TREES AND POLLUTION: INVESTIGATING THE IMPACT OF SULFUR DIOXIDE USING RING WIDTHS AND STABLE ISOTOPES
ZACHARY RAWLUK
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCE
NIPISSING UNIVERSITY SCHOOL OF GRADUATE STUDIES NORTH BAY, ONTARIO
© Zachary Rawluk, March 2016 SCHOOL OF GRADUATE STUDIES THESIS/DISSERTATION CERTIFICATE OF EXAMINATION
Certificate of Examination
Supervisor(s): Examiner(s)
Dr. Adam Csank Dr. Daniel Campbell
Dr. April James
Supervisory Committee:
Dr. Adam Csank
Dr. April James
Dr. Jeff Dech
The thesis by
Zachary Rawluk
entitled
Trees and pollution: investigating the impact of sulfur dioxide using ring widths and stable isotopes
is accepted in partial fulfillment of the requirements for the degree of
Master of Environmental Science
January 22, 2016 Dr. Dan Walters Date Chair of the Examination Committee
(original signatures on file)
Abstract
Excessive sulfur dioxide (SO2) pollution released into the atmosphere from mining and smelting operations in northeastern Ontario, Canada has devastated surrounding forest ecosystems. Over the course of the 20th century, smelting operations in
Sudbury, Ontario released more than 100 million tonnes of SO2 into the atmosphere. At peak production in 1960, Sudbury smelters generated 2.56 million tonnes of SO2 that represented approximately 4% of global sulfur emissions. Past studies have shown that in high enough doses, SO2 acts as a toxic environmental pollutant that can be associated with decreased growth in the annual growth rings of trees. However, the application and use of stable carbon-13 (δ13C) and sulfur-34 (δ34S) isotopes in dendrochemical studies involving sulfur pollution is still relatively new. This study aims to better understand the influence of atmospheric SO2 pollution loading on nearby forest ecosystems and tree physiology – from both a spatial and temporal perspective by combining tree-ring width and isotope data. Three sample sites along a 110-kilometer northeasterly transect from
Sudbury to Temagami were chosen in order to quantify the interplay of pollution and climate. Ring-width and isotope data provided useful information that allowed this study to comment on the magnitude of influence and impact that SO2 pollution had on eastern white pine (Pinus strobus) trees throughout Sudbury’s entire smelting history, as well as the success of Vale’s Emission Reduction Program (ERP) that has reduced overall SO2 emissions by 90% over the last 45 years. Results indicated that the closest study site to
Sudbury displayed growth ring trends that were influenced by SO2 during the peak emissions period and isotope data revealed a weakened relationship with climate variables before drastic emission reductions in the 1970s. Ultimately, this study provided
1 results in favour of white pine trees being good biorecorders of anthropogenic SO2 pollution. Because of the influence of SO2 emissions on tree growth and chemistry, failure to identify the associated signals of pollution in tree-ring widths and stable isotope records may lead to biased and inaccurate paleoclimatic reconstructions from regions affected by an increased pollution load.
2
To Nanny, for being the light to guide me in times of darkness.
3 Acknowledgments
First and foremost, I would like to offer a big thank you to my supervisors, Dr.
Adam Csank (Nipissing University) and Dr. April James (Nipissing University), for your valuable input, advice, guidance and support over the course of developing and conducting my thesis research project as well as throughout the entire duration of the
Master of Environmental Science (MESc) program at Nipissing University. You are both greatly respected and admired for the vast amount of knowledge you possess within your fields of specialization. Working with you proved to be extremely beneficial to my development as an inquisitive researcher and scientist, while also allowing me the freedom to critically think and attempt to answer questions on my own at each step of the scientific method. Second, I would like to thank all of the Nipissing University faculty members who were involved with designing and instructing the MESc graduate courses.
Our discussions were always thought-provoking and the feedback and constructive criticism provided in class greatly contributed to the evolution and improvement of my thesis. I would also like to thank Mr. Matt Rogers at the University of Alaska Anchorage
Stable Isotope Laboratory for his technical assistance in conducting the mass spectrometry work that was required for isotope analysis. I am also grateful to Dr. Jeff
Dech (Nipissing University) and Dr. Daniel Campbell (Laurentian University) for their feedback and comments on my written work in their roles as internal and external examiner, respectively. Furthermore, thanks to the Natural Sciences and Engineering
Research Council of Canada (NSERC) for funding the research project. Last, but certainly not least, a special thank you to my mom, Megan Williams, for providing constant motivation and encouragement that helped me make it to the finish line.
4 Table of Contents
Abstract ...... 1 Dedication ...... 3 Acknowledgements ...... 4 Table of Contents ...... 5 1. Introduction ...... 6 1.1. Sudbury mining and smelting history ...... 6 1.2. Trees, pollution and dendrochronology ...... 9 1.3. Project goals and significance ...... 13 2. Literature review ...... 14 2.1. Carbon isotope theory ...... 14 2.2. Sulfur isotope theory ...... 19 2.3. Biological and physiological effects of SO2 on plants ...... 21 13 34 2.4. Observed impacts of SO2 on ring widths, δ C and δ S isotopes ...... 22 3. Materials and methods ...... 30 3.1. Project overview and study design ...... 30 3.2. Site descriptions ...... 31 3.3. Fieldwork and tree-ring data ...... 34 3.4. Climate records and SO2 emissions data ...... 37 3.5. Sample measurement, dating and preparation ...... 37 3.6. Chemical processing of α-cellulose ...... 40 3.7. Isotope ratio mass spectrometry (IRMS) ...... 42 3.8. Statistical analyses ...... 43 4. Results and discussion ...... 45 4.1. RWI chronologies of White Bear, Hobbs Lake and Kukagami Lake ...... 45 4.2. RWI-climate analysis ...... 54 4.3. RWI-pollution analysis ...... 59 4.4. δ13C chronologies of White Bear, Hobbs Lake and Kukagami Lake ...... 61 4.5. δ13C-climate analysis ...... 64 4.6. δ13C-pollution analysis ...... 68 4.7. δ34S analyses ...... 72 5. Conclusions ...... 72 References ...... 77 Appendices ...... 85 Appendix I – Figures ...... 85 Appendix II – Tables and Images ...... 103
5 1. Introduction
1.1. Sudbury mining and smelting history
Mining, involving the extraction of copper and nickel ores, in the Sudbury region began in the late 1880s (SARA, 2008). The Canadian Copper Company was first to arrive on scene and locate significant nickel deposits. Following the company’s initial discovery, a sister company, the International Nickel Company, was created and established in 1902 in order to help increase production in the area. However, in 1919, after nearly three decades of production, the two companies would essentially merge and commonly be referred to as Inco Limited (presently, Vale Canada Limited) (Python
Group, 2010).
Nickel smelting (a term used to describe a set of complex reactions that occur in a furnace in which sufficient heat is added to raise the temperature to melt the constituents) commenced on December 22, 1888 at the Copper Cliff mine. By the end of 1889, the
Copper Cliff facility had already produced twice as much nickel as the rest of the world combined during the previous year (SARA, 2008). From 1888 to 1929, open roast yards were primarily where smelting activity took place at Copper Cliff. During this process, ore was stockpiled, ignited using wood as fuel, and left to burn for extended periods of time. During open roasting, 50 to 70% of the sulfur contained in the ore was oxidized using air or oxygen and carried away from the point of emission as sulfur dioxide (SO2)
(SARA, 2008). There were 11 roast yards in total, each having different operational durations. Owing to the lack of pollution controls in place during open roasting, roast yards were eventually relocated farther away from populated areas. This was because pollutants from roast yards were dispersed relatively close to the ground, as opposed to
6 when emitted from tall stacks. Over these four decades of roast yard production, approximately 11 million tonnes of SO2 were released into the atmosphere (SARA,
2008). During this time, the smelter being built at Copper Cliff was operational, but undergoing constant upgrading and would not become fully functional – with more modern reverberatory furnaces – until the early 1930s. It was also during this time period that a second major smelter, at Coniston, commenced operations in 1913.
In 1929, with both major smelters at Copper Cliff and Coniston now fully operational, roasting of copper and nickel ores in open roast yards was officially stopped
(SARA, 2008). The mark that the original open roast yards had left on Sudbury was quite evident on the ground, as most of the plant life in the immediate and surrounding areas
(~40-50 km radius) was either left with extensive foliage injuries, bark abnormalities or destroyed altogether, while rock outcrops were stained a smoky black colour (Linzon,
1966). From this point on, all smelting activity would be carried out in smelters with dispersal stacks that ranged from 53-137 meters in height (SARA, 2008). For example, from 1930-1936 a 53-meter (175 feet) tall stack was used; between 1937 and 1965 a 93- meter (304 feet) tall stack was primarily used; and from 1966-1978 a 137-meter (450 feet) tall stack was used to disperse emissions at higher elevations and, subsequently, to greater distances (SARA, 2008).
The 1930s and 40s saw increased production for Inco Limited – reflected by large
(and rapid) increases in SO2 emissions (Figure 1). This was largely due to the war effort requiring more nickel than ever before. During this time, Inco Limited produced more nickel than in its previous 54-year history (Python Group, 2010). A good portion of the nickel produced was supplied to all allied countries in order to meet their wartime
7 demands. This provided them with a necessary resource required for the production of such things as warplanes, ammunition and other support vehicles.
Until the early 1950s, more than 90% of sulfur contained in processed ores was being emitted into the atmosphere (SARA, 2008). The year 1952 saw the installation of new flash furnaces that were commissioned to replace the outdated reverberatory furnaces in the smelter at Copper Cliff. These new furnaces would allow for the produced
SO2 to be recovered in liquid form – a form of pollution control. Also taking place in the
1950s was the building of the world’s tallest smelter chimney. When construction was completed, the chimney measured in at 194 meters (637 feet) tall. However, this record would later be broken by the currently used Inco Superstack that was built in the 1970s
(operational in 1972) and measures 380 meters (1,250 feet) tall (Python Group, 2010).
By 1965, the Copper Cliff smelter (largely considered the main facility) consisted of two blast furnaces, 42 multi-hearth roasters, seven reverberatory furnaces, two oxygen flash smelting furnaces and 24 converters (SARA, 2008). By the end of the decade, SO2 gases were now being converted into sulfuric acid (H2SO4; a main component of acid rain) in order to help reduce direct SO2 emissions into the atmosphere and immediate environment.
The 1970s saw a slew of emission reduction measures and pollution controls implemented at the mining and smelting facilities in Sudbury. For example, installation of particulate control devices on smelting equipment helped contribute to the sharp reduction in SO2 and particulate matter emissions in the years following 1970. Also, the
Inco Superstack was now operational and officially replaced the three existing stacks, which were much shorter in height. Now, not only was copper being processed in flash
8 furnaces, but the technology had also developed to the point where it could be used on nickel ores (SARA, 2008). In line with the copper flash furnaces, SO2 emissions were now converted to sulfuric acid.
Following the emission reduction strategies in the 1970s, a sharp transition occurred during the 1980s and 90s with the installation of acid plants at Copper Cliff
(these acted to recover even more SO2 before the gasses were emitted). With these new changes, less than 10% of the sulfur in the ore is now discharged into the atmosphere
(SARA, 2008). Furthermore, in 1994, the remaining reverberatory furnaces were shut down in favour of the more environmentally friendly and less polluting flash smelting furnaces.
In summary, during the 1930s there was a sharp increase in emissions from
Sudbury smelters, which can be attributed to an increase in production and the scale of operations (e.g., the war effort). Emissions leveled off in the 1940s, 50s and 60s and then declined dramatically in the 1970s and 80s. The dramatic reduction in emissions was due to the use of new technologies, such as flash furnaces and acid plants, which allowed for maximal recovery of SO2 as intended by Vale Canada Limited’s newly implemented
Emission Reduction Program (ERP). That being said, copper and nickel ore smelting in
Sudbury during the major industrial period (1930-1982) resulted in an estimated 94 million tonnes of SO2 being emitted into the atmosphere (SARA, 2008).
1.2. Trees, pollution and dendrochronology
The City of Greater Sudbury is well known for its rich history of mining and smelting of high-sulfide ores. It is also well known for the barren and semi-barren
9 landscapes that are now the artifacts of the combined effects of open roast yard mining and, later on, smelting activity that sought to increase production of nickel, copper, iron and precious metals. As a result, large quantities of SO2 and other metal particulates were released into the atmosphere and dispersed up to tens of kilometers away (Linzon, 1966;
SARA, 2008). After the emission of more than 100 million tonnes of SO2 over the course of less than a century, well-established forest ecosystems within a 15-kilometer radius of smelting operations were almost completely eliminated (Gunn et al., 1995). According to estimates made by Gunn et al. (1995), approximately 100,000 hectares (1,000 km2) of land was affected (20,000 hectares left nearly completely barren and 80,000 hectares left semi-barren). SO2 can significantly affect and impact ring-width growth and stable isotope compositions in trees that are exposed to high pollution loads over both short and long timescales (Rinne et al., 2010). Increased mining and smelting activity in the first
th half of the 20 century drove SO2 emissions to their peak in 1960. During that year, operations generated 2.56 million tonnes (approximately 4% of global sulfur emissions) of SO2 that was released into the atmosphere (Gunn et al., 1995). Today, the effects of past smelting practices in northeastern Ontario can still be observed in the Greater
Sudbury area – and up to around 50 kilometers away – as tree populations and local forest ecosystems are still recovering.
Dendrochronology (tree-ring dating involving the analysis of patterns of tree/growth rings) and dendrochemistry (a related science involving the analysis of elemental abundances in tree rings to study the interaction of climate and air pollution in past times) are scientific fields that can provide the necessary tools for researchers and scientists to gauge – quantitatively – the ecological response of a forest ecosystem to
10 heavy pollution loads, including that of SO2. For example, it is already well established that variations in tree-ring widths may be used to date wood (Douglass, 1941) and reconstruct past climates (Fritts, 1971). Natural climate variables, such as precipitation
(e.g., Brienen and Zuidema, 2005) and temperature (e.g., Barber et al., 2000) may also be reconstructed from ring-width variations. More recently, however, isotope analysis has been used to elucidate complex interactions of climate and non-climate variables (e.g.,
Boettger et al., 2014). Since information on regional climate can be successfully recorded by trees and captured in their tree-ring width and isotope records, it should also be possible for trees to record additional information (i.e., anthropogenic signals) that is the product of more locally dominating site factors, such as enhanced pollution loads from intense mining and smelting activity during periods of industrialization.
When compared and contrasted with pre-industrially formed tree rings, growth rings formed during heavy industrial periods in the 20th and early 21st centuries occasionally exhibit ring-width and isotope anomalies that can’t be explained by variations in climate alone (Rinne et al., 2010). Indeed, studies involving tree-ring measurements in relation to industrial pollution, such as Larsson and Helmisaari (1998) and Guyette et al. (1991), were able to show a correlation between mining activity, tree- ring-width and tree-ring chemistry in tree populations growing in close proximity to smelters.
Thus, failure to identify significant time periods of anthropogenic SO2 pollution emissions and their associated signals in tree-ring width and stable isotope records may lead to biased and inaccurate paleoclimatic reconstructions for the region affected by the increased pollution load. For this study, failure to take into account the large volume of
11 SO2 emitted from Sudbury smelting activities – when interpreting tree-ring width and isotope records/chronologies – may lead researchers to infer climates in years past that were much hotter and drier than they actually were. This is because inferences would be based on the (likely) observation of a series of very narrow or compressed rings and/or enriched (i.e., more positive) δ13C isotope values that are often observed during periods of high pollution (e.g., Martin and Sutherland, 1990), but would ultimately turn out to be incorrect when the involvement and influence of industrial pollution is excluded or ignored in the analyses (e.g., Savard et al., 2009; Doucet et al., 2012).
Our tree-ring pollution study in northeastern Ontario aims to quantify the influence of anthropogenic SO2 pollution on eastern white pine (Pinus strobus) growth and is focused on providing clear insight on the following four hypotheses: (1) high aerial
13 34 emissions of SO2 influence tree-ring growth, δ C and δ S stable isotope compositions in white pine trees around the Sudbury area, (2) there is a spatiotemporal component to pollution-climate dominated growth along the study transect, (3) white pine trees show recovery upon implementation of emission reduction programs (ERPs) and (4) white pine trees are reliable passive biorecorders of anthropogenic SO2 pollution emissions and are useful in reconstructing regional pollution histories and in refining paleoclimatic reconstructions.
In summary, past studies have shown that anthropogenic pollution can have harmful and damaging impacts on some forested areas in close proximity to high- emission point sources (e.g., Thomas, 1961; Linzon, 1966). They have also shown that trees and their annual growth rings hold much promise for future work in the application of dendrochemistry to capturing anthropogenic pollution signals and observing how they
12 reflect – spatially and temporally – emission patterns. As overall SO2 emissions have been reduced by 90% over the last 45 years in response to emission reduction measures undertaken in 1972 (i.e., installation of the Inco Superstack and Vale Canada Limited’s
ERP), it will be interesting to see whether this study produces results similar to those of
Fox et al. (1986) and Thomas et al. (2013), where a return to environmentally (i.e., climate) controlled growth was observed after substantial emission reductions at a lead- zinc smelter in Trail, British Columbia (Fox et al., 1986) and implementation of the
Clean Air Act (equivalent to an ERP for the purpose of this study) in West Virginia, USA
(Thomas et al., 2013).
1.3. Project goals and significance
For dendrochronologists and dendrochemists, this study will help in determining whether or not trees (particularly white pine) are reliable passive biorecorders of periods of increased pollution emissions and whether their use in pollution studies should be continued. Furthermore, if sulfur isotopes successfully reflect SO2 emission patterns, the results will add credibility to the continued use of that stable isotope (δ34S) in dendrochemical pollution and paleoecological studies. This study will also aid in advancing quantitative knowledge on how pollution and climate interact with each other and how strongly the spatiotemporal effects of each can be felt, as indicated by the annual growth response and stable isotope ratios of each tree. This increased knowledge will allow researchers to refine their current models, as well as allowing for more accurate paleoclimatic reconstructions and inferences to be made for pollution-exposed regions in northeastern Ontario.
