The Influence of Climate and Atmospheric C02 On

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THE INFLUENCE OF CLIMATE AND ATMOSPHERIC C02

ON RADIAL GROWTH IN PSEUDOTSUGA MENZIESII AND

PINUS PONDEROSA OVER THE 20™ CENTURY

A thesis

Presented to

The Faculty of Graduate Studies

The University of Guelph

VANESSA STRETCH

In partial fulfillment of the requirements

for the degree of

Master of Science

July, 2011

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1+1 Canada ABSTRACT

THE INFLUENCE OF CLIMATE AND ATMOSPHERIC C02

ON RADIAL GROWTH IN PSEUDOTSUGA MENZIESII AND

PINUS PONDEROSA OVER THE 20Tff CENTURY

Vanessa Stretch Advisor: University of Guelph, 2011 Dr. Ze'ev Gedalof

This study illustrates changes in residual radial growth of Pseudotsuga menziesii (Douglas-fir) and Pmus ponderosa (ponderosa pine) in western North America over the past century. Using 382 tree-ring site chronologies, residual growth was statistically examined for evidence of direct CO2 fertilization and indirect changes in climatic sensitivities. Results demonstrate that a direct CO2 fertilization effect is unlikely in these species in western North America. Instead, a significant restriction in radial growth over the last century has been noted and associated with changing relationships to seasonal temper ature and precipitation parameters. Specifically, precipitation is more often becoming less limiting and temperature more limiting. These results may sug gest a change in the water-use efficiency of these species, a potential indirect implication of increased atmospheric CO2. These findings suggest that with a continually changing climate, the radial growth of P. menziesii and P. pon derosa in western North America will not respond uniformily and future forest ecosystem dynamics may be difficult to predict. ACKNOWLEDGMENTS

First, I have to thank my advisor, Ze'ev Gedalof. For all of the times I felt uneducated and completely overwhelmed, your belief in me and constant assurance that I could do this pushed me through. Thank you for making me use MATLAB and making me think in ways I never thought I could. Oh, and thanks for sending me to so many conferences! I look forward to working with you again next year. Also, I have to thank my advisory committee member, Aaron Berg, for all of his time and revisions. I must thank the Natural Sciences and Engineering Research Council, Le Fonds Quebecois de la Recherche sur la Nature et les Technologies, the University of Guelph, and Ze'ev Gedalof for the funding to support this research.

I'd also like to thank any and all dendrochronologists who have contributed data to the ITRDB, and particularly those who provided me with metadata. This thesis would not be possible without the sharing of data and knowledge within this community. It is one of which I am proud to now be a part.

A special thanks my CEDaR labmates: Graham, Vesta, Kerry, and Rob. An extra special shoutout goes to Kerry for dragging me out of the lab and up to the 'Gonq and for being the best Habs fan in Ontario! I want to thank all of my fellow Hutt Dogs. You guys are the best! Thanks specifically for T&T, all the beer and gin, the Hutt Dog sports and outings, and for keeping me sane throughout this process. A further thanks to the entire Geography department at the University of Guelph. You have all made Guelph feel like i home.

Also, I need to thank my family for supporting me throughout my lengthy university career. I am convinced you have no idea what I am studying, but your unconditional support is still very much appreciated. Erinn and

Lisa, thanks for being the bestest friends I could have ever asked for. I could have never made the move and done this without your support! And lastly,

Rashaad. Thank you for listening to me complain about my thesis (mostly

MATLAB), for forcing me to work on days when I didn't feel like working, for teaching me LaTeX, for making all of my beautiful maps, and for being the most supportive person I know! I look up to you.

n CONTENTS

Table of Contents iii

List of Figures vi

List of Tables vii

1 Introduction 1 1.1 Background 1 1.2 Problem Statement 2 1.3 Goals &; Objectives 2 1.4 Thesis Outline 3

2 Literature Review 4 2.1 Introduction 4 2.2 Climate Change & CO2: A Historical Evaluation 5 2.2.1 Climatic Shifts over Time 5 2.2.2 Greenhouse Gases & Carbon Dynamics 6

2.3 Plant Physiology & C02 9 2.3.1 Photosynthesis & Carbon Sequestration 10 2.3.2 Acclimation & Downregulation 14 2.3.3 Water-Use Efficiency 16 2.3.4 Between-Species Differences 17 2.4 Possible Research Methodologies 18 2.4.1 Growth Chambers & Greenhouses 18 2.4.2 Open-Top Chambers 19 2.4.3 Free-Air Carbon Enrichment (FACE) Sites 21 2.4.4 Dendrochronology 23 2.5 Conclusion: Uncertainties & Knowledge Gaps 25

111 3 Research Design & Methods 28 3.1 Introduction: Research Design 28 3.2 Study Region 28 3.2.1 Pseudotsuga menziesii (P. menziesii) 28 3.2.2 Pinus ponderosa (P. ponderosa) 31 3.3 Data Compilation & Formatting 33 3.3.1 Raw-ring Width Data 33 3.3.2 Metadata (Site Factor Data) 34 3.3.3 Climate Data 34 3.4 Removal of Known Growth Factors 35 3.4.1 Tree-age 35 3.4.2 Climate 36 3.5 Analysis of Residual Chronologies 38 3.6 Climate Re-analysis 38 3.7 Sensitivity Analysis 39 3.8 Conclusion 40

4 Results: Chronology Analyses 41 4.1 Introduction 41 4.2 Results 41 4.2.1 Study Sites 41 4.2.2 Climatically Sensitive Chronologies 42 4.2.3 Residuals 44 4.2.4 Spatial Patterns in Residuals 47 4.2.5 Trends in Climate-related Growth 47 4.2.6 Sensitivity Analysis 50 4.3 Conclusion 56

5 Analysis & Discussion 58 5.1 Scholarly Discussion of Results 58

5.1.1 C02 Fertilization? 58 5.1.2 Implications of a Changing Climate 60 5.1.3 Other Impacts on Growth 64 5.2 Conclusion 66 iv 6 Summary &z Conclusions 67 6.1 Overview of Findings 67 6.2 Future Forest Dynamics 69 6.3 Potential Future Research 70 6.4 Conclusions 71

Works Cited 72

A Site-specific Climate &z Residual Analysis Results 84

B Site-specific Sensitivity Results 104

v LIST OF FIGURES

2.1 The role of CO2 in plant productivity 10 2.2 The specificity of RuBisCO 13

3.1 Natural distribution of P. menziesii 29 3.2 Natural distribution of P. ponderosa 32

4.1 Map of study sites in western North America 42 4.2 Frequency of climatic variables included in AIC best-fit models .... 44 4.3 % Explained variance from multiple linear regression 45 4.4 Trends in residuals of P. menziesii 48 4.5 Trends in residuals of P. ponderosa 49 4.6 Differences in climate parameter correlations 51 4.7 Frequency of significant decreases in radial sensitivity to climate pa rameters 53 4.8 Frequency of significant increases in radial sensitivity to climate pa rameters 54 4.9 Frequency of significant changes in direction of radial sensitivity to climate parameters 55 4.10 The spatial distribution of sites with changing climatic sensitivities . 56

VI LIST OF TABLES

2.1 C02 budgets from 1750 to 1990 9 2.2 Internal & external biological factors influencing photosynthesis ... 11 2.3 Advantages of free-air carbon enrichment (FACE) studies 22

3.1 Climatic data for regional subdivisions of the range of P.menziesii . . 30

4.1 Proportion of climatically sensitive sites 43 4.2 Trends in residual chronologies 46 4.3 Species differences in residual trends 46 4.4 Significant trends in climate-related growth 50

A.l Site-specific climate & residual analysis results 86

B.l Site-specific sensitivity results 106

Vll CHAPTER 1

INTRODUCTION

1.1 Background

The responses of forests to post-industrial increases in atmospheric CO2 remain under considerable debate. Forests may simply emit excess CO2 and amplify the climatic influences via temperature-sensitive respiration or they may sequester it as organic carbon (Bazzaz, 1990; Gifford, 1994). Furthermore, there is debate concerning ac climation and downregulation responses to increased growth rates over time (Arp,

1991; Cook et al., 1998; Gunderson and Wullschleger, 1994; Idso and Kimball, 1991;

Mousseau and Saugier, 1992; Norby et al., 1999). Also, all forest species may not respond equally to such increases in atmospheric CO2 (Bazzaz et al, 1996). Species- specific responses to elevated C02 may not only have effects on individual growth, but on community structure and entire ecosystem function (Bazzaz et al., 1996). The species composition and competitive interactions of forest ecosystems may dictate an ecosystem being a source or a sink in the global carbon budget. It is important to study individual responses in order to infer responses of ecosystems, particularly because of the potential future range shifts under different climatic scenarios (Shafer et al., 2001).

Pseudotsuga menziesn (Mirb.) Franco and Pmus ponderosa Dougl. ex Laws co exist in the hot, dry forest ecosystems of western North America. The vast amount of forested land covered by these species' ranges makes the study of carbon dynamics in these ecosystems important. These species are also the most widely dendrochronolog- ically sampled species. In preliminary global research conducted by Gedalof and Berg 1 (2010), these two species exhibited slightly different growth responses to increases in atmospheric CO2 over the past century; little could be concluded in regards to their contributions to overall carbon sequestration, however.

1.2 Problem Statement

With increases in human-induced atmospheric CO2 expected to continue, a better understanding of the forest carbon cycle and the potential for differences in the re sponses of trees is required. It has been suggested that a higher concentration of atmospheric CO2 may have a direct fertilization effect and/or cause increased water- use efficiency in trees. The tree-ring record of P. menziesn and P. ponderosa in western North America has not been analyzed for evidence of direct CO2 fertiliza tion, changes in water-use efficiency, or species differences in radial growth responses over the past century. The hypothesis of this research is that inherent differences between P. menziesn and P. ponderosa, found in western North America, will lead to differences in their radial growth responses to increased atmospheric CO2 which will ultimately affect ecosystem dynamics. Understanding the mechanisms of such responses will help predict sites where this is most likely to occur in the future.

1.3 Goals &: Objectives

The overall goal of this research is to understand the mechanisms by which CO2 might affect forest carbon dynamics. Specifically, the aim is to assess the impacts of increased atmospheric C02 on the annual radial growth of P. menziesn and P. ponderosa in western North America. To achieve this purpose, four objectives have been established:

2 1. Compile an extant tree-ring chronology dataset of P. menziesn and P. ponderosa

for western North America.

2. Quantify and evaluate the effects of different climatic parameters on the radial

growth of these species over time.

3. Analyze residual tree-ring chronologies for evidence of differential radial growth

trends, and to assess the role of CO2 fertilization and improved water-use effi

ciency in contributing to these growth trends.

4. Explain and evaluate patterns of radial growth in terms of species and/or spatial

factors.

1.4 Thesis Outline

The following is an overview of the content of this thesis. Chapter 2 examines the potential role of CO2 in radial growth and an overview of the literature associated with different research methods. Chapter 3 addresses the design and methods of this research study. This chapter includes a description of the general study region, the species' environmental tolerances, the process of data compilation and formatting, the statistical techniques used in the analysis, and a justification of all research methods.

The resulting data of the methods described in Chapter 3 are presented in Chapter 4.

Chapter 5 provides an analysis of the results of this research study. This chapter specifically explores the trends in the residual tree-ring chronologies and the climatic sensitivity of these chronologies, in order to understand differential radial growth responses over the 20i/l century. Finally, Chapter 6 summarizes the main findings of this research, explores it's broader implications to forest ecosystem dynamics in western North America, and discusses avenues for further research.

3 CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

Climatic changes impact many components of the Earth's system. Impacts include those which both directly and indirectly affect the terrestrial biosphere. The compo sition, structure, and function of the biosphere can also feedback and enhance these climatic changes. Woody species play a significant role in these relations; they will be affected by changes in atmospheric CO2 concentrations. Woody species are able to sequester relatively large amounts of carbon (C). There are a multitude of variables affecting the interactions between CO2 and woody species. A greater understanding of these variables and the mechanisms that control and influence them is essential.

The purpose of this chapter is to provide a synthesis of previous research on the topic of C02 and trees, and to critically analyze these works in order to identify areas where research is needed. First, I discuss the history of climate change and the role of greenhouse gases (with a focus on CO2). Second, I describe the physiological impacts of CO2 on plant species, emphasizing photosynthetic responses and sequestration, water-use efficiency, and responses at the species level. Subsequently, I examine possible methods of studying the relationship between CO2 and trees, along with the advantages and disadvantages of each. Finally, I conclude with a synthesis of the identified knowledge gaps.

4 2.2 Climate Change &: CO2: A Historical Evaluation

A basic understanding of climate change principles and the evolution of this phe nomenon over time is essential to understanding its implications on the world's ter restrial biosphere. The following is an elementary overview of climate change.

2.2.1 Climatic Shifts over Time

Over the past few centuries, the global climate has been influenced by several factors resulting in relatively abrupt climatic shifts. Climate change is not solely a recent phenomenon; the global climate has experienced many fluctuations over it's history.

Glacial and interglacial periods have typically characterized major perturbations to

Earth's climate, altering both temperature and precipitation regimes resulting in climate changes (Anderson et al., 2007). Recent climatic shifts are differentiated from these for two reasons: (1) time-scale and (2) anthropogenic causes.

Most pre-historical changes to the Earth's climate happened over relatively long time periods, giving Earth's biosphere time to adapt (Anderson et al., 2007). The most recent climatic shift has occurred over a relatively short time period in compar ison to Earth's history. Never have the increases in greenhouse gases been as abrupt as they have been in the most recent accumulation period (Sundquist, 1993). This abrupt increase in greenhouse gases has resulted in alterations to the climate. The full impacts of this shift on Earth, specifically the terrestrial biosphere, are not yet completely understood.

The main causes of the most recent climatic shifts are anthropogenic factors

(IPCC, 2007). Industrial expansion is generally associated with having major influ ences on climate change; the burning of fossil fuels for energy purposes has combined to increase greenhouse gas concentrations (Fitter and Hay, 2002). The process of the 5 most recent climatic changes has resulted from an enhanced greenhouse effect caused by the abrupt global increase in greenhouse gas concentrations since industrialization.

2.2.2 Greenhouse Gases &: Carbon Dynamics

Greenhouse gases, the basis of the greenhouse effect, are significant factors influencing climatic shifts. The most important greenhouse gases to consider in terms of climatic shifts include water vapour, carbon dioxide, ozone, methane, and nitrous oxides (as well as synthetic gases). Naturally, greenhouse gases insulate the Earth from a loss of heat directly to space by absorbing infrared radiation and warming the lower reaches of the atmosphere (Schneider, 1989). Warmth is essential for the survival of most biotic life on the planet. Greenhouse gases can be produced from natural processes: water vapour results from evaporation and transpiration; CO2 results from plant and animal respiration, natural combustion, and organic decay; ozone is produced from chemical reactions within the atmosphere; methane is produced naturally from or ganic decay; and nitrous oxides are the result of chemical reactions that take place in soils (EIA, 2005). Most of these gases can be naturally removed from the atmo sphere via oceanic absorption and photosynthesis by plants (water vapour can be removed naturally via precipitation) (EIA, 2005). When these sources of greenhouse gases and their subsequent sinks are in balance, global concentrations remain stable.

When this balance is upset, the insulating property of the natural greenhouse effect becomes altered and subsequent climatic changes take place. Fossil fuel combustion and deforestation, as well as the inception of new, synthetic greenhouse gases with long residency times (e.g. chlorofmorocarbons (CFCs)) have resulted in enormous changes in this source/sink balance (Bazzaz, 1990). This enhanced greenhouse effect can cause abrupt shifts in Earth's climate, upsetting the usual temperature and pre cipitation regimes and decreasing climatic predictability. This enhanced greenhouse 6 effect will have large effects on Earth's terrestrial biosphere over a short period of time.

CO2 is an important greenhouse gas, as it has both natural and human sources and multiple natural sinks. CO2 is naturally emitted through plant and animal respiration, natural combustion, and via organic decay in soils. Presently, the biggest sources of

CO2 are fossil fuel combustion (for energy and transportation purposes) and extensive land-use changes (e.g. forest clearance) (Scarascia-Mugnozza et al., 2001; Sundquist,

1993). In regards to climate change, C02 is a key contributor to the expected increase in the greenhouse effect (Sundquist, 1993). Fortunately, there are a few ways in which

CO2 is naturally removed from the atmosphere as well; these include direct absorption by oceans and through photosynthetic uptake by plants.

Although carbon sinks are very important in removing C02, the enhanced emis sions of this gas in the past few centuries have resulted in the sinks not being able to catch up and/or store it all. The imbalance has, and is, resulting in climatic change.

Some reports indicate that increases in atmospheric CO2 concentrations anywhere from 270 ppm in preindustrial times (i.e. since 1750) to approximately 390 ppm cur rently are the cause of such changes. This accumulation amounts to overall increases of approximately 30% globally over this timeframe (Bazzaz et al., 1996; Fitter and

Hay, 2002; IPCC, 2007; Raven and Karley, 2006; Scarascia-Mugnozza et al, 2001).

CO2 concentrations are currently double what they were at the last glacial maximum

(ca. 18,000 years BP) and have not risen above 290 ppm in the preceding 400,000 years (Korner, 2003). Global atmospheric concentrations of CO2 are currently in creasing by 1% every year (Scarascia-Mugnozza et al., 2001). It is estimated that concentrations will reach 550 ppm by 2050 (Raven and Karley, 2006). Pre-historical increases in atmospheric CO2 concentrations were more gradual and the biosphere was subsequently able to adapt. 7 The resulting impacts on climatic changes from such abrupt increases in C02 are now obvious. Oceans have an absorption limit which will be difficult to alter and/or adapt (Sundquist, 1993). Another sink is potentially found in plants in the terrestrial biosphere. Plants may adapt to increased CO2 via increased photosynthesis, which may result in increased carbon sequestration.

Gifford (1994) and Bazzaz (1990) discuss the modern debate concerning the re sponse of the terrestrial biosphere to elevated atmospheric CO2. This debate concerns the responses of vegetation (and soils) to increases in C02 by means of emitting the excess CO2 and amplifying the situation (via temperature-sensitive respiration) or by sequestering it as organic carbon. There has been much uncertainty surrounding this debate due to a missing carbon sink (Table 2.1). Gifford (2004) believes that a portion of this missing sink is, and will continue to be, stored in the terrestrial biosphere. The two most probable locations for this sink are in coastal waters and/or terrestrial vegetation and soils. Forest re-growth in eastern North America has been described as this potential missing sink (Albani et al., 2006).

Woody species and whole forest ecosystems play a significant role in the under standing of carbon dynamics. In fact, although forest ecosystems cover only 27% of the global land surface, they store more than 60% of the carbon in the terrestrial biosphere. Specifically, more than 85% of the total plant carbon globally is found in forests, as well as approximately 60-70% of global soil carbon (Ceulemans et al.,

1999). Overall, forest ecosystems play an important role in carbon storage and in minimizing the impacts of climatic changes by acting as potentially enormous carbon sinks. There are two conceivable means by which forests could aid in carbon storage:

(1) reforestation and (2) growth/productivity (Ceulemans et al., 1999). Reforestation will require substantial effort, and the amount of reforestation needed to counteract

CO2 emissions will be hard to meet. The response of trees by increases in growth and 8 Table 2.1 CO2 budgets from 1750 to 1990. Estimates of carbon (gigatons) from sources and sinks (Adapted from Sundquist, 1993, p.937).

Reservoir Year

1750 to 1850 1850 to 1950 1950 to 1990 1750 to 1990

Sources Fossil fuels 1 61 ± 6 155 ± 16 217 ± 22 Land use 40 ± 12 69 ± 21 53 ± 16 162 ± 49 Total 41 ± 12 130 ± 22 208 ± 23 379 ± 54 Sinks Atmosphere 21 50 89 160 Oceans 20- 16 63-49 72-53 155- 118 Total 41 -37 113 - 99 161 - 142 315 - 278

Imbalance* 0-4 17-31 47- 66 64 - 101 Modeled residual** 40-36 52-38 6-13 98-61

Note: Two possible oceanic sink estimates have been calculated.

productivity may be an important response; this would rely upon a natural response, a CO2 fertilization effect.

2.3 Plant Physiology & C02

Modifications to the atmospheric concentrations of CO2 may have direct effects on plants because of the use of this greenhouse gas by plants during photosynthesis.

These effects may then have indirect effects on plant physiology. The following dis cussion concerns the impacts of changing concentrations of CO2 on plant physiology, as well as potential adaptations by plants. Although generally the processes are un derstood to be relatively similar for most plants, particular attention is given here to the study of tree species in regards to their enormous contributions to carbon storage. 9 2.3.1 Photosynthesis &: Carbon Sequestration

CO2 is the essential base of photosynthesis and can be limiting to rates of photosyn thesis in plants if concentrations are not sufficient (Long and Drake, 1992). Rates of photosynthesis will influence overall plant productivity (Figure 2.1).

The fate of carbon in plants

CO.

1 Litter production and decomposition 1— ,_ ^

Figure 2.1 The role of CO2 in plant productivity. A schematic representa tion of the fate of carbon in plants (Adapted from Korner, 2003, p.6).

Photosynthesis (or carbon assimilation) is the process through which sugar (glu cose) is created from CO2 and water by chlorenchyma (tissue containing chlorophyll) in the presence of light. This process is imperative for plants because all other or-

10 ganic substances within the plant are produced as by-products. Photosynthesis is influenced by other internal and external biological factors (Table 2.2).

Table 2.2 Internal & external biological factors influencing photosynthesis (Adapted from Korner, 1996, p.23).

Internal/external factor Factor is dependent on...

(1) Recruitment and establishment Seed quality Germination Stress resistance (2) Rate of photosynthesis As a function of age and position of leaves

(3) Carbon investment - Specific leaf area - Leaf area ratio - Stem mass ratio - Root mass ratio - Storage organs - Reproduction - Plasticity of resource foraging - Exudation - Mycorrhiza

(4) Rate of respiration - Specific rates for each of the above plant com partments

(5) Rate of biomass turnover - Leaf life span - Root turnover - Rate of plant development (maturation, senes cence)

(6) Morphological determinants - Leaf angle - Self shading - Branching patterns - Maximum plant height/width - Structural alteration of organs

Continued on next page

11 Table 2.2 - Continued from previous page Internal/external factor Factor is dependent on... (7) Plant interactions - Competition for resources (in space and time) - Other interactions (8) Plant-animal interactions - Herbivory - Pollination

(9) Microbial interactions - Symbiosis - Parasitism - Other pathogenic effects - Biomass (nutrient) recycling

Note: Not considering additional influences by the physical and chemical environment.

Due to the importance of CO2 as an input for photosynthesis, there may be direct impacts of increased CO2 on photosynthesis. The entry of carbon into the plant is cat alyzed by the carboxylation of the enzyme ribulose bisphosphate carboxylase oxidase (RuBisCO). Increases in CO2 concentrations increase the net rate of carboxylation of the RuBisCO enzyme (Stitt, 1991). Carboxylation results in photosynthesis through the photosynthetic carbon reduction cycle and oxygenation results in photorespiration through photosynthetic carbon oxidation (Long, 1991). Increases in RuBisCO activity mean that more carbon fixation (carboxylation) will take place; the higher CO2 concentrations increase the C02:02 ratio, which reduces photorespiration (oxy gen fixation) relative to carbon fixation (Saxe et al., 1998; Amthor, 1991; Gifford, 2004). Species vary markedly in their concentrations of active RuBisCO, which could account for inconsistent carbon fixation rates between species (Long, 1991). Climatic shifts may limit the amount of available nitrogen, which will have a direct effect on decreasing the RuBisCO specificity for C02- The specificity of RuBisCO to C02

12 should remain constant throughout this relationship; however, as demonstrated in

Figure 2.2, it may be complicated by variations in its response to rising temperatures

(Long, 1991).

O a o u a

10 20 30 40 50 Temperature ( C)

Figure 2.2 The specificity of RuBisCO. Specificity of RuBisCO for C02 relative to O2 (solid line) and the ratio of solubilities (a) of CO2 and O2 (dashed line) in water (pH 7) as functions of temperature (Adapted from Long, 1991, p.730).

The direct increases in photosynthetic capacity at higher concentrations of CO2

(CO2 fertilization) will support greater growth of the plants and higher carbon fix ation; more CO2 will be removed from the atmosphere and stored in these plants.

Although radial growth is a low priority in the allocation of carbon for tree produc tivity, increases in CO2 could allow for greater allocation to radial growth, as wider annual rings are produced during years of higher photosynthate production (Fritts,

1976). However, observed increases in carbon storage over short periods of time do not necessarily imply that these increases will persist (Kramer, 1981). The time scale over which such processes take place is important. Although some studies have sug-

13 gested that growth, productivity, and biomass will increase at quicker rates under elevated CO2, Korner (1996) states that if the residency time of sequestration is not changing, the increases will have little significance in the long term because the stored carbon will be released over a shorter timeframe.

2.3.2 Acclimation & Downregulation

The question as to whether carbon fixing plants will be able to maintain such high rates of photosynthesis in light of the current and future increases in atmospheric

CO2 concentrations remains debatable. Multiple studies have attempted to explain the long-term implications of high rates of carbon fixation via high photosynthetic rates (Arp, 1991; Cook et al., 1998; Gunderson and Wullschleger, 1994; Idso and Kim ball, 1991; Long and Drake, 1992; Mousseau and Saugier, 1992; Norby et al, 1999;

Stitt, 1991). The results of these studies are conflicting: some indicate a downregula tion response in plants over the long-term, while others indicate continued enhanced photosynthetic abilities.

Acclimation represents an adaptive response of species to elevated CO2 concen trations over a longer time period. Acclimation is any biochemical or physiological change which takes place in response to some change in growth (Gunderson and

Wullschleger, 1994). In this context, acclimation is the adjustment of species to their development under persistent exposure to elevated CO2 concentrations (Saxe et al.,

1998). Acclimation is most often associated with a downregulation in photosynthetic responses after prolonged periods of time; the amount of downregulation may be variable.

Downregulation has been reported in many studies after a few years of prolonged exposure to elevated CO2 levels (Arp, 1991; Cook et al., 1998; Mousseau and Saugier,

14 1992). Downregulation is the reduction in photosynthetic rates over the long-term following a relatively brief period of increased CO2 exposure. Downregulation ends up causing no overall positive effects on growth rates of plants over the long-term (Saxe et al., 1998). It is suggested that initial growth expanses will slow and eventually halt, returning to normal conditions. This response would counter the proposed persistent

CO2 fertilization effect found in other studies.

