Tree attributes (range) Changes in growth rate 1674−2015 Age (yrs) DBH (cm) Segment 1 Segment 2 Segment 3 Segment 4 Slope Break Slope Break Slope Break Slope A 1 +0.701 119 61−853 11.9−148.0 +0.0008 1919 +0.0295 1966 −0.0475 1995 −0.0132 B 1 −0.699 107 65−908 11.0−109.7 −0.0004 1916 +0.0090 1985 +0.1241 2005 −0.0273 C 2 +0.653 26 67−904 10.6−76.6 −0.0012 1915 +0.0240 1965 −0.0049 1993 +0.1413 D 2 −0.628 51 71−729 12.5−126.4 +0.0001 1911 +0.0132 1974 +0.0653 1996 −0.1156 72 Figure 4.2. Mean growth patterns of four groups of yellow-cedar trees from 1674-2015. The inset panel depicts growth patterns from 1959-2015, the period used for the principal components analysis. Group A (red) includes 119 trees positively correlated to PC1. Group B (blue) includes 107 trees negatively correlated to PC1. Group C (green) includes 26 trees positively correlated to PC2. Group D (purple) includes 51 trees negatively correlated to PC2. The standard chronologies for all four groups had a minimum of 10 samples going back to 1674 (Table 4.2). For the period 1674−2015, series intercorrelations ranged from 0.289 to 0.321. These series intercorrelations are low relative to other yellow-cedars in the region (e.g. 0.46-0.51, Stan et al. 2011) as the intent of the current study was to group all series by principal components, rather than choose series with the highest correlations, as is typically done in dendroclimatology. Mean sensitivity was 0.272 to 0.306 indicating modest variation in BAI from one year to the next. The EPS was above 0.85 for all chronologies starting in 1891, meaning the chronologies exhibited a coherent stand level signal throughout the period of climate-growth analysis. 73 Table 4.2. Characteristics of the basal area increment chronologies for groups A−D. Mean series intercorrelation and sensitivity are for the common period 1674−2015 when all four chronologies included at least 10 trees. Group n Full length of n ≥ 10 Mean series Mean EPS ≥ 0.85 chronology (year) intercorrelation sensitivity (year) A 119 1140−2015 1447 0.321 0.272 1691 B 107 1106−2015 1526 0.313 0.275 1866 C 26 1112−2015 1667 0.289 0.306 1891 D 51 1282−2015 1674 0.310 0.273 1866 Prior to the 20th century, growth rates were similar across the four groups and relatively constant over multiple centuries (Figure 4.2). Early in the 20th century all four standard chronologies exhibited an increasing growth trend, which began between 1911 and 1919 and continued for several decades (Table 4.1, Figure 4.3). The chronologies diverged starting in the 1960s. The chronology for group A exhibited the longest-term decreasing growth trend, which began decreasing rapidly in 1966, followed by a shallower decrease after 1995. The chronologies for groups B and D were most similar in pattern, although the timing of changes in their growth trends differed. Trees in chronologies B and D grew more rapidly beginning in 1985 and 1974 then abruptly switched to decreasing growth trends in 2005 and 1996, respectively. The chronology for group C switched to a decreasing trend in 1965, similar to chronology A. Chronology C then switched from decreasing to an increasing trend in 1993, contrasting the concurrent switches to decreasing trends in chronologies A and D. After 2005, only chronology C exhibited an increasing growth pattern. 74 Figure 4.3. Basal area increment (BAI) standard (top) and residual (middle) chronologies for groups A−D from 1674-2015. The black line superimposed on each standard chronology indicates significant breakpoints identified using segmented regression analyses. The bottom panels depict sample depth or the number of trees included in each chronology over time (n = 119, 107, 26 and 51 for groups A−D, respectively). 75 4.3.2 Distribution of age, size and crown health status The distribution of healthy, declining and dead trees differed among groups A−D, as follows (chi-squared=107.4, p<0.00001, Figure 4.4). Groups A and D included fewer healthy and more declining or dead trees than expected by chance. Group A included the lowest proportion of healthy trees (24%, n = 28 of 119 trees) and the highest proportion of dead trees (41%, n = 49 trees). Group D had a nearly equal proportion of healthy (41%, n = 21 of 51 trees) and declining trees (39%, n = 20). Groups B and C included more healthy and fewer declining or dead trees than expected by chance. Group B included the highest proportion of healthy trees (86%, n = 92 of 107). Group C included no dead trees and all trees produced rings through the year of sampling (2015). Figure 4.4. Observed (coloured) and expected (white) values of healthy, declining and dead trees for the four principal component chronologies. 76 All four groups included trees of a broad range of ages and sizes (Table 4.1, Figure 4.5). The distributions of age and DBH did not differ significantly across groups A−D (p > 0.05 for all pairwise tests). The declining growth patterns were not associated with the oldest and largest nor youngest and smallest trees. Similarly, the rapidly increasing growth patterns were not associated with only the youngest and smallest trees. Figure 4.5. Size and age distributions of yellow-cedar trees in the four principal component chronologies (A=red, B=blue, C=green, D=purple). Class sizes for each frequency distribution are (left) 100 years for age estimates and (right) 10 cm for diameter at breast height (n=303). 4.3.3 Climate and yellow-cedar growth Growth was significantly associated with temperature and precipitation although these relations varied among chronologies and through time (Figures 4.6-4.7). Figure 4.6 shows results for minimum temperature, as it had the strongest and most consistent associations with growth of the three temperature variables. Results for all climate variables, including maximum, minimum, and average temperature and total precipitation are presented in Appendices Q, R, S and T. 77 Figure 4.6. Correlation between monthly minimum temperature and the residual chronology for principal components A, B, C and D over the time periods based on the identified breakpoints of each chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05). Climate-growth relations were similar for all four chronologies in the earliest two periods and diverged in the latter two periods, after 1965-1985 (Figures 4.6-4.7). In the earliest time-period, spanning 1901 to 1915-19, chronologies A, B and C responded to climate in the same way (D was excluded as the period 1901-11 was too short). Specifically, warm March temperatures limited growth, while wet Augusts and cool Septembers facilitated growth. In the 78 second time-period, spanning 1912-20 to 1965-85, all four chronologies exhibited increasing growth and responded to climate similarly. For all four chronologies, wet Octobers and dry Novembers facilitated growth, although the latter association was significant for chronologies C and D only. In three of four chronologies, warmer minimum temperatures in September facilitated growth, opposite to the relationship in the previous period. Figure 4.7. Correlation between monthly precipitation and the residual chronology for principal components A, B, C and D over the time periods based on the identified breakpoints of each chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05). 