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The relation between climate and abundance cycles in barren-ground caribou herds of the Northwest Territories,… Zalatan, Rebecca 2006

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T H E RELATION BETWEEN CLIMATE AND ABUNDANCE C Y C L E S IN BARRENGROUND CARIBOU HERDS OF T H E NORTHWEST TERRITORIES, CANADA.  by  REBECCA Z A L A T A N  B.A., University of Ottawa, 2000 M.Sc., University of Ottawa, 2002  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF T H E REQUIREMENTS FOR T H E DEGREE OF DOCTOR OF PHILOSOPHY in  T H E F A C U L T Y OF GRADUATE STUDIES (Geography)  T H E UNIVERSITY OF BRITISH COLUMBIA  September 2006 © Rebecca Zalatan, 2006  ABSTRACT The central objective o f this research was to determine i f there is a relationship between longterm barren-ground caribou (Rangifer tarandus) abundance and climate patterns in the Canadian Arctic. A long-term dataset indexing caribou abundance was obtained from the frequency o f trampling scars on tree roots o f black spruce (Picea mariana [Mill.] B S P ) i n the forest-tundra o f central Northwest Territories. Samples were collected from roots o f live trees along well-used migration trails i n the forest tundra. T w o groups o f sites were sampled that roughly corresponded with the late summer migration routes o f the Bathurst and Beverly caribou herds. The scar frequency distributions were dated from A . D . 1760-2000 and both groups o f sites showed similar abundance patterns through time.  To best determine the relation between long-term climate and the proxy caribou abundance, local climate data were needed. However, the only annually-resolved climate data in the region is the short length o f record (63 years) available from Yellowknife (which is up to 300 k m away from the furthest sites i n this study). Therefore, I developed a series o f tree-ring chronologies at seven o f the nineteen sites where caribou abundance was reconstructed, i n an effort to increase the knowledge o f climate variability in this region. Summer temperatures (July-August) were reconstructed using standard dendroclimatological techniques.  The final objective o f m y research was to determine i f there was a correlation between patterns in the summer or winter index o f the Arctic Oscillation ( A O ) and long-term caribou abundance. I determined that the A O was most closely correlated to summer temperatures (June-August) at Yellowknife. The summer index o f the A O has undergone four major phase changes during the  n  last century. Wavelet coherence demonstrated that the relation between caribou abundance cycles and the A O S changed from inversely related during the first two phases o f the A O S , to i n phase during the final two phases o f the A O S . This study was the first to demonstrate the complexities associated with relating long-term trends i n the A O to abundance cycles o f barrenground caribou. Additionally, this study was the first to illustrate the importance o f obtaining long-term datasets when relating large-scale climate to caribou abundance cycles.  111  T A B L E OF CONTENTS ABSTRACT  II  T A B L E OF CONTENTS  IV  LIST OF T A B L E S  V  LIST OF FIGURES  VI  ACKNOWLEDGEMENTS  X  CHAPTER 1. INTRODUCTION  1  1.1 O B J E C T I V E S 1.2 THESIS O U T L I N E 1.3 R E F E R E N C E S  '.  8 9 10  CHAPTER 2. L O N G - T E R M ABUNDANCE PATTERNS OF BARREN-GROUND CARIBOU USING TRAMPLING SCARS ON ROOTS OF PICEA MARIANA IN T H E NORTHWEST TERRITORIES 2.1 2.2 2.3 2.4 2.5  INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES  16 16 18 28 33 37  •.  CHAPTER 3. SPATIO-TEMPORAL VARIATION OF TREE-RING G R O W T H A L O N G A TRANSECT AT TREELINE WITHIN T H E R A N G E OF BARREN-GROUND CARIBOU: A DENDROCLIMATIC APPROACH : 39 3.1 3.2 3.3 3.4 3.5  INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES  39 42 49 62 65  :  :  .-  CHAPTER 4. T H E RELATION B E T W E E N L O N G - T E R M ABUNDANCE C Y C L E S IN BARRENGROUND CARIBOU AND T H E ARCTIC OSCILLATION 4.1 4.2 4.3 4.4 4.5  I N T R O D U C T I O N .-. METHODS., RESULTS DISCUSSION REFERENCES  .' : '.  71 71 74 78 90 96  CHAPTER 5. THESIS SUMMARY AND SYNTHESIS  103  APPENDIX A  106  iv  LIST OF TABLES T A B L E 2.1 Geographic location, number of scars and samples for the northwest and southeast sites, Northwest Territories. Locations are also shown in Fig. 2.1 28 T A B L E 3.1 Site characteristics of Picea mariana chronologies along treeline in the N.W.T  43  T A B L E 3.2 Descriptive statistics for the seven chronologies developed using Picea mariana. s.d. = standard deviation; r l = first-order autocorrelation; SNR = signal-to-noise ratio; V A R p c l = variance explained by the first component; EPS = expressed population signal 51 T A B L E 3.3 Pearson's correlations among the seven residual chronologies developed from samples taken at treeline, N.W.T. A l l correlations were significant at p<0.05; «=88. 52 T A B L E 3.4 Pearson's correlations among the seven residual treeline chronologies and the ITRDB chronologies (Aus. = Austin; Copp. = Coppermine; Mac. = Mac Kinley; Pet. = Pethai.). Marked correlations are significant at p<0.05, «=62 53 T A B L E 3.5 Calibration and verification statistics for predicting July-August temperatures from eigenvectors derived from five chronologies at treeline in the N.W.T 59 T A B L E A . l Spearman's rank correlations between scar frequency distribution for both groups of sites (northwest and southeast) annually, in 2-year age-classes, 3-year age-classes and 4-year age-classes. Correlations are significant at p < 0.05 106 T A B L E A.2 Pearson's correlations among the seven tree-ring chronologies and mean monthly temperature. A l l marked correlations are significant atp<0.05; n = 59 109 T A B L E A.3 Pearson's correlations among the seven tree-ring chronologies and total monthly precipitation. A l l marked correlations are significant at j?<0.05; n = 59 110 T A B L E A.4 Response function analysis between the monthly index of the A O and the seven tree-ring chronologies over the entire study period from May of the previous growth year to August of the current growth year. Marked correlations are significant at p <0.05 Ill TABLE A.5 Spearman's rank correlation between the July index of the A O and the seven chronologies for each phase. Marked correlations are significant at p <0.05 Ill  LIST OF FIGURES FIGURE 1.1 The seasonal range and calving grounds o f some barren-ground caribou herds i n the Northwest Territories and Nunavut 2  FIGURE 1.2 Sea-level pressure and associated temperature anomalies as related to the A O from December-February. The A O is a major mode o f circulation variability in the Arctic 6  FIGURE 1.3 A conceptual model linking the phases o f the Arctic Oscillation, winter and summer climate conditions, and caribou abundance 8  FIGURE 2.1 Sampling sites o f scarred roots on Picea mariana (sampled 2002) and the summer migratory route o f barren-ground caribou. The geographic coordinates o f the sampling locations for the scarred roots are listed i n Table 2.1 19  FIGURE 2.2 This is an example o f a typical site where caribou scars were collected. This photo was taken from a helicopter (at approximately 300 m elevation) and is located in the foresttundra within the annual range o f the Bathurst caribou herd 20 FIGURE 2.3 Caribou migration trails across an esker taken from a helicopter i n the central N.W.T 21 FIGURE 2.4 Bathurst caribou migrating across the landscape in the N . W . T  21  FIGURE 2.5 A series o f black spruce roots trampled by barren-ground caribou. Trampling removes part o f the bark, leaving behind a scar which is used to represent caribou abundance 22 FIGURE 2.6 Tracks o f 10-20 satellite-collared caribou cows from the Bathurst herd (19962002). The black boxes outline the general area where the study sites are located 23 FIGURE 2.7 Schematic diagram o f a scar on a cross-sectional sample o f a root  24  FIGURE 2.8 Scar frequency distribution (a) using all samples from sites 1-15 (blue bars) and sites 16-19 (grey bars) and cumulative frequencies o f the number o f samples (lines) (r = 0.88; /?