13 In order to aid in further developing and advancing productive ERPs and policies for the present and future, it is crucial to understand the spatial and temporal extent to which high-magnitude industrial activity and economic development affects our forest resources (i.e., trees). More importantly, by increasing our understanding of how long- term high-pollution outputs affect the growth and health of nearby trees, we are more capable of designing mitigation strategies that work to minimize undesirable ecological side effects.
The overall significance of this type of pollution study to Ontarians and the general public is the resulting knowledge of how widespread atmospheric SO2 pollution affects tree growth and physiology. By using trees as representative biorecorders, this study will allow us to comment on how trees spatially and temporally respond to ERPs
(e.g., Vale Canada Limited’s ERP) and decreasing trends in pollution emissions, in general. This assessment will help determine the efficacy of such programs and suggest how they can be modified and/or improved in the future in order to better promote forest ecosystem recovery.
2. Literature review
2.1. Carbon isotope theory
Carbon, one of the three main elements that constitute wood, is present in the atmosphere in two stable forms (i.e., isotopes): carbon-12 (12C) and carbon-13 (13C).
Thus, in its most common and abundant form, 12C consists of six protons and six neutrons housed within its atomic nucleus. The less common stable isotope, 13C, carries with it an additional neutron. Though these stable isotopes share almost identical
14 chemical properties, the tiny difference in mass between the two allow for physical, chemical and biological processes to discriminate against one of them: the slightly heavier and more energy expensive 13C (McCarroll and Loader, 2004). When carbon discrimination and fixation occur (during photosynthesis) as trees take in CO2 from the atmosphere through their stomata, the resulting isotope value reflects a spatially and temporally unique environmental signal that is a combination of biotic and abiotic factors. In other words, the ratio between the two isotopes is an indicator of paleoclimate, plant physiology and soil conditions. For example, when a tree becomes stressed due to limited or complete loss of water access (potentially coupled with high temperatures and/or drought conditions), it will close its stomata to reduce further water loss (via transpiration). Closing of the stomata reduces CO2 flux (i.e., the net assimilation rate),
13 resulting in enrichment of δ C (Farquhar et al., 1982). Thus, climatic parameters can influence the normal physiological functioning of plants and alterations in δ13C values indicate a change in the amount of photosynthetic activity as a result of paleoclimate and physiology – an indication of how favorable an environment is for a given plant population (e.g., a tree stand).
As per convention, the ratio of 13C to 12C is expressed in delta (δ) notation with reference to a standard material for which the isotopic ratio is known (McCarroll and
Loader, 2004). The carbon isotope ratio (δ13C) is expressed, in parts per thousand (i.e., per mille [‰]), as:
13 δ C = (Rsample / Rstandard - 1)1000
13 12 Rsample and Rstandard are the C/ C ratios in a sample and standard, respectively. For carbon, isotope ratios are measured against Vienna Pee Dee Belemnite (Vienna-PDB or
15 VPDB). This is in reference to a fossil belemnite (an extinct order of cephalopods) from the Pee Dee formation of South Carolina. Using the VPDB as a standard, the present δ13C value of atmospheric carbon dioxide (CO2) is about -8.4‰ (Gessler et al., 2014). Wood yields δ13C values of -20‰ to -30‰ (McCarroll and Loader, 2004). Simply put, this means that trees are depleted in 13C relative to air. The Suess effect is a change in the ratio of atmospheric 13C concentrations towards lighter δ13C values resulting from large anthropogenic additions of CO2, derived from the burning of fossil fuels, which are
13 12 depleted in CO2 (i.e., mostly CO2) (Tans et al., 1979). In order to account for this, a correction factor must be added to the δ13C value of tree rings in order to quote values to a pre-industrial standard (elaborated on in later sections).
The term used to describe the above process, whereby a change in isotopic ratios occurs from the original external environment (i.e., atmosphere) to the newly integrated internal environment (i.e., the leaf and then wood), is fractionation. During fractionation, air (i.e., the CO2 component) enters the leaf through its stomata (epidermal pores that are used to control gas exchange). The CO2 that moves into the leaf then provides a substrate for the chemical reactions of photosynthesis that yield sugars, such as glucose (C6H12O6), and oxygen gas (O2). Following this pathway, from external atmosphere to leaf sugars, fractionation occurs at two main points (Farquhar and Lloyd, 1993).
The first point of fractionation occurs when air diffuses through the stomata.
12 Naturally, CO2 containing the lighter carbon isotope ( C) is able to diffuse more easily across the membrane because, by virtue of kinetics, the lighter isotope is able to move farthest with the least amount of resistance from surrounding atomic interactions. The effect of fractionation due to diffusion is an internal environment that is depleted in 13C
16 compared to the original value of outside air. The fractionation due to diffusion of CO2 into the leaf is -4.4‰ (Farquhar and Lloyd, 1993). After diffusion, carbon fractionation occurs a second time when CO2 is utilized in photosynthetic pathways. As the lighter form of carbon is more energetically favourable and less metabolically demanding on the tree to utilize, 12C is preferred over the heavier 13C. This second point of fractionation, during carboxylation, is also constant and has been estimated to result in a depletion of approximately -27‰ (Farquhar et al., 1982).
The net effect (i.e., resulting δ13C values) of the above interactions is additive, but also depends on the internal and external CO2 concentrations. For example, if internal
(leaf) concentrations of CO2 are higher than external (air) concentrations, then this means that stomatal conductance (defined as a measure of the rate of passage of CO2 entering, or water vapor exiting through the stomata of a leaf) is higher than the rate at which photosynthesis is occurring. With the high concentration of CO2 available, there will inevitably be discrimination against the heavier carbon isotope (13C) because of the abundance of lighter 12C that is easier for the tree to use. The result is depletion in 13C with lower (i.e., more negative) observed δ13C values. On the other hand, if the rate of photosynthesis is higher than that of stomatal conductance, internal CO2 concentrations drop and metabolic processes discriminate less now due to the diminished supply of external gas. With less carboxylation discrimination occurring, δ13C values tend to
13 increase by way of enrichment (i.e., δ Cplant values become less negative).
The fractionation events associated with diffusion and carboxylation that carbon isotopes undergo while being incorporated into each annual growth ring are also linked and related to intrinsic water-use efficiency (iWUE) values. iWUE values are another
17 way in which the response of a tree to changing environmental conditions (a stressor like
SO2 pollution or increased atmospheric CO2 levels) can be studied in more physiological detail. iWUE is calculated as (in µm/mol):
13 13 iWUE = ca(b - δ Cair + δ Cplant) / 1.6(b - a) where ca is the concentration of atmospheric CO2, a is the fractionation due to diffusion of
CO2 into the leaf (-4.4‰) and b is the fractionation due to carboxylation (-27‰).
However, when the different components of trees are manufactured using the products of photosynthesis (i.e., leaf sugars), there are additional stages of fractionation that take place that are much more difficult to quantify (e.g., Cernusak, 2001). McCarroll and Loader (2004) state that it is through these additional fractionations in other tree components, such as cellulose, that trees display lower δ13C values than those in the leaf where carbon is first integrated. Environmental factors that can influence the main controls on carbon isotope fractionation include, but are not limited and exclusive (i.e., may be a combination of two or more factors) to: leaf morphology, sunlight/irradiance, humidity, temperature, precipitation, cloud cover, snow-melt, rooting depth and soil status (pH) (McCarroll and Loader, 2004). In addition, anthropogenic factors might also influence how carbon is fractionated in plants. For example, atmospheric air pollution
(anthropogenic in origin) may impair stomatal conductance by damaging stomata or the guard cells around them that help mediate gas flow and exchange between the inner and outer environments – impacting fractionation by diffusion (Choi et al., 2014).
Additionally, air pollution might also impair or alter the efficiency of photosynthetic processes operating within the leaf, which, in turn, would influence fractionation due to carboxylation (Fuhrer et al., 1993). A phenomenon known as the juvenile isotope effect
18 (which will be elaborated on in a later section) may also play an important and noteworthy role in fractionation processes by causing an “artificial” enrichment of δ13C values during the early (first 10-20 years or so) years of tree growth (Freyer, 1979).
In summary, carbon isotopes in tree rings represent the net effect of (1) fractionation due to diffusion (the movement of CO2 from external to internal environment), (2) fractionation during carboxylation (photosynthetic processes), (3) additional stages of fractionation during the production of other tree components and (4) environmental and anthropogenic factors that impact stomatal conductance and stomatal regulation of water loss.
2.2. Sulfur isotope theory
Sulfur has four stable isotopes: 32S, 33S, 34S and 36S. Their abundances are
95.02%, 0.75%, 4.21% and 0.02%, respectively (Wieser, 2006). For this study, we are interested in δ34S. For sulfur, the standard for measuring isotope ratios is derived from
Canyon Diablo troilites (a variety of the iron sulfide mineral pyrrhotite) that were fragmented from the asteroid impact that created the Barringer Crater (aka Meteor Crater) in Arizona, USA approximately 50,000 years ago.
Compared to carbon, the analysis of sulfur from paleoarchives, such as tree rings, is not only a difficult and daunting task, but is rife with poor analytical resolution
(Thomas et al., 2013). Many paleoarchives (e.g., speleothems [stalactites and stalagmites], corals, ice cores and lake/ocean sediments) are situated far from pollution emission point sources and simply aren’t exposed to high enough loads to stand out from background (i.e., natural) sulfur levels in the environment (Wynn et al., 2014).
19 Furthermore, they often don’t respond quickly (i.e., annually) to immediate alterations in atmospheric composition.
Trees and their annual growth rings, on the other hand, take up nutrients directly from the external environment. This type of activity results in the production of yearly archives of changing environmental conditions at a very good annual resolution. Still, numerous dendrochronological and dendrochemical studies have shown that obtaining a recognizable sulfur signal (whether natural or anthropogenic) is difficult and challenging
(e.g., Yang et al., 1996; Thomas et al., 2013). This can be explained by the relatively low concentrations of sulfur present in the external environment and uncertainties in post- depositional mobility (i.e., cycling of sulfur through the soil or possible elemental movement radially within trees). These are a few reasons why a small number of tree- ring studies have been conducted using δ34S as the primary isotope of interest. Despite these challenges, Wynn et al. (2014) state that using stable isotopes of sulfur contained within tree rings and their associated signature is one of the most important diagnostic features available for conducting SO2 pollution studies.
Trees receive sulfur inputs from two clear sources: (1) as uptake of nutrients from soil and/or groundwater via root systems and (2) through the direct uptake of gases from the external environment (i.e., atmosphere) by way of penetrating stomata in the foliage.
Direct atmospheric uptake of SO2 is possible when foliage is regularly exposed to the atmosphere and SO2 gas is the medium in which most sulfur is assimilated through foliage (Wynn et al., 2014). During this time, isotopic fractionation is quite minimal and, thus, the resulting sulfur signature is similar to that of the atmosphere (i.e., the unique
δ34S signature of the point source). In cases where trees are exposed to prolonged high
20 concentrations of SO2 pollution, plant defense systems become active (e.g., stomata close) and sulfur compounds, such as hydrogen sulfide gas (H2S), are emitted from foliage to combat the added physiological stress. When this defense response occurs, δ34S enrichment is observed in trees relative to input values (Wynn et al., 2014).
In summary, sulfur isotopes in tree rings represent a weighted balance between sulfur that is (1) assimilated from soil and groundwater stores (products of acid rain), (2) incorporated through direct uptake by foliage from the atmosphere (diffusion through leaf
34 stomata) and (3) emitted as H2S as a stress response leading to δ S enrichment.
2.3. Biological and physiological effects of SO2 on plants
Mooney and Goldstein (1985) state that SO2 enters the leaves and needles of plants primarily in gaseous form through the stomata. Depending on the concentration and length of exposure (acute or chronic), SO2 can have varying biological and physiological effects on plants. Acute exposure refers to a large dose (i.e., high concentration) of SO2 over a short period, while chronic exposure refers to a small dose
(i.e., low concentration) over an extended period (e.g., days, weeks, months or years).
Acute exposure commonly results in observable damage to plants, while chronic exposure commonly manifests itself at a smaller scale (i.e., changes in cell structure or photosynthetic efficiency) (Mooney and Goldstein, 1985). In our study, we are interested in the impacts and/or effects of chronic exposure. Also, the degree to which SO2 is able to impact the health of a plant depends on the species involved, as different species are more tolerable (i.e., better able to detoxify a pollutant) than others and their physiological responses are not consistent (Mooney and Goldstein, 1985).
21 Mooney and Goldstein (1985) summarize a variety of acute and chronic effects on plants – from fumigation experiments and field studies – that SO2 has been linked to.
These effects include damaged trees in the form of foliar injury (e.g., chlorosis and bleaching of leaf pigment) and bark abnormalities; volume loss (i.e., reduced growth); altered root architecture due to acidified soil from acid rain and damage to symbiotic mycorrhizal fungi that help maintain proper nutrient balance; metabolic and biochemical pathway changes (e.g., altered enzyme activity, inhibition of ATP production, damage to other cellular components); damage to protective waxy coating and disruption to guard and epidermal cells (altering membrane permeability and impacting cell wall functions); increased susceptibility to cold/frost damage; increased susceptibility to insect outbreaks and pathogens; water stress; altered stomatal conductance and photosynthesis; mobilization of toxic heavy metals (e.g., Al, Cu, Ni, Co, Zn) into trees in place of normal nutrients (e.g., Ca and Mg); and even mortality/necrosis.
However, it should be noted that sulfur is not always bad for trees (Malhotra and
Hocking, 1976). Beneficial effects of SO2 on plants include positive impacts on health from low levels in species growing in sulfur deficient soils (sulfur is used in the formation of essential amino acids, proteins, and oils); aiding in chlorophyll formation; helping develop and activate certain enzymes and vitamins; and (in low doses) stimulating stomatal opening which may increase photosynthesis (Malhotra and Hocking,
1976).
13 34 2.4. Observed impacts of SO2 on ring widths, δ C and δ S isotopes
22 McCarroll and Loader (2004) identify the annual resolution that tree rings have to offer as one of the greatest advantages of using them as natural archives of environmental histories. Besides allowing dendrochronologists to tell the age of a tree through the exact dating of each annual ring, climate and anthropogenic effects can be reconstructed upon closer inspection and analysis of the layers of wood that are laid down in annual succession. Traditionally, this is done using incremental growth measurements of tree- ring widths (i.e., Fritts, 1976). However, more recent techniques have studied alterations in the chemical composition of the wood produced to form tree rings (McCarroll and
Loader, 2004). For example, Leavitt (2010) notes that tree-ring stable isotope measurements are often used in addition to tree-ring width measurements because the combination of both methods can be more useful for inferring and reconstructing past climate, plant ecophysiology and pollution history. McCarroll and Loader (2004) state that the primary added value of isotope records in tree-ring studies is not simply as samples of ancient air or water (i.e., climate), but as sensitive passive biorecorders of the way that the components of climate have interacted with one another and how the trees have responded to the environments in which they lived.
However, with ever increasing urbanization and industrialization of naturally forested ecosystems in various developed and – perhaps more importantly for the future – developing countries around the world, it is important to take into consideration the suite of non-climatic signals that could also be influencing tree growth, the stable isotopes contained within their rings and, ultimately, the overall health of the forests which are contiguous to these urbanized and industrialized areas (e.g., Savard et al., 2004; Savard et al., 2009). Therefore, it is not surprising that the existing literature examining this
23 concern seems to lend support to the general consensus that increasing emissions of anthropogenic pollution from industrialized areas significantly impacts the surrounding environment and manifests itself as disturbance events in tree responses that natural climate variables alone cannot fully explain (e.g., Boettger et al., 2014; Leonelli et al.,
2012).
Air pollution is a non-climatic, anthropogenic signal that is well known to cause harmful effects on forested ecosystems (Fox et al., 1986). In particular, exposure to SO2 has elicited physiological and morphological responses that have been observed in trees.
Evidence in the literature for altered physiological responses to SO2 is found, for example, in a study conducted in Turkey by Karaoz (2003) where high sulfur content in the leaves and wood of sampled trees corresponded to lower growth ring diameter increments in trees closer to the point source being investigated. Near the point source, most of the trees were found to lose their needles and acid rain burn marks were found on the remaining ones, indicating that most of the trees were unhealthy. The results of the above study clearly illustrate the types of adverse impacts from SO2 on tree physiology and health that would likely be observed (to some degree) in other forests exposed to significant pollution loading.
Furthermore, Keller et al. (1984) observed that tree-ring widths show a decreasing growth trend after being subjected to a continuous 10-week fumigation experiment that used different applications of SO2 concentrations. CO2 uptake and stomatal conductance in the fumigated tree leaves decreased with increasing sulfur content. This lends support to the hypothesis that decreased stomatal conductance eventually leads to stomatal closure – which then results in impaired photosynthesis and reduced growth during
24 episodes of high air pollution. In this particular study, tree-ring effects stemmed from increased SO2 uptake during experimental fumigation, but this is still a reassuring indicator of physiological change that could be mimicked in a natural environment if SO2 emissions are high enough. Keller et al. (1984) also looked at chlorosis (a condition relating to chlorophyll destruction) – a visible sign of SO2 injury – and found in their analyses that there was a significant decrease of chlorophyll in SO2 fumigated trees that brought about morphological changes (e.g., the presence of pale white to yellow leaves).
Once again – though experimental and in contrast to a field study – the results of this study clearly illustrate the types of harmful physiological impacts that SO2 can have on tree morphology and health that would likely be replicated in a natural environment exposed to high enough levels of common industrial pollutants.
In addition to purely elemental concentrations (e.g., Gratton, 1998), carbon-13
(δ13C) and sulfur-34 (δ34S) isotopes have been successfully detected, quantified and analyzed in past dendrochemical and dendroisotopic studies seeking to relate these chemical entities to anthropogenic pollution and emissions point sources (e.g., Boettger et al., 2014; Thomas et al., 2013). δ13C isotopes are commonly used in tree-ring pollution – and climate – studies and have been successfully used to show evidence for the influence of SO2 on their stable isotope ratios. For example, Martin and Sutherland (1990) found a correspondence between tree-ring stable isotope compositions and recent trends in SO2 emissions. Less negative (i.e., more enriched) δ13C isotope values were observed in the trees they studied during periods of elevated emissions. This phenomenon is consistent with the hypothesis from Keller et al.’s (1984) experimental study that stomatal closure
(and resulting decrease in stomatal conductance) resulted in impaired photosynthesis.