79 There was a divergence of both growth patterns and climate-growth relations after 1965-1985. Winter temperatures became more important for chronologies A and C, and summer temperatures became more important for chronologies B and D (Figure 4.6). During the 1966/67-1993/95 period, chronologies A and C exhibited decreasing growth and growth was negatively associated with warm January and April temperatures. Meanwhile, during the 1975/86-1996/2005 period, chronologies B and D exhibited increasing growth trends and growth was positively associated with warm April, May, June and July minimum temperatures, with significant associations for chronology D. Average and maximum June temperatures were significant for chronology B from 1986-2005 (Appendix R). During the two decades preceding 2015, only chronology C sustained an increasing growth pattern. From 1994-2015, growth of chronology C was positively associated with warm spring and summer temperatures, although not significant (Figure 4.6). Growth was positively associated with low winter precipitation, with February being the only significant association during this period (Figure 4.7). Conversely, low winter precipitation was negatively associated with growth for chronologies A and D, which exhibited decreasing growth starting in 1996/97, significant in December for both chronologies, and in January for chronology D. Warm January temperatures continued to be negatively associated with the growth of chronology A and became negatively associated with growth of chronology D as well in the most recent period, though the associations were not significant. Since growth of chronology B started to decrease in 2005, the most recent period was too short to assess climate-growth associations. 80 4.4 Discussion 4.4.1 Identifying four distinct growth patterns among yellow-cedar Identifying distinct growth patterns is a meaningful way to detect onset of decline, as decreasing radial growth often precedes crown symptoms of decline and mortality (Cailleret et al. 2017) and is commonly used as an indicator of tree decline (Cherubini et al. 2002, Bigler et al. 2004, Amoroso et al. 2012, Rodríguez-Catón et al. 2016). I derived BAI from ring-widths and applied PCA to objectively differentiate four subpopulations among the yellow-cedars at my study sites. Ring widths decrease through time as trees add wood to an increasing circumference, even under constant environmental conditions. This age- and size-related trend can obscure changes in growth patterns related to forest decline. Converting ring widths to BAI provides a two-dimensional representation of growth and avoids the confounding influences of age and size (LeBlanc 1990, Phipps 2005). Initially BAI increases during juvenile tree growth, then asymptotes once a full crown is formed. Flux and sustained directional change in BAI indicate short- to long-term variation in environmental or biophysical conditions. Therefore, BAIs provide a better means than ring-width to detect patterns of tree growth related forest decline (Johnson & Abrams 2009, Rodríguez-Catón et al. 2015). Using PCA to group trees based on their BAI, rather than crown condition, provides a more objective assessment of health status, growth trends and dieback (Rodríguez-Catón et al. 2015). By reducing the dimensionality of multivariate datasets, PCA facilitates classification of groups with common growth patterns, without assuming constant growth rates over time, and avoids grouping by surrogates, such as crown symptoms or time since death, that may not match growth patterns. Chapter 2 highlighted the discrepancies between internal and external symptoms of tree decline. Forest dieback processes are often complex, therefore detecting the onset of 81 growth decline is essential to understanding the primary causes. Identifying discrete dominant growth patterns within stands can more efficiently illuminate growth patterns related to dieback, which would not necessarily show up in the mean stand chronology. For example, Rodríguez-Catón et al. (2016) detected three distinctive long-term growth patterns in declining Nothofagus pumilio stands, identifying severe drought preceded by wetter periods as the inciting factor of decline. Comparable to other published literature, internal symptoms of yellow-cedar decline, identified as decreasing radial growth, corresponded with the external crown symptoms observed, with decreasing growth often acting as an ‘early warning sign’ (Amoroso et al. 2012, Cailleret et al. 2017). On Haida Gwaii, yellow-cedars affected by the earliest onset of decline were more likely to have died or to exhibit external crown symptoms, whereas yellow-cedars with more recent onset of decline were less likely to have died or exhibit crown symptoms. Trees that exhibited increasing growth at the time of sampling were all living and were the least likely to show crown symptoms. Each of the four subpopulations with distinct growth patterns from 1959-2015 included trees which cross the full range of ages and diameters, from sites across Haida Gwaii. Three of these subpopulations exhibited decline, entering a decreasing pattern of growth in the mid-1960s, 1990s or 2000s. These patterns provide evidence of three periods of onset of decline in yellow-cedars on Haida Gwaii, which coincide with elevated levels of mortality reported in Chapter 2, signifying that during these three time periods trees died and others entered decline. Mortality related to yellow-cedar decline on the mainland goes back to 1880, but has increased since 1950, with peaks in the 1970s and 1980s (Hennon & Shaw 1994). This timing corresponds to peaks in the onset of decline and mortality of yellow-cedars on Haida Gwaii in the 1980s (Chapter 2). 82 Group C was unique in that it was a “healthy” subpopulation made up of living trees with increasing growth and mostly asymptomatic crowns. Group C was composed of the fewest trees (n=26), with a greater proportion of trees under 20 cm in DBH, but also included trees up to 904 years old. 4.4.2 Patterns of growth divergence Yellow-cedars in declining stands on Haida Gwaii pose a particularly complex problem, as they exhibit divergent growth responses that differ among subpopulations and through time. Over time subsets of trees have exhibited increasing growth rates, responding positively to increasing growing season temperatures, while most trees have also exhibited periods of decreasing growth rates, associated with warm dry winter conditions. On Haida Gwaii, growth divergence follows more than 200 years during which trees experienced growth trends similar in both magnitude and direction. This includes an increasing growth trend concurrent with warming temperatures at the beginning of the 20th century. Divergence in growth patterns of yellow-cedar at these sites is a relatively recent phenomenon first appearing in the 1960s. This follows similar timing to climate-growth divergence seen across multiple species and geographies in the boreal and high elevation forests of the north (Wilmking et al. 2005, Lloyd & Bunn 2007, D’Arrigo et al. 2008). Growth responses to warming temperature in trees across the circumpolar north weakened or switched direction beginning around 1940-1950 and becoming more noticeable after 1970 (D’Arrigo et al. 2004, Wilmking et al. 2005, Lloyd & Bunn 2007). By contrast, some high elevation species such as alpine larch (Larix lyallii) did not show divergent radial growth patterns until as late as the 1990s (Montpellier et al. 2018). However, all of these divergent growth responses appear to be unique to the 20th century, with correlations to temperature weakening or changing direction after the 83 mid-20th century (Wilmking et al. 2004, Driscoll et al. 2005, D’Ariggo et al. 2009, Wong & Daniels 2016). 