<0.05, n - 24); and (b) using only scars on roots established before 1900 (r = 0.63, p<0.05, n = 24) 30 s  s  vi  FIGURE 2.9 Bathurst herd caribou numbers from aerial photography surveys (1984-2003; black line; means ± SE), and abundance patterns (high = dark grey shading or low = light grey shading) derived from traditional knowledge o f elders from the Dogrib First Nation relative to the scar frequency distribution for the northwest sites (Bathurst herd; bars) 32 FIGURE 2.10 Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites  33  FIGURE 3.1 M a p showing location o f tree-ring chronology sites (numbered sites), sites from the International Tree-Ring Databank ( I T R D B ) , Yellowknife meteorological station in the N . W . T . and the summer migratory route o f barren-ground caribou 43 FIGURE 3.2 The standardized Arctic Oscillation ( A O ) anomaly index for June to August for the period between 1900-2002, and a 5 order regression (data from http://www.jisao.washington.edU/ao/#monthly). The values o f the A O S have been multiplied by . -1 to illustrate the positive and negative phases 49 th  FIGURE 3.3 Ring-width chronologies from the seven sampled sites (ring-width sample depth is below each chronology curve). The thick line is a five-year moving average 50 FIGURE 3.4 Hierarchical cluster analysis o f seven residual chronologies  54  FIGURE 3.5 Plot o f the loadings for the first two principal components for each chronology. The percentage o f explained variance for P C I and P C 2 is 61.80% and 13.10%, respectively...54 FIGURE 3.6 Percent variance expressed by the first and second principal components o f P C A derived from the seven residual chronologies for the subperiods: 1916-1946, 1926-1956, 19361966, 1946-1976, 1956-1986, and 1966-1996. For the whole common period 1916-2003, the variance explained b y the first and second principal components is 74.9% 55 FIGURE 3.7 Response function coefficients relating mean monthly temperature and total monthly precipitation to ring-width chronologies at treeline sites, N . W . T . Shaded coefficients are significant at p<0.05 56-57 FIGURE 3.8 Observed (dashed line) and reconstructed (solid line) July-August temperatures at treeline, N . W . T . Darker solid line is a 3-year running mean o f the reconstructed July-August temperatures. Reconstruction covers the time span 1831-2002 60 FIGURE 3.9 The standardized Arctic Oscillation ( A O ) anomaly index for June to August for the period between 1900-2002, and a 5 order regression curve (data from http://tao.atmos.washington.edu/ao/#monthlv). The values o f the A O S have been multiplied by 1 to illustrate the positive and negative phases 61 th  FIGURE 4.1 (A) Relationship between the summer index o f the standardized Arctic Oscillation ( A O S ) and mean monthly summer temperatures for the period 1943-2002; (B) Relationship  vn  between the winter index o f the standardized Arctic Oscillation ( A O W ) and mean monthly summer temperatures for the period 1943-2001 78 FIGURE 4.2 The standardized Arctic Oscillation ( A O ) anomaly index for June to August and mean monthly temperatures for June to August for the comparative period o f 1943-2001 79 FIGURE 4.3 Percent variance expressed b y the first and second principal components o f P C A based on a matrix with the monthly index o f the Arctic Oscillation for the subperiods: 1900-1930, 1910-1940, 1920-1950, 1930-1960, 1940-1970, 1950-1980, 1960-1990, and 1970-2000. For the whole period 1900-2000, the variance explained by the first and second principal components is 50.3% 81 FIGURE 4.4 Standardized distribution o f the number o f trampling scars (residuals o f the loglinear regression) from 1900-2000 for the northwest and southeast sites 81 FIGURE 4.5 Residuals o f the log-linear regression on the frequency distribution o f trampling scars for both sets o f sites grouped into five-year age-classes and a 5 order regression o f the summer index o f the Arctic Oscillation (June-August) for the period 1900-2002 82 th  FIGURE 4.6 The standardized Arctic Oscillation ( A O ) anomaly index for October to M a y for the period between 1900-2002, (data from http://tao.atmos.washington.edu/ao/#monthly) 83 FIGURE 4.7 The cross wavelet transform ( C W T ) o f the northwest caribou abundance (top panel) and the A O (bottom panel) 85 FIGURE 4.8 The cross wavelet transform ( C W T ) o f the southeast caribou abundance (top panel) and the A O (bottom panel) 86 FIGURE 4.9 The cross wavelet transform ( C W T ) o f the northwest caribou abundance (top panel) and the southeast caribou abundance (bottom panel) 86 FIGURE 4.10 The wavelet coherence ( W T C ) between the southeast caribou abundance and the northwest caribou abundance time series 87 FIGURE 4.11 The wavelet coherence ( W T C ) between the northwest caribou abundance and the A O time series 88 FIGURE 4.12 The wavelet coherence ( W T C ) between the southeast caribou abundance and the A O time series 89 FIGURE A . l Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites i n 1-year age-classes 107 FIGURE A.2 Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites in 2-year age-classes 107  Vlll  F I G U R E A.3 Residuals of the log-linear regression on the scar-frequency distribution of trampling scars for both groups of sites in 3-year age-classes 108 F I G U R E A . 4 Residuals of the log-linear regression on the scar-frequency distribution of trampling scars for both groups of sites in 4-year age-classes 108  ix  ACKNOWLEDGEMENTS  I would like to thank m y supervisor Dr. Greg Henry, whose guidance taught me to become an independent thinker. Greg always supported m y decisions and allowed me to be where I needed to be to complete m y dissertation. H e always encouraged me to publish m y work early and to attend conferences to expand m y knowledge. I am indebted to m y committee: Lori Daniels, Anne Gunn, and Konrad Gajewski. L o r i was m y sole link to the dendro community and she always encouraged me in a supportive way. A n n e was an amazing person to have around, not just on m y committee but i n the field and around the office in Yellowknife. Konrad has been a constant source o f inspiration and support during m y Ph.D. I thank h i m for allowing me to work in his lab while I was living i n Ottawa. H i s energy and enthusiasm did not go unnoticed. A l s o , I would like to acknowledge the support o f the L P C for letting me take up space in their lab, and for all the great "fogs" that made m y time there more enjoyable. I thank D r . Shelly Rayback for her constant advice and support from the beginning o f m y Ph.D., right until the very end. She was an incredible source o f help and was always encouraging and supportive. M y experience i n the north would not have been the same without m y host K a r i n Clark and her loving family. Thank you so much for taking me in and showing me the beauty and spirit o f the north. I would like to thank J. Williams, D . Abernethy, G . Furniss, D . Cluff, J. K o d z i n , J. Mackenzie, P. Liske, J. Lee, and D . Johnson for field assistance and data collection. A support that cannot go unnoticed is m y husband and best friend Martin Turpin. He was always reminding me to make schedules and write down m y thoughts. Encouraging me to become a better person also made me a better academic. Thank you for standing by me until the very end. This research was financially supported by a Natural Sciences and Engineering Research Council o f Canada ( N S E R C ) Scholarship, N S E R C Northern Supplement, The Association o f Canadian Universities for Northern Studies Caribou A w a r d ( A C U N S ) , Royal Canadian Geographical Society ( R C G S ) , and a U B C University Graduate Scholarship to R. Zalatan. Fieldwork was aided b y a grant from the Northern Student Training Program (NSTP) to R. Zalatan, an N S E R C Discovery Grant and Northern Supplement to G . H . R . Henry and by the Department o f Environment and Natural Resources, G N W T , Yellowknife. I acknowledge the logistic support o f the Polar Continental Shelf Project ( P C S P project N o 636-04)).  x  C H A P T E R 1. I N T R O D U C T I O N  Caribou abundance across a landscape fluctuates from annual to decadal time scales (Skoog 1968). Determining changes i n barren-ground caribou (Rangifer  tarandus  groenlandicus)  abundance is limited by the practicalities o f counting large numbers o f animals whose movements and distribution are often unpredictable over thousands o f square kilometers (Messier et al. 1988). Barren-ground caribou are a migratory continental tundra subspecies that migrate for long distances from the forest to the open tundra (Mallory and H i l l i s 1998). The seasonal range o f some barren-ground caribou herds is located throughout portions o f the central Northwest Territories and Nunavut (Fig. 1.1). Arguably, the most important stop i n this arduous trek is the traditional calving grounds where the cows o f each o f the northern herds gather each year to bear their young. This territory makes up a small fraction o f their annual range, but they return to it each spring with some variation i n exact timing or location from year to year (Gunn and Fournier 2000). In fact, they are so important and distinct that each herd is defined and i n most cases named b y the use o f a common calving ground.  