25 Thus, it can be inferred from Martin and Sutherland’s (1990) field study that trees likely experienced a reduction in growth during past episodes of high air pollution, as reflected by δ13C values.
Another field study, conducted by Savard et al. (2004), studied the effects of copper smelters located in Rouyn-Noranda, Quebec on δ13C values. The study site was located nine kilometers from the point source (Horne copper smelter) of SO2 emissions, and similar to our study, focused on trees that were older than 100 years in order to evaluate conditions prior to and during periods of smelter operation. The carbon isotope data revealed a sudden increase (i.e., enrichment) of +︎4‰ after the onset of smelter operations. The observed shift was greater than all other variations in regional pre- smelter series that were attributed to natural climatic factors – even trees up to 116 kilometers downwind from the smelter showed positive δ13C shifts following commencement of smelting operations. The results of this study continued to support the hypothesis that trees exposed to high levels of SO2 decrease their level of CO2 uptake through activation of stomatal closure, resulting in an observed enrichment of δ13C isotope values. In fact, a review by Savard (2010) highlights a variety of studies where
δ13C enrichment (+1-5.5‰) is observed in tree species (including white pine) exposed to
SO2 pollution at distances ranging from 10-180 kilometers.
In contrast to stable carbon isotopes, Kawamura et al. (2006) state that sulfur content in tree-ring wood is generally insufficient for analysis using conventional methods, and thus δ34S isotopes are more difficult to work with. In support of this, a study done by Chung et al. (2011) showed that there were no observable physiological effects in trees exposed to a 40 parts per billion (ppb) SO2 environment, indicating that
26 there is likely a threshold concentration of SO2 exposure that must be met in order for the signal to be successfully detected. Because of this problematic issue, it may be more difficult to interpret and elucidate tree-ring sulfur isotope records. Therefore, δ34S isotopes in wood are not commonly used in dendrochemical and dendroisotopic studies due to the very low sulfur concentrations present in wood to begin with (Thomas et al.,
2013). That being said, Kawamura et al. (2006) found clear evidence that δ34S isotope values in tree-rings were dependent on the values of atmospheric sulfur sources, and although the tested trees from Chung et al. (2011) could tolerate their 40 ppb polluted environment, with SO2 concentrations as high as 3,500 ppb (measured in Skead, Ontario) in the vicinity of Sudbury during the 1950s and 1960s (Potvin Air Management
Consulting, 2004), it seems likely that the trees in this region would have responded in some way to a daily dose that was almost 90 times higher. Linzon (1966) suggests that
250 ppb is a good representative threshold value, as the condition of white pine improved remarkably in areas outside of Sudbury where the concentration of SO2 was consistently lower than this value (trees exposed to concentrations above 250 ppb were extensively damaged). Thus, Kawamura et al.’s (2006) evidence suggests that sulfur isotopes in tree- ring wood can indeed be used (albeit with some caution) to explain periods of past atmospheric SO2 pollution.
To support this statement, a recent study conducted by Wynn et al. (2014) in northeastern Italy showed that analyzed tree cores revealed trends in air pollution using sulfur concentrations and isotope measurements. Concentrations of sulfur were seen to increase towards the youngest growth, a reflection of increased SO2 aerosol emissions during the industrial period (circa 1960). Before this period of industrialization (i.e., pre-
27 1960), core samples showed a limited sulfur input, corresponding to a dominant control from the pre-industrial atmosphere (i.e., climate). Inputs of sulfur to the atmosphere, at this time, would largely be attributable to marine aerosols, volcanic eruptions and background contributions from vegetation and soil emissions. Bedrock weathering is another process by which soil and groundwater receive additional sulfur contributions in which a unique sulfur isotope signature is imprinted (Wynn et al., 2014). Concentrations of sulfur were relatively low throughout the pre-industrial period and the sulfur isotopic composition of soils closely reflected that of the atmosphere and the contributions from the underlying bedrock in the region. On the other hand, during the industrial period and even post-1970, the enhanced loading of SO2 into the atmosphere was still evident in the tree-ring concentration and isotope records, regardless of the recorded declines in SO2 emissions that occurred as a result of emission reduction programs and government legislation.
Additionally, supporting the hypothesis of a spatial component to pollution- climate dominated growth and the use of carbon and sulfur isotopes in tree-ring pollution studies, Fox et al. (1986) showed an inverse relationship between tree-ring width variation and distance from point source, whereby the variation explained by sulfur decreased with increasing distance from the smelter. At the same time, the variation explained by the control variables (i.e., climate) increased with distance from the smelter.
Therefore, these findings suggest that a transect design extending from Sudbury along the direction of prevailing winds (to the northeast) will be a useful way to study the spatial effects of climate and pollution.
28 Not only did the above studies find evidence for the negative influence of SO2 on tree-ring isotope compositions during peak industrial periods, they also found that subsequent increases in growth (release events) and the accompanying δ13C and δ34S isotope trends in the tree-ring chronologies of the trees being studied provided evidence for a distinct physiological response (e.g., increases in stomatal conductance and net photosynthesis) to decreases in SO2 emissions upon implementation of emission reduction programs (ERPs). This correlation signifies some of the positive impacts of landmark environmental legislation, such as the United States’ Clean Air Act of 1970, to facilitate recovery of forest ecosystems from exposure to prolonged periods of high atmospheric pollution emissions (the bilateral U.S.-Canadian Air Quality Agreement of
1991 might yield similar positive impacts in Canada).
According to Scheffer and Hedgcock (1955) and Linzon (1966), who looked at injuries to trees caused by SO2 emissions from smelting complexes, white pine trees were observed to be one of the most susceptible species to effects from smelter pollution.
Furthermore, Gunn et al. (1995) state that white pine is among the most sensitive species to SO2 injury. It is not entirely clear why white pine is sensitive to SO2, but Yang et al.
(1982) suggest that the explanation is genetically related, as their fumigation experiments
(using different concentrations of SO2 and in combination with other common pollutants) showed that sensitivity varied considerably between different pine species. According to these findings, white pine should be an optimal species to sample and analyze for establishing a pollution isotope-growth effect correlation. Additionally, white pine is
Ontario’s provincial tree, which provides context for the importance of understanding how SO2 pollution can impact its health and well-being. White pine is also usually easy
29 to recognize. From a distance, their windswept crowns poke high above the canopy of other trees and close up, they can be identified by their 5-7 centimeter long needles which typically grow in bundles of five. The bark of old trees is dark brown-gray and deeply furrowed – making them an ideal species for using an increment borer.
Ultimately, all of the aforementioned experiments and field studies support the use and analyses of ring widths and stable carbon-13 and sulfur-34 isotope records as useful spatiotemporal SO2 pollution proxies.
3. Materials and methods
3.1. Project overview and study design
To investigate our four hypotheses, this dendrochronological study involved the extraction and analysis of two cores from each of eight (White Bear), 18 (Hobbs Lake) and 10 (Kukagami Lake) trees at three sample sites along a 110-kilometer northeasterly
(direction of prevailing wind) transect from Kukagami Lake to Temagami (Figure 2).
Kukagami Lake was as close to Sudbury as we could get where white pine of suitable age were located and easily accessible, while White Bear and Hobbs Lake were selected partly because there was evidence from earlier studies that trees at these sites were climatically sensitive (e.g., Drobyshev et al., 2012). Also, having existing master chronologies for these two sites simplifies the cross-dating and provides a larger pool of ring widths to work with.
This type of study design was selected based on a combination of two (out of four) main approaches to illustrate the effects of industrial pollution on how isotopes behave in trees as outlined by Savard (2010). The first approach involved a comparison
30 of isotopic trends of tree-ring segments grown during a clean period with rings produced during pollution periods, while the second analyzed cause and effect links between pollution levels and isotopic changes on the basis of spatial trends and statistical relationships. The other two approaches Savard (2010) describes are (1) comparison of isotopic trends obtained for exposed and non-exposed trees (control site) and (2) modeling natural isotopic behaviour using pre-industrial series and inferring anthropogenic perturbations in measured isotope chronologies. If our study design was not transect-based, an exposed versus control site approach would have been an excellent alternative – and should be employed in future dendrochronology work using Sudbury as a pollution point source. On the other hand, the modeling approach was avoided because our analyzed time period was relatively short (would have been a good approach if we were dealing with a few hundred years) and we wanted to dedicate the bulk of our time to obtaining real data instead of attempting to generate an accurate model.
In applying these commonly used approaches, we aimed to maximize our chances of observing growth ring trends attributable to SO2 emissions and capturing carbon-13 and sulfur-34 isotope signatures as a unique response to regional climate variation and long-term pollution loads imposed on the area by mining and smelting activity from a globally recognized and significant pollution point source: Sudbury, Ontario. δ13C was selected due to its proven reliability and sensitivity to climate and pollution, while δ34S was selected for its relatively novel use in dendroisotopic studies.
3.2. Site descriptions
31 The three sites investigated in this study are located in northeastern Ontario. A single species, eastern white pine (Pinus strobus), was analyzed at each site: Kukagami
Lake (KL), Hobbs Lake (HL) and White Bear (WB) Old Growth Forest (located in
Temagami, Ontario). The climate of Ontario changes dramatically from season to season and, by virtue of the province’s latitudinal span, is quite different from one location to another. However, Ontario can generally be described as having a humid continental climate (Baldwin et al., 2000). Of the three main climatic regions in Ontario, the study region lies within Central and Eastern Ontario. This region can more specifically be classified as a moderate humid continental climate, where summers are usually warm and
(sometimes) hot while winters are long and cold with plentiful snowfall and precipitation that parallels the climatic region of Southern Ontario. Temperature and precipitation is affected by three main air sources: cold and dry arctic air from the north, Pacific polar air from western Canada, and warm, moist air from the south (Gulf of Mexico) and eastern
Canada (Atlantic Ocean) (Baldwin et al., 2000).
The White Bear collection site covered an area of approximately 0.1 km2
(100,000 m2). The soil in this area can be described as a well-drained humo-ferric podzol.
The selected white pines were located on a moderate slope and within 200 meters of a lake (Pleasant Lake). The trees were about 40 feet tall and their diameters ranged from 2-
2.5 feet. Their crowns were observed to breach the canopy, towering far above the lower levels of the forest. Other co-occurring species included red pine, cedar and birch.
Dispersed among the old growth were snags and dead wood. At this site white pine appeared to be in good health without any visible signs of injury (e.g., chlorosis or bark abnormalities).
32 The Hobbs Lake collection site covered an area of approximately 0.4 km2
(400,000 m2). The soil in this area can be described as a water-saturated organic mesisol.
The selected white pines were located on a moderate-steep slope that progressed to a hilltop. Trees were within 50 meters of various small lakes/ponds that were scattered about. The trees were about 30-40 feet in height and their crowns were observed to breach the canopy. Their diameters ranged from 2-2.5 feet. Other co-occurring species included red pine, cedar and birch. At this site white pine appeared to be in good health without any visible signs of injury.
The Kukagami Lake collection site covered an area of approximately 0.3 km2
(300,000 m2). The soil in this area can be described as a water-saturated organic mesisol.
The selected white pines were located on relatively flat ground within 100 meters of either Kukagami Lake or Portage Lake. The trees were about 40 feet tall and their crowns were observed to breach the canopy. Their diameters ranged from 2-3 feet. Other co- occurring species included red pine, birch and maple. At this site white pine appeared to be in good health without any visible signs of injury.
The total area bounded by the east-west and north-south extremes of the study sites is approximately 3,000 km2 and the topographic relief (change in elevation) across the region varies no more than 40 meters (Kukagami Lake at 285 meters above mean sea level [MSL] and Temagami at 320 meters above MSL).
The vegetation contained within the general study area belongs to the Great
Lakes-St. Lawrence forest region. This type of forest is dominated by hardwood trees, but features a variety of species such as maple, oak, yellow birch, red and white pine.
According to the Ontario Ministry of Natural Resources and Forestry, coniferous trees
33 such as white pine, red pine, hemlock and white cedar, are commonly found with deciduous broad-leaved species, such as yellow birch, sugar and red maples, basswood and red oak. Much of the growth in the Great Lakes-St. Lawrence forest is uneven aged.
This means that both young and old trees can be found within the same stand of trees.
The soils in the area, as outlined by the Canadian System of Soil Classification (Natural
Resources Canada), are largely of the organic (around Kukagami Lake and Hobbs Lake) and podzolic (around Temagami) orders. Organic soils (mesisol) are identified by horizons composed largely of organic materials that are frequently saturated with water for prolonged periods of time. Podzols (humo-ferric) are identified by B-horizons with accumulations of humified organic matter combined with aluminum (Al) and iron (Fe).
Baldwin et al. (2000) describe the geology of the study area as dominantly composed of Shield bedrock of Proterozoic age that is associated with the Grenville
Province (1.0-1.6 Ga) that borders on the older Southern Province (1.8-2.4 Ga) to the north. The Grenville Province is dominated by metasedimentary rocks that form the
Laurentian Highlands. In contrast, surficial deposits in the area are largely glaciolacustrine (silty and clayey till as well as clay-rich lake and marine deposits) that are the result of the northward retreat of the Laurentide Ice Sheet.
3.3. Fieldwork and tree-ring data
Fieldwork at the White Bear (Temagami) site was conducted during the months of
October and November in 2013 – following the cessation of the growing season.
Fieldwork at the Hobbs Lake and Kukagami Lake sites was conducted during the months of May and June in 2014 – before the trees started to put on their 2014 growth ring.
34 White Bear Old Growth Forest (N 47º03’15.5”, W 079º45’14.3”) is located roughly one kilometer southeast of Temagami, Ontario (approximately 100 kilometers north of North Bay, Ontario following Hwy 11). The forest itself can be accessed by foot using Red Fox Trail, which branches off from Jack Guppy Way. Accessing the oldest growth requires a few kilometers of hiking, but the trails are well maintained and marked and the old growth is easily identifiable. Cores of white pine were taken from trees located in the general area of the coordinates listed above, which correspond to an area near the east end of Pleasant Lake.
Hobbs Lake (N 46º43’53.1”, W 080º06’13.9”; also shown as Kennedy Lake on some maps) is located approximately 100 kilometers northwest of North Bay, Ontario following Trans-Canada Hwy 17 west to Sturgeon Falls; Route 64/539 northwest to River
Valley; then St. Joseph Road north to the coordinates listed above. White pine of appropriate age was accessed and cored no more than 100 meters off the south side of the road using old logging trails.
Kukagami Lake (N 46º43’18.7”, W 080º35’13.6”) can be found approximately
130 kilometers northwest of North Bay, Ontario following Trans-Canada Hwy 17 west past Sturgeon Falls and Verner and then heading 20 kilometers north on Kukagami Lake
Road. Suitable white pines were observed on both sides of the road near the Kukagami
Lake Emergency Helipad. Samples were collected along the stretch of road and adjacent forest between the helipad and Sportsman’s Lodge.
Fox et al. (1986) studied growth ring variation in trees that had been exposed to high SO2 emissions. Therefore, our study closely followed and adhered to the general sampling criteria used in Fox et al.’s (1986) study. First, the trees had to be dateable and
35 possess annual growth rings that could be accurately assigned to specific years. Second, the trees had to date prior to the onset of smelting activity and the high emissions period
(i.e., pre-1960) in order to establish a control period. Third, there had to be an abundance of the sampled tree species (at least 20) at each sample site to ensure adequate replication with respect to cross-dating and ring-width analyses – while only four trees are necessary for isotope work. Fourth, an effort was made to ensure that all sample sites were as homogeneous as possible in terms of macroclimate, topography, soil type, hill-slope, elevation and other various micro-environmental factors (though this was difficult to ensure given the relatively large geographical scale of the study). This was done to allow for the best possible site comparisons and for minimizing the contribution to variability in ring widths and isotope values by site-specific factors. Ensuring homogeneity (i.e., trees living in similar environments), often times determined qualitatively in the field, provides a good opportunity for observing common climate and SO2 pollution signals (and how they might manifest with varying differences) in tree-ring width and isotope records at the three study sites.
In order to test our hypotheses, standard dendrochronological and dendrochemical field techniques and laboratory methods were employed throughout the duration of this study (i.e., Fritts, 1976; Stokes and Smiley, 1968). Core samples of white pine were collected from the study sites using a 5.15 mm diameter Haglöf increment borer at roughly breast height. Two core samples per tree (180º apart around the circumference) were collected in order to minimize bias from uneven radial growth. Any available preexisting site chronologies were obtained from the International Tree-Ring Data Bank
(ITRDB) for use in cross-dating and to help verify dating accuracy.
36 3.4. Climate records and SO2 emissions data
Climate records for Sudbury and North Bay were obtained from the National
Climatic Data Center’s (NCDC) National Oceanic and Atmospheric Administration
(NOAA) Climate Data Online database. Fields including maximum temperature, minimum temperature, average temperature and total precipitation were gathered. The
Sudbury climate data was recorded at the Greater Sudbury Airport meteorological station
(CA006068150) with full coverage extending back to 1954. The North Bay climate data was recorded at the North Bay (aka Jack Garland) Airport meteorological station
(CA006085700) with full coverage extending back to 1939. The length of these two climate records is not ideal (i.e., extending back to 1900 would be optimal), but their coverage is excellent (i.e., consistent without any data gaps) for the period in which they span and they are the closest stations to our study sites, geographically.
SO2 emissions data for Sudbury smelters were extracted from a 2008 Sudbury
Area Risk Assessment report (SARA, 2008). The emissions record extends back to 1931
(when SO2 emissions first started to be recorded) and is complete to present. In this record, peak emissions in the 1960s are clearly identifiable as well as large reductions beginning around 1970 due to government (Ministry of the Environment) controls and limits on SO2 now in place.