The continued occurrence of high photosynthetic rates and of carbon fixation has been observed in other studies (Gunderson and Wullschleger, 1994; Idso and

Kimball, 1991; Norby et al., 1999). In these cases, downregulation was not found to be significant. Instead, photosynthetic and growth rates remained high over the long-term under elevated CO2 concentrations. These studies suggest a persistent CO2 fertilization effect in these plants over the study periods.

Differences in these two viewpoints include (1) the definition of long-term and (2) the species being studied. While some studies may consider 3 years of study long enough to conclude if the existence of downregulation is present (Idso and Kimball,

1991), other studies suggest that long-term research involves many more years of study (LaMarche et al., 1984). Secondly, studies that oppose each other on this topic have tended to study different species responses. Both conclusions may be of little significance because of innate species differences. Many of the differences between these studies are based on different methods of analysis. It is necessary to critically analyze the methods of these studies in order to determine the actual response of plant species to this elevation over the long-term (Section 2.4).

15 2.3.3 Water-Use Efficiency

Increases in CO2 levels may indirectly cause plant species to become more water-use efficient and, therefore, more productive. Water-use efficiency (WUE) is defined as the amount of CO2 assimilated via photosynthesis relative to transpiration (Hsiao and

Jackson, 1999). Clearly there are many complex components and interactive processes involved in WUE that can alter a plant's efficiency; clarifying a direct relationship between WUE and increased CO2 is difficult. The effects of increased atmospheric

CO2 on decreased stomatal aperture, conductance, and transpiration have been well- documented (Kramer, 1981; Davies and Pereira, 1992; Kimball, 1986; Field et al.,

1995). Increases in CO2 can cause a partial closure of the stomata because plants do not need to fully open their stomata to get high concentrations of C02- Subsequently, transpiration is reduced and the plant retains more water (Kramer, 1981), which may be used during drought years to maintain growth.

Plant stomata are considered important in this equation because of their inherent ability to conduct CO2 to the RuBisCO activity site. Increases in CO2 may play a role in altering stomatal density, as well. Woodward (1987) studied the relationship of stomatal density in relation to increases in atmospheric CO2 levels. Woodward observed a 40% decrease in stomatal density at high exposure levels to CO2, in all species studied. This was accompanied by decreased stomatal conductance. These ef fects on stomatal density and conductance have been associated with WUE in plants.

Changes in stomatal densities and conductance will lead to effects on RuBisCO ac tivity, due to the transport of CO2 to the site of fixation (the RuBisCO enzyme) via plant stomata.

Norby and O'Neill (1989) studied WUE in Quercus alba (white oak) in response

16 to different concentrations of elevated CO2. They found that WUE increased by

52% in medium concentrations (496 ppm) of CO2 and by 82% in high concentrations

(793 ppm). The increase in WUE was confirmed by Norby and O'Neill (1991) in

Lmodendron tuphpifera (yellow poplar or tulip poplar). Knapp et al. (2001) found that the growth of Juniperus occidentalis (western juniper) was significantly higher

(23% greater) under drought conditions post-1950 than it was under drought con ditions pre-1950, suggesting a change in WUE resulting from potential elevations in

CO2 concentrations over this time period. There are documented differences between species in their WUE (Davies and Pereira, 1992); any conclusions drawn from WUE studies should also consider such differences.

2.3.4 Between-Species Differences

Studies focusing on multiple aspects of CCVinduced growth responses, including photosynthetic responses, acclimation and downregulation, and WUE have all discussed differences concerning between-species responses. Species-specific responses to ele vated CO2 may not only have effects on plant growth and physiology, but on commu nity structure and entire ecosystem function (i.e. sources and sinks) (Bazzaz et al.,

1996). Graybill and Idso (1993) conducted one of the few studies that did focus on species differences. Graybill and Idso noted differences between species of trees in response to the same concentrations of elevated CO2, demonstrating that there may be different responses to CO2 fertilization and/or it may or may not occur in all species. In general, there are few studies that examine differences between species; most studies solely focus on one species and infer generalizations. There is a need for studies to focus on multiple species at longer time scales before concluding any of the effects of enhanced CO2 concentrations on the physiology of trees (Kramer, 1981;

17 LaDeau and Clark, 2001; Mousseau and Saugier, 1992).

2.4 Possible Research Methodologies

There have been many studies on the effects of increased atmospheric CO2 on growth responses in tree species. Outlined below are the different methods previously used to study this relationship, combined with a critical analysis of the methods and resulting conclusions.

2.4.1 Growth Chambers & Greenhouses

Some of the first studies involving the relationship between elevated CO2 and plants were carried out in growth chambers and greenhouses (Ceulemans and Mousseau,

1994). These studies are based on the concepts of controlled environmental vari ables and the manipulation of CO2 exposure. Researchers can manipulate other environmental variables, or combinations of other variables, along with CO2 to see the resulting effects. One of the major limitations of these experiments is the lack of representative environmental conditions (Ceulemans and Mousseau, 1994). Many of these conditions cannot be manipulated or may be excluded from these studies, which may significantly influence the relationship between atmospheric CO2 concentrations and any subsequent growth-induced responses in trees. For example, closed-top cham bers (CTCs) may suffer an inherent influence of greater temperature on growth. The closed nature of these studies tends to increase temperatures which may be unrealis tic compared to true environmental conditions (Saxe et al., 1998). These studies are carried out on very specific species; historically, this has been on agricultural crops as they were most lucrative (Ceulemans and Mousseau, 1994). It was only more recently

18 that tree species began being studied using this method; trees that have been studied in CTCs have been grown from seedlings, and therefore, inherently these have not been long-term studies. This method is not ideal for long-term studies because tree species easily outgrow the chambers. The importance of studying the effects on ma ture trees has largely been ignored by this method. In response to such criticisms, this approach has been modified to better include true conditions. For example, CTCs have been installed outdoors in order to let soil-plant interactions remain similar to those in true settings (Ceulemans and Mousseau, 1994).

Results from these studies have generally indicated no CO2 fertilization effect.

For example, Grulke et al. (1993) studied this relationship on seedlings of Pinus ponderosa (ponderosa pine) for a 4-month period and found no significant relationship between high growth rates and CO2 concentrations. Hirose et al. (1996) used a similar glasshouse method and determined increased growth (mainly increases in leaf-area index) coincided with the availability of nitrogen during times of elevated CO2 and not directly CO2 concentrations.

2.4.2 Open-Top Chambers

In response to the critiques of CTC studies, open-top chambers (OTCs) were intro duced to study the CCvgrowth relationship. This is currently the most commonly used method for studying the effects of elevated atmospheric CO2 over the long term

(Ceulemans and Mousseau, 1994). The main advantage of using OTCs is the ability to study a larger number of plants over a longer period of time (Saxe et al., 1998).

Greater inclusion of external environmental conditions is possible in comparison to

CTCs. These studies still mainly focus on the development and growth of trees from seedlings. There are still methodological problems in OTC studies (Ceulemans and

19 Mousseau, 1994; Saxe et al., 1998). These mainly focus on the continued alteration of true environmental conditions, known as chamber effects. Although OTCs do a much improved job at incorporating part of the environmental system, temperature, humidity, and wind speed issues have consistently resulted in potentially biased re sults. These studies have been praised for their ability to study long-term effects, but long-term is a relative concept, as previously discussed. These studies may not be able to contain the complexity of forest ecosystems as they evolve over decades, for example. Another disadvantage of using OTCs to study the growth responses of forest trees to elevated CO2 is the large amount of CO2 required to support them over long time periods, because what is not used by plants escapes the chamber.

This requires large tanks of C02, and therefore, it remains relatively rare and often prohibitively expensive to study this relationship in remote areas or with appropriate sample replication (Ceulemans and Mousseau, 1994).

Studies using this method note an overall increase in photosynthetic ability re sulting from increases in CO2; generally, downregulation responses have not been observed. Tissue et al. (1997) used this mode of study on seedlings of Pinus taeda (loblolly pine) which had been grown in a glasshouse for 1-year prior to OTC expo sure. This study found an overall increase in growth rates when exposed to elevated concentrations of CO2. Rey and Jarvis (1997) used the OTC technique to study the response of Betula pendula (silver birch) to elevated CO2 over a 5-year study period. They noted an overall persistent increase in growth rates apparently attributable to increased CO2. However, Idso and Kimball (1991) did note photosynthetic acclima tion in their OTC study involving Citrus aurantium (sour orange). These studies have tended to counter the results of closed chamber and greenhouse studies in their over all responses to enhanced CO2. This may note the importance of true environmental

20 conditions.

2.4.3 Free-Air Carbon Enrichment (FACE) Sites

Free-air carbon enrichment (FACE) experiments are a relatively recent response to many of the disadvantages of CTC and OTC studies; FACE studies were developed by George Hendrey in the mid-1980s (Gifford, 2004). FACE studies are based on the concept of full exposure to external environmental influences. These studies expose trees to increases in atmospheric CO2 without any type of enclosure; the trees remain in a natural environment (Saxe et al., 1998). The FACE method uses gas dispersion and feedback-control technology to expose study plots to increases in CO2 in the natural environment (Saxe et al., 1998). These experiments can expose entire ecosystems to elevated CO2, demonstrating effects from the individual tree/species level up to the community level.

The FACE system, like other studies in this area of research, was designed origi nally to study the effects on agronomic crops (Ceulemans and Mousseau, 1994). One of Hendrey's first experiments studied the effects of elevated CO2 on cotton (Hendrey et al., 1993), which found an increase in photosynthesis and an increase in overall growth of the plants. Table 2.3 lists the multiple advantages of FACE systems over other methods of studying the C02-induced growth responses of tree species.

While FACE studies tend to include most environmental conditions, their subse quent responses in ecosystems, and have the potential to study long-term responses, there still remain some disadvantages to this method. First, FACE systems are enor mously expensive to set up and to maintain. They require huge capital investment initially and are technically demanding, thus they commonly have an interdisciplinary

21 Table 2.3 Advantages of free-air carbon enrichment (FACE) studies (Adapted from Saxe et al, 1998, p.401).

Advantage Importance in research (1) Minimal alteration to plant Critical to conducting canopy-level studies under micro-environment. elevated CC^- (2) Large plot area or plant canopy Facilitates ecosystem-level studies in simple and volume that can be exposed to ele- complex forest systems.

vated CO2. (3) The capability of CO2 exposure Where natural climatic cycles and biotic interac- in ecosystems located in their natu- tions occur. ral environment. (4) The minimization of artefacts Such as large-scale soil disturbance and use of arti- common to previous studies. ficial soils, enabling detailed study of biogeochem- istry and soil science with realistic above-ground feedbacks under elevated CO2.

focus, involving studies from many different fields simultaneously (Ceulemans and Mousseau, 1994; Saxe et al., 1998). Second, whereas FACE experiments tend to expose the forest ecosystems to step increases in C02, true carbon fluxes are more gradual. Klironomos et al. (2005) and Luo (2001) demonstrate potential effects of this step increase. Models suggest that gradual exposure will result in a gradual increase in C sequestration; step increases in CO2 exposure will result in an initial, abrupt increase in C sequestration (10-fold increase), followed by a gradual decrease. Another disadvantage of FACE experiments is their large consumption of CO2 (Hen- drey et al., 1999). FACE systems are known to consume much more CO2 overall than chambered studies, which can be expensive and can be a contributor to at mospheric CO2 increases, although relatively minimal. The overall consumption of CO2 by FACE experiments also depends on wind speeds (Hendrey et al., 1999). The

22 method of dispersion in FACE systems can induce other problems (Okada et al.,

2001). FACE systems rely on blowers or fans to disperse the injected CO2. There are potential problems caused from this; for example, blowers and fans may alter inversion temperatures and/or increase wind speeds (Okada et al., 2001).

The most well-known FACE system is located at Duke University and contains a stand of Pinus taeda (loblolly pine) (Ceulemans and Mousseau, 1994). This system was the first of its kind to study a more mature (12-year old) forest stand (Saxe et al.,

1998). During its first phases of study, there were no differences detectable between the elevated CO2 exposed trees and the ambient exposed trees in their photosynthetic ability. After prolonged exposure, there was an increase in growth of the elevated

CO2 stand which slightly decreased (acclimated) over time (Saxe et al., 1998; Hendrey et al., 1999). Naidu and Delucia (1999) suggest that this acclimation is directly related to other nutrient limitations (e.g. nitrogen) in the study site over time. Furthermore, no evidence for increasing growth under elevated CO2 was found in a highly diverse, mature (80-120 year old) forest stand in Basel, Switzerland using an updated FACE technology known as web-FACE (Pepin and Korner, 2002). This technology uses fine tubing woven into the crowns of the forest to inject CO2, as opposed to older FACE technologies which use injector towers (Korner, 2003)

2.4.4 Dendrochronology

Dendrochronology can contribute a pre-historical perspective on the relationship be tween atmospheric CO2 concentrations and tree growth. Understanding how trees have responded to CO2 increases over the past century should provide a means to understanding how they will react in the future. Dendrochronology involves using tree-rings to understand and specifically date events related to the environment and

23 climate (Pritts, 1976). Studying tree-rings can help detect growth responses to rising atmospheric CO2 (Jones, 1992). Trees should react in some fashion to increases in CO2, most notably by an increase in growth rates resulting from increased photosynthetic ability, as well as increased water-use efficiency. Due to anthropogenic increases in CO2 over the past few centuries, any responses in trees to these past concentrations should be detectable in tree-rings. The analysis of tree-rings can contribute informa tion about climate-C02 relationships by evaluating if recent increased radial growth is unexplained by climate (and other growth-related factors) and, instead, is the result of the CO2 fertilization effect resulting from increased atmospheric CO2 concentra tions (Martinelli, 2004). Tree-ring behaviour is well understood, and chronologies can aid in the reconstruction of growth behaviours over time. The analysis of individual tree-ring widths can be used to isolate factors which may induce growth (Jones, 1992). This requires isolating known signals in the chronologies (e.g. climatic signals and age-related growth trends). This analysis can explain changes in behaviour which are related to new, introduced factors; this can then explain expected changes in the future, if these new factors remain persistent (Jones, 1992).

The few dendrochronological studies that have assessed the relationship between CO2 and growth responses have found minimal evidence for the CO2 fertilization effect (Graumlich, 1991; Jacoby and D'Arrigo, 1997; Kienast and Luxmoore, 1988). LaMarche et al. (1984) was one of the first to statistically remove climatic influences on radial growth and subsequently noted unexplained increases in growth responses, which may be attributable to recent increases in CO2. Graybill and Idso (1993) and Knapp et al. (2001) have found evidence to support the findings of LaMarche et al. (1984), though Salzer et al. (2009) suggest this increase was actually caused by climate rather than C02. The CO2 fertilization effect has been found in Pmus

24 ponderosa (ponderosa pine) (Soule and Knapp, 2006) and Abies cephalomca (Greek fir) (Koutavas, 2008). Knapp et al. (2001) found that dendrochronological evidence from Juniperus occidentals (western juniper) demonstrated a 23% increase in total growth rate in the second half of the 20t/l century. There is some uncertainty pertain ing to the overall existence of the CO2 fertilization effect within dendrochronological studies (Gedalof and Berg, 2010). A global analysis of tree-ring widths for evidence of increasing growth relative to drought by Gedalof and Berg (2010) suggested a small but highly significant proportion of trees exhibit increasing growth over the last 130 years. These growth increases could not be attributed to increasing water use ef ficiency, elevation effects, nitrogen deposition, or divergence. These results suggest that C02 fertilization is occurring at some locations but it does not appear to occur everywhere. Overall findings cannot rule out the existence of this phenomenon if species-specific responses are not considered. In fact, Graybill and Idso (1993) noted several differences between species in their growth responses to elevated CO2 and

Gedalof and Berg (2010) also noted a slight species difference between Pseudotsuga menziesn and Pmus ponderosa in their global analysis.

2.5 Conclusion: Uncertainties &: Knowledge Gaps

The impacts of such high rates of carbon storage in the future may affect the structure and function of ecosystems, as well as individual trees. Major uncertainties associated with ecosystem responses to climate change include inherent lags and feedback loops

(Saxe et al., 1998). Therefore, an understanding of the physiological responses of different tree species to such increases is crucial. A consistent understanding of such responses is currently non-existent; instead there are many inconsistencies in the apparent responses of different tree species to such increases based on many different

25 methodological techniques.

Climate changes are now imminent because of the high concentrations of green house gases in the atmosphere, particularly CO2. The full impacts of climate change remain uncertain, but it is known that climate change can, and will, greatly impact the Earth's terrestrial biosphere. As can be discerned from the previous discussion, such abrupt increases in CO2 will impact the growth of trees and the amount of car bon they can fix. Trees play an important role due to their inherent ability to store relatively large quantities of carbon over short time periods, as well as their sheer extent in the terrestrial biosphere. There remain many uncertainties in regards to the long-term impacts of elevated CO2 on trees. It is believed that increases in CO2 will affect the photosynthetic responses of trees, but whether increased photosynthe sis persists over time is still questionable. Some studies have reported acclimation and/or downregulation, while other studies have reported minimal changes in pho tosynthetic rates. There have been suggestions that CO2 fertilization (significant increases in residual unexplained growth) is inextricably linked to greater water-use efficiency in trees. There remains much to be determined in regards to the impacts on tree physiology. There have been many different methods of studying such impacts on trees, which have been outlined; there are many disadvantages which heavily out weigh the advantages of many of these methods. These studies report inconsistent results.

There is still much to be studied concerning the direct and indirect impacts of elevated CO2 on tree growth. If CO2 fertilization occurs, it may not occur in all tree species. There may be downregulation in photosynthetic responses over the long- term, as well. Dendrochronology has many advantages to studying this relationship based on real, pre-historical data. Between-species differences in their responses can

26 easily be noted using dendrochronological records from multiple tree species. Den drochronology provides a history of the changes in growth of these species over time. Therefore, this dendrochronological study analyzes the potential direct and indirect influences of climate and CO2 on unexplained radial growth in two dominent tree species in western North America.

27 CHAPTER 3

RESEARCH DESIGN & METHODS

3.1 Introduction: Research Design

The following research design uses dendrochronology and statistical analyses to study the potential relationship between radial tree growth and atmospheric CO2 concen trations, as well as analyze the stability of the growth-climate association over time, in P. menziesn and P. ponderosa in western North America.

3.2 Study Region

The hot, dry forested regions of western North America, mainly including Canada and the United States, are dominated by P. menziesu and P. ponderosa. This region is important in the carbon budget of this area because of its sheer size as a forested area and the dominance of these two species.

3.2.1 Pseudotsuga menziesii (P. menziesii)

P. menziesii are important and valuable timber trees globally. They have been abun dant in the forests of western North America since approximately 6,000 years BP

(Brubaker, 1992). The fossil pollen record has demonstrated this as the only native range of the species; more recently, introductions have occurred in temperate regions

(Brubaker, 1992; Hermann and Lavender, 1990). Two varieties are of consideration

28 here: Pseudotsuga menziesii (Mirb.) Franco var. menziesii, also known as coastal

Douglas-fir, and Pseudotsuga menziesii (Beissn.) Franco var. glauca, also called

Rocky Mountain or blue Douglas-fir. P. menziesii's native range is approximately

19 °N to 55 °N. Continuous cover is found in central British Columbia, Washington,

Oregon, California, Idaho, Montana, and Wyoming, with discontinuous distributions found in parts of northeastern Oregon, southern Idaho, Utah, Nevada, Colorado, New

Mexico, Arizona, western parts of Texas, and northern Mexico (Hermann and Laven der, 1990). Figure 3.1 depicts the combined native range of the two varieties of P. menziesii.

Figure 3.1 Natural distribution of P. menziesii (Adapted from USGS, 2006).

29 P. menziesn grows under a range of climatic conditions. Generally, these habitats are characterized by mild, wet winters and cool, dry summers, with an extended frost-free period (Table 3.1) (Hermann and Lavender, 1990). They range from sea- level to mid-elevations in mountains, and occasionally approach the treeline. Their altitudinal distributions increase from north to south along their range (Hermann and Lavender, 1990). The most limiting factor to growth in the north is temperature; in the southern portions of their distribution, moisture is most limiting to growth

(Hermann and Lavender, 1990).

Table 3.1 Climatic data for regional subdivisions of the range of P.menziesn (Adapted from Hermann and Lavender, 1990).

Region Mean July Mean Frost-free Mean Mean tempera January period annual pre annual ture (°C) tempera (days) cipitation snowfall ture (°C) (mm) (cm)

Pacific Northwest Coastal 20 to 27 -2 to 3 195 to 260 760 to 3400 0 to 60 Cascades & 22 to 30 -9 to 3 80 to 180 610 to 3050 10 to 300 Sierra Nevada Rocky Mountains Northern 14 to 20 -7 to 3 60 to 120 560 to 1020 40 to 580 Central 14 to 21 -9 to -6 65 to 130 360 to 610 50 to 460 Southern 7 to 11 0 to 2 50 to 110 410 to 760 180 to 300

Periodic wildfires have created a large amount of almost completely pure stands of P. menziesu in the area north of the Umpqua River in Oregon (Hermann and

Lavender, 1990). Logging activities have also removed many of the old-growth forests in the region; more recently, clearcutting and slash/burn forest management have maintained P. menziesu in most second-growth forests in the area (Hermann and 30 Lavender, 1990). P. menziesn is most commonly associated with P. ponderosa, Pi- nus contorta, and Larix occidentalism and has associations with Quercus garryana where coniferous forest transitions into steppe and with Libocedrus decurrens. Mi nor associates include Populus tremuloides and Jumperus occidentalis (Franklin and

Dyrness, 1973).

3.2.2 Pinus ponderosa (P. ponderosa)

P. ponderosa is one of the most widely distributed species of pine in western North

America (Oliver and Ryker, 1990). This species is a source of timber, habitat for many wildlife species, and important for recreational use and aesthetic value (Oliver and Ryker, 1990). There are two main varieties of this species: Pinus ponderosa var. ponderosa (Pacific ponderosa pine) and var. scopulorum (Rocky Mountain ponderosa pine). P. ponderosa's native range is from approximately 33°N to 55°N; it spreads from southern British Columbia, through Washington, Oregon, California and into

Montana, Idaho, North and South Dakota, Wyoming, and Nebraska. Discontinuous patches are also found in Arizona, Nevada, western Texas, New Mexico, Oklahoma,

Colorado, and northern Mexico. Figure 3.2 depicts the combined range of the P. ponderosa species.

Soil moisture is often the most limiting factor to growth in the range of P. pon derosa, particularly in summer when precipitation is minimal. Their preferred habitat is characterized by mostly dry areas in open forests (Oliver and Ryker, 1990). Mean annual temperatures are between 5 and 10°C, and mean summer temperatures are between 17 and 21 °C. The average frost-free season ranges from 90 to more than 200 days (Oliver and Ryker, 1990). P. ponderosa are typically found at low to moderate elevations (sea-level to 3050m) (Oliver and Ryker, 1990). Moving north to south 31 Figure 3.2 Natural distribution of P. ponderosa (Adapted from USGS, 2006). along their range, they grow at higher altitudes and at more restricted elevational limits.

Stands of P. ponderosa most often are small, even-aged groups; fires have had a significant effect on the distribution of these stands (Oliver and Ryker, 1990). Fire exclusion over the last 50 years has resulted in understories of P. ponderosa stands being composed of P. menziesn and other firs {Abies spp.). P. ponderosa is asso ciated with a wide variety of tree species including Jumperus occidentahs. Populus tremuloides, Pinus contorta, Quercus garryana, Abies grandis, P. menziesn, Larix oc cidentahs, Pmus monticola, Libocedrus decurrens, and Abies concolor (Franklin and

32 Dyrness, 1973).

3.3 Data Compilation &: Formatting

Multiple types of data were required for this analysis and collected from many sources.

These data requriements included:

• Raw-ring width data from the ITRDB.

• Metadata (site factor data) from literature/researchers.

• Climate data from the University of Delaware.

3.3.1 Raw-ring Width Data

Extant tree-ring data were collected from the International Tree-Ring Data Bank

(ITRDB). The ITRDB is a global repository for all types of dendrochronological data. The ITRDB's purpose is to provide a central location for permanent storage of archived, high-quality, global dendrochronological data (Grissino-Mayer and Fritts,

1997). Data found in the ITRDB are high-quality because submission of data must meet standard requirements including: (1) each chronology must be developed from at least ten trees, (2) minimum length of a final chronology must be at least 100 years, (3) raw tree-ring measurements are requested, (4) series have been principally investigated for cross-dating and other errors, and (5) all associated documentation is to be supplemented with the ring data, including site information and data publication information (Grissino-Mayer and Fritts, 1997). The ITRDB is freely available to researchers, which made it ideal for use in this research.

33 The data bank can be searched for data matching further criteria based on the goals of the researcher (Grissino-Mayer and Fritts, 1997). Search criteria for this study were minimal; criteria included raw ring width measurements for P. menziesii and

P. ponderosa in western North America, their natural range. Raw ring width mea surements were essential for this research project so that specified trends which were unrelated to the evidence of interest (i.e. CO2 fertilization and increased water-use efficiency) could be removed; for example, the age-related growth trend. Normally, previously compiled chronologies have undergone some form of detrending and stan dardization (age-related detrending is discussed in Section 3.4.1).

3.3.2 Metadata (Site Factor Data)

Because the primary data for this project was compiled from a data bank, associated metadata for individual sites also needed to be collected. An extensive review of the literature and personal communications with researchers were used to collect data concerning site description and related factors.

3.3.3 Climate Data

Climate data for the period 1900-2008 were compiled from the University of Delaware's

Willmott, Matsuura and Collaborators Center for Climatic Research. This is the highest resolution dataset available for the entire study area and time period. This dataset contains monthly grids of surface air temperature and precipitation data from

1900-2008, for western North America (JISAO, 2009). The 1-degree spatial resolution dataset was used for this analysis (JISAO, 2009).