Effective, quantitative methods to measure caribou abundance have only been used since the 1970s and 1980s through aerial surveys. The most recent and effective method o f monitoring distribution, which is the key to counting caribou, is through radio- and satellite-telemetry (Hearn et al. 1990; Cameron et al. 1993; Fancy 1994; Gunn et al. 2001). Despite attempts to use longer-term information from archeological sites, explorers and naturalists, company and mission journals, live trapping, aerial photos, and radio-telemetry (Elton 1942; V i b e 1967; Krebs et al. 1986; Meldgaard 1986; Messier et al. 1988; Fritz et al. 1993; Couturier et al. 1996),  1  reliable long-term records o f caribou population cycles are scarce (Gunn 2005). Recent progress in describing caribou movements and abundance over longer time periods comes from the application o f dendroecology.  Dendroecology is the study o f annual tree-rings as it applies to  ecological processes. This method has been an effective tool for reconstructing past population dynamics o f voles (Danell et al. 1981), snowshoe hare (Sinclair et al. 1993), porcupines (Spencer 1964; Payette 1987), beavers (Bordage and Filion 1988), and moose (McLaren and Peterson 1994).  Source: http://www.nwtwildlife.com/NWTwildlife/caribou/distribution.htm F I G U R E 1.1 The seasonal range and calving grounds o f some barren-ground caribou herds in the Northwest Territories and Nunavut.  2  Estimates o f vole population sizes were obtained from the amount o f bark gnawing on willows (Salix spp.) which roughly reflected vole population density (Danell et al. 1981). Dark bands in a cross-section o f a white spruce (Picea glauca (Moench) Voss) were used to date past scars formed by hare browsing (Sinclair et al. 1993). Similarly, the presence o f porcupines was evident from feeding scars on branches and stems o f pines (Pinus spp.) (Spencer 1964; Payette 1987). Periods o f beaver occupation were determined using a pattern o f tree-ring growth release recorded i n conifers not selected by beavers during a clearing for the building o f a dam (Bordage and F i l i o n 1988). Finally, ring-width suppression i n balsam fir (Abies balsamea var.  balsamea),  which makes up 59% o f the moose's winter diet, was used to indicate increases i n moose densities (McLaren and Peterson 1994).  Trampling scars left b y ungulates (hoofed animals) can be used to reconstruct their population dynamics.  Morneau and Payette (1998, 2000) introduced this method to determine  the  population dynamics o f the George River caribou herd in the Quebec-Labrador region. They used trampling scars left by caribou hooves on surficial roots o f black spruce (Picea  mariana  [Mill.] B S P ) trees along well-used migration trails that were presumed to be quite old. These trampling scars can also be the result o f grazing near or on tree roots, where caribou lichens (Cladina rangiferina and other species), a main caribou food source often grow (Morneau and Payette 2000). Scar frequency distributions are then computed based on dendrochronologicallydated scars from root samples. The scar frequency distribution is then interpreted as an index o f increasing or decreasing caribou abundance through time. Clearly, using dendroecology to reconstruct caribou abundance differs considerably from previous studies that have adopted dendroecological methods for understanding animal dynamics. This innovative approach as a  3  proxy for caribou abundance is all the more significant given the difficulty and expense o f obtaining direct estimates o f long-term changes in large mammal population activity.  Once the proxy abundance is constructed, the influence o f climate on these caribou abundance cycles can be explored. The synchrony in abundance patterns o f certain mammalian populations, such as caribou and snowshoe hares, at a regional or continental scale suggests that an external forcing variable is involved (Sinclair et al. 1993; G u n n 2005).  Northern biological systems  respond to changing climatic regimes over various time scales (Overpeck et al. 1997; M a n n and Bradley 1999; Welker et al. 2005). Comparison o f fluctuations in caribou abundance at the continental scale suggests a correlation with decadal climate shifts (Gunn 2005). Several studies have linked the patterns o f large-scale climate to mammal abundance. These studies have shown a correlation between abundance patterns in various animal species (snowshoe hares, reindeer, caribou, and muskoxen) and some form o f large-scale climate regime or pattern, such as the Arctic Oscillation ( A O ; Aanes et al. 2002), the North Atlantic Oscillation ( N A O ; Post et al. 1997; Forchhammer et al. 1998; Post and Stenseth 1999; Post et al. 1999; Forchhammer et al. 2002; Post and Forchhammer 2002) and sunspot cycles (Sinclair et al. 1993).  These oscillations have been shown to explain a considerable proportion o f annual variations in local temperature and precipitation patterns over large regions (Hurrell 1995), and hence affect mammal abundance through changes in climate. The A O and the N A O are the same physical entity, and are highly correlated however they are considered to be two separate climate oscillations (Thompson and Wallace 1998). The N A O index is calculated as a difference in the normalized monthly mean sea-level pressure between Lisbon, Portugal and Stykkisholmur,  4  Iceland (Hurrell 1996), while the A O index is calculated from the mean monthly sea-level pressure over the northern hemisphere north o f 20°N (Thompson and Wallace 2000). The A O explains more than half o f the temperature trends observed i n the eastern Arctic, and less than half o f the temperature trends over the western Arctic (Rigor et al. 2000). Thus, the A O is more suitable for studying the relation between large-scale climate and mammal abundance in the central N . W . T .  The winter index o f the A O has shown an upward trend during the last three decades, and is closely related to mean annual temperatures recorded in the Arctic (Overpeck et al. 1997; Thompson and Wallace 1998; Welker et al. 2005; F i g . 1.2). The high index o f the A O is defined as periods o f below normal Arctic sea-level pressure, as w e l l as enhanced surface westerly winds i n the north Atlantic. This results i n warmer and wetter than normal conditions, and is therefore called the "warm" or positive phase. The low index o f the A O is defined as the cooler phase (negative phase), since temperatures tend to be cooler and precipitation tends to be low.  Since the A O has been linked to meteorological conditions in the Arctic, it is an ideal region for investigating the interaction between climate variability and ecosystem dynamics (Aanes et al. 2002). M a n y caribou herds i n North America appear to have a population periodicity between 40 and 70 years (Gunn 2005). Similarly, large-scale climate oscillations fluctuate over decades from one mode to another. Thus it is essential that longer timescales are used to study the relation between large-scale climate and caribou. Additionally, sampling across the range o f neighboring herds may provide insight into the synchrony between herds.  5  Sea Level Pressure and Surface Wind AO positive - djf  AO negative - djf  AZZL Temperature at 2m AO negative  AO positive  -4  -2  -1  i  -0.5  0  -0.25  i  i  i  02.5  0.5  i  1  i  i  2  i  4  C  -m  Source: www.cas.sc.edu/geog/climatelab/climmock.htrnl  F I G U R E 1.2 Sea-level pressure and associated temperature anomalies as related to the A O from December-February. The A O is a major mode o f circulation variability in the Arctic.  6  In relating large-scale climate to caribou abundance, it is also important to understand the impact o f climate at smaller spatial scales. M a n y studies have quantified the impact o f climate on forage quality within caribou ranges (Chapin et al. 1995). Certain climatic conditions have been shown to alter the nutrient concentrations and availability o f caribou forage (Chapin and Shaver 1985; Shaver et al. 1986; Chapin et al. 1995), which directly influences body condition o f female caribou (Skogland 1985). Other effects o f climate can be seen through changes in the depth and density o f the snow pack which can either reduce or increase rates o f predation (Nelson and M e c h 1986; Huggard 1993; Post et al. 1999; Hebblewhite et al. 2002; Hebblewhite 2005). Changes in snow pack characteristics can also affect access to forage for caribou (Turner et al. 1994, M i l l e r and Gunn 2003).  Increased precipitation can result i n a greater abundance o f  mosquitoes and other insects, which has negative effects for caribou as they expend energy trying to avoid mosquitoes by constantly moving as they graze (Downes et al. 1986; N o e l et al. 1998). It is the interaction between the Arctic Oscillation and climate conditions that cause changes i n caribou abundance.  The following conceptual model illustrates the complex  interaction between the Arctic Oscillation, climate and caribou abundance (Fig. 1.3).  