4.4.3 Climatic drivers of yellow-cedar decline Multiple lines of evidence support climate change as the most probable driver of yellow-cedar decline. Each subpopulation included trees from across sites on Haida Gwaii, where the common factor is mesoclimate. The Pacific Northwest has been warming since around the 1850s, which marked the end of a cooler period known as the Little Ice Age (Mann 2002) when glaciers in the area last reached their maxima. Correspondingly, the first yellow-cedar decline dates back to the 1880s (Hennon et al. 1990c). Most of the yellow-cedars in this study established during this cooler time-period, with 88% establishing prior to 1880 (Chapter 2). Warming temperatures have accelerated over time, especially during the 20th century. The increase in growth rates of trees in all four groups beginning in the early 20th century reflect this. The earliest onset of decline was identified as beginning in the 1960s (groups A and C). However, the most dramatic warming has occurred since the 1980s (IPCC 2014). Corroborating this, the climate data from my study sites show that the warmest winter temperatures since 1900 have occurred between 1980 and 2015, with the greatest change to January and February temperatures (Chapter 3). The greatest increases in decline and mortality of yellow-cedar beginning in the second half of the 20th century coincide with increases in temperature. I also detected a switch in climate-growth responses around the same time. However, the strongest evidence for climate is that temporally unstable climate-growth relations corresponded to periods of increasing and decreasing growth of yellow-cedars. High latitude and high elevation forests are typically thought to be temperature limited (e.g. Nemani et al. 2003). Many tree species have exhibited increasing growth over the 20th 84 century in response to warmer and longer growing seasons, with earlier snowmelt and warmer fall temperatures (Wilmking & Juday 2004, Driscoll et al. 2005, Lloyd et al. 2011), known as a ‘greening response’ (Lloyd & Bunn 2007). This includes yellow-cedars in Alaska; a stand of healthy yellow-cedar was positively associated with growing season temperatures (Beier et al. 2008). Consistent with this, yellow-cedar on Haida Gwaii exhibited increasing growth in the early 20th century in response to a longer growing season, inferred from the positive correlation with September temperature after the mid-1910s, and in response to warmer growing season conditions in the late 20th century, with positive associations with growing season temperature from 1975-2015 during periods of increasing growth. This relationship was strongest with maximum temperature for groups B and D (shown in Appendices R and T) and minimum temperature for group C. Moreover, Myneni et al. (1997) found that plant growth increased in the northern high latitudes after the 1980s, concurrent with the greatest increases in growth of yellow-cedar. Conversely, during periods of decreasing growth, yellow-cedars have not been able to take advantage of warmer growing season temperatures. Yellow-cedars on declining sites on Haida Gwaii exhibited divergent growth responses to climate after 1965. This timing mirrors that of divergence of tree growth across the circumpolar north (Wilmking et al. 2005, Lloyd & Bunn 2007, D’Arrigo et al. 2008). However, the response of tree growth and forest productivity to increasing temperatures has been highly variable both spatially and temporally. In addition to the ‘greening response’, some trees have shown a negative response to increasing temperatures, which has led to decreasing growth or a ‘browning response’ (Lloyd & Bunn 2007). In most cases, growing season temperatures have had the most significant effect (D’Arrigo et al. 2004, Wilmking et al. 2004, Driscoll et al. 2005, Zhang & Wilmking 2010, Montpellier et al. 2018). Increasing growing season temperature has exceeded 85 physiological thresholds, causing the relationship with tree growth to weaken or change direction since the mid-20th century (D’Arrigo et al. 2008). Negative growth responses are present across multiple species in the boreal forests, (Lloyd & Bunn 2007). While the mechanisms for this are not fully understood, and vary by location and species (D’Arrigo et al. 2008), many negative relationships are driven by direct temperature stress or drought (Wilmking et al. 2004, Girardin et al. 2015). However, this is not the case with yellow-cedars, which are usually found on wet sites. For yellow-cedars with increasing growth rates, the positive relationship with growing season temperature is maintained through the end of the study period. Importantly, there was no positive relationship between growing season precipitation and growth during the second half of the 20th century (except with August precipitation for group A from 1967-1995). In the most recent period, when winter temperatures are at their warmest, the association with precipitation becomes most significant in the winter and spring. Unlike other circumpolar species, there is no evidence that warm growing season temperatures have become limiting to yellow-cedars in this study. Instead, warm winter temperatures and low winter precipitation were strong drivers of decreasing growth since the 1960s. This is consistent with the root-freezing hypothesis from Alaska (Hennon et al. 2012) and parallels patterns seen on the mainland, where yellow-cedars exhibited divergent climate-growth responses, with comparable limiting factors. The growth of healthy yellow-cedars on the mainland was facilitated by warmer growing season temperatures (Beier et al. 2008). While the growth of yellow-cedars at declining sites was limited by warm and dry winter conditions (Beier et al. 2008, Wiles et al. 2012). For yellow-cedar, associations with winter are biologically relevant, although the impacts of winter conditions on tree growth and dieback are indirect. Rather than direct drought limitation due to warmer or drier growing seasons, I propose declining yellow-cedars are limited 86 by physiological drought due to damaged roots. Growth during decreasing periods was limited by low winter precipitation and warm January temperatures. Corroborating this hypothesis, higher winter precipitation (theoretically as snow) and colder January temperatures, would maintain any snowpack present insulating trees from freezing damage. In years when these conditions are particularly lacking, fine roots become damaged in the event of freezing temperatures if roots are not cold hardened (Schaberg et al. 2008, 2011). This leads to physiological drought in the summer, an indirect impact of climate that limits growth (Hennon et al. 2012). Thaw-freeze events in conjunction with low snowpack have become more frequent in recent years in Alaska (Beier et al. 2008). On Haida Gwaii, 1986 was a