3.5. Sample measurement, dating and preparation
All core samples were packaged, labeled and transported back to the
Dendrochronology Laboratory at Nipissing University. Upon removal of the samples from their respective packaging straws, they were immediately mounted using wood glue
37 and string to hold the core firmly in place while the glue set overnight. The following day, the cores were sanded using progressively finer grits of sandpaper in order to make individual rings easier to see (i.e., identifiable) for cross-dating.
Cross-dating is a fundamental principle in dendrochronology that allows for the absolute dating of tree rings (Douglass, 1941). All ring-width series were first visually cross-dated following the methods of Stokes and Smiley (1962). Ring widths were then measured (to within 0.001 mm) using MeasureJ2X (VoorTech) and read into computer software in order to perform a quality control (QC) check for accuracy in the measured ring-width series. Visual cross-dating was checked for accuracy using COFECHA
(Holmes, 1983) and the “dplR” package in R (Bunn, 2008). The raw ring-width series were then detrended to remove any age-related growth trends and standard chronologies
(using ratios) were produced using the “dplR” package (Bunn, 2008). Measured values were detrended using a cubic smoothing spline (rigidity of 0.67) because this detrending method resulted in the lowest variability (i.e., flattest fluctuation) around the mean ring- width index (RWI). The mean value RWI chronologies were built using Tukey’s bi- weight robust mean, which works to minimize the effects of outliers (Bunn, 2008).
After all ring-width measurements had been made, preparation for isotope work began with the milling out of each annual growth ring using a drill. In order to quantify isotopic variability within each sample site, Leavitt and Long (1984) and Loader et al.
(2013) suggest that a minimum of four cores should be selected and their tree rings pooled for analysis (Leavitt, 2010). Rings from the same year were pooled together in order to achieve masses adequate for detection using mass spectrometry (typically 20-30 mg of wood). However, decadal years (e.g., 1900, 1910, 1920, etc.) were left unpooled
38 from each core in order to determine the between-tree (i.e., individual) variability. After collecting 114 years (1900-2013) of pooled rings, samples for δ13C analysis were purified to α-cellulose using the modified Brendel method (Brendel et al., 2000; Gaudinski et al.,
2005; Anchukaitis et al., 2008). Samples assigned for δ34S analysis, on the other hand, were pooled in 10-year blocks (e.g., 1900-1909, 1910-1919, 1920-1929, etc.) and left as dry wood (i.e., no chemical processing required) for mass spectrometry work (i.e.,
Thomas et al., 2013). Ten-year blocks were chosen because sulfur concentrations are typically very low in individual growth rings and we wanted to increase our chances of successfully detecting δ34S, so lower temporal resolution was deemed a necessary compromise in order to accumulate an adequate amount of material (~80 mg).
A strong effort was made to omit the first 15-20 years of growth rings from pith
(i.e., the first couple decades of a tree’s life) in order to minimize any possible “juvenile” isotope effect. Leavitt (2010) describes this effect as a phenomenon of increasing δ13C
(about 1.5–2‰) in the early years of tree growth. A possible explanation for this observed phenomenon is that juvenile wood may possess different physical and/or anatomical characteristics compared to those of normal wood. Freyer (1979) studied pine trees and observed the juvenile isotope effect lasting two decades, so this is why the first
15-20 years of growth were avoided in the sampling procedure and analyses of our white pine. Leavitt (2010) further states that the juvenile isotope effect tends to override trends attributable to changes in climate, which could also potentially be inferred as a false SO2 signal (i.e., climate interference event) if high emission years coincide with the juvenile years of tree growth.
39 3.6. Chemical processing of α-cellulose
All δ13C samples were chemically processed for isotope ratio mass spectrometry
(IRMS) work under a fume hood in Nipissing University’s Geography Department Wet
Laboratory using the modified Brendel method. The laboratory setup and equipment allowed for 40 samples to be fully processed at one time and involved nine distinct steps that are outlined as follows.
First (Step 1), drilled wood samples were appropriately pooled then weighed to a mass of 20-30 mg. Next, a hot plate (Fisher Scientific Isotemp) was turned on and heated to a temperature of 120 ºC. 240 µl of 80% acetic acid (CH3COOH) was added to each microcentrifgue tube along with 24 µl of 69% nitric acid (HNO3) in order to remove lignin and noncellulose polysaccharides. The tubes were capped and gently mixed to completely saturate the powdered wood and to homogenize the solution. Next, the tubes were placed in the aluminum hot block on the hot plate and were boiled at 120 ºC for 30 minutes. This combination of heat and time allowed for the initial digestion of the raw wood into its cellulose components. Analyzing the samples at any temperature below 120
ºC runs the risk of leaving chemical reactions incomplete and a lower level of α-cellulose purity (Brookman and Whittaker, 2012). However, too hot and the risk of explosion is increased as excessive pressure may build up within each tube. After 30 minutes the tubes were removed from the hot block and allowed to cool for 5-10 minutes.
Following the cooling period, 240 µl of 99% ethanol (C2H6O) was added (Step 2) to halt the chemical reactions that the acids and heat generated. The tubes were then capped and subjected to a five-minute vortex (Step 3) at 5,000 revolutions per minute
(rpm) using a Fisher Scientific accuSpin Micro 17 centrifuge. After mixing, the
40 supernatant was pipetted into an acid waste container. Next (Step 4), 400 µl of 99% ethanol was added to each tube and, again, centrifuged at 5,000 rpm for five minutes.
After mixing, the supernatant was pipetted into a solvent waste container.
Step 5 involved the addition and mixing of 400 µl of deionized water (H2O) to each tube and was repeated twice in order to ensure a thorough wash/rinse of the samples
(i.e., removal of residual acid and ethanol reagents).
For Step 6, the temperature of the hot plate was lowered to 70 ºC and 200 µl of
17% liquid sodium hydroxide (NaOH) was added to each tube. The tubes were mixed and then placed in the hot blocks for 10 minutes. This step allowed for the removal of holocellulose (defined as the portion of cellulose that does not dissolve in a 17.5% solution of NaOH). After heating, the tubes were removed and centrifuged for two minutes. The liquid NaOH was pipetted out followed by an addition of 400 µl of 1% hydrochloric acid (HCl). The tubes were then centrifuged and the solution was pipetted out once again.
Following this, Step 5 was repeated 2-3 times until the solution neutralized. Next
(Step 7), 400 µl of 99% ethanol was added, mixed and centrifuged. The supernatant was then pipetted into the appropriate waste container. In order to aid in the drying process,
Step 8 involved the addition of 400 µl of acetone (C3H6O). The tubes were centrifuged for a final time and the supernatant was pipetted out. The tubes were left uncapped and the chemically processed samples were allowed to dry overnight in the fume hood (Step
9). The following day the tubes were recapped and the labels double-checked for legibility before being packaged up and sent away for isotope analysis.
41 3.7. Isotope ratio mass spectrometry (IRMS)
Upon completion of chemical processing, all samples were sent to the University of Alaska Anchorage’s Environment and Natural Resources Institute Stable Isotope
Laboratory (ENRI SIL) for analysis. In the lab, two ThermoFinnigan Continuous Flow
Isotope Ratio Mass Spectrometers (Delta Plus XP and Delta V Advantage) with four gas preparation systems for the measurement of the stable isotopes of carbon and sulfur (δ13C and δ34S) were utilized on the processed and dry wood samples. The ThermoFinnigan isotope ratio mass spectrometers are fed by four analytical streams, however, for the purpose of this study, isotope analysis only required the use of the elemental combustion system for carbon and sulfur elemental analysis (EA). For this study, δ13C samples were analyzed using a Costech EA directly coupled to a ThermoFinnigan Delta V Advantage via continuous flow. Analytical precision of this instrument was 0.02‰.
Sartorius Microbalances (ME5 and CP2P) allowed the Stable Isotope Laboratory to weigh samples precisely to within 0.001 mg, or 1/100,000 of a gram. This level of precision was necessary for accurate determination of both mass percentage and isotopic composition of each sample. Each microbalance was connected to a computer via a RS-
232 interface to allow rapid transfer of weights into electronic sample lists. Calibrations, raw data and final results were compiled and then δ13C values during the industrial period
(considered post-1850) were corrected for the Suess effect using values for δ13C of atmospheric CO2 from McCarroll and Loader (2004) up to 2003 and a linear
13 extrapolation of the trend of decreasing δ C of atmospheric CO2 for the period from
2004 to 2013. Unfortunately, sulfur concentrations in our samples were discovered to be
42 far too low for detection by isotope ratio mass spectrometry and, thus, δ34S values were not obtained (expanded on in a later section).
3.8. Statistical analyses
Correlation analyses for each site were performed in order to elucidate relationships between climate, SO2 pollution, tree-ring widths and stable isotope compositions. Ring-width measurements and stable isotope ratio data were compared to
Sudbury and North Bay climate data using the “treeclim” package in R (Zang and Biondi,
2015) where Pearson correlation coefficients (r) were calculated using a significance level of α = 0.05 (5%). The “treeclim” package calculates significance by using a multivariate extension of a Monte Carlo method (Zang and Biondi, 2015). Monte Carlo methods are a class of computational algorithms that utilize repeated random sampling to obtain numerical results. For example, in the correlation function analyses a total of 1,000 random data sets are generated, where the climate time series are simulated as
Gaussian/statistical noise and the proxy series (i.e., ring widths or isotope values) as a linear combination of the climate parameters using the coefficients of the original correlation function (Zang and Biondi, 2015). An error component (variance equal to the variance unexplained by the individual climate predictors) is also applied and each repeated sampling calculates a moving correlation function using the same settings. The standard deviation (σ) over the individual windows for each climate predictor is then compared to the bootstrapped distribution (i.e., random samples) of standard deviations of the simulated data to test for significantly higher or lower low-frequency modulations
(Zang and Biondi, 2015). Correlations with SO2 emissions data were calculated using
43 Microsoft Excel. The significance level was selected because it is commonly used in the field of dendrochronology for similar climate-pollution studies (e.g., Leonelli et al., 2012;
Rinne et al., 2010).
Seasonal (partial) correlation analyses (“seascorr” function) were used to calculate partial correlations of tree-ring data with a primary (precipitation) and a secondary (temperature) climatic variable for seasons of different lengths (1, 3 and 6 months). The correlation of the primary variable with tree growth (i.e., RWI) or isotope value was computed as the Pearson correlation coefficient, while the influence of the secondary variable on tree growth was computed with the influence of the primary variable on tree growth removed (Zang and Biondi, 2015). These analyses provided information on significant growing season (current and previous) temperature and precipitation contributions that reflected the highest influence on ring-width growth and isotope composition.
Correlation function analyses (“dcc” function) were used to calculate moving correlation functions with ring-width chronologies, isotope chronologies and monthly climatic data. For the moving case, the calculation is performed repeatedly for consecutive time windows. This function allowed for the creation of arbitrarily complex selections of climate parameters for calibration (e.g., 3-month bins of temperature means and precipitation sums). Empirical non-exceedance probabilities were used to test the coefficients of the correlation function with the original data for significance (Zang and
Biondi, 2015). These analyses not only provided information on significant growth months/bins (like the seasonal correlation analyses), but also the years in which these
44 temperature and precipitation bins reflected the highest influence on ring-width growth and isotope composition.
4. Results and discussion
4.1. RWI chronologies of White Bear, Hobbs Lake and Kukagami Lake
Figures 3, 4 and 5 show segment plots for the White Bear (WB), Hobbs Lake
(HL) and Kukagami Lake (KL) study sites, respectively.
The WB site was composed of 16 eastern white pine (Pinus strobus) series and contained the youngest core samples of the three sites that were investigated. At this site, the oldest core (WB015A) that was collected dated back to the year 1844. The majority of others dated prior to the year 1900 and the remaining cores were about 25 years younger on average. This finding was somewhat surprising as the WB study site was located within an old-growth/ancient forest and some nearby red pine (Pinus resinosa) that were sampled had rings that extended into the late 17th century (1694). However, our study only required trees as old as the late 19th century and that objective was successfully met at WB. A previously established chronology (Cook, E.R., Temagami
Lake PIRE ITRDB CANA037, www.ncdc.noaa.gov/paleo/study/3035, September 10,
2013) located close to the WB study site exists. This chronology, compiled in 1983, was built from red pine and contains a number of core samples that date back to the first half of the 17th century. This is in line with our findings of the much older red pine at WB.
Since our collected tree species differs from the red pine studied by Cook, computed ring-width indices (RWIs) are likely not comparable (as (1) chronologies are generally compiled and contrasted using a single species and (2) preliminary visual inspection of
45 each species’ core samples revealed drastically different ring-width patterns), so cross- correlating the WB ring-width series to the previously established master chronology from Temagami Lake was not undertaken. Instead of visually cross-dating or cross- correlating with Cook’s chronology, the dating of each white pine series was first visually cross-dated with one another and then double-checked by computing correlations between all of the series from the study site (using the “dplR” package in R) to verify accuracy in dating. Results indicated that the majority of cores (based on the 50-year segments they are decomposed into) correlated above the critical value (at α = 0.05), indicating that the core samples were accurately dated. There were a few cores that the software flagged as correlating below the critical value – indicating the possibility of a dating error – but these cores were visually checked again in order to maintain dating accuracy and quality.
The HL site contained the oldest core samples that were collected from the three study sites and was composed of 36 series. The oldest core sample (HL002B) taken was from a white pine that dated back to 1816. One other core (HL002A) was close, whereas the remaining samples were no older than about 1875, with the majority not dating past the year 1900. Thus, minimum age requirements were successfully met for a certain number of cores in order to proceed with analysis. As with the WB site, a site chronology
(Guyette, R.P., Hobbs Lake PIST ITRDB CANA128, www.ncdc.noaa.gov/paleo/study/3450, May 2, 2014), compiled in 1994, had been established prior to the commencement of sampling at the HL site. The original Hobbs
Lake collection consists of white pine series, which was beneficial as it allowed for a site master chronology to be generated, visually cross-dated against and then correlated to
46 using the “dplR” package in R. Results indicated that most series were a good match to the original Hobbs Lake chronology and this provided support for our HL core dating accuracy. Additionally, each series was double-checked by computing correlations between the other series from the study site. This analysis – like WB – yielded positive results as only a few cores indicated segments with possible dating errors (which were visually checked again) while the majority correlated above the critical value to verify the accuracy of dating.
Dating of the KL core samples revealed that the oldest core (KL010B) at the KL site dated back to 1833, making it the intermediate site in terms of maximum sample age.
Most of the core samples – 20 in total – at this site indicated that the trees were around
100 years old on average with the majority of series extending back to the year 1900
(meeting the suitable age requirement for analysis). No previously constructed chronology existed on the International Tree-Ring Data Bank (ITRDB) near Kukagami
Lake, so there was not an original master chronology available for comparison. After visual cross-dating, the dating of each series was double-checked by computing correlations between the other series from the study site. This analysis yielded positive results similar to the WB and HL sites, as only a few cores indicated segments with possible dating errors while the majority correlated above the critical value. This finding helped in the verification of the accuracy and quality of tree-ring dating at this sample site.
Figures 6, 7 and 8 show RWI chronologies for the WB, HL and KL study sites, respectively. High RWI values indicate wide rings and low values indicate narrow rings.
The sample depth (shaded area) indicates how many core samples (i.e., series) are
47 contributing to the computed RWI. The chronologies are used to illustrate the high (and low) frequency changes that ring widths undergo over the course of multiple centuries
(complete 20th, but only partial 19th and 21st centuries). Values of the RWI chronologies at all three sites oscillate (rather uneventfully) around the mean over the time period in which they overlap (~1850 onwards) and examining the chronologies more closely reveals one interesting anomaly: a rather abrupt decrease in the KL RWI during the 1960s followed by a couple sharp peaks (of approximately equal, but opposite amplitude) of high RWI in the second half of the 1970s through the 1980s (see also Image 1). As this study site was closest in proximity to Sudbury smelters (the SO2 point source), it may be that the high pollution load over the time of abrupt RWI decrease was great enough to negatively affect the growth of the trees at KL and cause the sequence of narrow rings and resulting low RWI (e.g., Long and Davis, 1999; Rydval and Wilson, 2012;
Stravinskiene et al., 2013). From 1975 onward, substantial decreases in SO2 emissions may have been responsible for the “release event” (i.e., increase) that is seen in the KL
RWI. As white pine at this site were the tallest of all species, it may also be possible that the release event was simply related to the trees breaching the canopy and having competition removed (similar to what we observed at WB). The three chronologies will be investigated in more detail in subsequent sections, but they are introduced here to display how natural and anthropogenic variables, such as climate and pollution, respectively, might influence the RWI in a specific year or group of years over the short- and long-term.
Table 1 displays RWI summary statistics for the three study sites. Notation follows that of Cook et al. (1990) where Ntot is the total number of correlations
48 calculated, Nwt is the number of within-tree correlations computed, Nbt is the number of between-tree correlations computed, rtot is the mean of all the correlations between different cores, rwt is the mean of the correlations between series from the same tree over all trees, rbt is the mean interseries correlation between all series from different trees, ceff is the effective number of cores (takes into account the number of within-tree correlations in each tree), reff is the effective signal (incorporates both within- and between-tree signals), EPS is the expressed population signal (a measure of how the sample chronology portrays the hypothetically perfect chronology), and SNR is the signal-to- noise ratio (another possible measure of chronology quality). Cook et al. (1990) state that an EPS value of 0.85 is desirable to measure the chronology confidence and indicates acceptable statistical quality. The HL chronology (EPS = 0.87) exceeded this value and the KL chronology (EPS = 0.81) came very close. This indicated – if we are to be more liberal and lower the EPS threshold to 0.80 for the purpose of this study – that these two site chronologies were of acceptable statistical quality and, thus, reliable for further analysis. On the other hand, the WB chronology fell short with an EPS value of 0.72, which was lower than desired. This was likely the result of (1) lower between-tree correlations, (2) a weaker effective signal, (3) a relatively low SNR (2.58), or a combination of all three. However, all attempts were made to maximize the WB EPS and, after this, it was still decided to proceed with further analysis as it may have only been the lower number of trees sampled (eight) that “artificially” lowered the EPS. If this is the case, the WB chronology – along with KL – may have benefited from the sampling of about 10 additional trees in order to boost the EPS to match or exceed that of HL (and the
0.85 ideal).