It is clear that climate has both a direct (e.g. adverse weather) and indirect (e.g. forage quality) effect  on caribou abundance.  Therefore, understanding  how climate affects  vegetation is  important for providing a better explanation for the relation between climate and caribou. I investigated the impact o f regional ( A O index) and local (meteorological data) climate on the variation i n tree-ring growth using stems o f Picea mariana trees. Aanes et al. 2002 demonstrated that both plant growth (12-year time series) and Svalbard reindeer population growth rate (21year time series) were negatively related to positive values o f the A O index. M y assumption is  7  that i f ring-width growth is limited b y certain climate conditions, such as monthly temperature and precipitation, caribou abundance may also be affected either directly or indirectly b y the same factors.  Arctic Oscillation Positive phase  warm/wet winter  cloudy/cold/wet summer  greater snowdepth, increased predation, decreased access to forage  increased abundance of biting insects, sub-optimal growth conditions for forage  Negative phase  cold/dry winter  decreased predation easier access to forage  decreased caribou abundance  increased caribou abundance  F I G U R E 1.3 A conceptual model linking the phases o f the Arctic Oscillation, winter and summer climate conditions, and caribou abundance.  1.1 O B J E C T I V E S The main objective o f this study was to determine the influence o f climate on the abundance cycles o f barren-ground caribou (Rangifer tarandus groenlandicus)  i n the L o w Arctic o f central  8  Northwest Territories, Canada. The particular objectives o f this study were to: (1) reconstruct the long-term abundance cycles o f caribou using frequency distributions o f trampling scars on tree roots; (2) evaluate climatic factors correlated with radial growth o f Picea mariana trees and develop a climate reconstruction using tree-ring width as a proxy for climate; and (3) assess the impact o f large-scale climate regimes (the Arctic Oscillation) on the abundance cycles o f these caribou.  1.2 T H E S I S O U T L I N E I compiled this thesis as a series o f independent, but related chapters to be submitted for publication in scientific journals. Chapter 1 addresses the main objectives o f the research. Chapter 2 describes the reconstruction o f caribou abundance cycles using dendroecological analysis o f scar frequencies from roots o f Picea mariana and has been accepted for publication in Arctic, Antarctic and Alpine Research (In Press, September 2006). In Chapter 3, I present a series o f seven tree-ring chronologies developed from the same sites as those used to reconstruct the caribou abundance cycles from Chapter 2.1 also determined the main climatic factors driving tree-ring growth and reconstructed  July-August temperatures using five  o f the  longest  chronologies. Chapter 4 was dedicated to determining the relation between the Arctic Oscillation and caribou abundance from 1900-2002. The final chapter is a summary and synthesis o f the major findings o f this research.  9  1.3 R E F E R E N C E S  Aanes, R., Saether, B . , Smith, F . M , Cooper, E.J., Wookey, P . A . , and 0ritsland, N . A . 2002. The arctic oscillation predicts effects o f climate change in two trophic levels in a high-arctic ecosystem. Ecology Letters, 5: 445-453. Bordage, G . , and Filion, L . 1988. Analyse dendroecologique d'un milieu riverain frequente par le castor (Castor  canadensis)  au Mont D u Lac-Des-Cygnes (Charlevoix, Quebec).  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Messier, F., Huot, J., L e Henaff, D . , and Luttich, S. 1988. Demography o f the George River caribou herd: evidence o f population regulation b y forage exploitation and range expansion. Arctic, 41 (4): 279-287. M i l l e r , F . L . , and Gunn, A . 2003. Catastrophic die-off o f Peary caribou on the Western Queen Elizabeth Islands, Canadian H i g h Arctic. Arctic, 56: 381-390. Morneau, C , and Payette, S. 2000. Long-term fluctuations o f a caribou population revealed b y tree-ring data. Canadian Journal of Zoology, 78: 1784-1790. Morneau, C , and Payette, S. 1998. A dendroecological method to evaluate past caribou (Rangifer tarandus L.) activity. Ecoscience, 5(1): 64-76. Nelson, M . E . , and M e c h , L . D . 1986. Relationship between snow depth and gray w o l f predation on white-tailed deer. Journal of Wildlife Management, 50: 471-474. N o e l , L . E . , Pollard, R . H . , Ballard, W . B . , and Cronin, M . A . 1998. Activity and use o f active gravel pads and tundra by caribou, Rangifer tarandus granti, within the Prudhoe B a y O i l Field, Alaska. Canadian Field-Naturalist, 112: 400-409. Overpeck, J., Hughen, K . , Hardy, D . , Bradley, R., Case, R., Douglas, M . , Finney, B . , Gajewski, K . , Jacoby, G . , Jennings, A . , Lamoureux, S., Lasca, A . , MacDonald, G . , Moore, J., Retelle, M . , Smith, S., Wolfe, A . , and Zielinski, G . 1997. Arctic environmental change o f • the last four centuries. Science, 278: 1251-1256.  13  Payette, S. 1987. Recent porcupine expansion at tree line: a dendroecological analysis. Canadian Journal of Zoology, 65: 551-557. Post, E . and Forchhammer, M . C . 2002.  Synchronization o f animal population dynamics by  large-scale climate. Nature, 42: 168-171. Post, E . , Peterson, R . O . , Stenseth, N . C . , and McLaren, B . E . 1999. Ecosystem consequences o f w o l f behavioural response to climate. Nature, 401: 905-907. Post, E . , Stenseth, N . C . , Langvatn, R., and Fromentin, J . - M . 1997. Global climate change and phenotypic variation among red deer cohorts. Proceedings of the Royal Society of London Bulletin, 264: 1317-1324. Post, E . , and Stenseth, N . C . 1999. Climatic variability, plant phenology, and northern ungulates. Ecology, 80: 1322-1339. Rigor, I.G., Colony, R . L . and Martin, S. 2000. Variations in surface air temperature observations in the Arctic, 1979-97. Journal of Climate, 13: 896-914. Shaver, G . R . , Chapin, F.S., III, and Gartner, B . 1986. Factors limiting seasonal growth and peak biomass accumulation in Eriophorum vaginatum in Alaskan tussock tundra. Journal of Ecology, 74: 257-278. Skogland, T. 1985. The effects o f density-dependent resource limitation on the demography o f w i l d reindeer. Journal of Animal Ecology, 54: 359-374. Sinclair, A . R . E . , Gosline, J . M . , Holdsworth, G . , Krebs, C.J., Boutin, S., Smith, J.N.R., Boonstra, R., and Dale, M . 1993. Can the solar cycle and climate synchronize the snowshoe hare cycle in Canada? Evidence from tree rings and ice cores. American Naturaliste, 141(2): 173-198.  14  Spencer,  D . A . 1964.  Porcupine population  fluctuations  i n past  centuries  revealed  by  dendrochronology. Journal ofApplied Ecology, 1(1): 127-149. Thompson, D . W . J . , and Wallace, J . M . 2000. Annular modes i n the extratropical circulation, part I: Month-to-month variability. Journal of Climate, 13: 1000-1016. Thompson, D . W . J . , and Wallace, J . M . 1998. The Arctic Oscillation signature i n the wintertime geopotential height and temperature fields. Geophysical Research Letters, 25(9): 12971300. Turner, M . G . , W u , Y . , Wallace, L . L . , Romme, W . H . , and Brenkert, A . 1994. Simulating winter interactions among ungulates,  vegetation, and fire i n northern Yellowstone Park.  Ecological Applications, 4: 472-496. Welker, J . M . , Rayback, S., and Henry, G . H . R . 2005. Arctic and North Atlantic Oscillation phase changes are recorded in the isotopes ( d l 8 0 and d l 3 C ) o f Cassiope tetragona plants. Global Change Biology, 11: 997-1002. Vibe, C . 1967. Arctic animals i n relation to climatic fluctuations. Meddelelser om Grenland, 170: 1-227.  15  CHAPTER 2 . L O N G - T E R M ABUNDANCE PATTERNS OF BARREN-GROUND CARIBOU USING TRAMPLING SCARS ON ROOTS OF PICEA MARIANA IN T H E NORTHWEST TERRITORIES 1  2.1 INTRODUCTION Barren-ground caribou {Rangifer tarandus groenlandicus) tend to undergo relatively regular changes i n population size over decadal time scales (Skoog 1968). In the Northwest Territories, there are seven distinct barren-ground caribou herds. During August and September, the caribou move south from the tundra and into the forest-tundra transition zone along the treeline (Gunn et al. 2001). During the spring, caribou migrate to their calving grounds, which can be as far as 700 k m away from their wintering grounds. Barren-ground caribou adult males stand about 115 c m high and weigh approximately 105 k g (Kelsall 1968).  The average life span o f the barren-  ground caribou is between 10-15 years. The population o f the barren-ground caribou herds i n the studied region range between 186,000 (2003 estimate o f the Bathurst herd) to 276,000 (1994 estimate o f the Beverly herd) (http://www.nwtwildlife.com/NWTwildlife/caribou/herds.htm).  