34 3.4 Removal of Known Growth Factors

3.4.1 Tree-age

Naturally, the rate of a tree's growth is not stable over its lifetime. Instead, trees experience much quicker growth when they are younger. As they mature, growth slows but is maintained (Cook, 1992). Therefore, tree-rings from the youth years of a tree's growth (closest to the pith) are naturally thicker/wider than the outer rings. Few assumptions concerning the overall growth trends can be made unless this natural growth curve is considered (Cook, 1992). Specifically, the age-related growth trends must be removed from the tree-ring series. Age-related growth trends can be removed in the detrending process, by curve and/or line fitting (Graumlich,

1991). Standardization then involves dividing each ring width by the value of the fitted curve or line for that particular year. The resulting annual standardized index values represent growth not associated with tree-age (Fritts, 1976).

A conservative approach to detrending was used in this analysis so that all of the low-frequency variability would be preserved in the series (Cook et al., 1992).

Since the climatic dataset temporally ranges from 1900 to 2008, all chronologies were truncated at 1900 and then standardized by fitting them to a horizontal line (dividing each year by the mean growth rate of that site) using a script written in MATLAB.

In order to assure that this truncation removed the age-related growth trend, only chronologies that began in 1875 or earlier were used. This would assure that at least

25 years of growth were truncated. After detrending and standardization, individual chronologies were then combined using a robust mean to form site chronologies in the dendrochronology program ARSTAN (Mosteller and Tukey, 1977). ARSTAN was also used to minimize the exogenous and endogenous influences of stand disturbances.

35 Once the age trends were removed, the influences of climate were removed from the site chronologies, as well.

3.4.2 Climate

Climatic influences on tree-growth are significant. Temperature and precipitation are controlling factors of tree growth annually (Fritts, 1976). Cooler temperatures and a lack of precipitation can restrict tree growth, resulting in narrow rings in the tree-ring series. Contrarily, warmer temperatures and plentiful precipitation can increase growth, resulting in wider rings (Fritts, 1976). Although increases in atmo spheric CO2 will greatly affect climate, these impacts are better understood. Instead, the direct impacts of atmospheric CO2 increases on tree-growth are of interest here, specifically direct C02 fertilization and improved water-use efficiency. In order to determine the direct role of CO2, the climatic influences on past growth must be removed (Martinelli, 2004). Site-specific climate data was extracted from the Univer sity of Delaware's 1-degree global gridded dataset using a script written in MATLAB.

The closest coordinates of each study site were located within the dataset (within 0.25 degrees) and compiled for the years 1900 to 2008.

Seasonal climate variables (from monthly temperature and precipitation data) were calculated. According to Littell et al. (2008), approximations of ecophysiolog- ical mechanisms controlling annual growth-climate relationships are best studied as seasonal aggregations. Therefore, monthly climate variables were averaged into the associated seasonal variables known to impact the width of one ring (previous sum mer, previous fall, winter, current spring, and current summer), for a total of 10 seasonal climate aggregates. Five climate parameters were mean seasonal temper atures and the remaining five climate parameters were total seasonal precipitation

36 parameters. Previous year seasonal climate parameters are important to include as these seasons affect the amount of stored carbon during the winter which ultimately affects the growth of the antecedent year.

Combinations of different seasonal variables may impact radial growth at each study site differently. Climatic signals detected in site chronologies are unique to that site. Multiple linear regression was used to remove the influence of climatic vari ables; however, because a large number of potential regression models are possible, an

Akaike Information Criterion (AIC) analysis was used to select the optimal climatic model combination (Bozdogan, 1987). This model represents the most appropriate climate model of each individual site, without being too robust or too simplistic.

This was done using only sigificantly correlated climate parameters (Pearson Corre lation analysis). An AIC analysis ranks the total number of significant correlations from most significant to least significant and calculates AIC critical values (AICC) by adding on the next lesser correlation variable one at a time. An AICC is defined by: , fRSS\ „ 2K(K + 1) , , AIC = n-ln[ )+2K + K——{- 3.1 c \ n J n — K — 1 where n is the number of data points (observations), In is the natural logarithm, RSS is the residual sum of squares from regression, and K is the number of parameters in the model (Burnham and Anderson, 2002). The AIC best-fit model is represented when the antecedent AICC no longer increases.

Finally, multiple linear regression was used to remove the best-fit AIC model.

Multiple linear regression removes the impact on radial growth of each parameter selected in the AIC best-fit model subsequently. The residuals of this analysis were linearly regressed over time (in a second regression analysis). The residuals of the latter analysis, representing the unexplained variance in radial growth over time, was further analyzed for evidence of direct or indirect CCVinduced growth. 37 3.5 Analysis of Residual Chronologies

The residuals from the second multiple linear regression analysis were then analyzed for CCVrelated growth potential. This involved calculating significant trends in the residual chronologies. Any trend in the residuals can be attributed to growth that is unexplained by tree-age or climate. Significant trends may be related to changes in atmospheric CO2 concentrations over time. This may provide evidence of CO2 fertilization or changes in water-use efficiency. Non-significant trends are expected without the influence of post-industrial increases in atmospheric C02 (i.e. growth remains stable). Trends in the residuals were also analyzed for species differences.

This analysis involved reviewing the likelihood that CCVinduced growth is species- specific or unspecific.

3.6 Climate Re-analysis

In order to assure no spurious trends in the residuals, that could be misinterpreted as CCVinfluenced growth, a re-analysis of the climatic influences on radial growth was carried out. The initial predicted climatic influences on radial growth were re analyzed for trends over time. A significant trend in the predicted growth as a function of climate may be related to a significant trend in the residuals. Even though the supposed influence of climate on growth was removed via multiple linear regression, a significant change in the mean of any climatic parameter over this time may result in exaggerated growth impacts (possibly related indirectly to CO2). This would signify that the complete influences of climate were not removed initially.

In order to analyze the predicted growth as a function of climate for significant

38 trends over time, linear regression was used. This regression used the predicted growth based on the AIC best-fit model of climate parameters and regressed it directly with time. The predicted outcomes of this regression were then analyzed for significant trends.

3.7 Sensitivity Analysis

The previous climate-growth analysis has assumed that the relationships between the

AIC best-fit climate model and radial growth have remained constant over time based on the Uniformitarianism Principle of Dendrochronology. This principle states that the physical and biological processes linking environmental variables, such as climate, and tree growth have not changed over time (Fritts, 1976). However, as implied in the review of the literature, atmospheric increases in CO2 may also indirectly impact growth rates, via changes in water-use efficiency. Changes in the water-use efficiency of trees may be reflected in changing sensitivities to climatic variables, as water- use efficiency is related to temperature and precipitation relationships. In order to determine if trees (and sites) have altered their sensitivities to climate parameters over this time frame, a sensitivity analysis was carried out.

The sensitivity analysis involved calculating frequencies of changes in individual climate parameter and growth correlations (Pearson Correlation analysis). The aver age end date of all of the chronologies was calculated. This assured that an appropri ate number of chronologies would be included in the analysis (short chronologies were not excluded and long chronologies were not overly truncated). The start year of all chronologies (1900) was then subtracted from the average end date of all chronologies and divided by two. This provided two equivalent time periods for a paired Equality of Correlations analysis. Pearson Correlation coefficients were then recalculated for 39 each time period. An Equality of Correlations analysis then allowed for a determi nation of changes in correlation relationships that were significant (Zar, 1974). The sensitivity analysis only anaylzed significant changes in correlation coefficients (based on significant Z-values) between these two time periods. Z-values were calculated by:

Z = *Z^ (3.2)

where Z is the Z-value representing the difference between z\ and z2 and taking into consideration the standard deviations (azi_z2) of each set of data (Zar, 1974). Z\ and z2 are calculated by:

z = 0.5 • In (|±I) (3.3) where r is the Pearson Correlation coefficient for a dataset. The standard deviation of the dataset (az) is calculated using: a- = i/S) (3'4) where n is the number of observations in the dataset (Zar, 1974). Once the significant

Z-values were found, an analysis of the strength and direction of these changes was then performed in order to understand the intricacies of changing relationships and to infer possible causes (i.e. indirect CO2 impacts, such as changes in water-use efficiency).

3.8 Conclusion

The standard dendrochronological analysis techniques used here have been combined to allow for a robust analysis of changing growth responses of P. menziesit and P. ponderosa over the 204/l century. These changes in growth responses may then be further analyzed in order to relate them to possible changes in atmospheric C02. 40 CHAPTER 4

RESULTS: CHRONOLOGY ANALYSES

4.1 Introduction

Determining the direct impact of the post-industrial increase in atmospheric CO2 on tree growth response is complicated by the indirect impacts. The following section displays the results of a robust analysis (Chapter 3) of the potential direct and indirect impacts of changes in atmospheric CO2 on the radial growth responses of P. menziesu and P. ponderosa.

4.2 Results

4.2.1 Study Sites

Raw-ring width data for all P. menziesu and P. ponderosa sites in western North

America were collected from the ITRDB. After data compilation, manipulation, and chronology creation, 382 site chronologies were used in this analysis. Figure 4.1 de picts the frequency of locations of P. mensziesii chronologies, P. ponderosa chronolo gies, and sites with both a P. mensziesii and P. ponderosa chronology, used in this study.

41 Number of Sites • 2 •3#4#5 06 Q 7+

P. ponderosa P. ponderosa & P. menziesii • P. menziesii • • °o • m ••• • • • • •• • • • #° ••• » . > •. o • • • • • • • • • .. J' • • •• t^w • • • •••••

• o • ^ > , • • ry / • • ~- *- ^ • • *! - % \ • •

Figure 4.1 Map of study sites in western North America. Note: The left frame depicts the number of sites at approximate locations where P. pon derosa chronologies were compiled for this analysis and the right frame the number of sites at approximate locations where P. menziesii chronologies were compiled. The middle frame depicts the number of sites at approxi mate locations from which both P. ponderosa and P. menziesii chronologies were compiled.

4.2.2 Climatically Sensitive Chronologies

Ten seasonal climatic parameters were correlated with individual site chronologies to determine the climatic influences at each site. Sites where at least one climate param eter was significantly correlated were deemed climatically sensitive sites. Table 4.1 depicts the number and proportion of total sites analyzed that are climatically sensi tive to at least one climate parameter. Approximately 96% of sites were deemed cli matically sensitive. In order to determine the amount of unexplained growth, growth resulting from climatic influences must be removed. The 15 climatically insensitive sites did not require any climatic growth removal as there was no siginificant cli mate signal detected. These sites were thus linearly regressed against time and their residuals analyzed. 42 Table 4.1 Proportion of climatically sensitive sites.

Number of sites Proportion

Compiled chronologies 382 - Climatically sensitive 367 0.9607 Climatically insensitive 15 0.0393

The other climatically sensitive sites underwent a multiple linear regression with the AIC best-fit model first, in order to remove this climatic influence. AIC best- fit models were individually calculated for each site based on Pearson Correlation coefficients with each of the climatic parameters. Figure 4.2 depicts the number of times a climate parameter was included in all of the AIC best-fit models, as well as the number of times each climatic parameter was included first in the model (indi cating it was the most significantly correlated). Four climate parameters were most often significantly correlated: winter precipitation, current spring precipitation, pre vious summer temperature, and current summer temperature, with the latter being included most often and the most significantly correlated in the AIC models. It is important to note that current spring precipitation and current summer temperatures are significantly correlated with a current year's growth, but also that previous sum mer temperature and winter precipitation are also highly correlated with a current year's growth, reiterating that the preceding year's temperature and precipitation is important for growth in the following year.

43 150 Most correlated In AIC best-fit model Included In AIC best-fit model

i'7

200 t«8 178

155 150 145

itf>

lOO

4.2.3 Residuals

The individual AIC best-fit climate model and growth multiple linear regression anal ysis explained a wide range of the growth variance. As can be discerned Figure 4.3, the multiple linear regression explains more unexplained growth at certain sites than at others (ranging from 4% to 66%), and thus the results should be interpreted with caution.

44 lOO

c "I z

10 20 30 40 50 60 70 80 90 100 Explained variance from multiple linear regression 1%) Figure 4.3 Explained variance from multiple linear regression (%). His togram representing the distribution of explained variance from the multiple linear regression for all sites.

The regression residuals from the multplie linear regression were then regressed again with time (simple linear regression) in order to examine trends in the residuals over time. Table 4.2 shows the number and proportion of sites which had negative and positive growth trends, as well as the number and proportion of these that were significant.

A significant positive trend in the residuals, which could be associated with a di rect CO2 fertilization effect, was found in only 3.66% of sites. This is not a significant number of sites. Thus, it would be expected that there would be little to no unex- 45 Table 4.2 Trends in residual chronologies - unexplained growth.

Number of sites Proportion

Negative growth trend 330 0.8639 Positive growth trend 52 0.1361 Significantly negative growth trend 259 0.6780 Significantly positive growth trend 14 0.0366 No significant growth trend 109 0.2853

Table 4.3 Species differences in residual trends.

P. menziesn Proportion P. ponderosa Proportion

Significantly negative growth trend 105 0.4054 154 0.5946 Significantly positive growth trend 9 0.6429 5 0.3571 No significant growth trend 67 0.6147 42 0.3853

plained growth, represented by no significant trend in the residuals. Surprisingly, no significant trends were detected in only 28.53% of sites. Instead, almost two-thirds of the sites show a significantly negative growth trend in their residuals.

The significant and not significant trends in the residuals also do not show any par ticular species differences (Table 4.3). Both P. menziesu and P. ponderosa chronolo gies exhibit a significantly negative growth trend. This phenomenon does not appear to be species-specific in western North America. Based on this result, the remainder of analyses assumes these species respond to changes in growth similarly.

46 4.2.4 Spatial Patterns in Residuals

The significant and not significant trends in the residuals may have a spatial com ponent. P. menziesn and P. ponderosa naturally cover a wide expanse of land in western North America. To determine if the trends in the residuals are clumped in any particular area in western North America, the frequency of sites depicting these trends were plotted with an outline of their natural range (Figure 4.4 and figure 4.5).

This analysis shows no obvious spatial component in relation to their range limits or within the greater study area. Arguably, the majority of negative growth trends for P. menziesn appear to be distributed in the sparse range limits of the southeast ern portions of the study region, but this spatial pattern becomes less obvious when analyzing the distribution of negative growth trends in P. ponderosa.

4.2.5 Trends in Climate-related Growth

To determine if the residuals from the multiple linear regression represent only unex plained growth, a reanalysis of the predicted climate-related growth was carried out.

This analysis ensures that any spurious trends in the residuals are not associated with pronounced trends in the climate-related growth. For example, if the means of the climatic parameters have significantly changed over the study period, the ra dial growth sensitivities could be altered significantly (ie. exacerbated). The initial multiple linear regression analysis assumes stable sensitivities to climatic parameters over the study period (The Uniformitarianism Principle of Dendrochronology) (Fritts,

1976). Table 4.4 depicts the results of the second multiple linear regression performed on the climate data.

47 CO

o>» <0 c Q) •* 3 C/J c M— V o • LL CD .Q fl> b > 3 ** CO oin a.

Figure 4.4 Trends in residuals of P. menziesn and its natural range. The green represents the natural range. The left frame demonstrates the fre quency and location of significant negative trends in the residuals. The middle frame demonstrates the frequency and location of significant posi tive trends in the residuals. The right frame demonstrates the frequency and location of insignificant trends. 48 >. o r c a> 3 O" CO

+ 1^ n u i c O) CO

CD o z

'r • -fo >» u ^ (/> c CO -t 0) -t—' 3 r ! { CO tr o £ ^ J*J'&.-*^' • LL. cu > h 4-i _> CO (0 o QL

Figure 4.5 Trends m residuals of P ponderosa and its natural range The light blue represents the natural range The left frame demonstrates the frequency and location of significant negative trends in the residuals The middle frame demonstrates the frequency and location of significant positive trends m the residuals The right frame demonstrates the frequency and location of insignificant trends 49 Table 4.4 Significant trends in climate-related growth since 1900.

Number of sites Proportion

Significantly positive trend 71 0.1935 Significantly negative trend 231 0.6294 No significant growth trend 65 0.1771

As can be discerned from Table 4.4, almost two-thirds of sites show a significant negative trend in the predicted growth as a function of climate. This coincides with approximately two-thirds of sites which have a significant negative trend in their residuals, as well. Therefore, the negative trend in the residuals may be exacerbated by the negative trend in climate-related growth. A further analysis of potential sen sitivity changes to these climatic parameters is necessary in order to justify the use of multiple linear regression to remove the climatic influences in dendrochronological studies.

4.2.6 Sensitivity Analysis

The initial multiple linear regression to remove the influence of climatic parameters on radial growth assumed that the relationships between climate and radial growth have remained stable over time (at an annual resolution). However, the previous analysis of trends in the climatic parameters themselves revealed that climate-related growth has significantly changed over the study period. This indicates that annual radial growth responses to climate have changed in P. menziesii and P. ponderosa over this time period. Therefore, an analysis of changes in the relationships between radial growth and each of the ten climatic parameters considered in the AIC best-

50 2 —|

o — • II II II II 1 in

-1 —

•S .V $> c^ ±8

Figure 4.6 Box-and-whisker plot of the differences in climate parameter correlations between 1900-1940 & 1941-1981 calculated using an Equality of Correlations test. Boxes entirely above or below zero represent significant differences in correlations between these two time periods. Red plus signs indicate outliers. fit models was undertaken. This used an Equality of Correlations Test to test for significant differences between Pearson Correlation coefficients over two equal time periods. Figure 4.6 depicts the significant changes in the relationships of each of the ten climatic parameters over the common truncated study period.

Clearly depicted in Figure 4.6, the most significant changes in relationships have been with previous summer precipitation, current summer precipitation, and previous summer temperature; not every study site demonstrated these trends, however. A 51 more thorough analysis of the frequency, strength and directions of these changing relationships was carried out and the results of this analysis are depicted in Figure 4.7, Figure 4.8, and Figure 4.9.

Figure 4.7 shows the frequency, or number of sites, that show a significant decrease in sensitivity to each of the ten climate parameters. These are sites which were sensitive and are now becoming significantly less sensitive or completely insensitive now (correlation coefficient = zero). It is important to note from this figure that precipitation is generally positively correlated with radial growth and temperature is generally negatively correlated.

Figure 4.8 shows the frequency, or number of sites, that exhibited a significant in crease in their sensitivities to the studied climatic parameters. These are sites which were sensitive and are now becoming more sensitive or sites which were insensitive (correlation coefficient = zero) and are now significantly sensitive. Again here, pre cipitation generally shows a positive correlation and temperature shows a negative correlation with ring width. Overall, it should be noted that there are a lesser number of sites increasing their sensitivities.

Most interesting in the sensitivity anaylsis is the number of sites which exhibit a significant switch in their relationship to climatic parameters. This analysis doesn't examine the strength of the relationships, but the significant switch in direction of the relationship. Figure 4.9 depicts the frequency, or number of sites, which exhibited a significant switch in the relationship with ring width and each climate parameter over the study period. It is important to note here that temperature variables are most frequently switching from a negative relationship to a positive relationship and precipitation variables are most often switching from a positive relationship to a negative relationship. 52 Insensitive P Less sensitive P Insensible T 8o — Less sensitive T

53 Sensitive P More sensitive P Sensitive T 8o More sensitive T

60 —

Figure 4.8 Frequency of significant increases in radial sensitivity to climate parameters between 1900-1940 and 1941-1981. The top graph demonstrates the number of sites showing a significant increase in positive correlations with each climate parameter (correlation coefficient was positive and is now more positive or was zero and is now positive). The bottom graph demonstrates the number of sites showing a significant increase in negative correlations with each climate parameter (correlation coefficient was negative and is now more negative or was zero and is now negative). Blue bars represent precipitation parameters and red bars represent temperature parameters.

54 lOO

^ S^V^

55 Number of Sites • 2 •3#4#5 06 ^7+

Decreased Sensitivity Increased Sensitivity Switched Sensitivity

• • • V v, • • •• • • • • *'!• • • • • •_ • • • • • 4 • ••.

• • \ • • X X - • ^ ) <• • ) - H V ^ - V

Figure 4.10 The spatial distribution of sites with changing climatic sensitiv ities. Note: The left frame depicts the number of sites at each location that showed a significant decrease in their sensitivities to at least one climatic pa rameter. The centre frame depicts the number of sites at each location that showed a significant increase in their sensitivities to at least one climatic pa rameter. The right frame depicts the number of sites at each location that showed a significant switch in the direction of their relationship to at least one climatic parameter.

Figure 4.10 depicts the spatial distribution of changing sensitivities. All sites with decreasing sensitivities, increasing sensitivities, and switching sensitivities to different climatic parameters show scattered spatial distributions.

4.3 Conclusion

The results of the robust dendrochronological analysis show many interesting relation ships and changes. Not only does it denote the importance of climate in tree growth, but the potential for climatic changes to impart changes on tree growth in a manner 56 that is not currently expected. Indirectly, tree growth is experiencing changes in its sensitivity to different and individual climatic influences. This may have profound impacts in attempting to study unexplained growth and attributing any unexplained growth to direct or indirect CO2 influences. These results must be placed within a greater context. The following chapter describes these results in relation to previous studies and what they mean for future ecosystem dynamics.

57 CHAPTER 5

ANALYSIS & DISCUSSION

5.1 Scholarly Discussion of Results

The previous analysis has yielded some important results which must be discussed in a greater scholarly context in order to draw significant conclusions and examine avenues for future research.

5.1.1 C02 Fertilization?

CO2 fertilization is understood as a direct impact of increased atmospheric CO2.

Specifically, the higher abundance of this gas in the atmosphere may result in an increase in photosynthetic rates which may increase carbon storage and, ultimately, increase radial growth in tree species (Gunderson and Wullschleger, 1994; Idso and

Kimball, 1991; Norby et al., 1999). However, as previously discussed, radial growth re sults from a combination of factors, including biological affiliations and climatological responses (Cook, 1992; Fritts, 1976; Korner, 1996; Littell et al., 2008). If these factors can be removed, any unexplained growth may be attributed to C02-induced growth directly as CO2 fertilization or some other type of indirect response, such as changes in water-use efficiency. This distinction and process of elimination of growth-related factors is complex.

First, CO2 fertilization must be detected in unexplained growth as a significant

58 increase in growth over the last century. The previous analysis found 14 sites, out of a total of 382 in this study, had a significantly positive growth trend in the resid ual chronologies (unexplained growth). This percentage could result statistically by chance and, therefore, it can be concluded in this study that a direct CO2 fertiliza tion response was not detected in P. menziesii and P. ponderosa in western North

American sites. There also appeared to be no significant difference in the responses between species. P. menziesii and P. ponderosa are known to be biologically similar in their chmatological responses (Speer, 2010), and since the climatic influence in this analysis was stronger than expected, this result is not surprising. Overall, these results add to the large array of dendrochronological literature which has detected mixed responses in many tree species in many locations across the world (Graumlich,

1991; Graybill and Idso, 1993; Jacoby and D'Arrigo, 1997; Kienast and Luxmoore,

1988; Knapp et al, 2001; Koutavas, 2008; LaMarche et al, 1984; Soule and Knapp,

2006). Specifically, the smaller spatial scale and more precise climate data of this analysis provides results which may explain the unconvincing results of the global dendrochronological study by Gedalof and Berg (2010).

It would be expected, without an increase in growth over the last century, that growth has remained constant. Ideally, there would be zero unexplained growth.

However, radial growth is calculated by:

Rt = At + Ct + SDlt + SD2t + Et (5.1)

where Rt is the observed ring-width series, At is the age-size related trend in ring- width, Ct is the climatically related environmental signal, SDlt is the disturbance pulse caused by local endogenous disturbance, SD2t is the disturbance pulse caused by stand-wide exogenous disturbance, and Et is the unexplained year-to-year variabil ity not related to other signals (Cook, 1987). Inherently, the error term may result 59 from a combination of factors, such as elevation, nitrogen deposition, and the influence of atmospheric CO2 changes. Growth remained relatively constant in approximately one-third of sites studied in this research (Et remained constant). Instead, a signifi cant negative trend in Et over time was found at approximately two-thirds of the sites studied (259 sites out of 382). Gedalof and Berg (2010) found elevation and nitrogen deposition are minimally detected in the residual chronologies in their study, so the potential influences of these factors were excluded from this analysis. To analyze the prominent negative trend further, the climatic influence was revisited. Studying solely the climate-related growth over time, it was discovered that appropximately two-thirds of the sites exhibited a significantly negative trend in the climate-related growth over time (231 out of 367). This suggested the potential exaccerabation of climatic influences, meaning the residual chronologies may contain climatic influences that were not taken into account or removed. It also must be noted that the multiple linear regression removal of climatic influences explained anywhere from 4-66% of the variance in the data, meaning some chronologies were not well explained using this method of analysis.

5.1.2 Implications of a Changing Climate

In the residual chronology creation, the removal of climatic influences assumed that the individual and combined relationships between ring width and climate were stable over the last century. This assumption was based on the dendroclimatic Principle of Uniformitarianism (Pritts, 1976). However, a revisit of the climatic influences revealed a changing climate relationship over this time period, which suggested the possibility of a changing relationship with radial growth over the same time period.

This possibility was examined through an Equality of Correlations Test and it was

60 determined that there have been many interesting changes in this relationship over time.

A large number of sites are exhibiting decreasing sensitivities to individual cli mate parameters, particularly summer variables (i.e. previous summer temperature, current summer temperature, and current summer precipitation). In these instances, we are seeing that temperatures are negatively correlated and precipitation is pos itively correlated, meaning cooler temperatures and abundant precipitation result in increased radial growth. This is supported by general dendroclimatological work

(Fritts, 1976; Peterson and Peterson, 2001; Littell et al., 2008; Watson and Luck- man, 2002). However, the decreasing strength of these relationships may indicate a decrease in the importance of summer variables in particular. It is possible that the decrease in summer sensitivities will be accompanied by increases in winter and spring sensitivities in the future (Aerts et al., 2006; Kirdyanov et al., 2003).