49 Figures 9, 10 and 11 display continuous Morlet wavelet transforms for the WB,
HL and KL sites, respectively. A Morlet wavelet transform decomposes the RWI chronology into a multi-resolution analysis (i.e., the wavelets) from small-scale (e.g., 4- year period) through to large-scale (e.g., 256-year period). This is helpful for identifying larger scale climate patterns/oscillations that operate on multi-year or multi-decadal cycles as opposed to year-to-year (i.e., annual) climate variability. Examples include the
Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic
Multidecadal Oscillation (AMO), El Niño Southern Oscillation (ENSO) and Pacific-
North American teleconnection pattern (PNA). Some cycles are better understood than others and have been linked to findings in dendrochronological studies in the past (e.g.,
Rigozo et al., 2003; Wiles et al., 2009; Trouet and Taylor, 2010). The linkage to larger scale climate patterns and variability was not the main objective of this study – as we were more interested in how ring widths and stable isotope ratios change annually in response to climate and other driving factors (e.g., pollution) – but, it was exciting to observe that the Morlet wavelet transform analysis for the WB site indicated a strong and statistically significant ~30-year period signal outside the cone of influence (COI). The signal spanned the entire length of the chronology that was available for interpretation
(approximately 1885-1985). This 30-year signal is interpreted to be the result of an interaction of the Pacific-North American teleconnection pattern and other large-scale
Pacific patterns/oscillations. A teleconnection is a statistical association among climatic variables that are separated by large distances (typically thousands of kilometers).
Leathers et al. (1991) state that teleconnections are the result of large-scale ocean and atmosphere dynamics linking regional climates to one unified, global climatic system.
50 The PNA is arguably one of the most dominant and important modes of atmospheric circulation over North America (and more generally, the Northern Hemisphere) and strongly influences climate variability on seasonal, interannual and interdecadal time scales (Yarnal and Leathers, 1988; Ghanbari and Bravo, 2008). It is known to strongly influence winter and spring temperatures and precipitation, but can influence climate throughout the year as it is strongly linked to the position of the jet stream (NOAA,
2012). The PNA also exhibits variability over multi-decadal time scales (Trouet and
Taylor, 2010). Trouet and Taylor (2010) observed this climatic pattern in tree-ring series from comparable latitudes to our study sites (albeit more western longitudes) in Montana and Wyoming where a strong 30-year signal was observed over a roughly 100-year timespan (from 1870 onwards). Wiles et al. (2009) found an influence of the PNA on
Lake Erie water levels by correlating them with Gulf of Alaska (North Pacific) tree rings, where it is known positively that the PNA’s effect operates. Therefore, these two studies’ findings provide support and plausibility to the idea that the PNA can influence climate
(and ultimately the tree rings that are a product of climate) in northeastern Ontario, as our study sites are not that far off – latitude- and longitude-wise – from the combined locations of the aforementioned studies. St. George (2014) states that positive correlations between RWI chronologies and PNA index (PNAI) are nearly twice as common (and are stronger) than negative correlations. In northeastern Ontario (i.e., regions relatively close to our study sites), correlations are much weaker than those generated using tree-ring chronologies from Pacific/western locations and typically produce Pearson correlation coefficients within the range of -0.20 to +0.20 (St. George,
2014). Indeed, when we correlated our WB site chronology with PNAI, we obtained a
51 weak positive correlation of r = 0.16 (p < 0.05). When we correlated another Ontario white pine chronology from the ITRDB (Guyette, R.P., Dividing Lake PIST ITRDB
CANA127, www.ncdc.noaa.gov/paleo/study/3448, February 12, 2016) with the PNAI, a weak negative correlation of r = -0.11 (p < 0.05) was returned. Although opposite in direction (i.e., negative), the strength of the correlation is similar to that of our WB chronology and within the typical range that St. George (2014) presents. The same 30- year signal was also observed in the Morlet wavelet transform analyses for the HL and
KL sites. Interestingly, the signal strength (i.e., correlation) – though consistent over the length of the HL chronology – weakens compared to that of WB. Furthermore, the 30- year signal at the KL site was even weaker than that of HL and doesn’t consistently span the length of the site chronology. Correlations with PNAI at these sites were non-existent.
Ault and St. George (2010) identify northeastern Ontario as exhibiting significant decadal to multi-decadal signals in winter precipitation variability. They go on to say that these signals are not coherent across Canada and that they are not likely forced by a single common factor (e.g., the PNA). It may be that strong decadal to multi-decadal variability is the result of two or more large-scale climate oscillations interacting with one another, or the additive effect of another component of the regional climate system (Ault and St.
George, 2010). Therefore, some caution must be used when interpreting or inferring large-scale climate patterns using tree-ring widths.
One possible explanation for the weakening of the 30-year signal and absent correlations with PNAI is the influence of industrial SO2 pollution from Sudbury smelting activity, introduced prior to the start of the 20th century, which may be interfering with large-scale climate in some way. As the signal diminishes with
52 decreasing distance to Sudbury smelters (the pollution point source) and vice versa (i.e., strengthens with increasing distance from Sudbury), we suspect that SO2 pollution may have interfered with the PNA signal, almost entirely masking it at the KL site. Xu (2001) observed the effects of SO2 emissions on decadal climate patterns in China, as regional climate patterns (relating to flood and drought events) appeared to change/shift in response to a large presence of SO2 and sulfate aerosols in the atmosphere. As Canada and China are very close in geographic size, it seems reasonable that regional climate
(having an influence from cyclic patterns like the PNA) in northeastern Ontario might also be altered, so that over the multi-decadal time scales in which the PNA operates, the signal becomes muffled or undetectable. However, St. George (2014) states that ring- width responses to the PNA are less spatially coherent and may be connected through a more complex chain of causes linking climate modes, local climate and seasonal tree growth (i.e., coupled with other Pacific climate patterns or ENSO activity).
Diaz and Markgraf (2000) state that variability in the PNA pattern is also linked to large-scale ocean-atmosphere circulation patterns, specifically that of the El Niño
Southern Oscillation (ENSO). ENSO is a high frequency (i.e., 2-7 years) coupled ocean- atmosphere process in the eastern and central equatorial Pacific Ocean that is a primary driver of interannual climate variability in North America. In addition to the strong 30- year signal that we saw in our wavelet data, a weaker ~7-year period signal was observed.
If the 30-year signal is indeed the interaction of the PNA and other Pacific oscillations, then it seems possible that the 7-year signal present (relatively strong in WB, slightly weaker in HL and absent in KL) is that of the ENSO. Shabbar and Khandekar (1996) describe the impact of ENSO on climate across Canada and Fu et al. (2012) observe a
53 linkage of ENSO to streamflow across southern Canada, which aids in justifying our interpretation of the 7-year signal. It is, again, possible that the absence of this signal at the KL site is due to interference from SO2 pollution.
4.2. RWI-climate analysis
Figures 12, 13 and 14 display seasonal correlation analysis (SCA) plots for the
WB, HL and KL study sites, respectively, that were generated using the “treeclim” package in R (Zang and Biondi, 2015). Site RWIs were correlated with monthly North
Bay (WB and HL) and Sudbury (KL) climate data. The primary and secondary variables used were precipitation (monthly sums) and temperature (monthly averages), respectively. Correlations were computed for season lengths of 1, 3 and 6 months and statistical significance is reported for p < 0.05.
For the WB site RWIs, the highest statistically significant correlations with North
Bay climate data were found with current growth year (CGY) summer precipitation sums. Given a season length of 1 month, July precipitation (r = 0.22) correlated best;
July-September (r = 0.18) for a 3-month season length; and April-September (r = 0.22) for a season length of 6 months. The weak positive correlations mean that as precipitation
(i.e., rainfall) increases in each month for a given season length, the RWI increases as well, and vice versa. This makes sense, as you would expect ring widths to vary according to available water in a generally precipitation-limited climate regime like northeastern Ontario. As all significant correlations for all season lengths were associated with precipitation in the summer months (and some of the spring), it appears that summer
54 precipitation of the CGY is the most influential climatic control on RWI at the WB site and is the variable that our sampled trees respond to the most.
For the HL site, significant positive correlations were observed with CGY July precipitation (r = 0.27) data from North Bay for a 1-month season. This was also the case for CGY July-September precipitation (r = 0.24) for a 3-month season. Additionally, the previous growth year (PGY) seemed to impact RWI at this site, as summer precipitation
(July-September) correlated moderately with RWI (r = 0.38). For a 6-month season, significant correlations were seen with CGY spring/summer precipitation (April-
September r = 0.22) and PGY precipitation over the summer and fall months (highest r =
0.35). These findings indicate that not only does CGY precipitation influence RWI at the
HL site, but that accumulated and stored water from the previous year’s summer/fall is also an important (and according to the analysis, more statistically significant) control on ring widths and overall tree growth. This might be due to trees at this site utilizing stored water from the previous year to aid in kick-starting growth the following spring. As with
WB, it appears that summer precipitation of the CGY is a weak climatic control on RWI at the HL site. However, a key difference is the finding that PGY precipitation appears to explain more of the RWI and that precipitation in fall months also seems to contribute to correlations that are observed at this site.
For the KL site, significant positive correlations were once again observed with
CGY July precipitation (r = 0.29) using Sudbury climate data for a 1-month season. This, too, was the case for CGY March-May (r = 0.29), April-June (r = 0.35), May-July (r =
0.40) and June-August precipitation (r = 0.33) for a 3-month season. 6-month seasons yielded even higher correlations, with CGY February-July (r = 0.44), March-August (r =
55 0.44) and April-September (r = 0.37) precipitation yielding moderate correlations between climate and RWI. Similar to the HL site, 6-month seasons showed significant positive correlations with PGY precipitation over the summer/fall months (highest r =
0.36), indicating an influence (though not as much as at HL) from fall precipitation on growth of the following year. Like the first two sites, it appears that summer precipitation of the CGY is the dominant climatic control on RWI at the KL site, while precipitation of the PGY also had somewhat of an impact.
The SCAs of site RWIs and climate data was mostly unanimous among the three study sites. SCA revealed that RWIs were moderately correlated with summer precipitation of the CGY while summer/fall precipitation of the PGY also had an influence at two of the sites. This tells us that, in terms of RWI, tree growth from all three study sites is controlled more by seasonal precipitation than temperature.
Figures 15, 16 and 17 display correlation function analysis (CFA) plots for the
WB, HL and KL study sites, respectively, that were also generated using the “treeclim” package in R. Like the SCAs, site RWIs were correlated with monthly North Bay (WB and HL) and Sudbury (KL) climate data where the primary and secondary variables used were precipitation (monthly sums) and temperature (monthly averages), respectively.
Calculations were performed repeatedly for moving time windows and statistical significance is reported for p < 0.05.
Figure 15 seems to support (to an extent) the results of the WB site RWI SCA in that spring/summer precipitation is an influential control on growth at this site (highest r
= 0.61). This generally holds true from around 1960 until 2012 when the dataset ends
(although precipitation correlations weaken in the early 1970s and become stronger with
56 summer temperature). Before 1960, moderate correlations are observed with winter/spring temperature (highest r = 0.57) from 1941-1952 and summer temperature
(highest r = -0.51) from 1953-1959. This climate signal might be better understood and explained when considering the PNA teleconnection pattern. First, depending on which phase the PNA was in (positive or negative), the associated characteristic changes in climate patterns might be responsible for the alternating influence of precipitation and temperature over the analyzed time period. For example, a trend towards earlier growth onset might be observed in trees from 1941-1952 due to warmer spring temperatures that help to initiate tree growth earlier than normal in the growing season (e.g., Lechowicz,
1984; Menzel and Fabian, 1999). These warmer spring temperatures might be brought on by a positive phase of the PNA, which is characterized by above normal temperatures and below average precipitation. Wetter and cooler conditions, on the other hand, are associated with the negative phase of the PNA (Trouet and Taylor, 2010). Therefore, the
“flip-flopping” in climate correlations may be related to the phase in which the PNA was operating at a specific time. For example, the moderate to strong correlations with winter/spring temperatures may be related to a particularly dominant positive phase of the
PNA (which may be linked to the ENSO as well).
The CFA plot for HL (Figure 16) resembles the one for WB and its correlation pattern displays a striking similarity to that of the WB site as well. Most of the significant correlations are in line with the RWI SCA for HL in the sense that summer precipitation is an influential control (highest r = 0.41) on growth since 1960. Like WB, before 1960, moderate correlations are observed with winter/spring temperature (highest r = 0.60) from 1941-1952 and summer temperature (highest r = -0.37) from 1953-1959. As the
57 observed correlation pattern is similar to that of WB, we once again attribute the fluctuating climate variables to large-scale climate cycling, specifically that of the PNA and/or ENSO. That being said, there are some time windows that lack statistically significant correlations to climate data and the overall strength of the above correlations are weaker than at the WB site. A possible explanation for the much weaker (or totally absent) correlations over these periods (e.g., 1968-1974) might be an interference effect from SO2 emissions (as they were just coming down from their peak at that time) that masked the influence of natural climate (e.g., Rinne et al., 2010; Leonelli et al., 2012) as this site was much closer to the pollution point source than WB was.
Figure 17 appears to support the results of the KL site RWI SCA in that summer precipitation contributes to growth the most at this site (highest r = 0.62), albeit with moderate correlations to temperature (highest r = -0.59) over a good range of moving time windows also. Unlike the first two sites, the climate signal at KL is clear and distinct over the entire temporal range, without displaying any extended gaps or loss of climate signal. It appears that spring/summer precipitation has always correlated well with RWI at this site and statistically significant correlations span the majority of the moving time windows. Considering the SCA plot for KL, it appears that precipitation is the most influential climate variable over the time period that is included for analysis. Since acid rain is common in areas with exposure to high SO2 pollution (e.g., Hutchinson and
Whitby, 1977; Johnson et al., 1981), we hypothesize that the trees at this site were able to acclimate to the increased acidity and still respond well to precipitation. As temperature did not become significantly correlated until around 1970, and given that KL is the closest site to the pollution point source, we theorize that the SO2 pollution load may
58 have been great enough to inhibit the effect of temperature until emissions were reduced to much lower levels in the 1970s – thus unmasking the effect of summer temperature on
RWI.
The CFAs of site RWIs and climate data showed similarities between the WB and
HL sites (e.g., moderate correlations with summer precipitation) while the KL plot appeared unique. As previously mentioned, the Morlet wavelet transforms indicated a 30- year period signal in the RWI chronologies of WB and HL that was interpreted to be the interaction of the PNA and other large-scale Pacific climate patterns. If this is indeed the case, then it is possible that the “flip flopping” correlations between precipitation and temperature are a product of a large-scale climate pattern that we are detecting. We did not observe this signal in the Morlet wavelet transform for the KL site, so it is not surprising that we don’t observe this trend in the CFA. However, we must also consider the possibility that these findings stem from the use of two different meteorological datasets and that the CFAs we generated are simply a byproduct of the geographical proximity (i.e., spatial relationship) to the associated meteorological station. Another possibility is that our findings are simply a function of when precipitation is high enough
(i.e., when the trees are not water stressed) then tree rings are sensitive to temperature, but if precipitation is low (invoking a water stress response) than precipitation is what tree rings are most sensitive to.
4.3. RWI-pollution analysis
Pearson correlation coefficients (r) were calculated between site RWIs and SO2 emissions data from Vale smelters. The period selected for correlation analysis was from
59 1931-2013 because it spanned the length of available emissions data and it was desirable to know whether RWIs respond not only to years of high/peak emissions, but to substantial emission reductions toward the end of the time series as well.
The correlation coefficients for WB, HL and KL were -0.13, -0.13 and -0.25*, respectively (* = statistically significant). It makes sense that the r-value is highest at the
KL site as it is geographically closest to the emissions point source (e.g., Leonelli et al.,
2012). This finding also lends support to the hypothesis that the sequence of narrow rings
(low RWI values over the 1960s) in the KL chronology is related to SO2 emissions, where compressed rings are often observed during periods of high pollution and then followed by a release event (high RWI values) upon termination of the high pollution load (e.g., Hirano and Morimoto, 1999; Zeng et al., 2014). On the other hand, the other two sites’ correlation coefficients were lower and don’t appear to reflect a spatial pattern to RWI-SO2 interaction, as the correlation coefficient for HL is the same as WB. We would expect HL to be slightly higher, given its closer geographical position to Sudbury, if there was a spatial component to SO2 ring-width influence. Instead, this indicates that both the WB and HL sites are likely too far (i.e., the scale of distance too great) from
Sudbury smelters to have felt an impact and that RWI was largely a product of climate as indicated by the SCA and CFA plots. If this is true, then the SO2 must have been so dilute in the atmosphere – outside a ~50 km radius – to not pose a direct threat (i.e., measureable effect) to tree growth, thus allowing climatic variables (and larger climate patterns) to have more of an impact. This would certainly explain the insignificant correlations between RWI and SO2 that are observed at these two sites.
60 4.4. δ13C chronologies of White Bear, Hobbs Lake and Kukagami Lake
Figures 18, 19 and 20 show the δ13C chronologies for the WB, HL and KL study sites, respectively. All three chronologies span a 114-year period from 1900-2013. The chronology plots show both raw (uncorrected) and corrected δ13C values for comparison.