year of low growth for all four chronologies with 70-85% of trees per chronology exhibiting negative growth, this was also ranked as the fourth highest winter for potential thaw-freeze impact from 1950 to 2004 in Alaska, based on thaw-freeze magnitude and a proxy for snow cover (Beier et al. 2008). However, none of the other years ranked for high thaw-freeze magnitude and years of low growth from Haida Gwaii corresponded. Additionally, the majority of yellow-cedar forests on Haida Gwaii exist within the snow-rain threshold, where winter temperatures range between -2 and 2°C (Buma et al. 2016). Therefore, slight increases in temperature can have a large effect on both snowpack and the frequency of thaw-freeze events. Corroborating this, between 1900-2015 winter temperatures at the study sites ranged from -10 to 7°C, with decadal average minimum and maximum winter temperatures ranging from -4 to 2 °C. I propose the driving mechanism of decline on Haida Gwaii is consistent with that on the mainland, specifically low snowpack combined with increased thaw-freeze events damaging yellow-cedar’s shallow fine roots. Subsequent research should focus on snowpack accumulation 87 and persistence, as well as fine-scale thaw-freeze cycles at sites both exhibiting and lacking evidence of yellow-cedar decline. Identifying fine-scale climatic variation, focused on the time-periods when onset of decline and elevated levels of mortality occurred would also be beneficial in shedding light on the specific mechanism leading to decline on Haida Gwaii. 4.4.4 Fine scale drivers of yellow-cedar decline In addition to regional climate, there are likely also fine-scale drivers which function at the individual tree level. Both yellow-cedars which have died or have evidence of decline and those which are healthy and growing faster than at any other time in the past occur within the same stands. While most trees affected by a decrease in growth continued to exhibit that pattern until the end of the study period, trees in group C recovered from an early decrease in growth beginning in the 1960s, and became the only group exhibiting increasing growth in 2015. Trees in chronology C may be more resistant to decline or may be releasing due to a higher density of dead trees around them. Stem mapping at the stand level is needed to address this question. I found no evidence of stand dynamics contributing to elevated levels of decline and mortality, consistent with other forest declines worldwide (e.g. van Mantgem et al. 2009, Amoroso et al. 2015). Trees in declining patterns were neither the smallest and youngest, which would be consistent with competition, nor the oldest and largest, which would be consistent with the aging of a cohort (Franklin et al 2002). Instead, fine-scale drivers that affect individual trees can act as predisposing factors to decline (Manion 1991, Manion & Lachance 1992). Limiting factors at the tree-level can mediate response to a regional-scale driver, such as climate (Lloyd et al. 2017). The lack of knowledge around what predisposes or protects yellow-cedars from decline prevents accurate prediction of which trees are at risk. Possible fine-scale drivers of decline include microsite conditions, pathogens and genetics. 88 Wilmking et al. (2005) proposed that recent warming has led to trees becoming more sensitive to microsite differences which has caused some but not all trees at a site to become stressed by drought or other stressors. This has led to the divergence in growth among subpopulations. Additionally, microsite conditions are important in yellow-cedar decline on the mainland. Soil saturation and depth, canopy closure, aspect, snowpack and other microenvironmental factors all influence the likelihood of yellow-cedar decline on the mainland (D’Amore & Hennon 2006, Hennon et al. 2010). It is possible that microsite conditions affect yellow-cedars on Haida Gwaii as well, especially if the root-freezing decline mechanism is the same. Pathogens have been known to interact with climate in forest declines. For example, pathogens played a role in the decline of Austrocedrus chilensis (Vélez et al. 2012, Amoroso et al. 2015), Pinus mugo (Cherubini et al. 2002), and Pinus albicaulis (Wong et al. 2016). However, in the declining yellow-cedar forests of Alaska, pathogens have been extensively studied and excluded (Hennon et al. 1990b, Hennon & Shaw 1994). While at present there is no evidence of pathogens on Haida Gwaii affecting the decline or mortality of yellow-cedar, given the other discrepancies with decline on the mainland, they cannot be ruled out. Advances in DNA barcoding and metagenomics since the 1990s justify re-visiting the potential that pathogens contribute to decline (Stewart et al. 2018). Genetic variation affects how trees interact with climate; including resistance to drought and heat stress (Bansal et al. 2015, Allen et al. 2015) and tolerance of cold temperatures by way of cold hardiness (Aitken & Hannerz 2001). Genetic variation has been proposed as a predisposing factor in other forest declines, such as Austrocedrus chilensis decline (Amoroso et al. 2015) and may be important in yellow-cedar decline as well. Yellow-cedars are adapted to a 89 broader range of temperatures than most temperate trees. Russell and Krakowski (2012) suggested trees from populations within the core of yellow-cedar’s range are at minimal risk for maladaption with transfer and hypothesized that transfer to sites with warmer temperatures would increase productivity. However, as temperatures have warmed throughout the 20th century, yellow-cedars have experienced increased rates of decline and mortality. At present, not much is known about the role of genetic variation in protecting or predisposing yellow-cedars to decline. 4.5 Conclusion My findings highlight the importance of parsing out the multiple growth patterns that can exist within a declining stand to better understand the history and dynamics of decline over time. I uncovered four divergent growth patterns in yellow-cedars on Haida Gwaii, three of which are declining. Increasing growth over the second half of the 20th century was driven by increasing growing season temperatures consistent with patterns from across high latitude and high elevation forests. Whereas decline was driven by warmer winter temperatures and low winter precipitation, consistent with the drivers proposed on the mainland. These patterns emerged after 1965-1985, during the period of greatest climate warming. In addition to climate, there are likely multiple pre-disposing and contributing factors influencing decline, which are not currently understood. Subsequent research should focus on these fine-scale drivers in order to protect this important and vulnerable species. 