A smaller number of sites are exhibiting increasing sensitivities to climate pa rameters. Most of the increasing sensitivities are associated with the precipitation parameters, but the number of sites are overall lower than the number of sites showing decreasing sensitivities. Previous fall precipitation and current spring precipitation appear to have the highest number of sites seeing an increase in this sensitivity, per haps associated with the decreasing sensitivities of summer climatic variables. Due to the small number of sites exhibiting increasing sensitivities to all of the climatic parameters, this increase in precipitation sensitivities may also be related to site- specific, moisture availability characteristics, such as slope, aspect, elevation, or soil type. Since there was also little to no spatial pattern in the detected trends in the spatial analysis, spatial differences may occur at a smaller level than detected in this regional analysis. Overall, this may suggest that precipitation is a more important

61 controlling factor at a site-specific level (potentially associated with local moisture availability characteristics) but has a limited influence at the regional level. The site- scale factors affecting the variability of precipitation are not captured in the climatic dataset used for this analysis.

Lastly, a significant number of sites show complete changes in their relationships with climate parameters over this time period (ie. previous negatively correlated be comes significantly positively correlated and vice versa). This switch is predominantly seen with previous summer precipitation, current summer precipitation, and previous summer temperature. Not surprsingly, these three parameters showed a significant difference in their correlation coefficients initially in the Equality of Correlations Test.

What we are seeing in this latter analysis that is different from the changing strength relationships (increasing and decreasing sensitivities) is that precipitation variables are now negatively correlated and temperature variables are more often positively correlated, meaning increases in radial growth will be present in warm, dry years.

This result fits well with the literature on changes in water-use efficiency associated indirectly with changes in the atmospheric concentrations of CO2 (Davies and Pereira,

1992; Knapp et al., 2001; Norby and O'Neill, 1989).

Overall, the changing sensitivities of P. menziesu and P. ponderosa to different climatic parameters in both strength and direction have profound impacts on study ing unexplained growth. This analysis has revealed that removing climatic influences on growth is complex. A stable climatic relationship cannot be assumed, in fact oth ers have vouched for the nonstationary and potential nonlinearity of this relationship

(Biondi, 2000; Briffa et al., 1998; Carrer and Urbinati, 2001, 2006). The nonsta- tionarity of this relationship may be attributable to changes in many relationship forcing factors, such as temperature, precipitation, atmospheric CO2, and nitrogen

62 deposition (Carrer and Urbinati, 2006). Most studies analyzing the changing climatic sensitivity of tree-rings have found a change in sensitivity to temperature (Briffa et al.,

1998; Carrer and Urbinati, 2006). Interestingly, here we have found changes in the sensitivity to precipitation, as well. This change could be related to temperature- induced drought stress. What may be happening in relation to changing sensitivities is improved water-use efficiency. This would be seen via increased radial growth in relatively dry years. Higher concentrations of atmospheric C02 could cause a par tial closure of the stomata, reducing transpiration and retaining water. The retained water may then be used to stimulate growth in years when precipitation is limiting

(Kramer, 1981; Davies and Pereira, 1992; Kimball, 1986; Field et al, 1995). Radial growth would be minimally disrupted by annual changes in precipitation regimes.

Overall, the sensitivity to precipitation is decreased significantly. The physiological mechanisms behind changes in sensitivity to temperature parameters are more poorly understood, but may affect sensitivity changes to precipitation parameters (D'Arrigo et al., 2008).

Although this analysis studied the changes in sensitivity over two forty year time periods, perhaps smaller or larger time periods would reveal more about the changes in sensitivity. Given the relatively short length of the sensitivity analysis, perhaps a longer analysis would yield some insights into whether decreases in sensitivities are likely or if decreases in sensitivities are associated with a directional shift in the relationship instead. Those sites which have increased their sensitivities may see a decrease in their sensitivity over a longer time frame or they may see a decrease in the rate of sensitivity increase. Many studies have documented this reduction in correlation over longer time periods (Biondi, 2000).

Changes in the sensitivity of radial growth to climatic parameters may also be

63 related to decadal cycles of global dimming and global brightening (i.e. changes in surface solar radiation (SSR)) (Wild, 2009). Changes between direct and diffuse SSR can alter many components of the climate system, including diurnal temperature ranges, amounts of evaporation and precipitation associated with the hydrological cycle, timing and length of snow cover and spring melt, and rates of carbon uptake in the terrestrial biosphere (Mercado et al., 2009; Wild, 2009). All of these compo nents can directly or indirectly impact radial growth. Causes of cyclical changes may be externally associated with extraterrestrial factors or internally by anthropogenic changes to the transparency of the atmosphere (i.e. modifications of solar beams by clouds and/or aerosols) (Wild, 2009). These changes are of potential significance be cause of the temporal coincidence with the identified global dimming phase between the 1950s and 1980s (Wild, 2009). Specifically, Cutforth and Judiesch (2007) found evidence of global dimming in the Canadian prairies from 1958 to 1999, and Liepert

(2002) identified a 10% decrease in SSR in the United States between 1961 and 1990.

The temporal sensitivity analysis of this study examined the period between 1941 and 1981, in which many strength and directional changes were observed. The trees in western North America may have been exposed to a prolonged period of global dimming during this time, which may inherently be associated with changes to the climatic system and indirectly to the terrestrial biosphere. Therefore, it is possible that global dimming may have influenced the sensitivity of radial growth to changing climatic parameters during this time, as well.

5.1.3 Other Impacts on Growth

Attributing unexplained growth from the above analysis to a direct or indirect impli cation of rising atmospheric CO2 is an assumption. There are other potential known

64 and unknown factors that could factor into some of the unexplained growth. One of these, site characteristics, has already been briefly discussed in terms of explaining some discrepancies between sites and the lack of patterns at a regional scale. On a smaller scale, patterns may emerge resulting from differences in forest composition, density, slope, aspect, elevation, soil type, parent material, and endogenous distur bances. Site data on nutrient availability, such as nitrogen, may also be an important inclusion in growth trend detection analyses (Naidu and Delucia, 1999).

Another important growth factor to consider in this analysis is the role of ex ogenous disturbances, particularly insect outbreaks, on the studied tree species in the western North American region. Pandora moth, mountain pine beetle, western spruce budworm, and Douglas-fir tussock moth outbreaks have affected many conifer species in western North America over the last few decades and are continuously spreading across the region. Insect outbreaks have been known to decrease radial growth in their host species (Mason et al., 1997; Schmid and Mata, 1992; Speer et al.,

2001; Wickman et al., 1992). Many insect outbreaks have been reconstructed us ing dendrochronological techniques (Brubaker and Greene, 1979; Speer et al., 2001;

Weber and Schweingruber, 1995; Zhang and Alfaro, 2002). Although this analysis does not reconstruct and remove the impacts of specific insect outbreaks in affected sites due to the time constraints of the analysis, disturbances are accounted for in the ARSTAN chronology development stage. Exacerbated effects of certain attacks, however, should be noted with caution when interpreting the results of this analysis.

The significant negative trends found in the residuals may be associated with the restriction of radial growth during insect outbreaks.

65 5.2 Conclusion

Attributing radial growth to the multiple growth-related factors is complex. Assump tions of relationships, such as that with climate, are assumed stable. However, as can be discerned from the above discussion, climate is changing and many other growth related factors are also changing over time. The inherent interconnectedness of these factors and the instability of the relationships over time make it difficult to define unexplained growth. This makes it even more difficult to attribute any growth to changes in atmospheric CO2 concentrations.

This analysis and discussion has found very little support for a direct C02 ef fect. Instead, it has discovered changing relationships with climate over the past 100 years. These changing relationships are seen in all of the seasonal climatic variables, including both precipitation and temperature. Overall, these results suggest that temperature-induced drought stress could be important for radial growth responses under future climatic scenarios. Whether this relationship will continue to change over time and to what extent is unknown. The direction of the relationships may be in the midst of changing, as we have seen a large number of sites have experienced a change in the direction of correlation with particular climate parameters over the first 80 years of the last century. Many questions remain unanswered, which leaves room for future research in this field.

66 CHAPTER 6

SUMMARY & CONCLUSIONS

6.1 Overview of Findings

This research set out to address the potential influence of changes in post-industrial atmospheric CO2 concentrations on the radial growth of common species of trees, in order to infer implications for carbon dynamics within western North American ecosystems. Based on the findings of Gedalof and Berg (2010), it was noted, glob ally, that a small but significant proportion of trees exhibit an increase in their radial growth over the last 130 years. This increase was not attributed to elevation effects or nitrogen deposition. Therefore, this research sought to analyze a smaller geographic region, with more detailed climatic data. The effects of elevation and nitrogren de position were presumed minimal based on the results of Gedalof and Berg. However, opposing Gedalof and Berg's results, this research did not find a significant increase in growth at any number of sites. While the results of this analysis expand on those of Gedalof and Berg, the results are not methodologically comparable. Gedalof and

Berg used a 2.5-degree gridded PDSI dataset (representing a combination of mean moisture and temperature) to extrapolate climatic influences. The differentiation be tween precipitation and temperature influences was not analyzed. Here, I have used a 1-degree gridded dataset of monthly precipitation and temperature to analyze sea sonal influences. This dataset was used to perform a robust climate-related growth analysis. This analysis revealed the potential for changes in water-use efficiency to effect radial growth patterns over the last century, opposing Gedalof and Berg. Next, 67 I review the specific objectives of this research and the findings of this research.

The first objective was to compile a set of extant tree-ring chronologies for P. menziesn and P. ponderosa in western North America. This was completed using data from the International Tree Ring Data Bank (ITRDB). All previously collected and measured tree ring samples of P. menziesn and P. ponderosa in western North America were collected. Raw-ring width chronologies were used to remove the stan dard dendrochronological growth trends and to create site chronologies. Overall, 382 chronologies were created and used in this analysis.

The second objective was to quantify and evaluate the influences of different cli matic parameters on radial growth. This was completed using the University of Delaware's 1-degree global gridded dataset and an intensive statistical analysis. Grid data points of closest proximity to individual sites were used to represent the climatic signal of each area. Data were available from 1900 to 2008. Akaike Information Criterion (AIC) analysis and multiple linear regression analysis were used to remove the most likely interplay of 10 different climatic parameters on annual radial growth (aggregate seasonal temperature and precipitation parameters) at each site. Current summer temperature, previous summer temperature, winter precipitation, and cur rent spring precipitation were the climatic variables that were included most often in the AIC best-fit models across all sites.

The third objective was to examine residual chronologies for unexplained growth that may be attributed directly or indircectly to changes in atmospheric CO2 con centrations over the last century. This was completed using the residual chronologies from the previous multiple linear regression. It involved performing a second linear regression with time and analyzing the regression trend line of the predicted val ues. Significant positive trends in the residual chronologies were found in 3.66% of 68 sites. Interestingly, a significant negative growth trend was observed at 67.8% of sites, resulting in a further analysis of these chronologies.

The last objective was to explain patterns of unexplained growth in terms of species or site characteristics or differences. There were no significant species growth differences detected between P. menziesu and P. ponderosa. Further site charac teristics were not analyzed in regards to growth differences, as a major influence of climate appeared in the trends analysis. A revisitation of the climate analysis revealed changes in the sensitivity of radial growth to different climatic variables over time.

Both the strength and direction of individual correlations have changed over the first

80 years of this study period. Most sites appear to be decreasing their sensitivities to individual climatic parameters or changing their relationship altogether. A smaller portion of sites are showing increasing sensitivities to the same variables.

6.2 Future Forest Dynamics

The previous analysis demonstrates that climatic influences on radial growth can be more influential than assumed. The results of this analysis imply that forest dynam ics will continue to change with future climatic changes, as well. Relationships with climate are not stable and, therefore, relationships with other environmental param eters may not be stable either. The interplay of different environmental factors at different scales will mean that dynamics will not uniformly change across the study area. Instead, regional or local dynamics may differ. Elevation may become less crit ical in controlling tree growth with treeline advance up mountain sides resulting from rising temperatures, for example (Grace et al., 2002). Although Grace et al. (2002) state that the nutrient supply at higher elevations may limit this advance, as well.

The current study has shown that moisture availability may become less important. 69 This may indicate that local moisture controlling characteristics, such as slope and soil type, will have a reduced importance in growth. Changing environmental con ditions may favour certain species over others, altering competitive dynamics within the forest ecosystems and altering forest dynamics (Fonti et al., 2006; Franks and

Gedalof, prep). It is expected that dynamics will continue to change and the inter play of all of these factors make it difficult to predict the direction or extent of these changes. Shafer et al. (2001) predict, based on General Circulation Models (GCMs), that changes in atmospheric CO2 combined with temperature changes (bringing about temperature-induced drought stress) will cause western North American tree species to move into more arid/semi-arid environments. Will this improvement in water-use efficiency, which will allow this change, persist? Will this range shift be temporary?

More studies and modelling are required in order to grasp the potential spatial and temporal scales of predicted changes.

6.3 Potential Future Research

There are many questions left unanswered in the previous research which leave room for future research. What specific roles do micro-site characteristics have in explaining unexplained growth? Site-scale characteristics may influence the likelihood of having unexplained growth. Does the inclusion of site characteristics create greater regional patterns of unexplained growth? The change in climatic sensitivity discovered in this research also sets up further dendroclimatic research opportunities, including the spa tial component of changing sensitivities to specific climate parameters. Analyzing the changes in sensitivities at different time scales may also produce interesting insights into this nonuniform relationship and the potential relationship with global dimming.

The role of exogenous disturbances, such as insect outbreaks, should also be removed

70 from affected chronologies in future studies.

6.4 Conclusions

Changes in the atmospheric concentrations of CO2 have not had a significant direct impact on radial growth of P. menziesn and P. ponderosa in western North America.

However, CO2 may have indirectly altered the climatic influences on radial growth.

The unprecedented increase in atmospheric CO2 concentrations has been linked to changes in climate over the last century. The significant trends in the climate-related growth chronologies found in this analysis are strongly related to trends in the resid ual growth chronologies of P. menziesn and P. ponderosa, indicating an exaccerbated climatic influence. A robust correlation analysis has revealed that both the strength and direction of seasonal climatic relationships with radial growth have been nonuni form over the last century. Both species are becoming more often insensitive to the same climatic parameters or switching their relationship altogether. In the latter half of the century, temperature has become more positively correlated with radial growth and precipitation negatively. This opposes the relationships in the earlier half of the century at many sites. This response is not uniform across the region, however. This research has found no significant direct CO2 influence on radial growth in two conifer species in western North America. It has, however, revealed changes in the climatic sensitivity of annual conifer growth rings. This research has briefly analyzed the ex tent and direction of these changes, but there remains much to be studied in this complex relationship between radial growth and environmental variability in regards to future forest ecosystem dynamics.

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48(4):722-731. 83 APPENDIX A

SITE-SPECIFIC CLIMATE & RESIDUAL ANALYSIS RESULTS

The following is a site-specific list of the results (in alphabetical order based on site code) of the climate-related growth removal and residual chronology trend analysis

(Section 3.4.2 and 3.5). Listed in Table A.l is the designated site code, the site name

(as outlined in the ITRDB), the country in which the site is located, coordinates of the site, the species, and the researchers who originally collected this data. Following this are five columns with specific results from the analyses. First, the number of climate variables that were significantly correlated to the radial growth at the site (Sig. CV), then the AIC best-fit model components in order of decreasing significance (AIC model), the regression coefficient from the multiple linear regression that removed the climatic influences (AIC R2), the regression coefficient from the linear regression against time (Time R2), and the slope of the best-fit trend line in the residuals, that would represent unexplained growth (Trend slope).

The following are notes for interpretation of the table's contents:

In the AIC model column, the climatic variables are represented by numbers 1 through

10.

1 - Previous summer precipitation;

2 - Previous fall precipitation;

3 - Winter precipitation;

4 - Current spring precipitation;

84 5 - Current summer precipitation;

6 - Previous summer temperature;

7 - Previous fall temperature;

8 - Winter temperature;

9 - Current spring temperature;

10 - Current summer temperature.

In the Trend slope column, significant slopes in the residual trend lines are denoted by an asterisk (*).

Any N/A's found in the Sig. CV, AIC model, or AIC R2 columns, denote sites that were deemed climatically insensitive in the initial climate variable correlation analysis.

These sites had no significant correlations to any of the 10 climate variables and did not need to undergo climatic influence removal. These chronologies were regressed directly with time, and the residual chronologies were analyzed for significant trends.

85 Table A.l Site- specific climate & residual analysis results.

Site Site i Country Longitude Species Researchers S.g cv Trend slope Abouselman Spring USA 106W PIPO Swetnam, Capno, 6 4-3-2-10-7 0 58273 -0 00306* Lynch ALO-PP Alto Picnic Ground USA 40N 105W PIPO Graybill 2 6 012835 0 63685 -0 01382* ALR-PP Alcova Reservoir Site A USA 42 N 106W PIPO Stokes, Harlan 4 5-1 0 27821 0 0039216 -0 0010073 ALT-DF Annette Lake Trail USA 47N 121W PSME Earle, Brubaker, Se- 3 1-5-7 018571 0 20037 0 0037191* gura AMO-DF Pinal de Amole MEX 20N 99W PSME Stahle, Therrell 4 9-8-4-3 0 24691 0 0034528 -0 00048501 AMS-PP Aenaes Mountains USA 48N 119W PIPO Brubaker 6 10-3-1-4 0 26876 0 076092 -0 0043521* ANJ-PP Antelope Lake Recollection USA 40 N 120W PIPO Meko, Grow 6 10-6-7-3-2 0 35661 0 34853 -0 0042211* ANS-DF Antonito Site A USA 37N 106W PSME Stokes, Harlan 6 10-7-6-4-3-5 0 6137 0 25138 -0 0086967* ANT-PP Antelope Lake USA 40 N 120W PIPO Holmes, Adams 5 10-6-7-4-3 0 38269 0 16646 -0 0040075* ASF-DF Alpine San Francisco River USA 33N 109W PSME Stockton 7 3-7-6-1 0 41272 0 30925 -0 01072* Watershed ASH-PP Ash Canyon USA 42N 103W PIPO Woodhouse, Brown 6 10-6-5-9-2 0 26153 0 28693 -0 006633* ATR-DF Almont Triangle USA 38N 106W PSME Woodhouse, 5 2-1-7-9-10 0 3029 0 0044012 0 00063114 00 05 Losleben, Lukas PIPO Swetnam, Capno, 5 3-4-7-2-10 0 3791 Lynch Baldy Peak USA Briffa, Schweingru- 3 3-9 0 29197 0 00728 ber Cerro Baraja MEX Stahle, Cleaveland, 8 3-9-7-8-4-6-2 0 5547 0 11266 Burns BBB-DF Big Bend Boot Spring USA 29N 103W PSME Stokes, Harlan 6 4-2-10-3 0 30664 0 22014 -0 013091* BBC-DF Big Boulder Creek USA 48N 118W PSME Brubaker 5 4-6-10-1 0 48493 0 02965 -0 0019559 BCG-DF Black Canyon of the Gun USA 38N 107W PSME Stokes, Harlan 5 1-2-7-10-5 0 41672 0 00250 0 00062161 nison River BCR-DF Boulder Creek Colorado USA 40N 105W PSME Schweingruber 5 6-10-9-8 0 36663 0 57453 -0 012699* BCR-PP Beaver Creek USA 33N 109W PIPO Graybill 5 3-10-8-5-7 0 31626 0 14078 -0 004116* BCR2-PP Beaver Creek Watershed USA 34N 111W PIPO Biondi 2 10 0 20312 0 37138 -0 010306* BCV-PP Burning Coal Vein USA 46 N 103W PIPO Meko, Sieg 6 10-4-2-9-6-1 0 21798 0 33797 -0 0092982* BCW-PP Boulder Creek Washington USA 48N 120W PIPO Ferguson, Parker 1 6 0 049679 0 38238 0 026565* Wmthrop Continued on next page Table A 1 -- Continued from previous page Site code Site name Country Latitude Longitude Species Researchers Sig cv AIC model AIC R2 Time R2 Trend slope BEC-PP Bear Creek USA 48 N 120W PIPO Brubaker 5 2-1-4-10 0 28714 0 011319 -0 0013595 BEL-DF Bear Lake CAN 54N 122W PSME Schweingruber N/A N/A N/A 0 040741 0 0015289 BEN-PP Bennett Creek USA 40N 105W PIPO Woodhouse, Lukas, 6 10-6-9-4-7-2 0 38566 0 27136 -0 0057651* Huckaby, Barger BFE-PP Black Forest East USA 39N 104W PIPO Woodhouse, Brown 3 6 012017 0 66165 -0 017628* BHM-PP Buckhorn Mountain USA 43N 103W PIPO Meko, Sieg 4 4-10-5-9 0 14719 0 054438 0 002293* BIG-PP Big Elk Meadows USA 40N 105W PIPO Swetnam, Lynch, 5 10-6-4-2 0 42812 0 11304 -0 0038054* Raimo BIT-DF Big Thompson USA 40N 105W PSME Fritts, Holmes 8 6-7-9-2-4-10- 0 6261 0 013799 -0 0015329 1-3 BJS-PP Blue Jay Spring USA 42N 121W PIPO Speer, Swetnam 5 10-6-3-1 0 21958 0 038533 -0 0015219 BKF-DF Black Mountain USA 33N 108W PSME Graybill 6 4-3-2-1 0 44501 0 09457 -0 0042379* BKP-PP Black Mountain USA 33N 108W PIPO Graybill 6 4-3-2-10-7-5 0 41853 0 29535 -0 0081794* BLA-DF Cerro Baraja and Los An MEX 26 N 106W PSME Stahle, Cleaveland, 8 3-9-8-4-7-6- 0 62564 0 086705 -0 0016997* geles Sawmill Burns 10-2 BLM-PP Bally Mountain USA 45 N 118W PIPO Wickman, Swet 5 6-10-1-2-5 0 33559 0 20906 -0 0043435* oo nam, Baisan -4 BMT-PP Burned Mountain USA 36N 106W PIPO Swetnam, Harlan, 3 3-5-4 015628 0 24171 -0 0054323* Kennedy Suther land BOC-DF Bobcat Canyon USA 37N 108W PSME Dean, Robinson, 8 3-4-10-2-7-1 0 60113 0 1753 -0 0048136* Bowden, Cleaveland BOC-PP Box Canyon USA 45 N 120W PIPO Brubaker 2 7-6 016173 0 31689 -0 0060333* BOS-DF Boulder Shelter USA 47N 123W PSME Brubaker N/A N/A N/A 0 32253 -0 0037407* BPM-DF Barlow Pass am Mt Hood USA 45N 121W PSME Bnffa, Schweingru 2 4-7 011618 0 13894 -0 001739* ber BQR-DF Big Quilcene River Trail USA 47N 123W PSME Earle, Brubaker, Se- 2 1-6 011928 0 13469 0 002309*

gura BRF-DF Black River Arizona USA 33N 109W PSME 7 3-4-1-6 0 36935 0 099688 -0 0032693* Graybill BRP-DF Bryce Point USA 37N 112W PSME Grow 9 8-10-6-7-1-3- 0 54453 0 025588 -0 00089006 2-9-4 BRR-PP Boulder Ridge Road USA 40N 105W PIPO Woodhouse, 5 6-7-10-5-2 0 25924 0 12209 0 0020931* Losleben Conti nued on next page Table A 1 — Continued from previous page Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R/ Time R2 Trend slope Big Bend National Park USA 29N 103W PSME Cook, Montagu 2-9-3-5-10-4 0 34001 0 20198 -0 0050891* inci Camp Springs Big Sink USA Wickman, Swet- 5 0 23359 -0 0034201* nam, Baisan BTU-DF Big Thompson USA 40 N 105W PSME Woodhouse, Lukas 7 4-9-10-6-2-7- 0 49637 0 12073 -0 0037348* 3 Basin Area, North Rim, USA 36N 111W PIPO Wolf, Mast 4 6-10-5-1 0 25514 0 61604 -0 0077587* Grand Canyon NP BUT-DF Butte USA 45 N 112W PSME Ferguson, Parker 6 1-6-10-5-8-2 0 51834 0 075397 0 0039061* BWF-DF Bear Wallow USA 32N HOW PSME Grayblll 5 6-9-10-3 0 25734 0 34112 -0 0062428* CAB-PP Cahmus Butte USA 42N 121W PIPO Speer, Swetnam 3 1-6-5 017384 010847 0 001833* CAT-DF Cathedral Creek USA 38N 107W PSME Woodhouse, Lukas, 6 1-2-7-10-4-5 0 40073 0 001783 -0 00047332 Bolton, Losleben Cat Mesa USA 35N 106W PIPO Swetnam, Capno, 6 10-6-4-7-3 0 4278 0 32102 -0 0077399* Lynch Cross Canyon Oregon USA 45 N 117W PIPO Swetnam, Baisan, 6 2-4-10-6-3-1 0 3776 0 0102 -0 0006845 00 Wickman oo Canyon de Chelly USA Dean, Robinson 9-7-1-10-6-8- 0 5493 0 052274 -0 0015362 3-2

Cedar Butte USA Meko, Sieg 7 10-6-2-9-5-1- 0 42149 0 010905 -0 0011147 3 Clarks Fork of the Yellow- USA Waggoner, Graum- 3 6-9-10 0 28516

stone hch CHC-DF Chicago Creek USA 39N 105W PSME Stokes, Harlan 3 2-7-1 0 23736 0 070353 -0 0036864* CHM-DF Churchill Mountain USA 48 N 118W PSME Brubaker 5 6-4-9-1-10 0 38633 0 013817 0 00152 CIA-DF Creel International Airport MEX 27N 107W PSME Stahle, Cleaveland, 5 3-9-7-2 0 24833 0 39335 -0 0050989* Burns CIN-PP Cmega USA 33N 109W PIPO Parker, Harlan 4 3-6 017234 0 41894 -0 011187* CIR-DF Colville Indian Reservation USA 48 N 118W PSME Brubaker 5 6-10-1-4-2 0 41011 0 25778 -0 0045425* A Colville Indian Reservation USA Brubaker 5 6-10-4-1-2 0 42576 0 0062065 0 00069175

CLK-PP Crater Lake USA 42 N 122W PIPO Speer, Swetnam 2 10-6 0 13716 0 29957 -0 0040749* CNR-PP Canyon Road USA 41N 103W PIPO Woodhouse, Brown 4 7-9-6-4 0 20118 0 41558 -0 006326* Continued on next page Table A 1 - Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC R2 Time R2 Trend slope Cochetopa Dome USA 3SN 106W PIPO Woodhouse, Lukas, 7 3-2-10-4 7-5- 0 31655 0 083106 -0 003284* Bolton, Losleben 8 COP-PP Colockum Pass USA 47N 120VV PIPO Brubaker 1 6 0 074229 0 64469 -0 0082671* COR-PP Cornay Ranch USA 36N 103W PIPO Woodhouse, Brown 6 4-9-10-2 3 0 36744 0 33331 -0 0096518* COT-DF Colorado Transect USA 39N 105W PSME Harlan 5 6-7-2 0 27602 0 36562 -0 0096059* CPP-PP Cabresto Canyon USA 36N 105W PIPO Swetnam, Caprio, 2 4-2 0 1187 0 32617 -0 01202* Lynch