Aerial surveys have provided accurate data on caribou numbers over the past few decades (e.g. Gunn 2005); however, longer-term information is less available. Aboriginal knowledge o f caribou populations does extend further back i n time and has been compiled for the Bathurst herd i n the Northwest Territories, Canada (Dogrib Treaty 11 Council 2001). For other herds such as the Beverly herd, whose range is adjacent to the Bathurst herd, the traditional aboriginal information is not well compiled and census data are fewer.  ' A version of this chapter has been accepted for publication in Arctic, Antarctic and Alpine Research (In Press, September 2006). Zalatan, R., Gunn, A. and Henry, G.H.R. 2006. Long-term abundance patterns of barren-ground caribou using trampling scars on roots of Picea mariana in the Northwest Territories, Canada.  16  Caribou abundance among different herds has been shown to be i n synchrony at continental scales (Gunn 2005). This synchrony among herds in Alaska, Greenland, and eastern North America has been well studied (Gunn 2005). However, knowledge o f long-term abundance cycles for the central Canadian barren-ground caribou herds is not well understood. The only data that researchers and managers have to work with is derived from aerial photography and traditional knowledge ( T K ) . Caribou are large-bodied animals whose longevity necessitates a dataset that covers a larger geographic area and a longer timescale (Gunn 2005).  A recent method to estimate changes i n caribou abundance over decades is based on the application o f dendroecology. Morneau and Payette (1998, 2000) and Boudreau et al. (2003) used dendroecology to describe changes i n the size o f the George River caribou herd i n the northern Quebec-Labrador region. The authors aged the scars left by caribou hooves on the top o f surficial roots or low branches o f spruce trees during their summer migration to the tundra. The scars are formed when part o f the bark is removed due to trampling, which causes cambium death and stops radial growth i n that section o f the root. A scar lobe forms around the damaged cambial tissue i n subsequent years. The date o f scar formation can be accurately determined using dendroecology, and frequency distributions are then computed from the dated scars on root and stem samples. This method has made it possible to reconstruct caribou population activity and provides the longest available proxy record for changes i n caribou herd size.  The objective o f this study was to evaluate the long-term population dynamics o f barren-ground caribou herds i n the central Northwest Territories, Canada through the use o f dendroecology on trampling scars from spruce stands in the forest-tundra. Three aspects o f the methodology were  17  considered to properly evaluate the use of these data for reconstruction of caribou population dynamics: (1) the effect of root age on the scar frequency distribution; (2) the synchronicity of populations in neighbouring herds; and, (3) a comparison of the scar frequency distribution to historical and recent data (from aerial photography) available for the Bathurst herd.  2.2 M E T H O D S 2.2.1 Study Sites  The climate of this region is characterized by long, cold winters and short, cool summers with mean January and July temperatures of -26.8°C and 16.8°C, respectively (Meteorological Service of Canada 1971-2000 climate normals from Yellowknife airport, 62°27' N , 114°26' W, 206 m a.s.l.). Precipitation is low with an average annual total of 280.7 mm, falling mostly as rain during the summer months. The landscape consists of broad uplands and shallow lowlands, with rock outcrops, hummocky and ridged morainal deposits and eskers, and numerous lakes and wetlands (Traynor 2001). Permafrost is generally continuous. The vegetation consists of open lichen woodland with patches of closed forest in the southern areas, to forest-tundra consisting of thinning patches of forest and shrub tundra, to low arctic tundra in the north (Matthews et al. 2001).  The 19 study sites were located along the treeline to the northwest and southeast of Yellowknife (between 65°09'N, 115°37'W and 61°32'N, 105°52'W; Fig. 2.1). The sampling took place in the forest-tundra (Fig. 2.2). Fifteen study sites were selected based on information from Dogrib elders who identified several regions across the treeline within the Bathurst herd's annual range  18  that were frequented by caribou (Dogrib Treaty 11 Council 2001; Fig. 2.1, and Fig. 2.3). Figure 2.4 depicts a group of Bathurst caribou migrating across the landscape in the N.W.T.  125°W  120°W  115°W  Great Bear L.  Elevation (m) 0-100 •12  /  11  101-300  •10  14-15  301 - 500  * L  2  501 - 700  [Northwest sites  701 - 950  /  h65°N  •  Study sites — Treeline — • Migratory route!  Yellowknife  19  100 I  200 km —J 115°W  Southeast sites  110°W  J, 6  17  105°W  F I G U R E 2.1 Sampling sites of scarred roots on Picea mariana (sampled 2002) and the summer migratory route of barren-ground caribou. The geographic coordinates of the sampling locations for the scarred roots are listed in Table 2.1. A further four sites were selected on the late summer range of the Beverly herd. A targeted sampling approach was essential to find black spruce (Picea mariana [Mill.] BSP) roots with  19  trampling scars caused by caribou. To minimize bias in this study, sampling was done a few meters off the caribou trails to establish the presence o f trampling scars on roots growing across trails not currently used by caribou. One site was selected for this purpose. Laboratory analysis revealed that trampling scars were not found in the samples collected off the caribou trails. A s a result, this method o f sampling off trails was not applied to any subsequent sites.  F I G U R E 2.2 This is an example o f a typical site where caribou scars were collected. This photo was taken from a helicopter (at approximately 300 m elevation) and is located i n the foresttundra within the annual range o f the Bathurst caribou herd.  Once the general location o f the sites was chosen, a series o f highly trampled caribou trails were randomly chosen per site for sampling (Fig. 2.5). External features such as exposed xylem and resin accumulation were used to identify roots with scars.  Roots found to be buried under  understorey plants were all checked for possible scarring. Based on the density o f trails, the  20  number o f roots sampled per site differed. The roots sampled were all at the ground surface and therefore digging was not necessary.  Source: R. Zalatan FIGURE 2.3 Caribou migration trails across an esker taken from a helicopter i n the central N.W.T.  Source: R. Zalatan FIGURE 2.4 Bathurst caribou migrating across the landscape i n the N . W . T .  21  Source: R. Zalatan  F I G U R E 2.5 A series of black spruce roots trampled by barren-ground caribou. Trampling removes part of the bark, leaving behind a scar which is used to represent caribou abundance. Selecting sites used by barren-ground caribou populations increased our chances of finding old scars, thereby lengthening the scar chronology. The sites in this study cover a portion of the summer range of the Beverly, Bathurst and Ahiak herds, therefore the caribou abundance cycles derived from the scar frequency distribution are not defined as those of a specific herd. The analysis was performed on two groups of sites: (1) the northwest sites (n = 15); and, (2) the southeast sites (n = 4). These groups correspond generally to the range of the Bathurst and Beverly caribou herds, respectively. However, there can be considerable overlap in the ranges of  22  these  herds  (http://www.nwtwildlife.rwed.gov.ntxa/images/new_pa6.gi f)  in  some  years;  therefore, the sites w i l l be referred to as the northwest and southeast sites. Tracks o f satellitecollared caribou cows from the Bathurst herd over the period from  1996-2002  clearly  demonstrate that the area where the study sites are located has been well used by caribou (Fig. 2.6).  Source: A. Gurm 2002 F I G U R E 2.6 Tracks o f 10-20 satellite-collared caribou cows from the Bathurst herd (19962002). The black boxes outline the general area where the study sites are located.  23  A t each study site, trampled caribou trails were selected and surficial roots o f Picea mariana trees were collected where they crossed heavily trampled caribou paths. The number o f roots sampled per site varied from 10 to 417, depending on the density o f trails and the time available for sampling. For most o f the sampling, only one root was cut per tree. Caribou scars are formed on the top o f surficial roots and sometimes on l o w branches o f krummholz trees during the snowfree period.  The shape o f the scar is generally round, elliptical, or elongated with neat margins (Fig. 2.7). scar  year o f scar formation  pith  Source: R. Zalatan F I G U R E 2.7  Schematic diagram o f a scar on a cross-sectional sample o f a root.  