90 Chapter 5: Conclusion 5.1 Summary of main findings Yellow-cedar decline is one of the most severe forest declines in North America. Changes in climate, interacting with other key environmental factors have caused an extensive decline and mortality of yellow-cedar over the past century (Hennon et al. 2012). The global rise in temperature and associated changes in climate have also led to decline or dieback of forests around the globe, across multiple species and ecosystems (van Mantgem et al. 2009, Allen et al. 2010, Peng et al. 2011). Climate-driven forest dieback and decline has emerged as a critical issue. Yellow-cedar decline is an important example of how complex factors, including site conditions and physiology, can interact with changing climate and result in unexpected outcomes for forest health. On Haida Gwaii, I found abundant evidence of tree decline and mortality of yellow-cedar across multiple watersheds and over a range of elevations, including crown symptoms or death in 74% of trees, and decreasing basal area increment in an additional 53% of trees with asymptomatic crowns. Tree decline and death was present in yellow-cedars spanning size, age and canopy position. The decline on Haida Gwaii parallels the broader yellow-cedar decline in terms of spatial distribution, symptoms, magnitude and timing. Both onset of tree decline and rates of mortality on Haida Gwaii have intensified in recent decades, from the 1980s on. Although crown symptoms were observed more recently, causing concern in the 2000s (CHN & BC 2011), internal decline in basal area increment goes back much further. Including peaks in the onset of decreasing growth patterns in the 1940s and 1960s. This research was the first to use tree-ring analyses to compliment crown health to evaluate the decline of yellow-cedar. I 91 determined that internal symptoms of decline (radial growth decline), often preceded external symptoms (crown dieback). Decreasing radial growth has potential as an early indicator of tree decline in yellow-cedar trees or forests that do not currently exhibit crown symptoms. I investigated several possible drivers of yellow-cedar decline on Haida Gwaii both at the local and regional scale. My results, presented in Chapter 2, are inconsistent with stand dynamics as a driver of elevated decline and mortality. Neither increased competition, nor aging of a cohort explains the decline. Onset of basal area increment decline and mortality have been accumulating over time, with increased rates since the 1980s. Conversely, multiple lines of evidence, from Chapters 2-4, corroborate the hypothesis that freezing damage to roots during years of warm winter temperatures and low winter precipitation have driven yellow-cedar decline on Haida Gwaii. Chapter 2 demonstrates that both mortality rate and onset of decline have increased in concert with anthropogenic climate warming. Chapters 3 and 4 showed that yellow-cedars in declining stands on Graham Island, Haida Gwaii have common growth patterns over time, consistent with meso-climate, and are limited by common factors. Similarly, negative marker rings, reflecting years of very low radial growth, were common across declining sites on Haida Gwaii and the mainland coast of northern BC and southern Alaska (Chapter 3). I found that asymptomatic or ‘healthy’ trees and those affected by decline responded differently to climate over time. Chapter 3, presented strong evidence that this decline was driven by climate change, exacerbated by the Pacific Decadal Oscillation (PDO), rather than being associated with the positive phases of the PDO. Trees were stratified by health class and compared to climate over time, revealing that trees of all health status became negatively associated with warm dry winter conditions as temperatures warmed over the 20th century. In Chapter 4, I tested for different 92 growth patterns within these declining yellow-cedar stands and uncovered four divergent growth patterns in yellow-cedars on Haida Gwaii, three of which were declining. Increasing growth over the second half of the 20th century was driven by increasing growing season temperatures consistent with patterns from across high latitude and high elevation forests (Wilmking & Juday 2004, Driscoll et al. 2005, Lloyd et al. 2011). Whereas, decreasing growth was driven by warmer winter temperatures and low winter precipitation, consistent with the drivers of decline found on the mainland (Beier et al. 2008, Wiles et al. 2012). These patterns emerged after 1965-1985, during the period of greatest climate warming (IPCC 2014). Identifying this mechanism of decline allows for greater understanding of which stands may be at risk, though tree-level factors that protect trees from or predispose trees to decline remain unknown. I propose that warmer winter temperatures have led to a combination of decreased cold-hardening, earlier dehardening, and a reduced potential for snowpack to build-up, in conjunction with increased frequency of thaw-freeze cycles and freeze events during otherwise milder winters. Compared to drivers of yellow-cedar decline on the mainland, snowpack may play a less important role on Haida Gwaii. Warming temperatures have led to more direct effects. Warmer temperatures mean less cold-hardening throughout the winter which exposes yellow-cedar’s fine roots to varying degrees of freezing damage over multiple winter thaw-freeze cycles, causing physiological stress, tree decline and eventual death. Ultimately, I suggest that warming winter temperatures, accelerated by anthropogenic climate change, have had led to dieback and death of yellow-cedars on wet sites, even with the temperate ocean-moderated climate of the Haida Gwaii archipelago. 93 5.2 Drivers of yellow-cedar decline on Haida Gwaii In addition to climate, there are likely multiple predisposing and contributing factors influencing decline, which are not currently understood. Based on the decline spiral model, forest decline often involves a trigger or “inciting factor” which interacts with “predisposing factors”, such as age or site conditions and “contributing factors” such as insects or pathogens, ultimately leading to decline and death (Manion 1991, Manion & Lachance 1992). On Haida Gwaii the proposed inciting factor of yellow-cedar decline is freeze events following warmer temperatures when yellow-cedar’s fine roots are not protected by cold hardening (or an insulating snowpack, Figure 5.1). These events are increasingly frequent due to warming winter temperatures associated with climate change. Figure 5.1. Proposed spiral of decline model for yellow-cedar decline based on Manion’s (1991) decline disease spiral, depicting factors that predispose, incite and contribute to tree decline and death. 