CPP2-PP Capuhn Volcano USA 36N 103W PIPO Woodhouse, Brown 5 4-6 0 1707 0 23301 -0 0077469* CPP3-PP Crystal Cave Sequoia Na USA 36N 118W PIPO King, Graumlich 4 10-6-7-3 0 30694 0 32251 -0 0066664* tional Park CPS-DF Clark Peak Saddle USA PSME Harlan, Schwab 7 3-9-5-6 0 4973 0 22901 -0 0077801* Pmaleno Mountains CRP-PP Crags Hotel USA Woodhouse, 2 2 0 053116 0 25487 -0 0042594* Lukas, Nepstad- Thornberry CRY-DF Crystal USA 35N 108W PSME Cleaveland, Harlan 6 10-5-1-7-6-3 0 46942 0 11 -0 0029143* oo CRY-PP Crystal USA 35N 108W PIPO Cleaveland, Harlan 6 10-7-5-6-4 0 42271 0 26391 -0 0040337* DAL-PP Dalton Reservoir USA 41N 120W PIPO Holmes, Adams 5 10-6-9-7-1 0 47 0 012207 0 0013584 DCB-DF Dragon Creek Bright Angel USA 36 N 112W PSME Stockton 6 7-10-6-3-1 0 33599 0 31093 -0 0077546* Creek Watershed DCP-DF Deer Creek Pass USA 48 N 121W PSME Earle, Brubaker, Se- N/A N/A N/A 0 00061493 -0 00013988 gura DCW-DF Dry Creek Washington USA 45N 122W PSME Brubaker 3 6-1-4 0 20149 0 072136 -0 0013874* DEB-PP Defiance East (Fort Defi USA 35N 109W PIPO Stokes 8 3-7-10-9-6-5- 0 56324 0 049649 -0 0041655 ance) 2 DES-PP Deschutes USA 43N 121W PIPO Speer, Swetnam 3 3-10 0 15335 0 21876 -0 0043565* DET-PP Devils Tower National USA 44 N 104W PIPO Stambaugh, 5 10-6-3-9-8 0 34613 0 34507 -0 0066324* Monument Marschall DEW-DF Defiance West (Defiance- USA 35N 109W PSME Stokes, Harlan 8 7-6-3-10-9-1- 0 50463 0 020299 -0 0026903 Nazlini) 4-2 DHR-PP Drumhill Ridge USA 45 N 118W PIPO Wickman, Swet 2 6 0 073134 0 63273 -0 011771* nam, Baisan DIC-DF Ditch Canyon USA 36N 107W PSME Dean 6 10-6-1-3-4-2 0 40972 0 36149 -0 011008* DIC-PP Ditch Canyon USA 37N 107W PIPO Dean 7 4-3-10-1 0 34462 0 20063 -0 0087156* Continued on next page Table A 1 -- Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig cv AIC model AIC R2 Time R2 Trend slope DIC2-DF Ditch Canyon B USA 37N 107W PSME Cleaveland, Harlan 5 3-5-10-4 0 41732 0 36161 -0 011721* DIH-DF Dead Indian Hill USA 44N 109W PSME Ferguson, Despam 2 9-6 0 23569 0 18118 -0 0053523* DIL-DF Dillon USA 39N 10SW PSME Woodhouse, Lukas 2 4-10 0 082967 0 0015154 0 000414 DIT-PP Ditch Canyon B USA 36N 107W PIPO Cleaveland, Harlan 4 3-10-5-4 0 30272 0 11693 -0 0050258* DLK-PP Diamond Lake USA 43 N 121W PIPO Speer, Swetnam N/A N/A N/A 0 51688 -0 0064773* DMO-PP Deer Mountain USA 40N 105W PIPO Graybill 6 10-6-4-9-2 0 49309 0 26111 -0 0060535* DMU-PP Deer Mountain USA 40N 105W PIPO Woodhouse, Lukas, 6 4-10-9-6-2 0 50861 0 0057834 -0 00064427 Kaye DOL-DF Dolores USA 37N 108W PSME Harlan 8 10-3-6-1-4-9- 0 42375 0 1859 -0 005723* 2 DOU-DF Douglas Pass USA 39N 108W PSME Woodhouse, Lukas, 5 2-6-1 0 39963 0 00010069 0 000084795 Kaye DPB-DF Deer Park Burn USA 47N 123W PSME Brubaker N/A N/A N/A 0 49948 -0 0068623* DVG-PP Devil's Gulch USA 40N 105W PIPO Swetnam, Lynch, 4 4-9-10 0 2955 0 031585 0 0023525 Raimo EAG-DF Eagle USA 39N 106W PSME Stokes, Harlan 3 7-6-2 013484 0 0048572 -0 00097382 co BAG-PP Eagle Rock USA 39N 105W PIPO Woodhouse, 2 4-2 016579 0 20006 -0 0040061* Losleben EAP-DF Eagle Point USA 47N 123W PSME Brubaker N/A N/A N/A 0 52988 -0 0095081* ECA-DF Echo Amphitheater USA 36N 106W PSME Dean, Burns, 5 4-2-10-3-1 0 51404 0 077582 -0 0039312* Robinson, Bowden EDC-DF El Dorado Canyon USA 39N 105W PSME Fntts, Holmes 5 7-6-9-2-4 0 307 017583 -0 0051045* EDO-DF Eldorado Canyon USA 39N 105W PSME Graybill 1 1 0 055446 0 42421 -0 0068532* EDP-PP Eldorado Canyon USA 39N 105W PIPO Graybill 1 2 0 046983 0 1793 -0 0046228* ELC-DF Elbow Campground Jack- USA 43 N HOW PSME Ferguson, Parker 2 6 019766 0 31199 -0 010911*

ELD-PP Eldora USA 39N 105W PIPO Schweingruber N/A N/A N/A 0 46449 -0 0064809* ELE-PP Elevenmile Reservoir USA 38 N 105W PIPO Woodhouse, 7 4-10-9-6-7-1- 0 30304 0 034842 -0 0017035 Losleben 8 ELK-PP Elk Canyon USA 33N 106W PIPO Swetnam, Caprio, 4 2-3-9 0 20212 0 017201 -0 0018445 Lynch ELM-PP El Morro USA 35N 108W PIPO Dean, Woolfenden 7 3-10-6-4-1-7- 0 51182 0 054026 -0 0039367* 2

Continued on next page Table A 1 - Continued from previous page Site code Site name Country- Latitude Longitude Species Researchers Sig cv AIC model AIC R2 Time R2 Trend slope ELR-PP Elephant Rock USA 105W PIPO Swetnam, Caprio, 2 0 18649 0 15949 -0 0031188* Lynch El Salto recollect inci 3 Stahle, Burns, 1 0 086566 0 00053879 -0 00016117 Tucson LTRR sites Cleaveland El Tabacote and Tomochic PSME Stahle, Burns, 1 0 094314 0 5634 Cleaveland ELU-DF Eldorado Canyon 105W Woodhouse, 2 0 083378 0 17576 Lukas, Nepstad- Thornberry, Benton

ELV-PP El Valle USA 36 N 105W PIPO Dean, Robinson 2 4-2 0 11748 0 22212 -0 011487* EMN-DF El Malpais National Monu- USA 34N 108W PSME Gnssmo-Mayer 7 3-1-4-2 0 43653 0 0028858 0 00067406 ment Encampment USA Woodhouse, Lukas, 6 2-1-6-3-5-4 0 29909 0 022569 0 0012232 Losleben ENC-PP Eagle Nest Canyon USA 45 N 103W PIPO Meko, Sieg 8 10-2-4-3 0 36147 0 03644 -0 0018295 ESC-DF Escudilla-Paddy Creek USA 33N 109W PSME Stokes 5 3-6 0 28481 0 53484 -0 014268* co ESP-PP Emigrant Springs USA 45N 118W PIPO Wickman, Swet- 2 10 0 09229 0 57123 -0 0083704* nam, Baisan El Salto West Sierra Madre MEX 105W Stokes, Harlan, 2 0 010222 -0 00097379 Holmes EXF-PP Experimental Forest USA 43N 121W PIPO Speer, Swetnam 2 4-6 0 11973 0 296 -0 0050606* EXS-DF Exshaw Tunnel Banff CAN 51N 115W PSME Ferguson, Parker 5 4-1 017617 0 028925 -0 0018503 FEN-PP Fenton Lake USA 35N 106W PIPO Swetnam, Caprio, 4 3-4-2-10 0 35842 0 000048829 0 000067971 Lynch Wickman, Swet- 4 0 061614 -0 0014275* nam, Baisan FOL-PP Fort Lewis USA 47N 122W PIPO Brubaker N/A N/A N/A 0 74143 -0 017098* FPC-DF Fly Peak Chincahua USA 31N 109W PSME Briffa, Schweingru- 5 3-9-2-4-10 0 35988 0 24938 -0 003563* Mountains ber Fox Mountain San Fran- USA 108W Stockton 8 3-4-7-9-6-1- 0 48325 0 062671 -0 0038689* Cisco River Watershed 10-2 Fryday Ridge oberhalb USA Briffa, Schweingru- 5 6-10-7-8-5 0 41519 0 047703 -0 0010876*

GAM-DF Galiuro Mountains Site A USA Harlan, Holmes 0 41574 0 2687 Continued on next page Table A 1 -- Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig cv AIC model AIC R2 Time R2 Trend slope GAM2-DF Gallmas Mountains USA 34 N 105W PSME Stockton 4 4-3-2 0 34601 0 39167 -0 012071* GAR-DF Gardiner USA 45N HOW PSME Ferguson, Despain 6 2-6-10-3-4 0 45446 0 0015296 -0 00048302 GBP-PP Grizzly Bear USA 45N 117W PIPO Swetnam, Baisan, 7 2-3-4-10-1-7 0 45535 0 036652 -0 0012107 Wlckman GCP-PP Gila Cliff Dwellings USA 33N 108W PIPO Graybill 5 3-2-4-10-5 0 30165 0 34831 -0 0088638* GCP2-PP Grace Coolidge USA 43N 103W PIPO Meko, Sieg 7 10-3-6-5-9-4 0 49238 0 33186 -0 0083206* GMF-DF Green Mountain USA 32N HOW PSME Graybill 5 9-3-6 0 32324 0 22979 -0 0050849* GMR-DF Green Mountain Reservoir USA 39N 106W PSME Woodhouse, Lukas, 6 2-7-1-3-6-4 0 29162 0 0000088741 -0 000027729 Kaye GMT-PP Green Mountain USA 32N HOW PIPO Graybill 4 6-9 011324 0 45454 -0 013913* GOC-PP Government Camp USA 47N 120W PIPO Brubaker 3 6-2-1 0 22532 0 32035 -0 0066165* GPK-PP Garcia Park USA 36N 105W PIPO Swetnam, Harlan, 1 10 0 077411 0 31338 -0 0092649* Kennedy Suther land GPN-PP Gus Pearson USA 35N 111W PIPO Graybill 4 10-6-7-5 0 29592 0 46509 -0 010833* GRA-PP Grasshopper USA 34 N HOW PIPO Dean, Warren 5 7-10-6-3 0 40554 014204 -0 0073504* GRM-PP Granite Mountain (North USA 34 N 112W PIPO Stokes, Harlan N/A N/A N/A 0 053688 0 0061698 west of Prescott) GRP-PP Girl's Ranch USA 34N HOW PIPO Graybill 6 6-3-7 0 34042 0 035336 -0 0029966 GRR-PP Grasshopper Recollection USA 34N HOW PIPO Graybill 6 3-10-7 0 3573 0 04362 -0 0031322 GSD-PP Great Sand Dunes Lower USA 37N 105W PIPO Grissino-Mayer 6 7-2-4-3-10 0 34364 0 1118 -0 003265* GUA-DF Guadalupe Peak USA 31N 104W PSME Stahle, Montagu, 10 2-10-9-7-3-8- 0 54972 0 18807 -0 0047405* Cleaveland 6-4-5-1 HDE-PP Helen's Dome USA 32N HOW PIPO Graybill 4 6 0 113 0 54931 -0 015304* HEL-DF Helena USA 46 N 111W PSME Ferguson, Ferguson 5 10-6-7-9-8 0 39473 0 23502 -0 010636* HLT-PP Hualapai Laguna Tank USA 35N 113W PIPO Stokes 5 3-7-9-10 0 42688 0 044444 -0 0038301 HOO-PP Hoosier Canyon USA 32N 106W PIPO Swetnam, Caprio, 1 10 0 092417 0 062996 -0 0027301* Lynch HOS-DF Horsetooth Summit USA 40N 105W PSME Fritts, Holmes 7 10-2-6-9-4-3 0 45667 0 025307 -0 0025213 HOT-DF Hot Sulphur Springs USA 40N 106W PSME Woodhouse, 8 6-10-7-5-3-9- 0 36747 0 0045145 -0 0004608 Losleben, Lukas 2-4 HPP-PP Hodgdon Yosemite Na USA 37N 119W PIPO King, Graumlich 2 7-10 016698 0 27694 -0 0045961* tional Park HRC-PP Horsetooth Reservoir C USA 40N 105W PIPO Fritts, Holmes 5 10-4-9-6-5 0 37474 0 10326 -0 004949* Continued on next page Table A 1 — Continued from previous page Site code Site name Country Latitude Longitude Species Researchers Sig cv AIC model Time R2 Trend slope HTF-DF Horsetooth Reservior B USA 40N 105W PSME Graybill 4 6-10-5-9 0 28179 0 12199 -0 0031833* HTP-PP Horsetooth Reservior A USA 40N 105W PIPO Graybill 4 10-9-4-6 0 2626 0 12413 -0 0047398* HUA-DF Hualapai Peak USA 35N 113W PSME Stokes 6 7-3-10 0 3219 0 13839 -0 005971* HUM-PP Hualapai Mountains USA 35N 113W PIPO Harlan 5 7.3-4.6-IO 0 33606 0 098508 -0 0051601* HUR-DF Hurricane Ridge USA 47N 123W PSME Brubaker 1 7 013736 010246 -0 0019936* ICR-PP Indian Creek USA 39N 105W PIPO Swetnam, Lynch, 4 3-7 0 13193 0 70889 -0 01583* Raimo Indian Crossing Wickman, Swet 7 4-6-10-1-2-3- 0 40804 0 1504 -0 0027845* nam, Baisan 5 INR-PP Indian Ridge USA 46N 117W PIPO Brubaker 4 4-2 0 24744 0 39186 -0 0081866* JAM-PP Jamestown USA 40N 105W PIPO Woodhouse, Lukas, 5 10-6-4-5 0 25264 0 19573 -0 0043891* Kaye JCO-PP Jefferson County Colorado USA 39N 105W PIPO Graybill 6 7-2-9-4-6 0 18321 0 070621 -0 0022663* JCT-PP Junction of HWYS 51 and USA 43N 121W PIPO Speer, Swetnarn 1 1 0 060335 0 0067815 -0 00050104 97 Jefferson County Colorado USA 39N Woodhouse, 3 2-4-10 018024 014828 -0 003033* C£> Lukas, Nepstad- CO Thornberry JLK-PP Jacob Lake USA 36N 112W PIPO Stokes 4 3-6 0 20081 0 41463 -0 0073968* KAB-PP Kamiak Butte USA 46N 117W PIPO Brubaker 3 4-2-10 0 23901 0 37647 -0 0089378* KAF-DF Kassler Recollect USA 39N 105W PSME Graybill 5 4-2-1-7 018532 0 070955 -0 0028483* KAM-DF Kamloops CAN 50N 120W PSME Fritts 2 1-6 0 27698 0 051797 -0 0034664 KAM-PP Kamloops CAN SON 120W PIPO Fritts 1 1 016224 016268 -0 0068431* KAP-PP Kassler Recollect USA 39N 105W PIPO Graybill N/A N/A N/A 0 41909 -0 0093753* KAS-PP Kassler USA 39N 105W PIPO Fritts, Holmes 3 2-6 016786 0 35285 -0 0093644* KEN-PP Kenton USA 36N 103W PIPO Harlan, Cook 5 4-10-9-2-3 0 50508 0 24724 -0 0058224* KEW-DF Ketchum East Warm USA 43 N 114W PSME Ferguson 5 10-6-5-1-3 0 26153 0 18643 -0 0061282* Springs Kim Woodhouse, Brown, 6 4-9-10-2-6-3 0 26329 -0 010219* Losleben

KLA-PP Klagetoh 35N 109W PIPO Bradfield 6 3-7-6 0 41676 0 08091 -0 0043615* LAB-DF Las Bateas 25 N 100W PSME Villanueva-Diaz 4 9-8-3-4 0 20276 0 36426 -0 0060782* LAB2-DF Las Tinajas 30N 108W PSME Stahle, Burns, N/A N/A N/A 0 25094 0 0031398* Cleaveland Continued on next page Table A 1 — Continued from previous page

Site code Site name Country Longitude Species Researchers Sig CV AIC model Trend slope LAK-PP Lakeview USA 42N 120W PIPO Parker 3 1-5-10 0 24172 0 055792 -0 0033813 LAL-PP Los Alamos New Mexico USA 3SN 106W PIPO O'Brien 7 7-4-10 0 48921 0 048088 -0 0028151 LAN-DF Land's End USA 39N 108W PSME Woodhouse, 7 2-1-6-4-7-10- 0 4195 0 046024 0 0010562* Losleben, Lukas 5 LAN2-DF Los Alamos New Mexico USA 35N 106W PSME O'Brien 6 10-6-7-4-2 0 5019 0 055995 -0 0038404 LAS-DF Laramie Site A Woods USA 41N 106W PSME Stokes, Harlan 7 2-10-6-4-3-5 0 48156 0 01706 0 0017127 Creek Las Tinajas MEX Stahle, Burns, 5 4-3-9-2-8 0 47036 0 09617 -0 0026766* Cleaveland

LCA-PP Lake Coeur d'Alene USA 47N 116W PIPO Brubaker 3 10-2-1 0 281 0 0011237 0 00022775 LCA2-PP Lower Canyon, Stein Val- CAN 50N 121W PIPO Riccius 1 2 0 094824 0 25069 -0 0045507* ley Lower Canyon, Stem Val- CAN Riccius 1 9 0 06061 0 18691 -0 0030783* ley LGP-PP Long Pine Creek USA 42N 99W PIPO Woodhouse, Brown 3 10-6-9 0 23077 0 56231 -0 013822* LIL-DF Lily Lake USA 40N 105W PSME Woodhouse, Brown 1 5 0 052749 0 0090408 -0 00050589

4^ LIV-DF Livingston Montana USA 45N HOW PSME Ferguson, Parker 6 10-6-9-4-3-8 0 43015 0 064336 -0 003603* LML-PP Lookout Mountain Lower USA 43N 121W PIPO Speer, Swetnam 1 4 0 091285 0 36514 -0 0044193* LOL-DF Long Lake USA 48N 118W PSME Brubaker 4 6-4-1 0 34158 0 067311 -0 0025918* LOM-PP Lookout Mountain USA 45 N 117W PIPO Wickman, Swet 2 2-4 0 16439 0 062798 -0 001597* nam, Baisan Ladybug Peak Pinaleno USA Harlan, Schwab 7 3-7-9-6-5 0 4544 0 46261 -0 01458* Mountains Laird Park Palouse Ranger USA Brubaker 4 10-6-2-4 0 34747 0 36356 -0 0065056* District

LPS-PP Long Prairie Spring USA 45N 120W PIPO Brubaker 1 6 0 062096 0 46673 -0 012507* LSB-PP Laramie Site B Woods USA 41N 106W PIPO Stokes, Harlan 6 3-10-6-2-5-4 0 36765 0 13455 -0 0050341* Creek LSP-PP Lugar Springs USA Wickman, Swet- 5 nam, Baisan

Stokes, Harlan 8 3-7-10-6-1-4- 0 49088 5 LUP-PP Lucky Point USA 48N 118W PIPO Brubaker 1 1 0 15115 0 2358 -0 0057209* LYP-PP Lykms Gulch USA 40N 105W PIPO Graybill 5 10-6-2-4 0 34603 0 58641 -0 010232* Continued on next page Table A 1 -- Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R2 Time R2 Trend slope MAY-PP Mayer USA 34N 112W PIPO Stokes, Harlan 6 3-4-10 0 29043 0 42253 -0 013702* MCG-DF McGee Gulch USA 38 N 106W PSME Woodhouse, 5 2-7-1-5-4 0 34453 0 025368 0 0016973 Losleben MCK-PP McKenna Park USA 33N 108W PIPO Swetnam 4 3-4-10-5 0 23196 0 6037 -0 017432* MCN-PP Mill Creek Research Natu- USA 45 N 121W PIPO Knapp, Soule, 4 10-6-7-2 0 23838 0 25397 -0 0041831* ral Area Knight MDM-PP Mesa de Maya USA 37N 103W PIPO Woodhouse, Brown 3 9-4-3 0 13945 0 40259 -0 0086728* MEV-DF Mount Everts USA 44 N HOW PSME Graumhch, Wag 7 6-10-2-7-3-9- 0 40629 0 046015 -0 0019024* goner, King, Fergu 4 son MEV-PP Medicine Valley USA 35N 111W PIPO Dean, Bowden 7 9-6-10-3-7-1- 0 33296 0 00024904 0 00028925 4 MEY-PP Meyer Ranch USA 39N 105W PIPO Woodhouse, 1 2 0 044073 0 23177 -0 0052534* Lukas, Nepstad- Thornberry MHP-PP Mount Hopkin's USA 31N HOW PIPO Graybill 3 3-7-4 014471 0 44482 -0 015602* CO MIL-PP Mill Canyon USA 36N 104W PIPO Woodhouse, Brown 7 10-4-2-9-7-3- 0 447 0 13298 -0 0037072* 6 Mouth of La Junta 36N Swetnam, Caprio, 1 4 0 046387 0 34279 -0 0097103* Lynch MOP-PP Monarch Lake USA 40N 105W PIPO Graybill 2 6-10 017148 0 20914 -0 0025923* MOR-DF Mogollon Rim USA 34N 111W PSME Stokes, Harlan 5 6-3 0 22801 0 23547 -0 006098* MTA-DF Mt Ang USA 47N 123W PSME Briffa, Schweingru- 2 6-7 016994 0 34077 -0 0036892* ber Mt Lemon Briffa, Schweingru- 5 3-9-2-4-6 0 36256 0 00013128 -0 00010055 ber