This was done in order to demonstrate just how much of an impact the industrial period
th and increasing atmospheric CO2 concentrations – especially in the second half of the 20 century – have on δ13C values (e.g., Rinne et al., 2010). As can be seen, raw WB and KL
δ13C values clearly trend upward (i.e., become less negative) from 1900-1959 – albeit KL continuing this upward trend until around 1975, while HL values only display a very minor upward trend over this period. On the other hand, the low frequency trends in the latter half (from 1960-2013) of all three raw chronologies show the typical decline commonly seen in raw tree-ring δ13C series as a result of the decline in the atmospheric
13C/12C ratio (e.g., Rinne et al., 2010). In order to remove this trend, mathematical correction for the increasing atmospheric CO2 concentration and resulting decreased
13C/12C ratio was utilized to remove the effect of changes in the δ13C of the atmosphere
(Feng and Epstein, 1995). McCarroll and Loader (2004) present a table of annual correction factors (∆) necessary to quote tree-ring δ13C values after the year 1850 to a pre-industrial standard value of -6.4‰ (this value is widely accepted in the literature) to allow for better climate correlations (McCarroll et al., 2009). These corrections are developed from atmospheric δ13C values extracted from Antarctic ice core and firn
(granular snow) samples (Francey et al., 1999). In all three δ13C chronologies, the correction seemed to successfully remove the obvious declining trend post-1960, except for a slight declining trend still visible in the KL site chronology. However, this might
61 not be a declining trend that is attributable to atmospheric CO2 concentrations, but rather
13 a result of the enrichment of δ C over the 1960s and 1970s due to the influence of SO2 emissions (e.g., Wagner and Wagner, 2006) that causes the trend to continue upward before leveling off post-1980 (i.e., causing the appearance of a CO2-related decreasing trend). To better quantify the effect of the mathematical correction, the range of raw WB
δ13C is from -25.38‰ to -21.09‰ while the corrected data increases both minimum and maximum values to -24.80‰ and -20.32‰, respectively. The range of raw HL δ13C is from -26.43‰ to -22.55‰ while the corrected data increases both minimum and maximum values to -26.07‰ and -21.39‰, respectively. The range of raw KL δ13C is from -26.27‰ to -21.43‰ while the corrected data increases both minimum and maximum values to -25.98‰ and -20.54‰, respectively.
It is important to mention, at this point, that care was taken to not include in the analyses tree rings from selected cores that were within 15 years of pith in order to avoid any possible juvenile isotope effect (about a 1-2‰ enrichment in δ13C) that Leavitt
(2010) warns of. The HL and KL cores used for δ13C analysis did not contain any sample material that might be of concern. However, upon inspection of the cores used for δ13C analysis at the WB site, two cores (out of five) did include tree rings that were within 15 years of pith and a sharp ~1.5‰ increase is observed in the WB δ13C chronology before
1910. This rise could be attributed to an influence by juvenile material, but, that being said, the majority of cores used for analysis at this site were not of concern, so the increase observed in the chronology could just as likely be attributed to an environmental signal specific to this site (i.e., normal δ13C variability). Therefore, as the data were
62 deemed to be valid, there was no need to truncate any of the δ13C chronologies and the entire 114-year period was subsequently used for analysis.
With respect to the cores used for δ13C analysis, Table 2 shows a measure of between-tree (interseries) correlations for individually sampled (unpooled) cores. It is included to show how each tree’s δ13C correlated to the site as a whole. Overall, WB cores correlated best (rbt = 0.82); HL samples ranked second (rbt = 0.73); and KL cores had the lowest correlations of the three (rbt = 0.64). This order seems to follow a spatial pattern related to distance from Sudbury, however, it is impossible to tell whether this is the result of climate, an effect of SO2, site-specific microenvironmental factors, or purely coincidence. That being said, we believe that between-tree correlation differences at WB and HL were largely the result of site-specific microenvironmental factors, while differences at KL were the combined effect of site-specific microenvironmental factors and SO2 pollution. As WB was the farthest site from Sudbury, it is reasonable to assume that individual tree δ13C values varied according to microenvironmental factors such as topography and competition between younger and older growth. At WB, the sampled trees were all in close proximity – relative to the other two sites – and thus similar in microenvironment, which would explain the small rbt range of 0.10 (lowest range of the three sites). At HL, variation in δ13C values can also be attributed to microenvironmental factors. As some samples were taken up to a kilometer apart, it is plausible that a higher rbt range of 0.37 resulted from amplified differences in site-specific conditions (i.e., some sampled trees were located on steep hill-slopes while others were on flatter terrain and close to a swamp [moisture differences]). On the other hand, as we have some evidence
13 in favour of an SO2 influence at the KL site, we attribute variation in δ C values to a
63 combination of microenvironment and SO2. KL has the highest rbt range of 0.61, which we believe is the result of samples being collected from the largest and (potentially) most diverse geographic area relative to the other two sites (i.e., some sampled trees were located close to the lake while others were deeper in the adjacent forest near a camp site and cottages) as well as being exposed to the added stress of SO2 pollution which likely had varying degrees of impact on each individual tree. In order to be certain, though, in the future a detailed survey of each site should be undertaken prior to sample collection with a more quantitative focus – as opposed to a qualitative assessment of each site that was undertaken in this study – directed at individual tree responses as opposed to the site as a whole.
4.5. δ13C-climate analysis
Figures 21, 22 and 23 display SCA plots for the WB, HL and KL study sites, respectively. Site δ13C values were correlated with monthly North Bay (WB and HL) and
Sudbury (KL) climate data. The variables used were the same for RWI correlations: precipitation (monthly sums) as the primary variable and temperature (monthly averages) as the secondary variable. Correlations were computed for season lengths of 1, 3 and 6 months and statistical significance is reported for p < 0.05.
For the WB site, statistically significant correlations for δ13C data with climate included CGY winter/spring precipitation across 3 and 6-month seasons (highest r = -
0.35), while the PGY month of December precipitation seemed to have a weak influence given a 1-month season (r = -0.29). Therefore, it appears that winter/spring precipitation for the CGY is the largest determinant of the δ13C value at WB. This illustrates a seasonal
64 difference between precipitation that affects RWI and precipitation that affects δ13C values at the WB site: where ring widths responded to summer precipitation while δ13C responded more to winter/spring precipitation. This seems to indicate that the trees at this site put on most of their annual wood by accessing summer precipitation while the δ13C value is dictated by residual winter precipitation and early spring precipitation used to initially start growth in the CGY. Overall, it is precipitation that controls δ13C on a seasonal basis at the WB site with little to no relationship with temperature.
For the HL site, moderate correlations for δ13C data were observed with temperature. Over 1, 3 and 6-month seasons, winter, spring and summer temperatures
(highest r = 0.41) for the CGY all seemed to correlate well with the δ13C value. This means that there is not one season in particular that seems to best reflect the δ13C value, and instead, it is largely year round temperatures that influence δ13C.
For the KL site, moderate correlations for δ13C data were seen almost exclusively with summer precipitation (with some influence from spring months). 1, 3 and 6-month seasons indicated that precipitation for seasons ending in July and August (highest r = -
0.49) for the CGY best determined the δ13C value.
The SCAs for δ13C at all three sites generally show that the δ13C values are almost entirely related to the CGY climate, with little to no influence from the PGY. They also indicate that there is not a common seasonal climate control and that the δ13C values at each site are site specific and dependent on the interplay of microclimate in each geographic area. Sensula et al. (2015) came to a similar conclusion with respect to their
δ13C results from pine trees studied in close proximity to a power plant in Poland.
65 Figures 24, 25 and 26 display CFA plots for the WB, HL and KL study sites, respectively. Like the SCAs, site δ13C values were correlated with monthly North Bay
(WB and HL) and Sudbury (KL) climate data where the primary and secondary variables used were precipitation (monthly sums) and temperature (monthly averages), respectively. Calculations were performed repeatedly for moving time windows and statistical significance is reported for p < 0.05.
Figure 24 generally supports the findings of the WB δ13C SCA in that summer precipitation controls δ13C values the most at this site (highest r = -0.52). This is true from around 1970 to the end of the analyzed time period and also during the late 1950s.
Other than briefly showing statistically significant correlations during the late 1950s, the remaining time windows fail to correlate well with climate data and the precipitation signal is lost. Spring temperatures appear to take over in influencing δ13C values during the 1960s (highest r = -0.48). Since consistent climate correlations are lacking before the
1960s, it may be that SO2 emissions had enough of an impact on the trees at this site to interfere with normal climatically controlled growth (e.g., Leonelli et al., 2012; Boettger et al., 2014). For example, up until the 1970s, precipitation might have been too acidic for the trees to respond to before SO2 levels dropped significantly post-1970. Another possible explanation for the observed correlation is that severe drought in Ontario in the early 1960s caused the precipitation signal to be lost and a temporary switch to temperature controlled δ13C values took place (Gabriel and Kreutzwiser, 1993; Klaassen,
2000). Additionally, warming of global temperatures in recent decades may explain the stronger correlations to summer temperatures near the end of the analyzed time period,
66 especially if hot summer temperatures cause water stress and limit growth (Klaassen,
2000).
Figure 25 supports the results of the HL δ13C SCA in that winter, spring and summer temperatures control δ13C values the most at this site (highest r = 0.68 for summer and r = 0.63 for winter/spring). There were no statistically significant correlations with precipitation data – much like the δ13C SCA for the HL site. Similar to the WB site, a consistent climate signal is lacking before the early 1960s. This could, once again, possibly be due to the high pollution emissions in the 1950s (and prior open pit roasting) and subsequent reductions starting in the 1970s. Perhaps reduced SO2 emissions post-1970 allowed for temperature (i.e., a climate signal) to become identifiable and influential in the HL δ13C chronology without interference from high levels of pollution. A reasonable explanation for the earlier onset of a consistent climate signal (1963-1969) is drought conditions in the 1960s that may have strengthened the relationship to summer temperature.
Figure 26 supports the results of the KL δ13C SCA in that spring and summer precipitation strongly controls δ13C values at this site (highest r = -0.71). Strong correlations of δ13C with spring/summer precipitation essentially hold true for all moving time windows in the data frame. As the Sudbury dataset has fewer time windows than that of North Bay, we can’t observe whether the climate signal is lost before the mid
1950s. The correlations are still statistically significant pre-1960, so based on the available information it does not appear that the climate signal weakens – though this may seem counterintuitive as the KL site is closest to the SO2 pollution point source. An interesting, and isolated, correlation to summer temperature starting at the 1965 time
67 window and ending at the 1973 time window (highest r = 0.59) is also observed, which may capture the drought conditions of the 1960s (similar to WB and HL). From the KL
δ13C CFA, it does not appear that the δ13C climate signal experienced any interference, but this may have been observed if the Sudbury dataset extended farther back in time. It may also be that, as the trees at this site were (relatively) close to the pollution point source, they were forced to quickly acclimate to more acidic precipitation that continued to influence δ13C values.
4.6. δ13C-pollution analysis
Pearson correlation coefficients (r) were calculated between site δ13C values and
SO2 emissions data from Vale smelters. The entire time series of emissions (i.e., years with available SO2 records), from 1931-2013, was selected for correlation analysis. This period was chosen because we were interested in correlating δ13C with the entire history of smelter emissions in order to see if those long-term patterns were reflected in the annual δ13C chronologies (i.e., to identify an influence from pollution). We also isolated the period of 1955-1969 for finer temporal resolution as this period contained the years of highest/peak emissions. This way, we might have a better chance of identifying a distinct
13 SO2 signal that has a control/influence on δ C.
Results of the correlation analyses for both temporal periods of interest did not return any statistically significant correlations. Comparison of mean δ13C values before
(pre-1955), during (1955-1969) and after (post-1969) peak emissions also did not yield any results in favour of a temporal isotope pattern. These findings indicate that SO2 emissions do not likely directly impact δ13C values – at the scale of this study, at least –
68 nor do they indicate any type of spatial pattern (i.e., gradient) along the study transect.
This finding appears to contradict that of the RWI-SO2 analysis, where SO2 was found to significantly (though rather weakly) correlate to ring widths at the closest site (KL). This leads us to believe that pollution emissions were likely high enough to impact tree growth within a certain radius, but not strong enough to influence the annual isotopic composition of tree rings (e.g., Zeng et al., 2014). However, the following figures attempt to illustrate how SO2 pollution can interfere with temperature and precipitation to influence climate relationships within site δ13C chronologies.
Figures 27, 28 and 29 are line graphs of WB, HL and KL site δ13C, respectively.
δ13C values have been normalized and are plotted with normalized annual average temperature data for the three months (i.e., bin) that each corresponding δ13C CFA indicated strongest correlations with (e.g., JJA for WB). Normalized SO2 emissions are also included for comparative purposes. These graphs are provided as additional analyses in an attempt to illustrate the potential impact of pollution on climate by showing divergence between the δ13C chronology and temperature time series during the 1960s when pollution emissions were at their greatest (similar to figures in Rinne et al., 2010).
For WB, a divergence in δ13C and average temperature is observed between 1960 and 1973. For HL, a divergence is observed between 1965 and 1975. A divergence is also observed for KL, which occurs between 1962 and 1973. Keeping in mind the study of
Rinne et al. (2010), it seems reasonable to attribute the observed divergence to SO2 emissions, as their isotope series dramatically diverged with climate data upon onset of a high emissions period. This relationship could not be quantitatively demonstrated (i.e., produce statistically significant correlations) for the WB site – though this is not entirely
69 surprising, as WB δ13C is better correlated with precipitation. On the other hand, δ13C- climate correlations strengthened at HL when emissions were reduced (r = 0.20 from
1939-1970 and r = 0.40 from 1971-2012). Since relatively strong correlations existed with temperature at KL, a strengthening of δ13C-climate correlations was also observed at this site (r = 0.02 from 1954-1970 and r = 0.32 from 1971-2012). It should also be noted that all three sites appear to diverge over the late 1970s and early 1980s, but it is not known what factors are responsible for this observation. One possibility is that the sites are simply more correlated with precipitation over that time period – as the following precipitation-related graphs do not display divergence in the 1970s and 80s.
Figures 30, 31 and 32 are line graphs of WB, HL and KL site δ13C, respectively.
δ13C values have been normalized and are, this time, plotted with normalized annual precipitation sums for the three months (i.e., bin) that each corresponding δ13C CFA indicated strongest correlations with (e.g., AMJ for HL). Normalized SO2 emissions are also included for comparative purposes. These graphs are similar in nature to the above temperature comparisons and are intended for the same illustrative purpose.
For WB, a divergence in δ13C and precipitation is observed between 1962 and
1968. For HL, a divergence is observed between 1962 and 1967. A divergence is also observed for KL, which occurs between 1962 and 1969. As with the temperature analysis, it seems reasonable to attribute the observed divergence to SO2 emissions, as it is common across all three sites. To support this statement, δ13C-climate correlations strengthened at WB when emissions were reduced (r = -0.01 from 1939-1970 and r = -
0.31 from 1971-2012). A slightly stronger relationship to climate was also observed upon emission reductions at KL (r = -0.45 from 1939-1970 and r = -0.53 from 1971-2012).
70 HL, on the other hand, correlated best with temperature, so a strengthening relationship with precipitation was not observed.
Therefore, when we look at the above temperature and precipitation plots and quantitatively compare them, results generally indicate that δ13C-climate relationships strengthen as SO2 emissions decline beginning in the 1970s. After observing these coincident divergent series, we propose that climatic calibration or reconstruction over known SO2 pollution periods is unsuitable.
On a final note for pollution analysis, Figures 33, 34 and 35 show calculated intrinsic water-use efficiency (iWUE) values plotted with CO2 concentrations for WB,
HL and KL, respectively. The iWUE of HL follows the trend of increasing CO2 concentrations quite nicely (e.g., Feng, 1999), so no further explanation is required for that site. On the other hand, both WB and KL iWUE plots display anomalous behaviour from 1940-1985 (WB) and from 1950-1995 (KL). The increases in atmospheric CO2 concentrations are not enough to account for the increased iWUE values, so it is hypothesized that SO2 emissions from smelters in Sudbury might explain the pattern observed in the WB and KL iWUE series. At these sites, it is possible that exposure to
SO2 (if emissions were able to reach the great distance of the WB site) may have been sufficient enough to induce stomatal closure (i.e., inhibit stomatal conductance), which is reflected by the accelerated increase in iWUE over the above time periods (Rinne et al.,
2010 were able to illustrate a similar result). This result can be attained when SO2 interferes with stomatal openings, reducing them in size or destroying guard cells (i.e., physiological damage) to the point that it causes general water stress (Choi et al., 2014).
As SO2 emissions returned to low levels at the start of the 1980s, stomatal conductance
71 recovered (i.e., returned to normal) and iWUE returned to reflecting changes in atmospheric CO2 concentrations (e.g., Fuhrer et al., 1993). Why this effect was not seen throughout the entire spatial gradient (i.e., at the HL site) remains unknown. It could possibly be due to HL residing slightly south of the ideal transect and, thus, not being exposed to the prevailing winds that might carry SO2 in a more NNE direction (i.e., bypassing the HL site).
4.7. δ34S analyses
Unfortunately, due to an unexpected equipment failure in the lab at which the samples prepared for δ34S analyses were initially sent, results were unable to be obtained.
Upon sending them to a second facility, it was determined that the % sulfur (i.e., concentration) in the samples was much lower than expected as it was discovered that the
% sulfur was on the order of 0.02-0.002% (about an order of magnitude smaller than the concentrations used by Thomas et al., 2013). This is much too low for any mass spectrometer to generate a δ34S value, short of doing some additional processing for which we lack the equipment. Unfortunately, this was discovered at too late a date for us to resolve this issue for this study. Therefore, it is not possible to comment on δ34S with respect to SO2 pollution emissions for any of the study sites.
5. Conclusions
The RWI and δ13C chronologies generated and analyzed for the White Bear,
Hobbs Lake and Kukagami Lake study sites produced results that indicated weak to moderate strength correlations with climate variables. At all sites, RWI and climate
72 correlations on a seasonal level (i.e., the seasonal correlation analyses) displayed findings of statistically significant correlations with either temperature or precipitation records from North Bay and Sudbury meteorological stations. At all three sites, RWIs were moderately correlated with seasonal precipitation from the current growth year and also showed some influence from climate of the previous growth year. RWI correlation function analyses indicated a “flip flopping” between temperature and precipitation control at WB and HL, which, along with a multi-decadal signal observed in the Morlet wavelet transforms, was interpreted to be the combined influence of the Pacific-North
American teleconnection pattern and other large-scale Pacific climate oscillations. These relationships, in contrast, were lacking at the KL site – hypothesized to be a result of
13 climate masking by SO2 pollution. In terms of δ C, WB and KL values seasonally correlated best with precipitation, while HL values correlated best with temperature.
Interestingly, in contrast to RWIs, the previous growth year did not seem to influence
δ13C values. Correlation function analyses for δ13C indicated a relatively consistent climate signal post-1960 (showcasing what we believe to be a drought event in the
1960s), but before this time a strong climate signal is absent at WB and HL. KL managed to retain a consistent climate signal throughout, but this may have been a result of the trees rapidly acclimating to changing conditions (e.g., acidic precipitation and acidified soils) or the effect of a temporally limited Sudbury dataset.