94 Predisposing factors include long-term or slow-acting factors that alter a tree’s probability of exposure to injury or the inciting factor (Manion 1991). The predisposing factors for yellow-cedar decline may include microsite condition or genetics (Figure 5.1). Microsite conditions such as soil depth and soil moisture impact rooting depth, and therefore how exposed fine roots are to freezing. Supporting this, higher rates of yellow-cedar decline were concentrated at poorly drained sites on the mainland (Hennon & Shaw 1994) and on Haida Gwaii (personal observation). Landscape position, micro-topography, aspect and canopy closure influence local temperature regimes and risk of freezing. These factors all influence the likelihood of yellow-cedar decline on the mainland (D’Amore & Hennon 2006, Hennon et al. 2010) and likely affect yellow-cedars on Haida Gwaii as well. Genetics likely influence the overall cold tolerance, as well as timing and extent of cold hardening of yellow-cedar’s roots. Cold hardiness related traits in conifers are often under strong genetic control; Douglas fir (Pseudotsuga menziesii) and lodgepole pine (Pinus contorta) are both good examples of this (Aitken & Hannerz 2001). Other phenotypic or genetic traits may play a role in predisposing yellow-cedars to decline as well. In the weakened state caused by the predisposing and inciting factors, trees are additionally vulnerable to other stressing agents, these are referred to as contributing factors (Manion 1991). Contributing factors to yellow-cedar decline on Haida Gwaii may include Armillaria spp., which were commonly found on declining and dead yellow-cedars (Armillaria sinapina and A. nabsona, N. Feau, personal communication, May 7, 2019), or other pathogens (Figure 5.1). For example, two Pythium species (P. emineosum and P. undulatum) identified on yellow-cedars on Haida Gwaii have both been related to root rot disease and tree declines (N. Feau, personal communication, May 7, 2019). There may be additional predisposing or contributing factors leading to decline and death which have not yet been identified. Further 95 research is needed to investigate these predisposing and contributing factors to yellow-cedar decline on Haida Gwaii to determine which trees will be protected from or exposed to yellow-cedar decline in the future. 5.3 Implications for management and conservation The direct loss of yellow-cedar has important ecological, economic and cultural implications (Oakes et al. 2016). Yellow-cedar is important ecologically in forests with relatively limited coniferous species diversity and is an important long-term component of forest structure, living nearly twice as long as co-occurring Sitka spruce and hemlock trees (Hennon et al. 2016). The loss of yellow-cedar from stands has important implications for the successional dynamics of the forests (described for Alaska by Oakes et al. 2014). Yellow-cedar is culturally important to Indigenous peoples, including the Haida (CHN 2016) and has many spiritual and practical uses. Its wood and bark are used extensively for multiple purposes. The wood of declining and dead yellow-cedars can still be used, but as trees experience decline and the cambium dies-back, the inner bark becomes dry and unusable in cedar bark weaving. Yellow-cedar also has high economic value, important to the local logging industry, and healthy forests are an important part of the local tourism industry. Therefore, yellow-cedar decline has the potential to negatively impact the local economy. For management, in the absence of tree-ring data from across stands to pinpoint the onset of decline, crown symptoms are good indicators of decline, as there is a general agreement between external and internal symptoms of tree decline. Internal symptoms of tree decline appeared earlier than external crown symptoms, meaning when decline does become apparent in the crown it has likely already been going on for some time. Additionally, monitoring of future 96 trends in crown symptoms and growth patterns at both currently declining stands and over the broader landscape will be important. There is some indication that yellow-cedars have the potential to recover from radial growth decline. In Chapter 4, the subset of trees with increasing growth had suffered a period of decreasing growth from 1965-1993, and recovered to become the only group with increasing growth and mostly asymptomatic crowns in 2015. However, it is unclear if trees already exhibiting crown symptoms can do the same. On Haida Gwaii, decline appears to be concentrated on wetter sites, where rooting depth is shallower, canopy conditions are more open and there are likely greater extremes in temperature. Given that the decline on Haida Gwaii follows a similar mechanism to that proposed for the mainland, planting yellow-cedars on wetter sites that are likely to be affected by decline should be avoided. Conversely, yellow-cedars should be planted on drier, more productive sites where rooting depth is greater and yellow-cedar decline is not expected to occur. Additionally, these could be areas to set aside for conservation of healthy yellow-cedar populations, not predicted to be affected by decline. The drivers of yellow-cedar decline on Haida Gwaii parallel those on the mainland. Therefore, models developed in for the mainland (e.g. Wooton & Klinkenberg 2011) could be adapted to predict where future decline is most likely to occur and where yellow-cedars are likely to be protected on Haida Gwaii. Identifying where yellow-cedar is likely to remain unaffected by decline will be critical for the conservation of the species. Protecting these identified areas from logging and other human disturbance will be important in preserving this species for future generations. 97 5.4 Future research questions As a result of this research, a number of new hypotheses and questions have emerged around yellow-cedar decline both on Haida Gwaii and on the mainland. Associations between climate variables at the monthly scale and growth of yellow-cedar revealed evidence of climate as a driver of yellow-cedar decline. Going forward, it would be beneficial to test for climatic drivers at a range of temporal (e.g. hourly, daily, seasonally) and spatial (e.g. individual tree, stand, regional) scales on Haida Gwaii and throughout the range of yellow-cedar decline. Additionally, identifying the facilitating and limiting climate factors to tree growth of other co-occurring species within stands containing yellow-cedar would highlight limiting factors which are unique to yellow-cedar and may help expose drivers of decline. On Haida Gwaii, yellow-cedars of a range of health statuses exist within the same stands, including yellow-cedars that are healthy and increasing in growth, those recently affected by decline, and yellow-cedars long ago affected by decline or dead. Therefore, in addition to regional climatic drivers of yellow-cedar decline, there exist fine-scale tree-level factors which influence which trees are affected by decline. As suggested by the spiral of decline model (Manion 1991) it is important to identify predisposing and contributing factors affecting a tree’s vulnerability to decline and death. Research from Alaska has identified many landscape level predisposing factors to yellow-cedar decline (Hennon et al. 2010, 2012). These include canopy closure, soil moisture, landscape position, aspect and elevation. In the future, testing for fine-scale factors that predispose yellow-cedars to decline could include monitoring the above-ground and below-ground temperature (i.e. with temperature loggers), as well as testing for rooting depth, and the importance of micro-topography. Monitoring accumulation and persistence of snowpack at a range of elevations on Haida Gwaii would also be beneficial. Testing for the 98 influence of genetics on yellow-cedar decline could include testing for traits associated with cold hardiness or susceptibility to root damage by freezing. Finally, testing for pathogens associated with yellow-cedar decline should continue, as it often takes continuous monitoring over a long period of time to identify contributing pathogens to a forest dieback or decline. There are also questions around what the future of the forest stands affected by yellow-cedar decline will look like. Oakes et al. (2014) investigated the stand development patterns