MTP-PP Muletank USA 34 N HOW PIPO Graybill 5 10-6-7-5-9 0 33506 0 17096 -0 00322* NAG-DF Nantack Gap USA 33N 109W PSME Harlan, Parker 6 3-6-1-7-9-2 0 49219 0 16382 -0 0055922* NAR-PP Naramata Penticton CAN 49 N 119W PIPO Ferguson, Parker 5 4-5-2-10-1 0 49791 0 035513 -0 001956 NAV-PP Navajo Mountain USA 37N HOW PIPO Dean, Bowden 4 3-10 0 40004 0 22487 -0 007424* NAZ-PP Nazhm Canyon USA 35N 109W PIPO Bradfleld 7 7-10-6-3-5 0 51006 0 22593 -0 0083917* NED-PP Ned Tanks USA 35N 107W PIPO Dean 6 4-3-10-7-2 0 45365 0 008092 0 0014111 NEW-DF Newport USA 48 N 117W PSME Brubaker 6 6-10-4-1-5-3 0 4858 0 13323 -0 0039223* NFR-DF North Fork Ridge USA 45 N 111W PSME King 4 6-1 0 25529 0 00011854 0 000053196 Continued on next page Table A 1 -- Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig cv AIC model AIC R2 Time R2 Trend slope NIO-PP Niobrara Valley Preserve USA 42N 100W PIPO Brown, Woodhouse, 4 10-9-6-2 0 28369 0 28055 -0 0062781* Bragg NMC-DF Nine Mile Canyon High USA 39N HOW PSME Stokes, Harlan 6 1-9-3-10-2 0 57472 0 00050267 -0 00026559 NNM-DF Navajo National Monu USA 36N HOW PSME Dean 7 7-1-6-9-10-8- 0 46273 0 0058789 0 0011261 ment 3 NNP-DF New North Park USA 40N 106W PSME Stokes, Harlan 4 6-7-2-10 0 21165 0 040583 0 0023812 NOF-DF Nooksak Falls USA 48 N 121W PSME Brubaker 1 6 011137 0 011667 0 00081047 NOO-PP Noon Creek USA 32N 109W PIPO Graybill 3 3-9-10 0 2081 0 24275 -0 0082347* NOS-DF North Slope USA 32N HOW PSME Graybill 4 6 0 11725 0 48781 -0 012495* NPU-DF New North Park USA 40N 106W PSME Woodhouse, 6 2-7-6-10-5-1 0 28919 0 013669 0 00098413 Losleben NSE-PP North Slope USA 32N HOW PIPO Graybill 5 6 0 13754 0 55745 -0 013779* OAK-PP Oak Creek USA 38N 105W PIPO Swetnam, Lynch, 4 6 0 20816 0 47863 -0 011996* Raimo OLR-DF Olympic Road 3116 USA 48 N 124W PSME Earle, Brubaker, Se- 1 1 0 064103 0 025466 -0 0008645 gura OPH-PP Ophir Creek USA 38N 105W PIPO Swetnam, Lynch, 4 10-6-7 0 24944 0 12733 -0 0044688* Rairno OPP-PP Oak Flat Road Yosemite USA 37N 119W PIPO 3 10-6 019001 0 48378 -0 0055924* National Park ORD-PP Ord Mountain USA 33N 111W PIPO Graybill 7 6-3 0 24751 0 34231 -0 0099582* ORM-DF Organ Mountains Site 3 USA 32N 106W PSME Stokes, Naylor, Har- 7 9-2-3-4-10-7- 0 38481 0 17886 -0 0065208* 5 ORP-PP Ormes Peak USA 38N 104W PIPO Swetnam, Lynch, 5 10-6-7-9-4 0 41006 0 30415 -0 0065012* Raimo OSM-PP Osha Mountain USA 36N 105W PIPO Swetnam, Harlan, 2 5-10 0 099077 0 29959 -0 0047823* Kennedy Suther land PAL-DF Pavilion Lake CAN 50N 120W PSME Fritts 2 1-6 0 34291 0 004019 0 00090174 PAU-PP Paulina USA 44N 119W PIPO Parker 2 10 0 21158 0 21769 -0 0073521* PCD-DF Pinery Canyon USA 31N 109W PSME Graybill 4 3-4-9-2 0 26187 0 37855 -0 00836* PED-PP Pedro Mountains Site A USA 42N 106W PIPO Stokes, Harlan 3 10-3-5 0 31574 0 077111 -0 003439* Prarie Continued on next page Table A 1 -- Continued from previous page Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R2 Time R2 Trend slope PEP-DF Peak to Peak USA 40N 105W PSME Woodhouse, Brown, 1 5 0 064786 0 00065831 -0 00018617 Lukas PFL-DF Peter's Flat Pinaleno USA 32N 109W PSME Grissmo-Mayer, 4 10-3-6-5 0 23942 0 60463 -0 0091512* Mountains Adams, Swetnam PIB-DF Point Imperial Bright An USA 36N 111W PSME Stockton 6 7-10-3-6-1-5 0 45539 0 35841 -0 0075401* gel Creek Watershed PIM-PP Pilger Mountain USA 43N 103W PIPO Meko, Sieg 6 5-10-3-4 0 25776 0 15824 -0 0046726* PIM2-PP Pilger Mountain Lookout USA 43N 103W PIPO Fritts 6 10-5-3 0 49681 0 26293 -0 0078773* PIP-DF Pipestone Canyon Twisp USA 48N 120W PSME Ferguson, Parker 2 2-4 0 12725 0 051654 -0 0050335 PLB-PP Piatt Bradbury USA 37N 106W PIPO Woodhouse, 8 4-10-7-1-6-9- 0 39092 0 12892 -0 0031642* Losleben 2-3 POC-DF Post Creek USA 32N 109W PSME Graybill 6 3-9-5-4-10 0 34259 013978 -0 0036976* POR-DF Point Reyes Bear Valley USA 38N 122W PSME Brown 4 9-10-6 0 19616 0 49677 -0 0092099* POT-DF Cerro Potosi MEX 24N 100W PSME Stahle, Therrell, 4 9-8-3-4 0 24952 0 29791 -0 0071433* Cleaveland, Paull POW-DF Powerhouse CAN 51N 115W PSME Ferguson, Parker 6 1-4-10-6-5 0 51752 0 011044 -0 0010016 co PPF-PP Prmgle Falls Prescribed USA 43N 121W PIPO Speer, Swetnam 2 6 0 052951 0 50036 -0 0083749* Fire PPP-PP Panorama Point Sequoia USA 36N 118W PIPO King, Graumlich 4 3-8-4-10 0 25357 012409 -0 0039309* National Park PRD-DF Princeton USA 38N 106W PSME Woodhouse, 6 4-2-1-10-7-5 0 34846 0 0043675 -0 00062137 Losleben, Lukas PRF-PP Pringle Falls RNA USA 43N 121W PIPO Speer, Swetnam 2 6-4 011568 0 44068 -0 0087716* PTP-PP Pool Table Pines USA 37N 106W PIPO Ryerson, Swetnam, 6 10-7-6-3-2-1 0 392 0 014032 -0 0012927 Lynch PUC-DF Pueblita Canyon USA 36N 107W PSME Cleaveland, Harlan 4 3-4-10-2 0 41395 0 16435 -0 006532* PUC2-DF Puebhto Canyon USA 36N 107W PSME Dean, Robinson, 4 3-4-10-2 0 48841 0 36477 -0 010184* Bowden PYL-DF Pyramid Lake Patricia CAN 52N 118W PSME Ferguson, Parker 6 2-10-1 0 33424 0 021602 -0 0024263 Lake RAP-DF Rainy Pass 2 USA 48N 120W PSME Brubaker 1 6 011262 0 022936 -0 0011949 RCF-DF Rhyohte Canyon USA 32N 109W PSME Graybill 4 3-9-7-4 0 2766 0 23233 -0 0076684* RCK-DF Red Creek USA 38 N 107W PSME Woodhouse, 7 1-6-7-10-8-9- 0 42356 0 26295 -0 0038179* Losleben, Lukas 5

Continued on next page Table A 1 - Continued from previous page

Site code Site name Latitude Longitude Species Researchers Sig CV AIC model AIC B/ Time RJ Trend slope RDF-PP Rito de los Frijoles 35N 106W PIPO Dean, Robinson 8 4-10-7-3-2-9- 0 54044 0 0065375 -0 0014237 6-5 USA Ryerson, Swetnam, 5 2-1-4-7-8 0 4234 Lynch Reno Gulch Meko, Sieg 7 10-6-5-4-9-3- 0 4916 0 000012734 0 000027546 1 RES-PP Rancho Escondido Sierra 9-7-4-6-3 0 33574 0 12348 -0 0053175* Madre RGP-PP Rocky Gulch USA 34N 111W PIPO Grayblll 4 10-6-7-3 0 35203 0 16578 -0 0078326*

RIM-DF Rimrock Valles Ground USA 46 N 121W PSME Kaiser N/A N/A N/A 0 078725 -0 0039497* White Pass Rio Pueblo Swetnam, Caprio, 1 0 090025 0 368 -0 0098794* Lynch

RIR-PP Ridge Road USA 36N 105W PIPO Woodhouse, Brown 2 10 0 076175 0 58771 -0 011534* RMP-PP Robinson Mountain Recol- USA 39N 104W PIPO Grayblll 5 9-3-4-10-8 0 23736 0 018177 0 0017773 lection oo Rainy Mesa San Francisco USA Stockton 5 0 46831 0 2733 -0 0076345* River Watershed ROL-DF Ross Lake USA 33N 108W PSME Brubaker 4 6-1 016159 0 016584 0 00090336 ROM-PP Robinson Mountain USA 48N 121W PIPO Dean, Bowden 8 9-3-10 0 23558 0 0075102 -0 0015422 ROP-PP Rose Peak USA 35N 111W PIPO Harlan 4 3-6-1 0 30277 0 21742 -0 013852* ROR-DF Reef of Rocks USA 33N 109W PSME Wright, Ababneh, 7 9-6-4-7-3 0 31848 0 061122 -0 0023668* Kirkby, Grow, Towner, Glueck, et al RPE-PP Rhyolite Canyon USA 32N HOW PIPO Grayblll 2 7-5 011272 0 50788 -0 012446* RPP-PP Rose Peak Recollection USA 32N 109W PIPO Grayblll 5 3-4 0 21426 0 33502 -0 01321* RSP-PP Ranger Station Peak Se- USA 33N 109W PIPO King, Graumlich 4 7-10-6-3 0 25843 0 16743 -0 0032912* quoia National Park Ruidosa Ridge USA Dean, Robinson, 4 0 069546 -0 0058229* Matlock Rustic USA 105W Woodhouse, Lukas, 6 10-4-9-6-7-5 0 4175 0 17866 -0 0035952* Huckaby, Barger Continued on next page Table A 1 - Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R Time R Trend slope SAC-DF Sandia Crest Briffa, Schweingru- 8 10-7-4-3-5 0 42905 0 038348 -0 0019055 ber SAL-DF Salida USA 35 N 107W PSME Stokes, Harlan 1 4 0 1035 0 29954 0 0080868* SAP-DF Satan Pass USA 38N 105W PSME Dean, Bowden, 8 3-4-1-2-7-6-9 0 63774 0 015646 -0 0018181 Robinson, Burns SAP-PP Sapmero Mesa Woodhouse, Lukas, 6 4-2-10-5-3-9 0 34725 0 020776 0 0013559 Bolton

SAR-DF Santa Rita USA 31N HOW PSME Graybill 5 3-6-4 019356 0 52475 -0 013317» SAR2-DF Sargents USA 38N 106W PSME Woodhouse, Lukas, 4 2-10-3 0 2083 0 029176 -0 0019161 Bolton Sierra Blanca Ruidoso USA PSME Briffa, Schwemgru- 8 10-9-6-3-7-1- 0 34527 0 021161 -0 0013562 ber 2-5 Scheehte Canyon USA PSME Adams, Baird 6 3-7-6-2 0 36071 0 22525 -0 0059253" Huachuca Mountains Baxter, Pedicmo, Scotti, et al SCM-DF Santa Catahna Mountains USA PSME Harlan 4 0 0079536 -0 0012768 CO High CO Sierra del Carmen Madera MEX Stockton, Stokes 6 3-4-10-9-2 0 40689 0 20003 -0 0054075* Canyon Sierra del Nido Site B MEX PSME Naylor 7 7-6-10-2-9-3- 0 36091 0 13327 -0 0046208* 1

SEE-DF Seedhouse USA 40 N 106W PSME Woodhouse, 4 5-6-10-1 0 34621 0 04581 -0 0011341* Losleben, Lukas SEP-DF Segelson Pass USA 48N 121W PSME Brubaker N /A N/A N/A 0 029713 -0 0012371 SFK-PP South Fork USA 37N 106W PIPO Woodhouse, 6 10-5-2-7-4 0 46164 0 00069769 0 00022436 Losleben, Chowan- ski SFP-DF San Francisco Peaks B USA 35N 111W PSME Graybill 7 10-6-7-3-8-1 0 47245 0 38502 -0 0070748* SGP-PP Sit Gravel Pit USA 34N 109W PIPO Graybill 7 3-10-6-7-1 0 37192 0 13261 -0 0071046* SIC-DF Sliver Creek USA 46 N 121W PSME Earle, Brubaker, Se- 1 1 0 068162 0 2405 0 002923* gura SJH-DF San Juan Hill USA 47N 121W PSME Earle, Brubaker, Se- N /A N/A N/A 0 0080445 -0 00046862 gura Continued on next page Table A 1 -- Continued from previous page Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R2 Time R2 Trend slope SJM-PP St John Mountain USA 39N 122W PIPO Holmes, Adams, 2 9-1 011372 0 36046 -0 0047366* White, Lloyd SKB-PP Skookum Butte USA 43 N 121W PIPO Speer, Swetnam N/A N/A N/A 0 35324 -0 0067293* SLG-PP Show Low Gravel Pits USA 34 N 109W PIPO Dean, Bowden 6 3-6 0 29297 0 22421 -0 010878* SLM-PP Santa Lucia Mountains USA 36 N 121W PIPO Biondi 5 10-6-4-7 0 20042 0 17073 -0 0036603* SLM2-PP Slate Mountain USA 35N 111W PIPO Dean, Bowden 4 7-6-3 0 18045 0 19282 -0 011848* SLR-DF Summit Lode Road USA 46N 121W PSME Brubaker 2 4-6 011392 0 0029168 0 00028505 SMP-PP Slate Mountain Recollec USA 35N 111W PIPO Graybill 4 10-7-6-3 0 21418 0 1456 -0 0087414* tion SMR-DF Saddle Mountain Road USA 36N 112W PSME Young 6 10-3 017637 0 54738 -0 0096804* SNA-PP Snake River USA 42N 100W PIPO Woodhouse, Brown 1 9 0 043042 0 73466 -0 01374* SNO-PP Snow White Ridge USA 38N 120W PIPO Holmes, Adams 2 8-7 019575 0 23616 -0 0039082* SNR-DF Snackout Road USA 48N 117W PSME Brubaker 5 6-1-4-5-2 0 451 0 0026669 0 00055032 SOT-DF Schulman Old Tree Num USA 37N 108W PSME Schulman 8 10-3-2-7-9-1- 0 53059 0 11658 -0 006035* ber 1 Mesa Verde 4 SOT2-DF Schulman Old Trees USA 37N 108W PSME Schulman 9 2-10-3-9 0 58843 0 069842 -0 004882* I—1 o (Navaho Canyon) o SPC-DF Spanish Creek USA 45 N 111W PSME Ferguson, Despam, 2 6 0 20596 0 32374 -0 010055* Houston SPC-PP Sheep Pen Canyon USA 37N 103W PIPO Woodhouse, Brown 7 4-10-6-9-7-2- 0 53103 0 093445 -0 003459* 3 SPC2-DF Spruce Canyon USA 37N 108W PSME Cleaveland, Harlan 8 10-4-6-3-9-1- 0 44577 0 17704 -0 004385* 2 SPC3-DF St Peter's Creek USA 48N 118W PSME Brubaker 6 6-4-1-10-9-5 0 47144 0 0062432 -0 00077123 SPL-DF Spring Lake CAN SIN 121W PSME Schweingruber 3 1-6 0 13001 0 14094 -0 0046706* SPM-PP San Pedro Martir Low MEX 31N 115W PIPO Stokes, Harlan, Cle- 5 3-7-2-4-1 0 34084 0 028081 -0 0019481 mans SPP-PP Soap Creek USA 38N 107W PIPO Woodhouse, 5 6-10-7-2-3 0 24167 010605 -0 00249* Losleben, Lukas SPR-PP Spring Canyon New Mex USA 32N 106W PIPO Swetnam, Caprio, 1 7 0 045499 013327 -0 0035403* ico Lynch SRC-DF Spider Rock Canyon de USA 36N 109W PSME Dean, Robinson 9 10-7-6-9-1-2- 0 66122 0 085799 -0 0037007* Chelly 8-5-3 SRD-PP Salt River Draw USA 34N HOW PIPO Dean, Warren 6 6-7-10-3 0 34835 0 14441 -0 0080965* Continued on next page Table A 1 -- Continued from previous page

Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R2 Time R2 Trend slope SRM-DF Santa Rita Mountains High USA 31N HOW PSME Stokes, Harlan, Fel 4 7-4-3-6 0 23307 0 55659 -0 018045* (Florida Canyon) lows SRS-DF Salmon River South USA 44N 113W PSME Ferguson 4 6-10-1-4 0 32853 0 30788 -0 0091894* SRV-DF Salmon River Valley USA 44 N 114W PSME Biondi, Perkins 6 10-6-1-5-4 0 33531 0 0010992 0 00027582 SSC-DF Silver Springs Canyon USA 33N 105W PSME Stokes, Harlan 7 10-9-7-2-5-6 0 35428 0 0082816 -0 001147 SSP-PP Summit Springs USA 45 N 117W PIPO Wickman, Swet- 5 4-3-2-10 0 41275 0 22488 -0 0057739* nam, Baisan STU-DF Stultz Trail USA 38N 105W PSME Woodhouse, 3 1-6 0 11722 0 39068 -0 0075874* Losleben SUN-DF Sunspot USA 32N 105W PSME Stahle, Montagu 6 10-9-6-3-4-2 0 22717 0 38596 -0 0086387* SVF-PP Surveyor Flow USA 43 N 121W PIPO Speer, Swetnam 2 6-10 0 12975 0 013893 0 0012781 SWA-DF Swauk Pass N Of Ellens- USA 47N 120W PSME Kaiser 4 5-1-9-4 019579 0 34244 0 0054188* burg TAJ-PP Tajique Canyon USA 34 N 106W PIPO Dean, Robinson 5 4-7-5-2-3 0 34698 0 091771 -0 0067011* TCP-PP Turkey Creek Bluff USA 38N 104W PIPO Graybill 8 4-9-3-10-2 0 48071 0 00097128 -0 00041768 TCT-DF Tahoma Creek Trailhead USA 46N 122W PSME Earle, Brubaker Se- 3 6-7 0 1627 0 010478 -0 00051256 gura TDS-PP Telephone Draw South USA 42N 121W PIPO Speer, Swetnam 2 10 0 1024 0 57376 -0 010078* TLD-PP Telephone Draw USA 42N 121W PIPO Speer, Swetnam 1 1 0 050895 0 16112 0 0048451* TLP-PP Terrace Lake Pines USA 37N 106W PIPO Ryerson, Swetnam, 6 4-10-7-2-3 0 41737 0 013591 -0 001479 Lynch TRS-PP Tres Rios Sierra Madre MEX 30N 108W PIPO Stokes, Harlan, 5 9-7-2-4-3 0 367 0 12506 -0 0049182* Holmes TSE-PP Tucson Side USA 32N HOW PIPO Graybill 5 10-6-7-9-3 0 2765 0 39293 -0 010911* TUS-PP Turkey Springs USA 35N 108W PIPO Dean, Woolfenden, 5 6-7-10-3-1 0 31384 0 00073928 -0 0005108 Robinson, Burns UHC-DF Upper Henderson Canyon USA 37N 111W PSME Grow 7 3-10-1-2-6-7- 0 43335 0 058054 0 0012674* 5 UIM-DF Uinta Mountains Site C USA 40N 109W PSME Stockton, Harsha, 1 2 0 11701 0 35207 -0 014607* Jacoby UNI-PP Union USA 45 N 117W PIPO Parker 5 4-3-1-2-5 0 51897 0 21668 -0 0056386* UPG-DF Upper Gunnison USA 38N 106W PSME Stokes, Harlan 4 1-5-2 0 36738 0 073331 -0 0039493* UWB-DF Upper Watershed Bright USA 36N 112W PSME Stockton 6 7-10-6-3-1 0 33599 0 31093 -0 0077546* Angel Creek Continued on next page Table A 1 -- Continued from previous page

2 2 Site code Site name Country Latitude Longitude Species Researchers Sig CV AIC model AIC R Time R Trend slope VAM-DF Vasquez Mountain USA 40N 106W PSME Woodhouse, 5 6-5-7-10-4 0 34435 0 000000240950 0000028292 Losleben, Lukas

VBP-PP Van Bibber Creek USA 40N 105W PIPO Graybill 6 6-10-9-4-7-2 0 36461 0 47873 -0 0082289* VED-PP Vedauwoo USA 41N 105W PIPO Woodhouse, 3 6-10-5 0 26025 0 00015349 -0 000084682 Losleben

VIL-DF Villareal MEX 19N 97W PSME Stahle, Ther- 5 4-9-6-3-10 0 28257 0 22525 -0 0040313* rell, Cleaveland, Villanueva-Diaz

VVR-DF Valley View Ranch USA 39N 104W PSME Woodhouse, Brown 3 4-2-9 0 29001 0 017391 -0 0015671 WAC-DF Walnut Canyon Site A USA 35N 111W PSME Stokes, Harlan 3 3-10 0 1753 0 00012669 -0 00027797 WAC-PP War Creek USA 48 N 120W PIPO Brubaker 4 6-10-1-2 0 21163 0 06668 -0 0028617* WAL-PP Wallace USA 47N 115W PIPO Brubaker 3 10-6 0 26737 0 25903 -0 0044908* WAT-PP Waterdale USA 40N 105W PIPO Fntts, Holmes 7 10-2-4-9-3 0 44583 0 015817 -0 0019891 WCB-PP Water Canyon Bryce USA 37N 112W PIPO Stokes, Harlan 7 7-10-6-3-2-8- 0 52214 0 099677 -0 0031195* Canyon National Park 1 WCK-DF White Creek USA 33N 108W PSME Swetnam 7 3-4-10-6-2-1- 0 28866 0 50515 -0 010714* o 5 K3 WCN-PP Walnut Canyon National USA 35N 111W PIPO Stokes, Harlan 6 3-8-9-6 0 33487 014943 -0 0075312* Monument WCP-PP Walnut Canyon USA 35N 111W PIPO Graybill 7 10-3-6-7-8-9- 0 37799 0 028786 -0 0018807 4 WDF-DF Walnut Canyon USA 35N 111W PSME Graybill 5 10-6-3-8 0 23128 0 10085 -0 0041861* WEB-DF Webb Peak Pinalenos USA 32N 109W PSME Falk, Adams 6 3-9-10-4-7-6 0 41921 0 087407 -0 0023104* WEN-PP Wenaha 1 and 2 USA 45N 117W PIPO Wickman, Swet 4 3-2-4 0 20264 0 42546 -0 0081829* nam, Baisan WHC-DF White Canyon USA 37N HOW PSME Dean, Bowden 5 3-10-6-2-1 0 32743 0 061774 -0 0033514* WHE-PP Wheelman USA 40N 105W PIPO Schweingruber 6 6-10-2-7-4 0 32706 0 099606 -0 0031202* WHH-PP White Horse Hills USA 35N 111W PIPO Dean, Bowden 5 3-9-7-1-4 0 24967 0 000089811 0 00019071 WHI-DF White Pass B USA 46N 121W PSME Kaiser 1 6 0 046134 0 0053998 0 00033397 WIM-PP Windy Meadows USA 45 N 118W PIPO Brubaker 3 6-10-1 0 21857 0 61885 -0 013283* WIR-PP Wilson Ranch USA 37N 106W PIPO Ryerson, Swetnam, 4 10-6-3-7 0 23054 0 28579 -0 0069099* Lynch WLT-DF Wofford Lookout Tower USA 32N 105W PSME Stokes, Harlan 8 10-9-7-2-6-3- 0 45309 0 036885 -0 0030994 5 Continue d on next page Table A 1 - Continued from previous page 2 Site code Site name Country- Latitude Longitude Species Researchers Sig CV AIC model AIC R^ Time R Trend slope WMC-DF Wet Mountains USA 37N 105W PSME Woodhouse, 8 4-7-9-1-2-6-8- 0 50204 0 071552 -0 003549* Losleben 3 WPR-PP Windy Point Ridge USA 46N 120W PIPO Brubaker 1 10 0 052906 0 05863 -0 002421* WSK-DF Whiskey Mountain USA 43N 109W PSME Waggoner, Hill 6 10-2-4-6-9-3 0 37368 0 060848 -0 002873* WTK-DF Webb Peak LTRR 2001 USA 32N 109W PSME Adams 5 3-4-9-10 0 39789 0 093774 -0 0027475'' WWG-DF Wagon Wheel Gap USA 37N 106W PSME Schulman 3 4-2 0 22284 0 10808 -0 01343* YDF-DF Yosemite Valley Yosemite USA 37N 119W PSME King 2 6 0 081673 0 00090518 -0 00023011 National Park YMR-DF Yellow Mountain Ridge 2 USA 45N 111W PSME King, Waggoner, 4 6-10-1 0 35529 0 0034388 0 00029934 Graumhch

o co APPENDIX B

SITE-SPECIFIC SENSITIVITY RESULTS

The following is a compilation of the results from the sensitivity analysis (Section

3.7). Table B.l contains 21 columns of data. The first column denotes the site, based on the site codes initially assigned (further information on the site name, country, coordinates, species, and researchers can be found in Appendix A). The following

10 columns contain site-specific Pearson Correlation coefficients for the ten climatic variables of interest and radial growth from 1900 to 1940. The remaining 10 columns contain the corresponding correlation coefficients from 1941 to 1981. In the latter, an asterisk (*) represents a significant change in the correlation coefficient of that climate variable from the previous time period (1900-1940).

CV1 - Previous summer precipitation;

CV2 - Previous fall precipitation;

CV3 - Winter precipitation;

CV4 - Current spring precipitation;

CV5 - Current summer precipitation;

CV6 - Previous summer temperature;

CV7 - Previous fall temperature;

CV8 - Winter temperature;

CV9 - Current spring temperature;

CV10 - Current summer temperature.

104 Any rows containing N/A's represent sites with chronologies that did not span the time period of the sensitivity analysis (ie. end dates were before 1981). These sites did not undergo any form of sensitivity analysis.

Site codes with a plus (+) indicate sites which were initially deemed climatically insensitive (no significant climate variable correlations over the entire study period).

They were included in the sensitivity analysis in order to observe any possible changes in their sensitivity over smaller time periods which would explain their overall insen- sitivity.