Scars can be formed b y other ungulates or human activity (such as hiking trails) or by fire, however there was little evidence that the study region has been affected b y any o f these disturbance agents other than caribou. The scars found in this study were clearly trampling scars  24  because charcoal was not detected and the samples were taken on roots and not on the stems o f trees. In addition, fire (Barrett and A r n o 1988) and frost scars are structured differently and are therefore easy to differentiate from trampling scars.  Cross-sections were sanded using progressively finer grades o f sandpaper (240, 320, 400, and 600) and were prepared for crossdating as outlined in Stokes and Smiley (1968). Each annual ring was measured using a Mini-scale and Mate System Encoder connected to an AcuRite III digital counter with a precision o f 0.001 m m . Scars were dated by visually and statistically crossdating the rings prior to the scar. Crossdating ensures the exact year o f formation o f annual rings and it verifies the presence o f missing, false or locally absent rings (Fritts 1976). Most scars were dormant season scars, which were formed i n late summer/early fall when caribou migrate through the study sites upon returning from the calving grounds. However, it is possible during a winter with low snow-pack, that some scars are formed in the spring when caribou are on their way to the calving grounds. The seasonal dormant phase o f the cambium extends over 2 calendar years (Morneau and Payette 2000). It is not possible to determine the exact date that scars formed, as the year o f scar formation could vary by +1 year. A s a standard, the year o f scar formation is taken as the most recent year (Morneau and Payette 1998). A t times, annual rings were wedged (or pinched) near the edge o f the scar, which made it difficult to identify the exact scar ring. A s a consequence, we grouped the scars into 5-year age-classes to obtain the most accurate record. Only scars that were successfully crossdated were included i n subsequent analysis.  25  Once the date o f scar formation was determined, the scar frequency distribution (5-year ageclasses) was computed as an index o f caribou abundance (Morneau and Payette 1998, 2000). Although the scars are accurately dated, they are representing caribou abundance and therefore a 5-year age-class is a more conservative way o f capturing any variability i n this index o f caribou abundance. The scar frequency distribution must be interpreted i n terms o f successive periods o f increasing and decreasing caribou abundance. Scar frequency distributions were also computed using scars at an annual level, a 2-year age-class, a 3-year age-class and a 4-year age-class to determine whether there were any significant changes i n the trends o f the scar frequency distributions for both groups o f sites (northwest and southeast sites; Appendix A ; F i g . A 1 - A 4 ) . Spearman's rank correlations were computed between the two groups o f sites for each age-class (Appendix A ; Table A . l ) .  A Kolmogorov-Smirnov test for goodness-of-fit was used to determine i f the scar frequency distributions for both herds were significantly different. To ensure the validity o f the results, the scar frequency was compared to traditional knowledge ( T K ) and animal counts from aerial surveys and photography. Research on the T K data was conducted b y the West Kitikmeot Slave Study i n conjunction with the aboriginal elders o f the Dogrib Nation. The data were used to index Bathurst caribou herd abundance to the 1920s (Dogrib Treaty 11 Council 2001). The T K data were only recorded back to the 1920s because the elders would not provide any information on caribou population abundance and migration that they had not directly witnessed or for which they felt certain about.  26  Aerial surveys o f caribou herds i n the Northwest Territories are performed by the Department o f Environment and Natural Resources ( E N R ) . Biologists estimate the number o f breeding females in a herd by performing aerials surveys above the calving grounds. Since all breeding females calve around the same time in early June and in the same general area, it makes it possible to ensure that all the animals counted belong to a single herd. The percentage o f the total herd that is breeding females is used to estimate the size o f the entire herd. A series o f photos (e.g. 2600 photos were taken for the 2003 estimate) are taken while flying at 600 meters above ground level (http://www.nwtwildlife.com/NWTWildlife/caribou/Counting_Caribou.pdf). A standard error is calculated for each year that a survey is done.  For the reconstruction o f caribou abundance using dendroecology, two analyses were performed. The first involved determining the influence o f root age to obtain an objective scar frequency distribution independent o f the age o f the root itself. This was done by plotting the scar frequency distribution using only roots that had established prior to 1900 (the year to which the data set was eventually truncated). This analysis was used to account for the increasing underestimation o f caribou abundance with the increasing age o f the roots. A second analysis was used to address the underestimation o f caribou activity with time, assuming a constant loss o f scars. Various factors contribute to the loss o f scars through time such as the death o f scarbearing roots, fading o f scars by weathering, decomposers, and repeated caribou trampling activity (Morneau and Payette 2000). A log-linear regression was applied to remove the error associated with the loss o f scars through time and the residuals were used to obtain a more accurate depiction o f the abundance patterns o f these herds. Departures from the negative exponential model were used to demonstrate the years o f high and l o w caribou abundance.  27  2.3 R E S U L T S 2.3.1 Scar frequency distributions Statistical and visual crossdating o f 2477 root samples collected from Picea mariana trees yielded 1991 trampling scars (Table 2.1). T A B L E 2.1 Geographic location, number o f scars and samples for the northwest and southeast sites, Northwest Territories. Locations are also shown i n F i g . 2.1.  Location Number of Scars (% of all sites)  Number of Samples  Number of Samples (% of all sites)  Latitude  Longitude  3.1  36  1.5  64° 27.2' N  112° 45.6'W  2  61 19  1.0  21  1.1  64° 27.9' N  113° 09.4'W  3  12  0.6  10  0.5  64° 22.7' N  113° 23.3'W  4  8  0.4  12  0.6  64° 17.5'N  113° 49.3'W  Site  Number of Scars  Northwest sites 1  5  62  3.1  108  5.4.,  63° 29.1'N  112° 23.2'W  6  190  9.5  282  14.2  63° 32.7' N  112° 18.8'W  7  47  2.4  143  7.2  63° 21.9' N  111° 46.2' W  8  42  2.1  139  7.0  63° 13.9'N  110° 55.2'W  9  41  2.1  100  5.0  64° 04.8' N  112° 31.1* W  10  34  1.7  72  3.6  64° 37.7' N  115° 04.4'W  11  90  4.5  136  6!8  64° 47.7' N  115° 12.2'W  12  125  6.3  121  6.1  65° 00.8'N  115° 29.1'W  13  77  3.9  93  4.7  65° 09.8'N  115° 37.5'W  14 15  37  1.9  32  1.6  64° 30.8'N  114° 15.0'W  102  5.1  153  7.7  64° 30.5'N  114° 14.6'W  Total  947  1458  Southeast sites 16  225  11.3  245  12.3  61°45.1'N  106° 29.0'W  17  321  16.1  282  14.2  61° 32.5' N  105° 52.3'W  18  436  21.9  417  20.9  61° 36.5' N  106° 07.3'W  19  62  3.1  75  3.8  61° 36.0'N  106° 49.3'W  Total scars  1044  Total (all sites)  1991  1019 100  2477  100  28  The number o f scars found was evenly distributed between the two groups o f sites: 48% (n = 947) within the northwest sites (sites 1-15) and 52% (n = 1044) within the southeast sites (sites 16-19). For all sites, 97% (n =1933) o f the scars were dated with certainty.  The earliest scars in our samples were formed in the northwest sites during the 1760s, although they were scarce until the 1870s (Fig. 2.8a). The scars from the southeast sites appear i n the 1835 age-class. The majority (97%) o f the scars were formed after 1900, although, no scars formed from 1910-1930 i n the northwest sites. The frequency o f scars increased steadily through time with the highest frequency o f scars i n the 1945, 1990 and 1995 age-classes, i n addition to the 1975 age-class for the southeast sites.  There was a general trend toward increasing scars through time until the present, with a sudden drop in the number o f scars i n 2000. Similar scar frequency distributions were observed at both groups o f sites (Spearman's rank correlation, r = 0.88; p<0.05, n = 24), and the scar frequency s  distributions were not different ( K S , p>0.05). The cumulative frequency curves o f the number o f samples i n Figure 2.8a were used to demonstrate how the number o f samples increased steadily through time. The cumulative frequency curves were not applied to Figure 2.8b because this figure did not include the entire dataset.  