following yellow-cedar decline in Alaska. Similar studies should be undertaken in BC, both on the mainland coast and on Haida Gwaii. It is likely that the stand development process will differ from what was observed in Alaska. There are some important differences both in where decline has occurred and what species may succeed yellow-cedar in areas where it is being lost. Notably, western redcedar was not present in the stands studied in Alaska, but is found throughout the areas affected by yellow-cedar decline in BC. Finally, testing for early radial growth decline in ‘healthy’ or more productive stands could indicate early warning signs of decline in these stands, identifying at risk stands and healthy, unaffected stands. This could help to identify trees which are likely to experience decline in the future and therefore may be less of a priority to conserve. This could also identify trees that are unlikely to be affected by decline and are therefore a high priority for conservation and protection from logging and other activities. This could be highly effective in combination with mapping local to regional predisposing factors. These techniques could be used to help ensure healthy stands containing yellow-cedar persist on Haida Gwaii and across the coast of northern BC and southern Alaska. 99 5.5 Conclusion While tree death is a natural part of forest dynamics, increasing evidence suggests that higher rates of forest dieback and background mortality are occurring under anthropogenic climate change. Understanding and predicting the consequences of climate change to forests worldwide is emerging as an important challenge. Future changes in climate will likely result in increasing levels of climate-induced forest mortality. This affects ecological communities, ecosystem functioning and ecosystem services (Anderegg et al. 2013). In the Pacific Northwest, the high rates of mortality of yellow-cedar across stands and forests have far reaching ecological, cultural and economic effects (Oakes et al. 2016). 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Widespread decline of Congo rainforest greenness in the past decade. Nature. 509 (7498):86-90. 114 Appendices Appendix A Results of the Kolmogorov-Smirnov tests for all pairwise combinations stratified by health class (healthy, declining and dead), testing the distribution of trees by diameter at breast height (DBH), basal area (BA) and age adjusted using the Duncan method. Attribute Distributions compared D p-value DBH Healthy – Declining 0.35714 0.3338 DBH Healthy – Dead 0.21429 0.9048 DBH Declining – Dead 0.21429 0.9048 BA Healthy – Declining 0.22222 0.7658 BA Healthy – Dead 0.16667 0.9639 BA Declining – Dead 0.11111 0.9999 Age Healthy – Declining 0.27273 0.8079 Age Healthy – Dead 0.27273 0.8079 Age Declining – Dead 0.18182 0.9934 115 Appendix B Yellow-cedar tree densities at each of the 15 sites. Trees were stratified by health status based on their crown attributes as healthy, declining and dead to determine density and proportion of trees per health class (n=1016). Site Sample size (trees) Density in trees/ha (Proportion of trees) Total Healthy Declining Dead Declining and Dead AWU1 132 660 170 (0.26) 325 (0.49) 165 (0.25) 490 (0.74) BRE1 127 635 180 (0.28) 160 (0.25) 295 (0.46) 455 (0.72) CHI1 30 150 50 (0.33) 25 (0.17) 75 (0.50) 100 (0.67) DAT1 46 230 85 (0.37) 50 (0.22) 95 (0.41) 145 (0.63) DAT2 89 445 120 (0.27) 120 (0.27) 205 (0.46) 325 (0.73) DEL1 52 260 50 (0.19) 70 (0.27) 140 (0.54) 210 (0.81) DIN1 51 255 105 (0.41) 65 (0.25) 85 (0.33) 150 (0.59) FLO1 37 185 45 (0.24) 35 (0.19) 105 (0.57) 140 (0.76) GHO1 25 125 45 (0.36) 50 (0.40) 30 (0.24) 80 (0.64) IRA1 148 740 130 (0.18) 215 (0.29) 395 (0.53) 610 (0.82) PHA1 40 200 70 (0.35) 5 (0.03) 125 (0.63) 130 (0.65) PHA2 71 355 90 (0.25) 105 (0.30) 160 (0.45) 265 (0.75) REN1 36 180 25 (0.14) 35 (0.19) 120 (0.67) 155 (0.86) REN2 84 420 85 (0.20) 100 (0.24) 235 (0.56) 335 (0.80) SKO1 48 240 75 (0.31) 35 (0.15) 130 (0.54) 165 (0.69) Average 339 88 (0.26) 93 (0.28) 157 (0.47) 250 (0.74) 116 Appendix C Summary of yellow-cedar mean tree diameter at breast height (DBH), with range in brackets, for all trees within the transects at the 15 sites. Trees were stratified by health status based on their crown attributes as healthy, declining and dead (n=1016). Site (n) Mean (range) DBH in cm All Trees Healthy Declining Dead AWU1 (132) 24.2 (10.1- 71.4) 27.9 (10.1-62.5) 21.8 (10.2-71.4) 24.9 (10.9-53.0) BRE1 (127) 28.0 (10.1- 62.8) 26.2 (10.4-52.3) 28.1 (10.1-62.8) 29.1 (10.1-60.9) CHI1 (30) 46.9 (11.5-77.8) 46.2 (11.5-77.8) 54.2 (25.6-74.2) 44.9 (12.5-74.4) DAT1 (46) 50.3 (10.2- 126.4) 44.9 (10.2-126.4) 52.6 (18.9-94.2) 54.0 (22.8-98.5) DAT2 (89) 33.5 (10.2- 118.6) 39.6 (11.3-118.6) 30.9 (10.2-64.7) 31.5 (11.5-68.4) DEL1 (52) 39.3 (12.6- 83.6) 33.0 (12.6-72.0) 36.7 (13.6-66.9) 42.9 (17.7-83.6) DIN1 (51) 36.6 (11.7-107.4) 31.6 (11.7-107.4) 37.2 (15.8-67.2) 42.5 (14.5-73.3) FLO1 (37) 40.3 (11.2- 79.5) 33.6 (11.2-72.0) 35.2 (13.8-66.8) 44.9 (14.6-79.3) GHO1 (25) 32.1 (10.5- 61.2) 24.8 (11.0-42.9) 39.4 (13.5-57.9) 31.0 (10.5-61.2) IRA1 (148) 22.3 (10.0- 76.6) 27.2 (12.4-57.4) 22.4 (10.3-76.6) 20.6 (10.0-55.1) PHA1 (40) 40.2 (13.0- 140.0) 41.7 (15.3-96.5) 16.8 (16.8-16.8) 40.4 (13.0-140.0) PHA2 (71) 38.2 (10.1- 130.8) 45.3 (14.0-130.8) 36.8 (10.1-69.8) 35.2 (11.5-70.3) REN1 (36) 37.0 (15.0- 84.1) 52.4 (25.0-84.1) 35.1 (17.0-56.9) 34.3 (15.0-75.5) REN2 (84) 28.8 (10.1- 80.8) 21.9 (10.1-50.3) 25.8 (11.7-71.4) 32.6 (10.8-80.8) SKO1 (48) 29.5 (10.6- 59.4) 29.3 (10.6-55.0) 26.3 (13.3-39.8) 30.5(12.3-59.4) Average 31.9 (10.0- 140.0) 33.1 (10.1-130.8) 29.4 (10.1-94.2) 32.8 (10.0-140.0) 117 Appendix D Summary of yellow-cedar mean tree basal area, with range in brackets, for all trees within the transects at the 15 sites. Trees were stratified by health status based on their crown attributes as healthy, declining and dead (n=1016). Site (n) Mean (range) Basal Area in m2 All Trees Healthy Declining Dead AWU1 (132) .058 (.008-.400) .076 (.008-.307) .047 (.008-.400) .060 (.009-.221) BRE1 (127) .075 (.008-.310) .063 (.008-.215) .079 (.008-.310) .079 (.008-.291) CHI1 (30) .201 (.010-.475) .211 (.010-.475) .255 (.051-.432) .177 (.012-.435) DAT1 (46) .256 (.008-1.255) .237 (.008-1.255) .260 (.028-.697) .270 (.041-.762) DAT2 (89) .114 (.008-1.105) .165 (.010-1.105) .096 (.008-.329) .095 (.010-.367) DEL1 (52) .142 (.012-.549) .110 (.012-.407) .120 (.015-.352) .164 (.025-.549) DIN1 (51) .135 (.011-.906) .114 (.011-.906) .132 (.020-.355) .162 (.017-.422) FLO1 (37) .155 (.010-.496) .121 (.010-.407) .119 (.015-.350) .182 (.017-.496) GHO1 (25) .100 (.009-.294) .058 (.010-.145) .137 (.014-.263) .103 (.009-.294) IRA1 (148) .048 (.008-.461) .066 (.012-.259) .051 (.008-.461) .039 (.008-.238) PHA1 (40) .177 (.013-1.539) .179 (.018-.731) .022 (.022-.022) .182 (.013-1.539) PHA2 (71) .140 (.008-1.344) .211 (.015-1.344) .120 (.008-.383) .113 (.010-.388) REN1 (36) .133 (.018-.555) .266 (.049-.555) .109 (.023-.254) .113 (.018-.448) REN2 (84) .082 (.008-.513) .045 (.008-.199) .068 (.011-.400) .102 (.009-.513) SKO1 (48) .078 (.009-.277) .077 (.009-.238) .062 (.014-.124) .083 (.012-.277) Average .105 (.008-1.539) .118 (.008-1.344) .088 (.008-.697) .108 (.008-1.539) 118 Appendix E Summary of yellow-cedar mean ages, adjusted using the Duncan method, with range