105 Table B.l Site- specific sensitivity results.

Site code 1900- 1940 1941-1 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 ABS-PP 0 11 0 28 0 48 0 46 0 15 -0 31 -0 44 0 01 -0 33 -0 53 -0 08* 0 45* 0 44 0 56 -0 04* 0 05* -0 14* 0 02 -0 2 -0 07* ALO-PP 0 18 0 07 0 16 0 16 0 35 -0 44 -0 12 0 04 -0 11 -0 29 -0 03* 0 16 -0 13* 0 21 0 28 -0 16* -0 17 -0 1 -0 25 -0 19 ALR-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ALT-DF 0 27 0 24 0 11 0 12 0 09 -0 02 0 41 0 24 0 06 -0 04 0 02* -0 15* -0 17* 0 09 -0 03 -0 41* 0 33 -0 07* -0 14* -0 05 AMO-DF 0 07 0 08 0 19 0 17 0 13 -0 07 0 09 -0 4 -0 21 0 01 -0 05 -0 35* 0 22 0 48* 0 02 -0 05 0 01 -0 03* -0 32 -0 12 AMS-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ANJ-PP 0 13 0 14 0 28 0 34 0 16 -0 65 -0 48 -0 11 -0 3 -0 64 0 05 0 33* 0 24 0 13* 0 26 0 08* 0 28* 0 43* 0 15* -0 28* ANS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ANT-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ASF-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ASH-PP 0 43 0 34 0 12 0 17 0 52 -0 6 -0 34 -0 1 -0 35 -0 62 -0 14* 0 2 0 09 0 09 0 11* 0 09* -0 27 0 2* 0 01* -0 16* ATR-DF 0 39 0 39 0 13 0 3 0 34 -0 12 -0 34 -0 03 -0 32 -0 28 0 42 0 36 0 27 0* 0 1* -0 08 -0 33 -0 25* -0 33 -0 34 BAC-PP -0 01 0 13 0 45 0 26 0 22 -0 45 -0 38 0 11 -0 24 -0 41 -0 21* 0 29 0 45 0 42* -0 11* 0 22* -0 14* 0 07 -0 07 0 01* BAP-DF 0 16 0 2 0 58 0 14 0 07 -0 26 0 04 0 06 -0 29 -0 22 0 2 0 19 0 36* 0 36* 0 03 0 11* -0 26* -0 01 -0 24 -0 12 BAR-DF -0 37 0 11 0 53 0 43 -0 24 -0 1 -0 25 -0 37 -0 35 -0 14 0 23* 0 46* 0 59 0 52 0 59* -0 23 -0 44* -0 36 -0 44 -0 25 BBB-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BBC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BCG-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BCR-DF 0 36 0 24 0 12 0 23 0 18 -0 6 -0 24 -0 1 -0 23 -0 42 0 06* 0 18 0 03 0 19 0 23 -0 24* -0 08 -0 22 -0 32 -0 21* BCR-PP 0 09 0 09 0 27 0 0 34 -0 47 -0 19 0 23 -0 17 -0 49 -0 06 0 27* 0 27 0 35* -0 01* 0 37* -0 1 0 28 -0 11 0 03* BCR2-PP 0 21 0 16 0 19 0 09 0 37 -0 28 -0 01 0 02 -0 26 -0 22 0 12 -0 12* 0 05 -0 07 0 1* -0 15 0 12 -0 02 -0 03* -0 27 BCV-PP 0 28 0 43 0 33 0 29 0 23 -0 3 0 -0 11 -0 14 -0 39 0 16 0 26* 0 02* 0 31 0 13 -0 01* -0 19* -0 01 -0 2 -0 3 BCW-PP 0 28 0 29 0 22 0 3 0 16 -0 37 -0 09 -0 06 -0 27 -0 36 -0 05* -0 21* 0 21 -0 12* 0 02 0 23* 0 01 0 07 0* 0 1* BEC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BBL-DF -0 01 -0 03 -0 04 0 06 -0 17 -0 14 -0 12 -0 02 -0 09 -0 02 0 1 -0 18 -0 34* 0 18 0 02* -0 06 0 24* 0 24* -0 01 0 BEN-PP 0 13 0 26 0 06 0 42 0 22 -0 49 -0 2 0 12 -0 4 -0 49 -0 3* 0 29 -0 14* 0 37 0 16 -0 11* -0 5* -0 02 -0 35 -0 36 BFE-PP 0 44 0 35 -0 03 0 28 0 37 -0 67 -0 36 -0 12 -0 25 -0 51 -0 08* 0 05* 0 08 0 31 0 15* -0 12* -0 01* -0 03 -0 16 -0 28* BHM-PP 0 12 -0 09 0 11 0 31 0 34 -0 35 -0 06 -0 22 -0 37 -0 4 0 02 0 12* 0 23 0 31 0 21 -0 06* 0 07 -0 05 0 03* -0 21* BIG-PP 0 01 0 23 0 19 0 3 0 16 -0 51 -0 05 0 07 -0 18 -0 56 -0 18* 0 37 0 08 0 35 0 21 -0 12* -0 48* -0 1 -0 29 -0 36* BIT-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BJS-PP 0 36 0 13 0 39 0 13 0 25 -0 3 -0 15 -0 04 -0 2 -0 36 0* 0 08 -0 05* 0 01 -0 05* -0 25 -0 07 0 03 0 28* -0 39 Continued on next page Table B 1 -- Continued from previous page Site code 1900-1940 1941-1981 CV1 CV2 CV3 CV4 CVS CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 BKF-DF 0 29 0 34 0 38 0 48 0 27 -0 27 -0 3 -0 17 -0 33 -0 41 0 18 0 44 0 62* 0 28* -0 13* -0 01* -0 34 -0 04 -0 36 -0 1* BKP-PP 0 24 0 31 0 41 0 43 0 36 -0 32 -0 4 -0 13 -0 35 -0 42 0 14 0 48* 0 51 0 25* -0 08* -0 01* -0 44 -0 07 -0 46 -0 17* BLA-DF -0 32 0 07 0 55 0 41 -0 23 -0 09 -0 21 -0 38 -0 32 -0 16 0 19* 0 4* 0 65 0 53 0 56* -0 25 -0 46* -0 43 -0 49* -0 25 BLM-PP 0 27 0 22 -0 18 0 09 0 16 -0 51 -0 14 0 2 -0 16 -0 51 0 47* 0 19 -0 05 0 35* 0 37* -0 28* -0 15 -0 03* 0 17* -0 2* BMT-PP -0 11 0 02 0 33 0 25 0 28 0 04 -0 17 -0 04 -0 02 0 0 18* 0 31* 0 25 0 09 0 12 -0 36* -0 28 -0 14 -0 06 -0 4* BOC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BOC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BOS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BPM-DF 0 31 0 07 -0 02 0 37 -0 06 -0 13 0 06 0 23 -0 17 0 11 0 12* 0 13 -0 06 0 13* 0 -0 27 0 41* 0 03* -0 07 0 07 BQR-DF 0 31 0 08 -0 03 0 25 0 13 0 01 0 14 0 31 0 19 0 17 0 15 -0 05 -0 01 -0 03* -0 06* -0 41* 0 31 -0 04* -0 29* -0 07* BRF-DF 0 4 0 15 0 4 0 27 0 06 -0 47 -0 18 -0 16 -0 5 -0 31 0 12* 0 32 0 62* 0 39 -0 2* 0 07* -0 19 -0 12 -0 18* -0 06* BRP-DF 0 42 0 36 0 21 0 18 0 06 -0 38 -0 22 -0 27 -0 28 -0 24 0 19* 0 1* 0 55* 0 29 -0 04 -0 24 -0 23 -0 5* -0 25 -0 46* BRR-PP 0 05 0 27 0 13 -0 01 0 38 -0 6 -0 23 -0 13 0 -0 56 -0 01 0 11 -0 19* -0 23* 0 31 -0 34* -0 35 -0 05 0 01 -0 45 BSC-DF 0 11 0 48 0 35 0 16 0 34 -0 23 -0 26 -0 16 -0 58 -0 4 0 11 0 42 0 41 0 26 0 41 0 02* -0 3 -0 16 -0 37* -0 15* BSK-PP 04 0 37 0 41 0 32 0 29 -0 5 -0 07 0 04 -0 14 -0 44 0 09* 0 28 0 07* 0 48* -0 03* -0 25* -0 09 -0 14 0 1* -0 33 1—1 o BTU-DF 0 35 0 31 0 29 0 53 0 15 -0 45 -0 33 0 04 -0 49 -0 43 -0 02* 0 27 0 16 0 57 0 02 -0 09* -0 22 -0 05 -0 48 -0 12* BUA-PP 0 24 0 31 0 22 0 03 0 31 -0 62 -0 32 -0 05 -0 36 -0 54 0 24 -0 19* -0 16* -0 18* 0 24 -0 07* 0 28* 0 04 0 25* 0 06* BUT-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A BWF-DF 0 19 0 02 0 34 0 3 0 02 -0 56 -0 29 -0 03 -0 45 -0 29 -0 1* 0 05 0 24 0 34 -0 03 0 05* 0 13* 0 02 -0 16* 0 02* CAB-PP 0 35 0 05 0 46 0 01 0 24 -0 29 -0 12 0 07 0 -0 17 0 24 -0 23* -0 05* -0 46* 0 08 -0 13 0 05 0 03 0 2* -0 02 CAT-DF 03 0 3 0 08 0 22 0 01 -0 27 -0 32 -0 25 -0 18 -0 19 0 48* 0 23 0 2 0 27 0 22* -0 26 -0 36 -0 15 -0 29 -0 4* CAT-PP 0 09 0 04 0 37 0 26 0 25 -0 41 -0 44 0 03 -0 24 -0 48 -0 04 0 37* 0 33 0 41 -0 03* -0 08* -0 21* 0 02 -0 13 -0 13* CCT-PP 0 42 04 0 25 0 23 0 28 -0 44 0 02 0 14 -0 12 -0 37 0 15* 0 31 0 2 0 3 0 01* -0 2* -0 18* -0 1* 0 1* -0 29 CDC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CED-PP 0 41 0 47 0 15 0 12 0 37 -0 45 -0 3 -0 09 -0 42 -0 58 0 15* 0 29* 0 31 0 25 0 15* -0 12* -0 24 -0 07 -0 2* -0 27* CFY-DF 0 22 0 25 0 11 0 18 -0 03 -0 58 -0 13 -0 2 -0 42 -0 28 0 06 0 1 0 14 0 17 0 32* -0 38* 0 08* -0 01* -0 18* -0 55* CHC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CHM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CIA-DF 0 09 0 4 0 54 0 4 0 18 0 03 -0 15 -0 09 -0 22 0 14 -0 2* 0 24 0 43 0 27 0 24 -0 15 -0 31 -0 38* -0 43* -0 29* CIN-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CIR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CIR2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CLK-PP 0 36 -0 04 0 17 -0 05 0 17 -0 17 -0 22 0 26 0 02 -0 39 0 15* 0 04 0 02 0 04 0 25 -0 32 0 08* 0 2 0 12 -0 41 Continued on next page Table B 1 - Continued from >revious page Site code 1900- 1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CNR-PP 0 39 0 24 -0 12 0 29 0 28 -0 51 -0 52 -0 14 -0 48 -0 47 -0 31* 0 12 0 02 0 17 0 1 0 17* -0 38* 0 13* -0 17* 0 07* COD-PP 0 2 0 29 0 34 0 32 0 32 -0 09 -0 17 -0 19 -0 09 -0 18 0 21 0 21 0 19 0 12* -0 1* -0 27* -0 36* -0 3 -0 27* -0 31 COP-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A COR-PP 0 41 0 38 0 21 0 49 0 47 -0 48 -0 24 -0 06 -0 42 -0 46 -0 15* 0 2* 0 27 0 62* 0* 0 1* -0 05* -0 08 -0 43 -0 15* COT-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CPP-PP -0 09 0 14 0 29 0 38 0 23 -0 11 -0 24 -0 04 -0 15 -0 39 -0 15 0 31 0 13 0 42 -0 05* 0 14* -0 2 -0 14 -0 25 -0 12* CPP2-PP 0 47 0 29 0 07 0 32 0 42 -0 48 -0 28 0 02 -0 32 -0 23 -0 03* 0 24 0 25 0 39 -0 04* 0 11* -0 14 -0 1 -0 32 -0 23 CPP3-PP -0 17 0 05 0 19 0 21 -0 25 -0 46 -0 13 0 09 -0 3 -0 62 -0 23 -0 03 0 18 0 12 -0 32 -0 23* -0 22 0 13 0 21* -0 31* CPS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CRP-PP 0 34 0 25 0 21 0 06 0 25 -0 51 -0 15 0 21 -0 05 -0 21 -0 09* 0 38 0 2 0 09 -0 01* 0 05* -0 07 -0 01* -0 15 -0 2 CRY-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A CRY-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DAL-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DCB-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DCP-DF -0 04 -0 29 0 -0 05 0 22 -0 21 -0 09 0 1 0 25 0 06 -0 16 0 05* -0 1 -0 06 -0 03* -0 02 -0 12 -0 25* -0 15* -0 23* I—1 O DCW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 00 DEE-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DES-PP 0 39 0 28 0 39 0 13 0 31 -0 4 -0 01 -0 06 -0 29 -0 37 -0 03* 0 09* 0 24 0 27 -0 12* -0 36 0 -0 03 0 21* -0 48 DET-PP 0 23 0 08 0 37 0 17 0 24 -0 55 -0 1 -0 33 -0 25 -0 53 -0 13* 0 -0 06* 0 16 -0 06* -0 18* 0 04 0 01* -0 34 -0 32* DEW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DHR-PP 0 33 0 21 -0 16 0 17 0 1 -0 49 -0 19 0 13 -0 14 -0 47 0 48 -0 03* -0 24 0 3 0 32* -0 36 -0 07 0 02 0 14* -0 25* DIC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DIC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DIC2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DIH-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DIL-DF 0 17 0 09 0 02 0 32 0 05 -0 07 -0 22 0 04 -0 12 -0 02 -0 09* 0 32* 0 11 0 25 0 06 -0 42* -0 46* -0 1 -0 32* -0 37* DIT-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DLK-PP 0 38 0 2 0 01 -0 05 0 13 -0 33 -0 22 -0 04 -0 29 -0 39 0 02* 0 12 -0 11 0 05 -0 02 -0 17 0 13* 0 18* 0 02* -0 27 DMO-PP 0 21 0 36 -0 05 0 55 0 11 -0 57 -0 17 0 23 -0 27 -0 5 -0 18* 0 08* -0 02 0 41 0 21 -0 15* -0 29 -0 23* -0 44* -0 37 DMU-PP 0 13 0 37 0 0 55 0 03 -0 4 -0 15 0 19 -0 34 -0 46 -0 29* 0 11* 0 05 0 57 0 27* -0 11* -0 35* -0 23* -0 51* -0 49 DOL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A DOU-DF 0 25 0 48 0 07 0 09 0 08 -0 6 -0 22 -0 09 -0 24 -0 42 0 41 0 28* 0 14 0 05 -0 05 -0 49 -0 32 -0 21 -0 27 -0 39 DPB-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 - Continued from >revious page Site code 1900-1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 DVG-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EAG-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EAG-PP 0 18 0 2 -0 09 0 52 0 04 -0 32 -0 35 -0 03 -0 5 -0 27 0 01 0 4* 0 04 -0 02 -0 05* -0 29 -0 12 -0 24* -0 19 EAP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ECA-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EDC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EDO-DF 0 49 0 16 0 03 0 27 0 01 -0 52 -0 25 0 1 -0 16 -0 11 0 02* 0 4* -0 06 0 08* -0 14 -0 15* -0 08 -0 02 -0 1 -0 04 EDP-PP 0 26 0 34 -0 01 0 0 2 -0 16 -0 18 0 16 0 -0 05 -0 11* 0 17 0 14 0 19* -0 19* 0 17* 0 02* -0 06* -0 08 0 06 ELC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ELD-PP 0 07 0 22 0 09 0 1 0 17 -0 4 -0 25 0 11 -0 17 -0 39 -0 24* 0 06 -0 13* 0 12 0 0 03* 0 02* 0 04 -0 02 -0 12* ELE-PP 0 27 0 IS 0 0 54 0 01 -0 35 -0 43 -0 23 -0 37 -0 39 0 24 0 11 0 4* 0 33* 0 23* -0 12* -0 11* -0 17 -0 21 -0 27 ELK-PP 0 12 0 4 0 34 0 32 0 36 -0 38 -0 34 -0 08 -0 52 -0 39 -0 05 0 31 0 28 0 02* -0 09* 0 24* -0 16* 0 08 -0 11* 0 27* ELM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ELR-PP 0 1 -0 08 0 13 0 18 0 3 -0 06 -0 23 -0 14 -0 29 -0 29 -0 08 0 13* 0 0 47* 0 34 0 34* -0 15 0 06* -0 23 -0 21 ELS-DF -0 44 0 12 0 44 0 28 0 03 0 09 -0 07 0 21 0 17 0 07 -0 13* 0 04 -0 09* -0 08* 0 -0 13* -0 05 0 19 -0 05* -0 34* (—> o ELT-DF 0 04 0 24 0 38 0 48 -0 07 -0 1 -0 27 -0 31 -0 44 0 08 0 18 0 21 0 34 0 19* -0 02 0 12* 0 07* -0 01* -0 05* 0 14 to ELU-DF 0 42 0 19 0 07 0 34 -0 06 -0 51 -0 42 -0 01 -0 37 -0 2 -0 08* 0 41* -0 05 0 15* -0 21 -0 11* -0 15* -0 08 -0 16* -0 06 ELV-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EMN-DF 0 36 0 26 0 39 0 32 0 05 -0 31 -0 26 -0 14 -0 35 -0 31 0 49 0 28 0 62* 0 33 -0 23* -0 06* -0 06* -0 11 -0 35 -0 18 ENC-DF 0 48 0 25 0 24 0 12 0 26 -0 39 -0 15 -0 22 -0 28 -0 29 -0 01* 0 41 0 28 0 21 0 06* -0 4 -0 39* -0 11 -0 02* -0 46* ENC-PP 0 38 0 35 0 33 0 35 0 24 -0 38 -0 15 -0 19 -0 35 -0 5 -0 13* 0 31 0 15* 0 38 0 3 -0 07* -0 42* -0 14 -0 2 -0 37 ESC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ESP-PP 0 24 0 22 -0 18 0 11 0 15 -0 48 -0 13 0 22 -0 13 -0 6 0 46* -0 02* -0 13 0 31* 0 27 -0 33 -0 02 -0 06* 0 17* -0 25* ESW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A EXF-PP 0 36 0 07 0 07 -0 12 0 28 -0 24 -0 02 -0 19 -0 34 -0 23 -0 11* 0 2 0 05 0 13* -0 09* -0 25 -0 03 -0 07 0 24* -0 36 EXS-DF 0 62 0 23 -0 11 0 57 0 37 -0 64 -0 13 0 01 -0 34 -0 56 0 11* 0 13 0 0 14* 0 03* 0 11* 0 06* -0 17 0 13* 0 27* FEN-PP 0 07 0 11 0 44 0 29 0 24 -0 31 -0 35 0 11 -0 28 -0 48 -0 04 0 33* 0 53 0 52* -0 06* 0 2* -0 04* 0 03 -0 2 0 03* FLP-PP 0 27 0 36 -0 04 0 01 0 4 0 1 0 3 0 43 0 33 -0 12 0 41 0 13* 0 15* 0 32* 0 13* -0 32* -0 07* -0 06* 0 25 -0 23 FOL-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A FPC-DF 0 26 0 23 0 46 0 21 0 23 -0 32 -0 2 -0 02 -0 42 -0 32 -0 02* 0 38 0 52 0 29 -0 2* -0 02* -0 16 -0 13 -0 26 -0 08* FPS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A FRR-DF -0 04 -0 01 0 17 -0 06 0 34 -0 39 -0 39 0 25 0 22 -0 39 0 09 -0 06 -0 21* 0 08 0 06* -0 54 -0 08* 0 21 0 06 -0 34 GAM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 -- Continued from jrevious page Site code 1900- 1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 GV6 CV7 CV8 CV9 CV10 GAM2-DF 0 13 0 54 0 37 0 41 0 39 -0 11 -0 31 -0 08 -0 41 -0 36 0 22 0 49 0 29 0 2* 0 15* 0 3* 0 04* -0 23 0 07* 0 19* GAR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A GBP-PP 0 49 0 37 0 31 0 26 0 25 -0 42 -0 22 0 01 -0 09 -0 41 0 2* 0 38 0 23 0 39 0 02* -0 05* -0 25 -0 11 0 16* -0 29 GCP-PP 0 14 0 35 0 42 0 18 0 11 -0 25 -0 36 0 01 -0 33 -0 32 0 12 0 39 0 39 0 01 0 13 -0 04* -0 15* -0 01 -0 1* -0 25 GCP2-PP 0 2 0 03 0 31 0 18 0 41 -0 57 -0 04 -0 3 -0 35 -0 7 -0 03* 0 01 0 29 0 2 0 2* 0* -0 13 0 18* -0 09* -0 04* GMF-DF 0 45 0 18 0 44 0 25 0 05 -0 4 -0 3 -0 06 -0 56 -0 03 -0 21* 0 36* 0 32 0 48* -0 27* -0 01* -0 01* -0 12 -0 37* 0 09 GMR-DF 0 29 0 32 -0 11 0 18 0 02 -0 43 -0 31 -0 09 -0 22 -0 25 0 08* 0 3 0 48* 0 17 -0 05 -0 1* -0 39 -0 05 0* -0 22 GMT-PP 0 36 0 04 0 21 0 06 0 25 -0 32 -0 27 0 03 -0 4 -0 13 -0 21* 0 05 0 16 0 22 -0 06* 0 04* 0 03* -0 06 -0 16* 0 04 GOC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A GPK-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A GPN-PP 0 19 0 12 0 3 0 05 0 33 -0 42 -0 26 -0 05 -0 17 -0 4 0* 0 11 0 15 -0 03 0 03* -0 11* -0 06* -0 2 -0 03 -0 26 GRA-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A GRM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A GRP-PP 0 3 0 07 0 3 0 03 0 28 -0 39 -0 24 -0 04 -0 42 -0 22 -0 1* 0 17 0 45 0 24* -0 1* -0 12* -0 34 -0 23* -0 23* -0 29 GRR-PP 0 27 0 14 0 29 0 07 0 22 -0 31 -0 25 -0 05 -0 37 -0 29 -0 28* 0 18 0 46* 0 56* -0 27* 0 07* -0 24 0 09 -0 22 -0 18 GSD-PP 0 31 0 27 0 41 0 28 0 14 -0 16 -0 19 -0 01 -0 02 -0 04 0 15 0 51* 0 14* 0 26 -0 01 -0 3 -0 63* -0 18 -0 31* -0 34* GUA-DF 0 35 0 56 0 36 0 32 0 32 -0 43 -0 47 -0 41 -0 49 -0 52 0 1* 0 59 0 29 0 17 0 36 -0 14* -0 26* -0 4 -0 36 -0 35* HDE-PP 0 0 07 0 3 0 18 0 16 -0 41 -0 24 -0 18 -0 28 -0 27 -0 22* 0 05 0 09* 0 08 -0 01 -0 08* -0 2 -0 2 -0 14 0 02* HEL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HLT-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HOO-PP 0 12 0 17 0 08 -0 1 0 26 -0 46 -0 31 0 09 -0 28 -0 55 0 18 -0 02* 0 11 -0 09 0 2 0 05* -0 13* -0 06 -0 05* -0 22* HOS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HOT-DF 0 24 0 38 0 03 0 32 0 3 -0 57 -0 27 0 06 -0 26 -0 53 -0 09* 0 16* 0 47* 0 08* 0 26 -0 13* -0 4 -0 24* -0 07* -0 16* HPP-PP 0 05 0 19 0 21 0 16 -0 2 -0 23 -0 28 0 1 -0 16 -0 39 -0 24* 0 3 0 07 0 12 -0 12 0 02* -0 16 0 11 0 22* -0 24 HRC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HTF-DF 0 29 0 07 0 12 0 15 0 44 -0 44 -0 2 0 09 -0 26 -0 44 -0 06* 0 28* 0 11 0 03 0 3 -0 19* -0 01* -0 14* -0 14 -0 11* HTP-PP 0 12 0 18 0 13 0 29 0 23 -0 37 -0 15 0 -0 31 -0 51 -0 31* 0 28 0 15 0 25 0 12 0 17* -0 25 -0 05 -0 35 -0 23* HUA-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HUM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A HUR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ICR-PP -0 22 -0 05 -0 25 0 04 -0 11 -0 34 -0 04 0 25 0 03 -0 32 -0 21 -0 14 -0 14 0 09 -0 14 0 38* 0 29* 0 24 0 2 0 42* IDC-PP 0 44 0 29 0 16 0 24 0 47 -0 36 0 23 0 23 0 16 -0 41 0 42 0 13 0 03 0 42* 0 12* -0 39 0 06 -0 12* 0 22 -0 28 INR-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Cont nued on next page Table B 1 — Continued frompreviou s page Site code 1900- 1940 1941-1 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CVS CV9 CV10 JAM-PP 0 05 0 1 0 23 0 27 0 36 -0 44 -0 13 0 11 -0 21 -0 42 -0 1 0 2 -0 17* 0 29 0 29 -0 06* -0 23 -0 03 -0 26 -0 23* JCO-PP 0 25 0 25 0 06 0 2 0 09 -0 43 -0 26 -0 04 -0 26 -0 27 -0 29* 0 27 0 17 0 32 -0 26* -0 16* -0 34 -0 23* -0 33 -0 26 JCT-PP 0 4 0 19 0 1 0 03 0 14 -0 37 -0 11 0 03 -0 21 -0 32 0 09* 0 04 0 08 -0 04 0 17 -0 03* 0 05 0 38* -0 02* 0 13* JFU-PP 0 36 0 27 -0 02 0 25 0 18 -0 56 -0 32 0 03 -0 28 -0 36 -0 22* 0 33 0 17* 0 25 -0 26* -0 16* -0 31 -0 24* -0 39 -0 3 JLK-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KAB-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KAF-DF 0 31 0 14 0 05 0 37 0 11 -0 49 -0 28 -0 04 -0 32 -0 25 0 04* 0 42* 0 1 0 3 -0 23* -0 04* -0 25 -0 23* -0 19 -0 26 KAM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KAM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KAP-PP 0 24 0 3 0 08 0 25 0 21 -0 57 -0 26 0 18 -0 21 -0 45 -0 09* 0 11* -0 07 0 24 0 05 0 06* -0 05* 0 02 -0 01* -0 05* KAS-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KEN-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KEW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A KIM-PP 0 41 0 46 -0 09 0 36 0 34 -0 45 -0 27 0 08 -0 4 -0 37 -0 05* 0 21* 0 38* 0 52* 0 02* -0 16* -0 19 -0 14* -0 42 -0 28 KLA-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAB-DF 0 21 0 24 0 33 0 39 -0 05 -0 47 -0 31 -0 56 -0 61 -0 32 -0 29* 0 07 0 11* 0 04* 0 04 0 45* 0 25* -0 06* -0 03* 0 14* LAB2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAK-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAL-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAN-DF 0 3 0 39 0 26 0 01 0 09 -0 5 -0 37 -0 06 -0 26 -0 19 0 4 0 3 0 1 0 22* 0 19 -0 26* -0 11* -0 03 -0 23 -0 36 LAN2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LAT-DF 0 17 0 31 0 36 0 47 0 16 -0 25 -0 31 -0 25 -0 4 -0 22 0 25 0 39 0 53* 0 47 0 07 0 07* 0 01* -0 37 -0 26 -0 16 LCA-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LCA2-PP 0 26 0 34 -0 2 -0 01 0 01 -0 05 -0 19 0 19 0 09 -0 11 0 22 0 43 0 01* 0 12 0 08 0 27* 0 23* 0 07 0 15 0 LCS-DF 0 17 0 22 -0 2 -0 01 -0 13 -0 02 -0 12 0 07 0 15 -0 03 0 29 0 2 0 02* 0 15 0 05 0 01 0 26* -0 02 0 25 0 13 LGP-PP 0 5 0 42 0 0 22 0 49 -0 42 -0 07 -0 01 -0 17 -0 54 0 05* 0 19* 0 05 0 07 0 25* -0 05* 0 11 0 28* -0 22 -0 18* LIL-DF 0 27 0 06 0 14 -0 14 0 21 -0 04 -0 25 -0 07 0 0 14 0 01* 0 36* 0 09 0 12* 0 22 -0 14 -0 21 -0 3* 0 -0 44* LIV-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LML-PP 04 0 15 0 09 0 03 0 28 -0 24 -0 07 -0 08 -0 32 -0 27 -0 01* 0 2 -0 09 -0 1 0 23 -0 13 0 06 0 08 0 08* -0 38 LOL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LOM-PP 0 22 0 42 0 0 06 0 26 -0 02 0 12 0 31 0 15 -0 09 0 28 0 17* -0 04 0 43* -0 01* -0 16 -0 02 0 02* 0 02 -0 01 LPP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 — Continued frompreviou s page Site code 1900-1940 1941-1 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 LPP-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LPS-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LSB-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LSP-PP 0 46 0 43 0 39 0 28 0 27 -0 21 -0 05 0 -0 07 -0 31 0 12* 0 26* 0 01* 0 48* -0 08* -0 32 -0 01 -0 16 0 13* -0 36 LUN-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LUP-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A LYP-PP 0 25 0 39 0 1 0 41 0 22 -0 48 -0 14 0 05 -0 24 -0 44 -0 03* 0 41 0 14 0 35 0 08 -0 33 -0 44* -0 32* -0 38 -0 33 MAY-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A MCG-DF 0 34 0 45 0 11 0 43 0 34 -0 07 -0 27 0 0 01 -0 21 0 34 0 4 0 15 0 02* 0 21 -0 07 -0 35 -0 24* -0 25* -0 14 MCK-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A MCN-PP 0 24 0 39 0 16 0 26 0 05 -0 46 -0 34 0 06 -0 27 -0 55 0 04* 0 11* -0 12* 0 3 0 09 -0 15* 0 1* -0 12 0 23* -0 21* MDM-PP 0 19 0 11 0 1 0 13 0 25 -0 43 -0 1 -0 1 -0 14 -0 38 -0 26* 0 14 0 28 0 38* -0 12* 0 17* -0 12 -0 09 -0 41* -0 08* MEV-DF 0 07 0 46 0 28 0 38 0 05 -0 55 -0 38 -0 26 -0 26 -0 39 0 31* 0 4 0 22 -0 01* 0 24* -0 31* -0 21* 0 05* -0 17 -0 52 MEV-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A MEY-PP 0 33 0 27 0 08 0 28 0 22 -0 4 -0 29 0 19 -0 29 -0 31 -0 1* 0 29 -0 1 0 33 -0 08* 0 09* -0 1* 0 03 -0 16 -0 17 MHP-PP -0 06 0 01 0 28 0 23 0 07 -0 34 -0 31 -0 03 -0 25 -0 24 -0 15 0 14 0 23 0 32 -0 29* 0 14* -0 2 -0 01 -0 09 0 26* MIL-PP 0 IS 0 3 0 35 0 46 0 39 -0 26 -0 28 -0 11 -0 39 -0 52 0 1 0 46* 0 24 0 3* 0 14* -0 12 -0 27 -0 26 -0 34 -0 29* MOJ-PP -0 24 0 08 0 21 0 25 0 27 -0 16 -0 35 -0 05 -0 04 -0 3 -0 14 0 18 0 23 04 0 22 0 22* -0 15* -0 15 -0 17 -0 07* MOP-PP 0 11 0 14 0 13 0 05 0 -0 57 -0 14 -0 07 0 03 -0 29 -0 01 -0 2* -0 24* -0 17* 0 3* 0 11* 0 19* 0 12* -0 01 -0 13 MOR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A MTA-DF 0 11 0 3 -0 13 0 05 0 02 -0 27 -0 05 0 14 -0 04 -0 23 0 16 -0 13* -0 18 -0 3* -0 02 -0 37 0 57* 0 31 -0 15 -0 01* MTL-DF 0 29 0 29 0 5 0 26 0 14 -0 36 -0 25 -0 07 -0 46 -0 19 0 05* 0 3 0 49 0 24 -0 01 -0 1* 0 05* -0 11 -0 29* -0 17 MTP-PP -0 02 -0 03 0 2 -0 01 0 19 -0 54 -0 2 0 19 -0 28 -0 49 0 2* 0 13 -0 34* 0 05 0 32 -0 07* -0 25 0 09 -0 14 -0 15* NAG-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NAR-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NAV-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NAZ-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NED-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NEW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NFR-DF 0 36 0 08 -0 27 0 27 0 19 -0 65 -0 21 0 06 -0 06 -0 54 0 34 0 1 -0 38 -0 02* 0 01 -0 13* -0 06 0 11 -0 17 0 09* NIO-PP 0 18 0 34 0 03 0 21 0 13 -0 5 -0 08 -0 17 -0 42 -0 58 -0 05* 0 39 -0 08 0 03 0 17 -0 19* -0 01 0 17* -0 29 -0 36* NMC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NNM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 — Continued frompreviou s page Site code 1900-1940 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 GV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 NNP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NOF-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A NOO-PP 0 26 0 13 0 37 0 06 0 4 -0 24 -0 24 0 03 -0 36 -0 29 -0 08* 0 22 0 41 0 24 -0 1* -0 01* -0 1 0 19 -0 27 -0 09* NOS-DF 0 34 0 16 0 21 0 13 0 33 -0 45 -0 24 -0 04 -0 31 -0 26 -0 19* 0 16 0 02* 0 3 -0 14* 0 1* -0 02* -0 17 -0 17 0 1* NPU-DF 0 39 0 4 -0 15 0 07 0 23 -0 35 -0 28 -0 26 -0 21 -0 32 -0 12* 0 27 0 32* 0 08 0 02* -0 26 -0 5* -0 06* 0 02* -0 3 NSB-PP 0 09 0 14 0 31 0 09 0 28 -0 4 -0 27 -0 1 -0 27 -0 27 -0 09 0 21 0 38 0 12 -0 01* -0 29 -0 14 -0 17 -0 21 -0 2 OAK-PP 0 2 0 02 0 02 0 36 0 06 -0 63 -0 48 -0 33 -0 31 -0 46 0* 0 12 0 29* 0 39 0 14 0 11* -0 01* 0 14* -0 06* -0 04* OLR-DF 0 18 0 3 0 0 31 -0 1 0 09 -0 28 -0 13 -0 2 -0 35 0 23 -0 15* -0 03 0 09* 0 15* -0 37* 0 19* 0 02 -0 13 -0 07* OPH-PP 0 08 0 12 0 14 0 19 0 08 -0 42 -0 36 -0 11 -0 26 -0 39 0 08 0 08 0 2 0 24 0 41* 0 03* -0 17* 0 06 -0 15 -0 29 OPP-PP -0 02 0 25 -0 12 0 06 -0 14 -0 45 -0 32 0 24 0 01 -0 38 -0 21* 0 3 0 02 0 03 -0 16 -0 08* -0 13* 0 16 0 36* -0 22 ORD-PP 0 38 0 16 0 36 0 21 0 29 -0 43 -0 22 -0 04 -0 4 -0 1 -0 09* 0 22 0 22 0 24 -0 2* -0 13* -0 3 -0 15 -0 14* -0 21 ORM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ORP-PP 0 02 0 14 -0 39 0 15 0 15 -0 47 -0 4 -0 03 -0 33 -0 56 0 0 24 0 3* 0 47* 0 24 -0 17* -0 31 -0 01 -0 23 -0 39* OSM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PAL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PAU-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PCD-DF 0 3 0 31 0 38 0 31 0 19 -0 36 -0 41 0 04 -0 52 -0 25 0 08* 0 26 0 38 0 38 -0 18* 0 07* 0 02* -0 08 -0 19* 0* PED-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PEP-DF 0 4 -0 01 0 09 -0 09 0 14 0 07 -0 36 -0 17 -0 21 0 21 -0 04* 0 19* -0 13* 0 09 0 47* -0 06 -0 13* -0 07 -0 07 -0 36* PFL-DF 0 34 0 06 0 39 0 1 0 57 -0 55 -0 2 0 06 -0 29 -0 44 0 03* 0 19 0 37 0 21 0 08* 0 03* 0 03* -0 07 -0 12 -0 2* PIB-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PIM-PP 0 09 -0 17 0 14 0 27 0 5 -0 23 0 15 -0 17 -0 19 -0 37 0 19 0 1* 0 23 0 24 0 23* -0 14 -0 02 0 1* -0 12 -0 22 PIM2-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PIP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PLB-PP 0 23 0 35 0 26 0 44 0 14 -0 08 -0 13 -0 06 -0 1 -0 19 0 48* 0 22 0 21 0 26* 0 21 -0 19 -0 42* -0 25* -0 37* -0 36 POC-DF 0 29 0 13 0 52 0 17 0 43 -0 28 -0 19 -0 03 -0 37 -0 14 -0 08* 0 32* 0 35* 0 34 0 09* -0 13 -0 11 -0 1 -0 39 -0 34* POR-DF -0 09 0 12 0 11 0 28 -0 08 0 26 0 37 -0 11 0 01 0 21 0 06 0 01 -0 1* 0 19 0 18* -0 16* -0 25* -0 1 -0 17* -0 28* POT-DF 0 0 09 0 4 0 35 0 16 -0 07 -0 19 -0 47 -0 37 -0 22 0 0 28* 0 43 0 23 0 29 0 09 -0 06 -0 41 -0 37 -0 35 POW-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PPF-PP 0 36 0 15 -0 03 -0 11 0 2 -0 25 -0 14 -0 16 -0 33 -0 22 -0 1* -0 02 -0 11 0 15* -0 16* -0 32 -0 05 -0 08 0 04* -0 33 PPP-PP -0 13 0 1 0 43 0 29 -0 3 -0 04 0 16 0 27 -0 05 -0 24 -0 06 0 05 0 26* 0 22 -0 13 -0 08 -0 18* 0 3 0 17* -0 21 PRD-DF 0 36 0 34 0 08 0 46 0 19 -0 12 -0 15 0 03 -0 02 -0 25 0 31 0 29 0 29* 0 17* 0 19 -0 05 -0 22 -0 23* -0 42* -0 27 PRF-PP 0 39 0 11 0 1 -0 07 0 29 -0 25 -0 03 -0 16 -0 29 -0 25 -0 02* 0 02 -0 07 0 11* -0 13* -0 35 0 03 -0 13 0 12* -0 4 Continued on next page Table B 1 - Continued from irevious page Site code 1900-1940 1941-1 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 PTP-PP 0 29 0 11 0 24 0 23 0 26 -0 37 -0 25 0 01 -0 26 -0 41 0 21 0 39* 0 24 -0 06* 0 15 -0 23 -0 37 -0 23* -0 06* -0 18* PUC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PUC2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A PYL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A RAP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A RCF-DF 0 3 0 16 0 35 0 26 0 25 -0 38 -0 43 -0 15 -0 61 -0 19 0 04* 0 36* 0 5* 0 35 -0 16* 0 13* -0 11* -0 19 -0 22* 0 19* RCK-DF 0 37 0 34 -0 01 0 1 0 25 -0 38 -0 39 -0 29 -0 39 -0 25 0 49 0 17* 0 28* 0 13 0 11 -0 32 -0 38 -0 4 -0 31 -0 45* RDF-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A RDS-PP 0 31 0 49 0 1 0 65 0 14 -0 23 -0 43 -0 19 -0 27 -0 26 0 44 0 47 0 05 0 07* 0 18 -0 19 -0 43 -0 35 -0 24 -0 16 REN-PP 0 3 0 03 0 19 0 37 0 39 -0 56 -0 16 -0 25 -0 39 -0 63 0 21 0 01 0 36 0 2 0 33 -0 26* -0 04 -0 15 -0 05* -0 32* RES-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A RGP-PP 0 39 0 17 0 24 0 1 0 32 -0 26 -0 09 0 06 -0 2 -0 21 0 02* -0 03* 0 38 0 18 -0 32* -0 09 -0 2 -0 11 -0 16 -0 27 RIM-DF 0 12 0 16 -0 09 0 1 0 01 -0 13 -0 1 0 11 -0 05 0 02 -0 06 0 36* 0 18* 0 3* -0 03 -0 16 -0 08 -0 2* -0 14 -0 22* RIO-PP -0 28 0 2 0 29 0 42 0 37 -0 1 -0 29 -0 04 -0 19 -0 38 -0 2 0 25 0 28 04 0 08* 0 13* -0 23 -0 1 -0 23 0 04* RIR-PP 0 28 0 27 -0 02 0 22 0 33 -0 47 -0 1 -0 06 -0 1 -0 45 -0 14* 0 07* 0 11 0 39* 0 09* -0 08* -0 01 0 07 -0 2 -0 16* RMP-PP 0 38 0 15 0 3 0 28 0 11 -0 43 -0 25 -0 15 -0 41 -0 36 -0 01* 0 21 0 4 0 23 -0 05 0 12* -0 19 -0 42* -0 36 -0 23 RMS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ROL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ROM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ROP-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A ROR-DF 0 22 0 11 0 34 0 3 0 12 -0 39 -0 21 -0 11 -0 5 -0 17 -0 07* 0 42* 0 32 0 41 -0 25* 0 04* -0 18 -0 2 -0 5 0 RPE-PP 0 14 0 09 0 22 0 2 0 44 -0 38 -0 43 -0 07 -0 53 -0 41 -0 07* 0 28* 0 16 0 09 0 18* 0 22* -0 05* 0 03 -0 03* 0 15* RPP-PP 0 33 0 24 0 44 0 15 0 23 -0 38 -0 17 -0 06 -0 3 -0 35 0 01* 0 33 0 52 0 26 -0 03* 0 13* 0 02* 0 12* -0 13 -0 07* RSP-PP -0 09 0 12 0 04 0 12 -0 29 -0 46 -0 29 0 09 -0 23 -0 59 -0 05 -0 06 0 41* 0 25 -0 29 -0 13* -0 15 0 26 0 26* -0 14* RUR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A RUS-PP 0 17 0 23 -0 01 0 53 0 24 -0 5 -0 19 0 09 -0 41 -0 53 -0 31* 0 17 -0 09 04 0 32 -0 06* -0 45* -0 1* -0 32 -0 41 SAC-DF -0 06 0 23 0 42 0 29 0 39 -0 2 -0 32 -0 12 -0 27 -0 45 0 18* 0 27 0 24* 0 27 0 06* -0 13 -0 31 -0 14 -0 27 -0 32 SAL-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SAP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SAP-PP 0 12 0 46 0 25 0 2 0 42 -0 35 -0 21 -0 22 -0 14 -0 25 0 28 0 09* 0 27 0 41* 0 11* -0 19 -0 12 -0 21 -0 23 -0 34 SAR-DF 0 23 0 16 0 34 0 37 0 2 -0 46 -0 4 0 01 -0 38 -0 17 0 2 0 37* 0 39 0 45 -0 15* -0 14* -0 12* -0 23* -0 32 -0 05 SAR2-DF 0 19 0 33 0 23 0 33 0 37 -0 17 -0 13 -0 18 -0 06 -0 22 0 2 0 21 0 41* 0 09* -0 12* -0 24 -0 19 -0 26 -0 25* -0 14 SBR-DF 0 23 0 48 0 29 0 06 0 17 -0 42 -0 37 -0 07 -0 37 -0 47 0 33 0 05* 0 46* -0 02 0 3 -0 17* -0 1* -0 27* -0 27 -0 3* Continued on next page Table B 1 -- Continued from previous page Site code 1900-1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 SCH-DF 0 21 0 2 0 45 0 25 0 17 -0 56 -0 45 -0 17 -0 43 -0 12 0 19 0 51* 0 47 0 3 -0 07* -0 26* -0 39 -0 29 -0 5 -0 18 SCM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SDC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SDN-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SEE-DF 0 42 0 16 0 02 -0 21 0 49 -0 33 -0 29 -0 12 -0 04 -0 28 0 11* -0 02 -0 17* -0 04 0 42 -0 12* 0 03* 0 06 -0 01 -0 18 SEP-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SFK-PP -0 02 0 28 0 29 0 26 0 39 -0 33 -0 23 -0 07 -0 02 -0 37 0 08 0 1 0 23 0 07* 0 32 -0 11* -0 31 -0 02 -0 04 -0 18* SFP-DF 0 24 -0 03 0 33 0 17 0 19 -0 58 -0 24 -0 14 -0 34 -0 54 0 07 0 16* 0 37 -0 07* -0 17* -0 31* -0 07 -0 33* -0 01* -0 36* SGP-PP 0 26 0 1 0 25 0 01 0 08 -0 4 -0 29 -0 08 -0 34 -0 28 0 01* 0 23 0 47* 0 25* -0 34* -0 08* -0 16 0 03 -0 2 -0 27 SIC-DF 0 18 0 27 -0 01 0 4 -0 04 -0 23 0 2 0 04 -0 23 -0 11 0 28 -0 12* -0 05 -0 18* 0 09 -0 27 0 13 0 08 -0 07 0 23* SJH-DF 0 14 0 15 0 04 0 14 0 1 -0 2 -0 15 0 22 0 07 -0 22 -0 2* -0 04* -0 2* -0 04 -0 39* 0 06* 0 03 -0 2* 0 02 0* SJM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SKB-PP 0 43 0 14 0 06 0 01 0 28 -0 28 -0 15 -0 01 -0 17 -0 36 0 07* -0 01 -0 12 0 18 0 02* -0 38 0 22* 0 05 0 19* -0 38 SLG-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SLM-PP -0 26 0 33 -0 02 0 15 -0 32 -0 3 -0 23 -0 07 -0 37 -0 62 -0 24 -0 15* 0 33* 0 39* 0 23* -0 11* -0 03* 0 03 -0 07* -0 19* SLM2-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A en SLR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SMP-PP 0 2 0 08 0 19 0 12 0 1 -0 13 -0 11 -0 09 -0 22 -0 16 -0 31* 0 18 0 44* 0 26 -0 43* 0 -0 22 -0 23 -0 17 -0 06 SMR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SNA-PP 0 12 0 36 0 11 0 07 0 08 -0 52 -0 07 -0 14 -0 32 -0 47 0 08 0 29 0 01 -0 05 0 03 -0 11* 0 07 0 22* -0 28 -0 17* SNO-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SNR-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SOT-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SOT2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SPC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SPC-PP 0 31 0 38 0 22 0 45 0 36 -0 58 -0 39 -0 18 -0 48 -0 54 0 1* 0 22 0 28 0 56 0 06* -0 24* -0 37 -0 17 -0 4 -0 32* SPC2-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SPC3-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SPL-DF 0 39 -0 03 0 07 0 11 0 16 -0 25 0 09 -0 13 -0 16 -0 37 0 36 -0 26* -0 35* 0 26 0 29 -0 36 0 28* 0 17* 0 06* 0 02* SPM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SPP-PP 0 02 0 34 0 32 0 07 0 26 -0 58 -0 33 -0 19 -0 28 -0 3 0 11 0 05* 0 28 0 34* -0 04* -0 14* -0 18 -0 07 -0 09* -0 26 SPR-PP 0 15 0 3 0 08 -0 05 0 27 -0 16 -0 21 0 08 -0 25 -0 35 -0 07* 0 19 0 32* 0 01 0 02* 0 34* -0 31 0 04 -0 05* -0 01* SRC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 -- Continued from p revious pag Site code 1900- 1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 SRD-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SRM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SRS-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SRV-DF 0 33 0 17 0 18 0 21 0 3 -0 47 -0 17 -0 16 -0 13 -0 56 0 21 0 17 0 08 0 29 0 16 -0 34 -0 27 0 13* -0 14 -0 27* SSC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A SSP-PP 0 46 0 35 0 34 0 35 0 48 -0 41 0 1 0 11 0 03 -0 52 0 3 0 24 0 16* 0 41 0 09* -0 36 -0 03 -0 18* 0 15 -0 38 STU-DF 0 S 0 25 0 21 0 36 0 08 -0 4 -0 48 -0 34 -0 37 -0 3 0 19* 0 19 0 24 0 2 0 23 -0 2* 0 03* 0 08* -0 09* -0 15 SUN-DF 0 32 0 5 0 29 0 08 0 27 -0 44 -0 47 -0 04 -0 59 -0 61 0 1* 0 21* 0 37 0 12 0 06* 0 1* -0 02* 0 06 -0 18* 0 17* SVF-PP 0 43 0 26 0 26 0 06 0 3 -0 41 -0 04 -0 04 -0 27 -0 37 0 09* -0 11* -0 11* 0 08 -0 12* -0 44 -0 01 -0 11 0 09* -0 38 SWA-DF 0 31 0 29 0 29 0 41 0 32 -0 4 0 14 0 13 -0 24 -0 25 0 18 0 09* 0 19 0 02* 0 3 -0 16* 0 11 0 12 -0 28 -0 17 TAJ-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A TCP-PP 0 34 0 15 0 1 0 58 0 32 -0 34 -0 31 -0 23 -0 38 -0 36 -0 07* 0 45* 0 42* 0 45 0 13* -0 11* -0 29 -0 21 -0 45 -0 25 TCT-DF 0 03 0 47 -0 18 0 29 -0 22 -0 23 0 0 25 -0 03 -0 01 0 18 -0 21* -0 15 -0 08* -0 14 -0 36 0 4* 0 17 -0 29* 0 07 TDS-PP 0 41 0 1 0 33 0 09 0 27 -0 35 -0 12 0 02 -0 07 -0 32 0 04* 0 01 -0 04* 0 15 0* -0 36 -0 03 -0 07 0 08 -0 41 TLD-PP 0 31 0 09 0 39 0 07 0 24 -0 29 -0 08 0 03 -0 13 -0 31 -0 06* 0 04 0 03* -0 3* 0 07 0 22* -0 26* 0 18 0 38* -0 03* TLP-PP 0 18 0 3 0 32 0 46 0 22 -0 04 -0 07 0 -0 1 -0 19 0 33 0 26 0 36 0 29* 0 06 -0 08 -0 29* -0 26* -0 3* -0 26 Oi TRS-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A TSE-PP 0 08 0 21 0 4 0 18 0 19 -0 34 -0 26 -0 18 -0 35 -0 24 -0 06 0 16 0 16* 0 25 0 06 -0 15* -0 25 -0 05 -0 31 -0 16 TUS-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A UHC-DF 0 29 0 33 0 4 -0 04 0 24 -0 38 -0 23 -0 17 -0 17 -0 41 0 33 0 27 0 27 0 4* 0 25 -0 24 -0 29 -0 17 -0 19 -0 38 UIM-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A UNI-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A UPG-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A UWB-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A VAM-DF 0 24 0 25 -0 16 0 16 0 33 -0 49 -0 24 0 1 -0 06 -0 43 -0 07* 0 01* 0 2* 0 28 0 38 -0 18* -0 35 0 0 03 -0 06* VBP-PP 04 0 32 0 23 0 37 0 31 -0 54 -0 33 -0 04 -0 48 -0 39 -0 08* 0 33 0 16 0 41 -0 01* -0 11* -0 34 -0 21 -0 42 -0 17* VED-PP 0 17 0 09 0 18 0 23 0 32 -0 64 -0 14 -0 09 -0 08 -0 61 -0 17* 0 27* -0 06* 0 07 0 31 -0 25* -0 31* 0 12* 0 17* -0 26* VIL-DF -0 04 0 06 0 33 0 48 -0 29 -0 3 0 16 -0 38 -0 25 -0 09 -0 03 -0 12 0 2 0 51 0 17* 0 15* 0 1 -0 01* -0 39 -0 3* VVR-DF 0 26 0 22 0 13 0 46 0 09 -0 36 -0 24 -0 16 -0 23 -0 33 -0 19* 0 23 0 23 0 53 0 02 0 04* 0 04* -0 06 -0 22 -0 14* WAC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WAC-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WAL-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WAT-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Continued on next page Table B 1 — Continued from previous page Site code 1900-1940 1941-1981 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10