Therefore, it appears that precipitation is generally the dominant climate variable for controlling RWI and δ13C at WB and KL and that temperature is the variable that HL is most sensitive to. That being said, these are general statements, and the truth of the matter is that these two climate variables are constantly mingling and contributing to the
73 correlations we see with respect to ring widths and δ13C – not to mention additional climate variables and microenvironmental factors specific to each site. Simply put, this means that tree growth and isotope composition is not always – and unsurprisingly so – influenced by a single variable and that clear relationships are difficult to establish and elucidate. From this study, findings suggest that there is interplay of not only climatic variables (at least two that we are able to comment on), but also of an added influence from SO2 pollution that operates with climate to impact annual ring growth and carbon isotope ratios.
In regards to pollution, it was difficult to elucidate a clear and direct relationship with SO2 emissions from the Vale smelters in Sudbury. Correlation analysis with RWI chronologies yielded a statistically significant result for the KL site (r = -0.25) while WB and HL did not produce any statistically significant findings. This means that the sequence of narrow rings that were visible in the core samples from KL may have been related to the peak SO2 emissions period. On the other hand, correlations between annual emissions data and δ13C values were not observed directly, but there was some
13 convincing indirect evidence for the influence of SO2 (e.g., divergent δ C-climate plots, absent climate correlations pre-1960 and strengthened climate correlations post-1970).
Therefore, our findings suggest that SO2 influenced ring widths at the KL site and interfered with natural climate to mask (to an extent) δ13C correlations at all three study sites. Another piece of evidence in support of an inferred SO2 signal comes from the calculated iWUE values for WB and KL. At these two sites the values are offset from atmospheric CO2 concentrations during periods of peak smelting activity and associated high emissions. It is possible that during these observed periods of offset, SO2 pollution
74 affected stomatal conductance (i.e., induced stomatal closure or led to physiological impairment) that resulted in an iWUE increase. Why this phenomenon isn’t also observed in the iWUE values of the intermediate site (HL) is unknown and is somewhat mysterious.
With respect to the four hypotheses put forward at the beginning of this study:
13 34 “(1) high aerial emissions of SO2 influence tree-ring growth, δ C and δ S stable isotope compositions in white pine trees around the Sudbury area, (2) there is a spatiotemporal component to pollution-climate dominated growth along the study transect, (3) white pine trees show recovery upon implementation of emission reduction programs (ERPs) and (4) white pine trees are reliable passive biorecorders of anthropogenic SO2 pollution emissions”, it is felt that the evidence obtained from our study provides support in favour of all four, albeit not very strong. (1) Ring widths at the closest site (KL) weakly correlated (with statistical significance) to SO2 emissions data and the hypothesized PNA signal (i.e., large-scale climate) was not detected in the KL chronology; δ13C lacked statistically significant correlations to climate data pre-1960, but maintained a consistent and strengthened climate signal after this time; δ34S relationships cannot be commented on. (2) It is difficult to make a strong case for a spatial component. For example, a RWI-
δ13C-pollution correlation gradient was not entirely observed, especially beyond the KL site. Also, WB iWUE inferred an SO2 influence while HL iWUE did not. However, temporally, the observed δ13C patterns make sense when emission history is considered and this is the stronger half of the spatiotemporal component. (3) A release event was observed in the RWI chronology for KL and, additionally, the normalized data line graphs indicate a (strengthened) return to following temperature and/or precipitation
75 trends after the divergence in series is observed during the peak emissions period. (4) At up to 50 kilometers away from the point source, white pine trees at the KL site were able to record an observable and quantifiable effect attributed to SO2 pollution, rendering them as reliable biorecorders. It is not unreasonable to suggest that the other two sites were, ultimately, too far away from the point source or that the concentrations of SO2 in the atmosphere were so diluted below threshold response levels by the time they reached these far sites that the pollution signal wasn’t strong enough to leave a more detectable – and convincing – footprint.
In summary, identifying the influence and impact of anthropogenic SO2 pollution from Sudbury smelters in the RWI and δ13C analyses for each study site proved challenging and difficult as trees at our sites still responded and correlated well with climate variables. However, we did manage to generate some evidence supporting traces of a pollution signal and given the spatial scale of the study, this proved satisfactory.
Given this finding, it is our opinion that dendrochronologists should use caution if performing paleoclimatic reconstructions using ring widths or δ13C measurements in areas of close proximity (~50 km) to known exposure of SO2 emissions, while sites farther away may be of only minor concern. In the future, it would be advisable to perform a pollution study similar to this on a much smaller scale using the exposed versus control site study design. This way, stronger correlations and links between ring
13 34 widths, δ C, δ S and anthropogenic SO2 pollution might more easily be revealed and clearer pollution relationships established. Partnering with the mining industry may also prove to be beneficial, as both parties stand to gain valuable knowledge on how pollution impacts tree growth and chemistry.
76 References
Anchukaitis, K. J., Evans, M. N., Lange, T., Smith, D. R., Leavitt, S. W., & Schrag, D. P. (2008). Consequences of a rapid cellulose extraction technique for oxygen isotope and radiocarbon analyses. Analytical chemistry, 80(6), 2035-2041.
Ault, T. R., & St. George, S. (2010). The Magnitude of Decadal and Multidecadal Variability in North American Precipitation. Journal of Climate, 23(4), 842-850.
Baldwin, D., Band, L., & Desloges, J. (2000). Physical Geography of Ontario. Ecology of a Managed Terrestrial Landscape: Patterns and Processes of Forest Landscapes in Ontario. UBC Press, 2000. Print.
Barber, V. A., Juday, G. P., & Finney, B. P. (2000). Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress. Nature, 405(6787), 668-673.
Boettger, T., Haupt, M., Friedrich, M., & Waterhouse, J. (2014). Reduced climate sensitivity of carbon, oxygen and hydrogen stable isotope ratios in tree-ring cellulose of silver fir (Abies alba Mill.) influenced by background SO2 in Franconia (Germany, Central Europe). Environmental Pollution, 185, 281-294.
Bouillon, D. (2003). Preliminary assessment of 100 years of Pb emissions from the Inco Sudbury operations. Internal INCO document. NV.
Brendel, O., Iannetta, P. M., & Stewart, D. (2000). A rapid and simple method to isolate pure alpha-cellulose. Phytochemical Analysis, 11(1), 7-10.
Brienen, R. J., & Zuidema, P. A. (2005). Relating tree growth to rainfall in Bolivian rain forests: a test for six species using tree ring analysis. Oecologia, 146(1), 1-12.
Brookman, T., & Whittaker, T. (2012). Experimental assessment of the purity of α‐ cellulose produced by variations of the Brendel method: Implications for stable isotope (δ13C, δ18O) dendroclimatology. Geochemistry, Geophysics, Geosystems, 13(9).
Bunn, A. G. (2008). A dendrochronology program library in R (dplR). Dendrochronologia, 26(2), 115-124.
Cernusak, L.A., Marshall, J.D., Comstock, J.P, Balster, N.J. (2001). Carbon isotope discrimination in photosynthetic bark. Oecologia 128, 24–35.
Choi, D., Toda, H., & Kim, Y. (2014). Effect of sulfur dioxide (SO2) on growth and physiological activity in Alnus sieboldiana at Miyakejima Island in Japan. Ecological research, 29(1), 103-110.
77 Chung, C., Chung, P., & Liao, S. (2011). Carbon fixation efficiency of plants influenced by sulfur dioxide. Environ Monit Assess. 173, 701-707.
Climate Data Online. NCDC NOAA. www.ncdc.noaa.gov/cdo-web. Web. 26 November 2014.
Cook, E.R., Briffa, K., Shiyatov, S., Mazepa, A., and Jones, P.D. (1990). Data analysis. In: Cook, E.R., Kairiukstis, L.A. (Eds.), Methods of Dendrochronology: Applications in the Environmental Sciences. Kluwer Academic Publishers, Dordrecht, pp. 97–162.
Diaz, H.F. & Markgraf, V. (2000) El Nino and the southern oscillation: multiscale variability and global and regional impacts. Cambridge University Press, Cambridge.
Doucet, A., Savard, M. M., Bégin, C., Marion, J., Smirnoff, A., & Ouarda, T. B. (2012). Combining tree-ring metal concentrations and lead, carbon and oxygen isotopes to reconstruct peri-urban atmospheric pollution. Tellus B, 64.
Douglass, A. E. (1941). Crossdating in dendrochronology. Journal of Forestry, 39(10), 825-831.
Drobyshev, I., Goebel, P. C., Bergeron, Y., & Corace, R. G. (2012). Detecting changes in climate forcing on the fire regime of a North American mixed-pine forest: A case study of Seney National Wildlife Refuge, Upper Michigan. Dendrochronologia, 30(2), 137-145.
Farquhar, G.D. & Lloyd, J. (1993). Carbon and oxygen isotope effects in the exchange of carbon dioxide between terrestrial plants and the atmosphere. In: Ehleringer, J.R., Hall, A.E., Farquhar, G.D. (Eds.), Stable Isotopes and Plant Carbon–Water Relations. Academic Press, New York, pp. 47–70.
Farquhar, G.D., O’Leary, M.H., Berry, J.A. (1982). On the relationship between carbon isotope discrimination and intercellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9, 121–137.
Feng, X. (1999). Trends in intrinsic water-use efficiency of natural trees for the past 100– 200 years: a response to atmospheric CO2 concentration. Geochimica et Cosmochimica Acta, 63(13), 1891-1903.
Fox, C., Kincaid, W., Nash, T., Young, D., & Fritts, H. (1986). Tree-ring variation in western larch (Larix occidentalis) exposed to sulfur dioxide emissions. Canadian Journal of Forest Research, 16(2), 283-292.
78 Francey R. J., Allison C. E., Etheridge D. M., Trudinger C. M., Enting I. G., Leuenberger M., Langenfelds R. L., Michel E. and Steele L. P. (1999). A 1000-year high 13 precision record of δ C in atmospheric CO2. Tellus 51B, 170–193.
Freyer, H. D. (1979). On the 13C record in tree rings. Part I. 13C variations in northern hemispheric trees during the last 150 years. Tellus, 31(2), 124-137.
Fritts, H. C. (1971). Dendroclimatology and dendroecology. Quaternary Research, 1(4), 419-449.
Fritts, H. (1976). Tree rings and climate. Academic Press, London, 567. Reprinted in: Methods of Dendrochronology Vols. II and III, L. Kairiukstis. Z. Bednarz and E. Feliksik (ed). Proceedings of the Task Force Meeting on Methodology of Dendrochronology: East/West Approaches, 24 June, 1986, Krakow, Poland.
Fu, C., James, A. L., & Wachowiak, M. P. (2012). Analyzing the combined influence of solar activity and El Niño on streamflow across southern Canada. Water Resources Research, 48(5).
Führer, G., Payer, H. D., & Pfanz, H. (1993). Effects of air pollutants on the photosynthetic capacity of young Norway spruce trees. Response of single needle age classes during and after different treatments with O3, SO2, or NO2. Trees, 8(2), 85-92.
Gabriel, A. O., & Kreutzwiser, R. D. (1993). Drought hazard in Ontario: a review of impacts, 1960–1989, and management implications. Canadian Water Resources Journal, 18(2), 117-132.
Gaudinski, J. B., Dawson, T. E., Quideau, S., Schuur, E. A., Roden, J. S., Trumbore, S. E., & Wasylishen, R. E. (2005). Comparative analysis of cellulose preparation techniques for use with 13C, 14C, and 18O isotopic measurements. Analytical Chemistry, 77(22), 7212-7224.
George, S. S. (2014). An overview of tree-ring width records across the Northern Hemisphere. Quaternary Science Reviews, 95, 132-150.
Gessler, A., Ferrio, J. P., Hommel, R., Treydte, K., Werner, R. A., & Monson, R. K. (2014). Stable isotopes in tree rings: towards a mechanistic understanding of isotope fractionation and mixing processes from the leaves to the wood. Tree physiology, tpu040.
Ghanbari, R. N., & Bravo, H. R. (2008). Coherence between atmospheric teleconnections, Great Lakes water levels, and regional climate. Advances in water resources, 31(10), 1284-1298.
79 Gratton, W. (1998). Metal analysis and random amplified polymorphic DNA characterization of Jack pine (Pinus banksiana) populations from the Sudbury region. MSc Thesis, 1, 1-99.
Gunn, J., Keller, W., Negusanti, J., Potvin, R., Beckett, P., & Winterhalder, K. (1995). Ecosystem recovery after emission reductions: Sudbury, Canada. Water, Air, and Soil Pollution, 85(3), 1783-1788.
Guyette, R. P., Cutter, B. E., & Henderson, G. S. (1991). Long-term correlations between mining activity and levels of lead and cadmium in tree-rings of eastern red- cedar. Journal of environmental quality, 20(1), 146-150.
Hirano, T., & Morimoto, K. (1999). Growth reduction of the Japanese black pine corresponding to an air pollution episode. Environmental pollution, 106(1), 5-12.
Holmes, R. L. (1983). Computer-assisted quality control in tree-ring dating and measurement. Tree-ring bulletin, 43(1), 69-78.
Hutchinson, T. C., & Whitby, L. M. (1977). The effects of acid rainfall and heavy metal particulates on a boreal forest ecosystem near the Sudbury smelting region of Canada. Water, Air, and Soil Pollution, 7(4), 421-438.
Johnson, A. H., Siccama, T. G., Wang, D., Turner, R. S., & Barringer, T. H. (1981). Recent changes in patterns of tree growth rate in the New Jersey pinelands: a possible effect of acid rain. J. Environ. Qual.;(United States), 10(4).
Karaoz, M. (2003). Air pollution effects on forest trees in Balikesir, Turkey. Water, Air, and Soil Pollution: Focus, 3, 269-279.
Kawamura, H., Matsuoka, N., Momoshima, N., Koike, M., & Takashima, Y. (2006). Isotopic evidence in tree rings for historical changes in atmospheric sulfur sources. Environmental Science & Technology, 40(18), 5750-5754.
Keller, T., Rutter, A., Keller, T., Elsden, S., Bell, J., & Huttermann, A. (1984). Direct Effects Of Sulphur Dioxide On Trees [and Discussion]. Philosophical Transactions of the Royal Society B: Biological Sciences, 305(1124), 317-326.
Klaassen, J. (2000). A Climatological Assessment of Major 20th Century Drought Years in the Grand River Basin. Atmospheric Science Division, Meteorological Service of Canada-Ontario Region.
Larsson, C., & Helmisaari, H. S. (1998). Accumulation of elements in the annual rings of Scots pine trees in the vicinity of a copper-nickel smelter measured by scanning EDXRF. X-Ray Spectrometry, 27(2), 133-139.
80 Leathers, D. J., Yarnal, B., & Palecki, M. A. (1991). The Pacific/North American teleconnection pattern and United States climate. Part I: Regional temperature and precipitation associations. Journal of Climate, 4(5), 517-528.
Leavitt, S. (2010). Tree-ring C-H-O isotope variability and sampling. Science of the Total Environment, 408(22), 5244-5253.
Leavitt, S. W., & Long, A. (1984). Sampling strategy for stable carbon isotope analysis of tree rings in pine.
Lechowicz, M. J. (1984). Why do temperate deciduous trees leaf out at different times? Adaptation and ecology of forest communities. American Naturalist, 821-842.
Leonelli, G., Battipaglia, G., Siegwolf, R., Saurer, M., Morra di Cella, U., Cherubini, P., & Pelfini, M. (2012) Climatic isotope signals in tree rings masked by air pollution: a case study conducted along the Mont Blanc Tunnel access road (Western Alps, Italy). Atmos Environ 61:169–179.
Linzon, S. N. (1966). Damage to eastern white pine by sulfur dioxide, semimature-tissue needle blight, and ozone. Journal of the Air Pollution Control Association, 16(3), 140-144.
Loader, N. J., Young, G. H. F., McCarroll, D., & Wilson, R. J. S. (2013). Quantifying uncertainty in isotope dendroclimatology. The Holocene, 23(9), 1221-1226.
Long, R. P., & Davis, D. D. (1999). Growth variation of white oak subjected to historic levels of fluctuating air pollution. Environmental pollution, 106(2), 193-202.
Malhotra, S. S., & Hocking, D. (1976). Biochemical and cytological effects of sulphur dioxide on plant metabolism. New Phytologist, 227-237.
Martin, B., & Sutherland, E. K. (1990). Air pollution in the past recorded in width and stable carbon isotope composition of annual growth rings of Douglas-fir. Plant, Cell & Environment, 13(8), 839-844.
McCarroll, D., & Loader, N. (2004). Stable isotopes in tree rings. Quaternary Science Reviews, 23(7), 771-801.
McCarroll, D., Gagen, M. H., Loader, N. J., Robertson, I., Anchukaitis, K. J., Los, S., & Waterhouse, J. S. (2009). Correction of tree ring stable carbon isotope chronologies for changes in the carbon dioxide content of the atmosphere. Geochimica et Cosmochimica Acta, 73(6), 1539-1547.
Menzel, A., & Fabian, P. (1999). Growing season extended in Europe. Nature, 397(6721), 659-659.
81 Mooney, H. A., & Goldstein, R. A. (Eds.). (1985). Sulfur dioxide and vegetation: physiology, ecology, and policy issues. Stanford University Press.
National Pollutant Release Inventory. Government of Canada, Environment Canada, Science & Technology Branch, Pollution Data Division. www.ec.gc.ca/inrp-npri. Web. 14 May 2015.
Natural Resources Canada (2014). Forests. www.nrcan.gc.ca. Web. 15 December 2014.
NOAA Climate Prediction Center. Pacific/North American (PNA). 10 Jan. 2012. Web. 4 Aug. 2015.
Ontario Ministry of Natural Resources and Forestry (2014). Forestry. www.ontario.ca. Web. 18 December 2014.
Potvin Air Management Consulting (2004). City of Greater Sudbury, Ontario. Air Quality Trends, 1, 1-78.