To assess the influence o f age o f roots on the scar data, the scar frequency distribution was plotted using only roots established prior to 1900 present over the entire period o f the truncated scar frequency distribution (Fig. 2.8b). Thus, the relative frequency o f scars is compared with the number o f roots that existed from 1900 to the present. The scar frequencies for both groups o f  29  (a) Northwest sites Southeast sites 80  15  60 S 10 40  m x  o 20  1760 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Year (5-year age-classes)  (b)  12  Northwest sites Southeast sites  10  a.  4  1  Lliifi/jlii  0 1760 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Year (5-year age-classes)  F I G U R E 2.8 Scar frequency distribution (a) using all samples from sites 1-15 (blue bars) and sites 16-19 (grey bars) and cumulative frequencies o f the number o f samples (lines) (r = 0.88; p<0.05, n = 24); and (b) using only scars on roots established before 1900 (r = 0.63, p<0.05, n = 24). s  s  30  sites, showed the same patterns o f major increases and decreases (Spearman's rank correlation, (r = 0.63; /?<0.05, n = 24). Both groups o f sites showed low numbers until the 1920s, again from s  1955-1970 and i n 2000. The scar frequency distributions showed an increase during the 1940s until the mid-1950s, and from 1980-2000. However, the complete scar frequency distribution (Fig. 2.8a) showed a much more marked peak i n the 1990s than the restricted distribution (Fig. 2.8b).  The scar frequency distribution from the northwest sites (corresponding to the Bathurst herd; truncated to 1900) was compared to information on caribou abundance from Dogrib elders and from animal counts based on aerial photography (Fig. 2.9). A similar calibration o f the data for the southeast sites (Beverly herd) was not possible because aboriginal knowledge has not been compiled and there have been fewer population estimates based on aerial photographic surveys. The qualitative description o f "high" or " l o w " population o f caribou was derived from aboriginal elder's narratives and describes the abundance o f caribou from the 1920s to the 1970s (Dogrib Treaty 11 Council 2001).  The population estimates were from aerial photographic surveys and describe caribou abundance from 1984 until 2003. The information from the Dogrib elders and the scars both showed low numbers o f caribou during the 1920s, followed by a high peak in caribou numbers during the mid-1940s, then a low period from 1950 to 1970. The aerial photography data showed an increasing trend in caribou abundance after the 1970s, with a peak i n the mid-1980s, followed by a significant drop in caribou abundance in 2000. In the 1990s, the scar frequency distribution  31  showed a rapid increase, after a period of little change from the 1950s-70s, followed by a notable decrease after 2000.  500000  1900  1910  1920  1930  1940  1950  1960  1970  1980  1990  2000  Year (5-year age-classes)  F I G U R E 2.9 Bathurst herd caribou numbers from aerial photography surveys (1984-2003; black line; means ± SE), and abundance patterns (high = dark grey shading or low = light grey shading) derived from traditional knowledge of elders from the Dogrib First Nation relative to the scar frequency distribution for the northwest sites (Bathurst herd; bars).  2.3.2 C a r i b o u abundance cycles  The scar frequency distributions for both groups of sites demonstrated an exponential decrease in scars from the 2000 age-class towards the 1760s. Figure 2.10 illustrates the residuals of the loglinear regression for both groups of sites, which was used to remove the long-term depletion pattern in the number of scars and to accentuate the fluctuations in the scar frequency distributions.  32  1900  1920  1940  1960  1980  2000  Year of scar formation (5-year age-classes)  F I G U R E 2.10 Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites.  The large oscillations i n the residuals around the regression line follow a pattern similar to that o f the fluctuations i n the scar frequency distributions. The chronologies o f residuals from both sites were positively correlated (Spearman's rank, r = 0.55; p<0.05). The correlations remained s  significant when the recent (1950-2005) and older (1900-1950) parts o f the chronologies were considered separately (Spearman's rank, r = 0.55 and r = 0.87; p<0.05, respectively). s  s  2.4 D I S C U S S I O N The scar frequency distribution developed in this study is the longest proxy record o f barrenground caribou abundance yet available. The proxy record developed i n this study extends caribou abundance data back to the 1760s, although the scars were scarce until approximately  33  1900. The most prominent peaks in the scar frequency distribution were seen during the m i d 19408, and the late 1980s and 1990s. Periods o f l o w scar frequency occurred i n the 1920s, 1950s-70s and at the turn o f the 2 1 century. The loss o f scars with time did not influence the st  population reconstruction since the scar frequency distribution using only roots established before 1900 demonstrated a broadly similar abundance pattern. Both groups o f sites showed similar patterns i n scar frequency, indicating that the different herds within the sampling area experienced similar changes i n abundance over time. Given the scale o f these synchronous changes in caribou abundance, it is likely they are linked to changes and variability i n large-scale climate, such as the Arctic Oscillation (Aanes et al. 2002; Post and Forchhammer 2002; Post and Forchhammer 2004). The link between the abundance patterns and climate is investigated i n Chapter 4.  H i g h numbers o f scars were associated with the growth o f the herds between the mid-1940s to the 1990s, as seen from the Dogrib traditional knowledge ( T K ) and aerial photography data. The trends seen i n data from T K are similar to those depicted i n the scar frequency distribution from 1920-1970. H i g h numbers o f caribou during the 1980-1990s and sudden drop i n the 2000 ageclass were observed i n the aerial photography data. The large standard error associated with the aerial photography data may account for the earlier peak observed during the mid-1980s (SE±72,000 i n 1986). This provides evidence for the strength and accuracy o f the scar frequency distribution as a proxy for caribou abundance. Despite the earlier peak i n the mid-1980s from the census data, there were synchronous patterns in caribou abundance using the different methods. Morneau and Payette (2000) found a similar synchronous relationship between estimates o f the herd size and trampling scar data for the George River caribou herd, with increasing caribou  34  numbers from the early 1940s until the early 1990s. Synchronous changes i n the scar frequency distribution at both groups o f sites and among the different datasets demonstrate the strength o f the spatiotemporal pattern i n caribou abundance i n this region. Changes in caribou migration patterns have not been quantified over the last 100 years, however, the synchrony i n the scar frequency data to the T K and aerial photography data, suggests that any changes i n migratory routes has not affected the results, W i t h the use o f dendroecology, I have shown that it is possible to reconstruct the abundance patterns o f these barren-ground caribou herds. The presence o f trampling scars at all sites suggests that the forest-tundra is an ideal region for estimating past caribou activity. Assuming the capacity o f conifer roots to produce scars remains constant with time (a reasonable assumption), the scar frequencies can be used as a proxy for caribou abundance (Morneau and Payette 1998, 2000).  A log-linear regression was applied to account for the loss o f scars with time, assuming that this loss is constant (Morneau and Payette 2000). The residuals o f the regression were then used to depict the abundance patterns o f the barren-ground caribou at these sites. The chronology o f residuals illustrated the overall trends in caribou activity as seen i n the scar frequency distributions. The similar trends i n the T K , caribou numbers and trampling scars suggest that changes in the scar frequency distributions corresponded to changes i n the rate o f scar formation. Subsequently, variations i n the rate o f scar loss with time, which corresponds to caribou movements along trails and root mortality, were most likely minor i n comparison to the changing rate o f scar formation.  