WCB-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WCK-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WCN-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WCP-PP 0 25 0 09 0 32 0 17 -0 05 -0 27 -0 1 -0 21 -0 28 -0 25 -0 13* 0 21 0 5* 0 32 -0 17 -0 11 -0 21 -0 25 -0 2 -0 36 WDF-DF 0 17 -0 OS 0 23 0 1 0 -0 18 0 1 -0 23 -0 27 -0 03 -0 11* 0 24* 0 55* 0 37* -0 21* -0 2 -0 12* -0 3 -0 33 -0 5* WBB-DF 0 16 0 21 0 66 0 25 0 37 -0 26 -0 19 0 04 -0 31 -0 3 -0 02 0 26 0 37* 0 45* 0 11* -0 02* -0 16 -0 13 -0 47 -0 28 WBN-PP 0 22 0 31 0 26 -0 01 -0 05 -0 21 0 16 0 21 0 02 -0 15 0 15 -0 05* -0 16* 0 44* -0 05 -0 44* 0 1 -0 07* -0 01 -0 23 WHC-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WHE-PP 0 31 0 28 0 21 0 34 0 37 -0 42 -0 23 0 03 -0 27 -0 32 -0 12* 0 38 0 08 0 29 0 09* -0 11* -0 36 -0 23* -0 27 -0 18 WHH-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WHI-DF -0 2 0 21 -0 07 0 11 -0 1 -0 38 -0 04 0 09 0 22 -0 2 0 04* 0 09 -0 24 -0 09* 0 17* -0 11* -0 06 0 13 0 14 0 03* WIM-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WIR-PP 0 07 0 11 0 27 0 23 0 21 -0 29 -0 11 0 14 -0 08 -0 18 0 13 0 13 0 32 -0 1* 0 15 -0 19 -0 17 -0 19* -0 06 -0 2 WLT-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WMC-DF 0 62 0 33 0 02 0 43 0 04 -0 29 -0 41 -0 23 -0 41 0 0 15* 0 34 0 34* 0 51 -0 22* -0 07* -0 33 -0 39 -0 57* -0 18 WPR-PP N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A WSK-DF 0 35 0 3 0 11 0 24 0 1 -0 49 -0 23 -0 12 -0 35 -0 43 0 06* 0 51* 0 3* 0 37 0 2 -0 25* -0 18 0 01 0 04* -0 53 WTK-DF 0 1 0 2 0 65 0 24 0 39 -0 21 -0 16 0 06 -0 29 -0 33 -0 09* 0 21 0 36* 0 51* 0 09* 0 09* -0 12 -0 07 -0 48* -0 21 WWG-DF N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A YDF-DF 0 03 0 03 0 03 -0 01 0 02 -0 61 -0 42 -0 18 -0 28 -0 5 -0 17* 0 03 -0 18* -0 06 -0 21* -0 23* -0 09* 0 1* 0 18* -0 12* YMR-DF 0 34 0 06 -0 3 0 25 0 24 -0 71 -0 23 0 04 -0 04 -0 62 0 33 0 -0 4 -0 12* 0 05* -0 3* 0 02* 0 07 -0 14 -0 03*