in brackets, for all trees within the transects at the 15 sites (n=377). Trees were stratified by health status based on their crown attributes as healthy, declining and dead. Site (n) Mean (range) Age in years All Trees Healthy Declining Dead AWU1 (28) 367 (88-1004) 274 (91-1004) 570 (225-829) 352 (88-603) BRE1 (29) 230 (29-576) 192 (29-576) 316 (173-443) 221 (118-485) CHI1 (28) 297 (79-556) 252 (79-545) 421 (198-556) 298 (146-464) DAT1 (21) 389 (135-973) 359 (135-973) 436 (231-578) 414 (361-481) DAT2 (29) 235 (93-665) 223 (93-619) 271 (105-665) 215 (124-355) DEL1 (17) 339 (121-445) 327 (121-424) 422 (387-445) 315 (251-422) DIN1 (22) 207 (85-370) 164 (95-283) 262 (85-364) 230 (157-370) FLO1 (27) 226 (80-536) 228 (80-496) 171 (93-238) 246 (112-536) GHO1 (26) 315 (114-646) 271 (114-571) 340 (216-432) 393 (274-646) IRA1 (22) 253 (34-680) 186 (34-360) 174 (107-246) 373 (195-680) PHA1 (24) 347 (112-768) 335 (112-768) 366 (191-584) 344 (194-525) PHA2 (25) 324 (90-641) 331 (242-641) 346 (244-479) 304 (90-502) REN1 (27) 329 (155-640) 290 (155-640) 403 (277-587) 315 (217-444) REN2 (24) 288 (93-757) 213 (93-490) 428 (190-757) 315 (149-570) SKO1 (28) 216 (62-432) 232 (85-432) 293 (254-318) 180 (62-425) Average 288 (29-1004) 256 (29-1004) 358 (85-829) 290 (62-680) 119 Appendix F Distribution of yellow-cedar trees in trees/ha stratified by height class, with proportion in brackets for all 15 sites (n=1011). Site (n) Density (proportion) of trees by height class in trees/ha Dominant Co-dominant Intermediate Suppressed AWU1 (132) 60 (0.09) 195 (0.30) 275 (0.42) 130 (0.20) BRE1 (127) 125 (0.20) 200 (0.31) 215 (0.34) 95 (0.15) CHI1 (30) 70 (0.47) 40 (0.27) 25 (0.17) 15 (0.10) DAT1 (46) 55 (0.24) 80 (0.35) 60 (0.26) 35 (0.15) DAT2 (84) 65 (0.15) 175 (0.42) 135 (0.32) 45 (0.11) DEL1 (52) 90 (0.35) 90 (0.35) 45 (0.17) 35 (0.13) DIN1 (51) 20 (0.08) 100 (0.39) 75 (0.29) 60 (0.24) FLO1 (37) 65 (0.35) 75 (0.41) 40 (0.22) 5 (0.03) GHO1 (25) 20 (0.16) 55 (0.44) 30 (0.24) 20 (0.16) IRA1 (148) 15 (0.02) 225 (0.30) 340 (0.46) 160 (0.22) PHA1 (40) 50 (0.25) 75 (0.38) 55 (0.28) 20 (0.10) PHA2 (71) 35 (0.10) 210 (0.59) 95 (0.27) 15 (0.04) REN1 (36) 40 (0.22) 60 (0.33) 50 (0.28) 30 (0.17) REN2 (84) 100 (0.24) 155 (0.37) 110 (0.26) 55 (0.13) SKO1 (48) 50 (0.21) 95 (0.40) 25 (0.10) 70 (0.29) Average 54 (0.17) 115 (0.36) 100 (0.31) 51 (0.16) 120 Appendix G Summary of the ability to determine the outer ring date of trees and the reasons for which outer ring dates could not accurately be ascribed (n=444). Health Status Accurate outer ring date Outer ring date not accurate Reason outer ring date could not be identified Unable to crossdate Suppressed outer rings Missing outer rings Healthy 213 8 4 2 2 Declining 93 11 3 4 4 Dead 70 49 4 2 43 Total 376 68 11 8 49 121 Appendix H Outer ring date estimates for trees where an accurate outer ring date could not be determined, for living (top; n=17) and dead trees (bottom; n=46) between 1724 and 2015. 122 Appendix I Mortality rates of yellow-cedar. Annual (bars) and decadal (line) mortality rates of 116 yellow-cedars, 70 for which the year of death could be accurately determined, and 49 for which year of death is an estimate (top). Changes in sample depth (blue) due to cumulative mortality of dead trees (black) and cessation of ring formation in healthy (green) and declining (grey) trees (bottom). n = 439 trees for both trees where accurate outer ring dates could be determined and where outer ring dates are estimates. 123 Appendix J Characteristics of the site level ring width chronologies. Mean series intercorrelation and sensitivity are for the full length of the chronology. Site n Full length of n ≥ 10 Mean length Mean series Mean chronology (year) of series intercorrelation sensitivity AWU1 21 1112-2015 1734 328.1 0.391 0.263 BRE1 16 1440-2015 1817 293.4 0.376 0.259 CHI1 23 1472-2015 1734 287.7 0.384 0.236 DAT1 26 1500-2013 1575 356.8 0.461 0.281 DAT2 21 1378-2015 1780 223.6 0.348 0.247 DEL1 20 1568-2015 1626 286.0 0.362 0.280 DIN1 16 1431-2015 1797 229.8 0.374 0.272 FLO1 22 1324-2015 1840 223.3 0.342 0.253 GHO1 22 1282-2015 1639 345.6 0.386 0.280 IRA1 16 1287-2015 1805 307.6 0.373 0.308 PHA1 28 1562-2013 1726 241.8 0.363 0.257 PHA2 23 1380-2015 1683 338.4 0.366 0.270 REN1 20 1515-2013 1784 254.9 0.361 0.273 REN2 22 1252-2015 1723 288.2 0.414 0.278 SKO1 24 1575-2015 1725 213.5 0.346 0.273 *Chronologies for sites DAT1, PHA1, REN1 were cut off in the 1500s to exclude sections with low correlations. 124 Appendix K Summary of sample size for site-level and regional chronologies, and the reasons for which trees were removed from the regional chronologies. Site Trees Highly correlated Northern Health status Low Regional cored at site level sites adjusted correlations chronology Healthy 221 152 -21 -11 -21 99 Declining 104 80 -7 +11 -20 64 Dead 119 85 -16 0 -12 57 Total 444 317 -44 -53 220 125 Appendix L Ring width index of standard chronologies for healthy (green), declining (grey) and dead (black) trees, from 1524 to 2015, beginning when all three chronologies had ≥10 samples (n = 99, 64 and 57 for the healthy, declining and dead groups respectively). 126 Appendix M Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for the ‘healthy’ group, over the time periods 1902-1945 (left), 1946-1976 (middle) and 1977-2015 (right). Bars are the correlation coefficients, a dot indicates significance (α=0.05). 127 Appendix N Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for the ‘declining’ group, over the time periods 1902-1945 (left), 1946-1976 (middle) and 1977-2015 (right). Bars are the correlation coefficients, a dot indicates significance (α=0.05). 128 Appendix O Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for the ‘dead’ group, over the time periods 1902-1945 (left), 1946-1976 (middle) and 1977-2015 (right). Bars are the correlation coefficients, a dot indicates significance (α=0.05). 129 Appendix P Distribution of the four principal components (A=red, B=blue, C=green and D=purple) across Graham Island, Haida Gwaii. Pie charts represent the percent of trees that make up each principal component at the 15 sites. 130 Appendix Q Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for principal component A, over the time periods based on the identified breakpoints of the chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05). 131 Appendix R Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for principal component B, over the time periods based on the identified breakpoints of the chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05). 132 Appendix S Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for principal component C, over the time periods based on the identified breakpoints of the chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05). 133 Appendix T Correlation between monthly average precipitation, monthly mean temperature, monthly maximum temperature, monthly minimum temperature and the residual chronology for principal component D, over the time periods based on the identified breakpoints of the chronology. Bars are the correlation coefficients, a dot indicates significance (α=0.05).
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Freezing to death in a warming climate : drivers of yellow-cedar decline on Haida Gwaii
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