Python Group Mining Consultants (2010). History of nickel mining in Sudbury. Web. 25 January 2015.
Rigozo, N. R., Vieira, L. E. A., Echer, E., & Nordemann, D. J. R. (2003). Wavelet analysis of solar-ENSO imprints in tree ring data from southern Brazil in the last century. Climatic Change, 60(3), 329-340.
Rinne, K. T., Loader, N. J., Switsur, V. R., Treydte, K. S., & Waterhouse, J. S. (2010). Investigating the influence of sulphur dioxide (SO2) on the stable isotope ratios (δ13C and δ18O) of tree rings. Geochimica et Cosmochimica Acta, 74(8), 2327- 2339.
Rydval, M., & Wilson, R. (2012). The impact of industrial SO2 pollution on north Bohemia conifers. Water, Air, & Soil Pollution, 223(9), 5727-5744.
Savard, M. M., Bégin, C., Parent, M., Smirnoff, A., & Marion, J. (2004). Effects of Smelter Sulfur Dioxide Emissions. Journal of Environmental Quality, 33(1), 13- 26.
Savard, M.M., Begin, C., Marion, J., Aznar, J.C., & Smirnoff, A. (2009). Air quality changes in an urban region as inferred from tree-ring stable isotopes. In: Legge, A. (Ed.), Relating Atmospheric Source Apportionment to Vegetation Effects: Establishing Cause Effect Relationships. Developments in Environmental Science. Elsevier, Amsterdam.
Savard, M. M. (2010). Tree-ring stable isotopes and historical perspectives on pollution– An overview. Environmental Pollution, 158(6), 2007-2013.
82 Scheffer, T., & Hedgcock, G. (1955). Injury to Northwestern forest trees by sulfur dioxide from smelters. Washington: Dept. of Agriculture.
Sensuła, B., Wilczyński, S., & Opała, M. (2015). Tree Growth and Climate Relationship: Dynamics of Scots Pine (Pinus Sylvestris L.) Growing in the Near-Source Region of the Combined Heat and Power Plant During the Development of the Pro- Ecological Strategy in Poland. Water, Air, & Soil Pollution, 226(7), 1-17.
Shabbar, A., & Khandekar, M. (1996). The impact of el Nino-Southern oscillation on the temperature field over Canada: Research note. Atmosphere-Ocean, 34(2), 401- 416.
Stable Isotope Lab. Stable Isotope Laboratory. UAA ENRI. Web. 21 January 2015.
Stokes, M. and Smiley, T. (1968). An Introduction to Tree-Ring Dating. University of Chicago Press, 73.
Stravinskiene, V., Bartkevicius, E., & Plausinyte, E. (2013). Dendrochronological research of Scots pine (Pinus sylvestris L.) radial growth in vicinity of industrial pollution. Dendrochronologia, 31(3), 179-186.
Sudbury Area Risk Assessment (SARA) (2008). Historical review of air emissions from the smelting operations. Sudbury Area Risk Assessment Final Report, 1(3).
Tans, P. P., De Jong, A. F. M., & Mook, W. G. (1979). Natural atmospheric 14C variation and the Suess effect. Nature, 280(5725), 826-828.
Thomas, M. D. (1961). Effects of air pollution on plants. Air Pollution, 239.
Thomas, R., Spal, S., Smith, K., & Nippert, J. (2013). Evidence of recovery of Juniperus virginiana trees from sulfur pollution after the Clean Air Act. Proceedings of the National Academy of Science, 110(38), 15319-15324.
Trouet, V., & Taylor, A. H. (2010). Multi-century variability in the Pacific North American circulation pattern reconstructed from tree rings. Climate dynamics, 35(6), 953-963.
Wagner, R., & Wagner, E. (2006). Influence of air pollution and site conditions on trends of carbon and oxygen isotope ratios in tree ring cellulose. Isotopes in environmental and health studies, 42(4), 351-365.
Wieser, M. E. (2006). Atomic weights of the elements 2005 (IUPAC Technical Report). Pure and Applied Chemistry, 78(11), 2051-2066.
83 Wiles, G. C., Krawiec, A. C., & D'Arrigo, R. D. (2009). A 265-year reconstruction of Lake Erie water levels based on North Pacific tree rings. Geophysical Research Letters, 36(5).
Wynn, P. M., Loader, N. J., & Fairchild, I. J. (2014). Interrogating trees for isotopic archives of atmospheric sulphur deposition and comparison to speleothem records. Environmental Pollution, 187, 98-105.
Yang, W., Spencer, R. J., & Krouse, H. R. (1996). Stable sulfur isotope hydrogeochemical studies using desert shrubs and tree rings, Death Valley, California, USA. Geochimica et cosmochimica acta, 60(16), 3015-3022.
Yang, Y. S., Skelly, J. M., & Chevone, B. I. (1982). Clonal response of eastern white pine to low doses of O3, SO2, and NO2, singly and in combination. Canadian Journal of Forest Research, 12(4), 803-808.
Yarnal, B., & Leathers, D. J. (1988). Relationships between interdecadal and interannual climatic variations and their effect on Pennsylvania climate. Annals of the Association of American Geographers, 78(4), 624-641.
Zang, C., & Biondi, F. (2015). treeclim: an R package for the numerical calibration of proxy‐climate relationships. Ecography.
Zeng, X., Liu, X., Xu, G., Wang, W., & An, W. (2014). Tree-ring growth recovers, but δ13C and δ15N do not change, after the removal of point-source air pollution: a case study for poplar (Populus cathayana) in northwestern China. Environmental Earth Sciences, 72(6), 2173-2182.
84 Appendix I – Figures
Figure 1. Time series of SO2 emissions (in megatonnes) from Vale smelters in Sudbury, Ontario. Data obtained from Bouillon (2003) and Environment Canada’s NPRI.
Figure 2. Map showing the locations of the three study sites (green pins). The approximate study transect is designated by the yellow dashed line.
85 1850 1900 1950 2000 WB013B WB013A WB018A WB016B WB014B WB011B WB018B WB014A WB016A WB011A WB017A WB012B WB017B WB012A WB015B WB015A
1850 1900 1950 2000 Year
Figure 3. Segment plot for WB site showing the length of each series (core sample).
1850 1900 1950 2000 HL005B HL023B HL021B HL006B HL022B HL010A HL012A HL024B HL016A HL013B HL013A HL006A HL011A HL018A HL010B HL012B HL011B HL017B HL017A HL009A HL020B HL023A HL024A HL015B HL020A HL015A HL016B HL018B HL009B HL022A HL021A HL004A HL005A HL004B HL002A HL002B
1850 1900 1950 2000 Year
Figure 4. Segment plot for HL site showing the length of each series (core sample).
86 1850 1900 1950 2000 KL007B KL002A KL001A KL007A KL001B KL006B KL008A KL004B KL009A KL004A KL006A KL002B KL005A KL003B KL008B KL003A KL009B KL010A KL005B KL010B
1850 1900 1950 2000 Year
Figure 5. Segment plot for KL site showing the length of each series (core sample).
WBstd 1850 1900 1950 2000 2.0 15 1.5 10 RWI Sample Depth 1.0 5 0.5
1850 1900 1950 2000 Year
Figure 6. WB site chronology showing standardized ring-width index (RWI) values (grey line) to a mean of 1 (black horizontal line). The shaded area reflects sample depth.
87 HLstd 1850 1900 1950 2000 3.0 35 30 2.5 25 2.0 20 RWI 1.5 Sample Depth 15 10 1.0 5 0.5 0 1850 1900 1950 2000 Year
Figure 7. HL site chronology showing standardized ring-width index (RWI) values (grey line) to a mean of 1 (black horizontal line). The shaded area reflects sample depth.
KLstd 1850 1900 1950 2000 20 15 1.5 RWI 10 1.0 Sample Depth 5 0.5
1850 1900 1950 2000 Year
Figure 8. KL site chronology showing standardized ring-width index (RWI) values (grey line) to a mean of 1 (black horizontal line). The shaded area reflects sample depth.
88 1850 1900 1950 2000 2.0 1.5 RWI 1.0 0.5 512
256
128
64
32
Period 16
8
4
2
1850 1900 1950 2000 Time
0.0000 0.0101 0.0471 0.1467 0.3449 Power2 Figure 9. Morlet wavelet transform for the WB site with RWI displayed above. The crosshatched area designates the cone of influence (COI) that is not to be interpreted. Power2 is representative of the strength of correlation/cohesion. Periods are in years.
1850 1900 1950 2000 3.0 2.5 2.0 RWI 1.5 1.0 0.5 512
256
128
64
32
Period 16
8
4
2
1850 1900 1950 2000 Time
0.0000 0.0700 0.1959 0.6249 2.0366 Power2 Figure 10. Morlet wavelet transform for the HL site with RWI displayed above. The crosshatched area designates the cone of influence (COI) that is not to be interpreted. Power2 is representative of the strength of correlation/cohesion. Periods are in years.
89 1850 1900 1950 2000 2.0 1.5 RWI 1.0 0.5 512 256 128 64 32
Period 16 8 4 2
1850 1900 1950 2000 Time
0.0000 0.0152 0.0742 0.2184 0.4647 Power2 Figure 11. Morlet wavelet transform for the KL site with RWI displayed above. The crosshatched area designates the cone of influence (COI) that is not to be interpreted. Power2 is representative of the strength of correlation/cohesion. Periods are in years.
1 month 3 months 6 months
0.2
0.1 primary
0.0
-0.1
-0.2 significant FALSE TRUE 0.2 Correlation coefficient 0.1 secondary
0.0
-0.1
-0.2
a s o n d J F M A M J J A S a s o n d J F M A M J J A S a s o n d J F M A M J J A S Ending month Figure 12. WB RWI-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
90 1 month 3 months 6 months 0.4
0.2 primary
0.0
-0.2 significant FALSE 0.4 TRUE
Correlation coefficient 0.2 secondary
0.0
-0.2
a s o n d J F M A M J J A S a s o n d J F M A M J J A S a s o n d J F M A M J J A S Ending month Figure 13. HL RWI-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
1 month 3 months 6 months
0.25 primary
0.00
significant -0.25 FALSE TRUE
0.25 Correlation coefficient secondary
0.00
-0.25 a s o n d J FMAMJ J AS a s o n d J FMAMJ J AS a s o n d J FMAMJ J AS Ending month
Figure 14. KL RWI-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
91 White Bear (North Bay)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef 0.6
JFM temp 0.3
0.0 JJA ppt -0.3 MJJ ppt
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1941-1965 1942-1966 1943-1967 1944-1968 1945-1969 1946-1970 1947-1971 1948-1972 1949-1973 1950-1974 1951-1975 1952-1976 1953-1977 1954-1978 1955-1979 1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 15. WB RWI-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
Hobbs Lake (North Bay)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef 0.50 JFM temp 0.25
JJA ppt 0.00 -0.25 MJJ ppt
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1941-1965 1942-1966 1943-1967 1944-1968 1945-1969 1946-1970 1947-1971 1948-1972 1949-1973 1950-1974 1951-1975 1952-1976 1953-1977 1954-1978 1955-1979 1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 16. HL RWI-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
92 Kukagami Lake (Sudbury)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef 0.6
JFM temp 0.3
0.0 JJA ppt -0.3 MJJ ppt
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 17. KL RWI-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
Figure 18. δ13C chronology for the WB site including both raw/uncorrected (solid line) and corrected (dashed line) δ13C values (in ‰) for comparison.
93 Figure 19. δ13C chronology for the HL site including both raw/uncorrected (solid line) and corrected (dashed line) δ13C values (in ‰) for comparison.
Figure 20. δ13C chronology for the KL site including both raw/uncorrected (solid line) and corrected (dashed line) δ13C values (in ‰) for comparison.
94 1 month 3 months 6 months
0.2
0.0 primary
-0.2
significant FALSE TRUE 0.2 Correlation coefficient
0.0 secondary
-0.2
a s o n d J F M A M J J A S a s o n d J F M A M J J A S a s o n d J F M A M J J A S Ending month Figure 21. WB δ13C-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
1 month 3 months 6 months 0.4
0.2 primary
0.0
-0.2 significant FALSE 0.4 TRUE Correlation coefficient 0.2 secondary
0.0
-0.2
a s o n d J F M A M J J A S a s o n d J F M A M J J A S a s o n d J F M A M J J A S Ending month Figure 22. HL δ13C-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
95 1 month 3 months 6 months
0.2
0.0 primary
-0.2
-0.4 significant FALSE TRUE 0.2 Correlation coefficient
0.0 secondary
-0.2
-0.4
a s o n d J F M A M J J A S a s o n d J F M A M J J A S a s o n d J F M A M J J A S Ending month Figure 23. KL δ13C-climate seasonal correlation analysis (SCA) plots for season lengths of 1, 3 and 6 months. Uppercase = current year of growth and lowercase = previous year of growth. The letter on which the bar falls is the last month in the respective season. “Primary” is precipitation and “secondary” is temperature.
White Bear (North Bay)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef
0.25 JFM temp 0.00 JJA ppt -0.25
MJJ ppt -0.50
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1941-1965 1942-1966 1943-1967 1944-1968 1945-1969 1946-1970 1947-1971 1948-1972 1949-1973 1950-1974 1951-1975 1952-1976 1953-1977 1954-1978 1955-1979 1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 24. WB δ13C-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
96 Hobbs Lake (North Bay)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef 0.6 JFM temp 0.4 0.2 JJA ppt 0.0 -0.2 MJJ ppt -0.4
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1941-1965 1942-1966 1943-1967 1944-1968 1945-1969 1946-1970 1947-1971 1948-1972 1949-1973 1950-1974 1951-1975 1952-1976 1953-1977 1954-1978 1955-1979 1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 25. HL δ13C-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
Kukagami Lake (Sudbury)
JJA temp
MJJ temp
AMJ temp
MAM temp
FMA temp coef
0.3 JFM temp 0.0 JJA ppt -0.3
MJJ ppt -0.6
AMJ ppt
MAM ppt
FMA ppt
JFM ppt
1956-1980 1957-1981 1958-1982 1959-1983 1960-1984 1961-1985 1962-1986 1963-1987 1964-1988 1965-1989 1966-1990 1967-1991 1968-1992 1969-1993 1970-1994 1971-1995 1972-1996 1973-1997 1974-1998 1975-1999 1976-2000 1977-2001 1978-2002 1979-2003 1980-2004 1981-2005 1982-2006 1983-2007 1984-2008 1985-2009 1986-2010 1987-2011 1988-2012 Figure 26. KL δ13C-climate correlation function analysis (CFA) plot for the computed moving time windows (x-axis). “temp” bins = average temperature for the given months and “ppt” bins = precipitation sums for the given months. “*” indicates significance.
97 13 Figure 27. Combined line graphs of normalized WB δ C, SO2 and North Bay JJA average temperature data (z-scores) for visual comparison. Divergence between δ13C and temperature is observed during the peak period of SO2 emissions (1960-1970).
13 Figure 28. Combined line graphs of normalized HL δ C, SO2 and North Bay MJJ average temperature data (z-scores) for visual comparison. Divergence between δ13C and temperature is not observed during the peak period of SO2 emissions (1960-1970).
98 13 Figure 29. Combined line graphs of normalized KL δ C, SO2 and Sudbury MJJ average temperature data (z-scores) for visual comparison. Divergence between δ13C and temperature is observed during the peak period of SO2 emissions (1960-1970).
13 Figure 30. Combined line graphs of normalized WB δ C, SO2 and North Bay JJA precipitation sums data (z-scores) for visual comparison. Divergence between δ13C and precipitation is observed during the peak period of SO2 emissions (1960-1970).
99 13 Figure 31. Combined line graphs of normalized HL δ C, SO2 and North Bay AMJ precipitation sums data (z-scores) for visual comparison. Divergence between δ13C and precipitation is not observed during the peak period of SO2 emissions (1960-1970).
13 Figure 32. Combined line graphs of normalized KL δ C, SO2 and Sudbury JJA precipitation sums data (z-scores) for visual comparison. Divergence between δ13C and precipitation is observed during the peak period of SO2 emissions (1960-1970).
100 Figure 33. Time series of calculated iWUE values (µm/mol) for the WB site plotted with atmospheric CO2 concentrations (ppm) on a secondary axis. The red line is the linear line of best fit for iWUE. Note the close relationship with CO2 and the anomalous period with higher iWUE values.
Figure 34. Time series of calculated iWUE values (µm/mol) for the HL site plotted with atmospheric CO2 concentrations (ppm) on a secondary axis. The red line is the linear line of best fit for iWUE. Note the close relationship with CO2 over the entire length of the time series.
101 Figure 35. Time series of calculated iWUE values (µm/mol) for the KL site plotted with atmospheric CO2 concentrations (ppm) on a secondary axis. The red line is the linear line of best fit for iWUE. Note the close relationship with CO2 and the anomalous period with higher iWUE values.
102 Appendix II – Tables and Images
Table 1. Summary of descriptive statistics for site RWI chronologies. Notation follows Cook et al. (1990) and a description is given in the text.
Statistic White Bear (WB) Hobbs Lake (HL) Kukagami Lake (KL) # of trees 8 18 10 # of cores 16 36 20 Ntot 120 630 190 Nwt 8 18 10 Nbt 112 612 180 rtot 0.23 0.31 0.29 rwt 0.44 0.58 0.49 rbt 0.21 0.30 0.28 ceff 2.00 2.00 2.00 reff 0.30 0.38 0.37 EPS 0.72 0.87 0.81 SNR 2.58 6.74 4.26
13 Table 2. Summary of δ C between-tree (interseries) correlations (rbt) for individually sampled (unpooled) cores.
Statistic White Bear (WB) Hobbs Lake (HL) Kukagami Lake (KL) Core # 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 rbt 0.82 0.77 0.87 0.84 0.82 0.75 0.76 0.49 0.77 0.86 0.77 0.26 0.53 0.78 0.87 rbt average 0.82 0.73 0.64
Image 1. Photograph of a KL core sample (KL002A) showcasing a sequence of very narrow and compressed rings during the 1960s (red line), followed by a “release” sequence of wide rings over the 1970s and 80s (green line).
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