35  This study further validates the use o f dendroecology for understanding the population dynamics o f barren-ground caribou herds (Morneau and Payette 1998, 2000). The identification o f similarities i n the scar frequency distributions among sites can lead to further investigation regarding the spatial and temporal patterns throughout the range o f the barren-ground caribou herds. The sites selected for this study represent a substantial portion o f the range o f barrenground caribou. However, the length and lack o f synchrony i n the scar frequency prior to 1900 needs to be addressed with further sampling and analysis. Nevertheless, further use o f this method has the potential o f providing valuable information about the long-term abundance cycles o f all barren-ground caribou herds across northern North America. This study was the first to reconstruct  long-term abundance  cycles for barren-ground  caribou o f the Northwest  Territories.  36  2.5 R E F E R E N C E S Aanes, R., Saether, B - E . , Smith, F . M . , Cooper, E . J . , Wookey, P . A . , and 0ristland, N . A . 2002. The Arctic Oscillation predicts effects o f climate change i n two trophic levels i n a higharctic ecosystem. Ecology Letters, 5: 445-453. Barrett, S.W., and Arno, S.F. 1988. Increment-borer coniferous  forests.  General  Technical  methods for determining fire history in  Report,  Report  INT-244.  Ogden,  UT:  Intermountain Research Station. Boudreau, S., Payette, S., Morneau, C , and Couturier, S. 2003. Recent decline o f the George River caribou herd as revealed by tree-ring analysis. Arctic,  Antarctic,  and  Alpine  Research, 35: 187-195. Dogrib Treaty 11 Council. 2001. Caribou migration and the state of their habitat. Final Report to the West Kitikmeot/Slave Study Society, Yellowknife, N T , 90 pp. Fritts, H . C . 1976: Tree Rings and Climate. N e w Y o r k : Academic Press, 567 pp. Gunn, A . , Dragon, J., and Boulanger, J. 2001. Seasonal movements of satellite-collared from  the Bathurst  caribou  herd. Final Report to the West Kitikmeot Slave Study Society,  Yellowknife,  NWT,  72  pp.  http://www.wkss.nt.ca/HTML/08_ProjectsReports/PDF/SeasonalMovementsFinal.pdf Gunn, A .  2005. Voles, lemmings and caribou - population cycles revisited?  Rangifer  Special Issue, N o . 14: 105-112. Kelsall, J.P. 1968. The migratory  barren-ground  caribou of Canada. Ottawa: Queen's Printer,  340 pp.  37  Matthews,  S., E p p , F L , and Smith, G . 2001. Vegetation classification for  the West  Kitikmeot/Slave Study region. Final report to the West Kitikmeot/Slave Study. Society, Yellowknife, N T , 42 pp. Morneau, C . , and Payette, S. 1998. A dendroecological method to evaluate past caribou (Rangifer tarandus L.) activity. Ecoscience, 5: 64-76. Morneau, C . , and Payette, S. 2000. Long-term fluctuations o f a caribou population revealed b y tree-ring data. Canandian Journal of Zoology, 78: 1784-1790. Post, E . , and Forchhammer, M . C . 2002. Synchronization o f animal population dynamics b y large-scale climate. Nature, 420: 168-171. Post, E . , and Forchhammer, M . C . 2004. Spatial synchrony o f local populations has increased i n association with the recent Northern Hemisphere climate trend. Proceedings of the National Academy of Sciences of the United States of America, 101: 9286-9290. Skoog, R . O . 1968. Ecology of the caribou (Rangifer tarandus grand) in Alaska. P h . D . Thesis, University o f California, Berkeley. Stokes, M . A . , and Smiley, T . L . 1968. An Introduction to tree ring dating. Chicago: University o f Chicago Press, 73 pp. Traynor, S. 2001. Esker habitat characteristics and traditional use study in the Slave Geological Province. Final report to the West Kitikmeot/Slave Study Society. Yellowknife: West Kitikmeot/Slave Study Society, 66 pp.  38  CHAPTER 3. SPATIO-TEMPORAL VARIATION OF TREE-RING GROWTH A L O N G A TRANSECT A T TREELINE WITHIN T H E RANGE OF BARREN-GROUND CARIBOU: A DENDROCLIMATIC APPROACH 3.1 INTRODUCTION The region to the northwest and southeast o f Great Slave Lake is home to four barren-ground caribou herds, whose movements are monitored through satellite telemetry as they migrate from the boreal forest across the latitudinal treeline into the tundra (http://www.nwtwildlife.com/). Studies have demonstrated the direct impacts o f climate at different scales on the migratory patterns and dynamics o f this ungulate species. For example, Telfer and Kelsall (1984) found that an abundance o f soft snow made it easier for caribou to avoid predation. A case-study on the Peary caribou demonstrated the detrimental impact o f snow and ice conditions which resulted i n a cataclysmic decline i n the number o f caribou due to a reduction in forage availability (Miller and Gunn 2003).  Large-scale climatic oscillations have been shown to explain a considerable proportion o f annual variations in temperature and precipitation over large areas (Hurrell 1995; Broccoli 2001). Specifically, the Arctic Oscillation ( A O ) is closely related to the mean annual temperature recorded at different stations in the Arctic (Overpeck et al. 1997). The A O is a climate cycle and its index reflects the mean deviation from the average sea level pressure throughout the Northern Hemisphere at latitudes poleward o f 20°N (Thompson and Wallace 1998, 2001). The A O switches between two phases based on the deviation in mean pressure. In its positive phase, the Arctic experiences lower than average atmospheric pressure, which usually results i n an increase in winter temperature and amounts o f precipitation (Thompson et al. 2000). However, during the summer, the positive phase o f the A O usually results in cloudy conditions, decreased  39  temperatures and increased amount o f precipitation. The negative phase o f the A O corresponds to decreased  winter temperatures and precipitation. The meteorological conditions o f the  negative phase during the summer are not well documented possibly because their impacts are not as pronounced.  Recently, studies have demonstrated the indirect impacts o f large-scale climate oscillations on mammals and vegetation (Post and Stenseth 1999; Patterson and Power 2001; Aanes et al. 2002; Post and Forchhammer 2002; Hebblewhite 2005). In the Canadian Rockies, large-scale climate was influencing snow depth and winter climate, which resulted i n greater difficulty for elk to avoid predation b y wolves (Hebblewhite 2005). Other studies have shown that large-scale climate oscillations have influenced plant growth conditions, through changes in temperature and precipitation, which has then affected the population growth rate o f ungulates (Aanes et al. 2002). In one case, large-scale climate directly influenced plant phenology, as well as the reproduction and demography o f ungulates, and explained a greater amount o f the variation than the local climate (Post and Stenseth 1999).  There are direct and indirect influences o f climate on caribou and the more information that is available on the impacts o f climate, the better managers w i l l be able to interpret the complex dynamics o f this interaction. A t present, the only annually-resolved climate data that are available for this area o f the N . W . T . are from the distant meteorological station at Yellowknife, and this record is only consistently available back to 1943. M a k i n g inferences about climatecaribou relationships could be problematic when only one distant meteorological station is being used to study various caribou herds, occupying a vast geographic region.  40  Tree-rings have been used to reveal past climates at various spatial and temporal scales (Hughes 2002). Several studies have used tree-rings to reconstruct temperature in northern regions (Jacoby and Cook 1981; Jacoby et al. 1988; Jacoby and D ' A r r i g o 1989; Briffa et al. 1994). However, i n the Northwest Territories dendroclimatological records are few and far between. The closest tree-ring chronologies to the sites i n this study were developed from trees i n the Franklin and Richardson Mountains, to the west o f Great Bear Lake (Szeicz and MacDonald 1995). These sites are approximately 400km from the sites in this study, and in a different climatic and physiographic region. Although the mountainous regions o f the Northwest Territories are well studied, the dendroclimatological potential o f trees growing i n the lowland areas o f the Northwest Territories to the southeast o f Great Bear Lake has not been investigated.  In an effort to increase the knowledge o f climate variability and its potential impacts on biological systems, as well as to expand on the number o f tree-ring chronologies i n this region, I present a dendroclimatic reconstruction o f summer temperatures using a network o f tree-ring chronologies o f black spruce (Picea mariana [Mill.] B S P ) . The goal was to have a better understanding o f the local impact o f climate on the trees growing within the range o f the barrenground caribou o f the N . W . T . , more specifically at the same sites where caribou population abundance cycles were reconstructed i n Chapter 2. In addition, I have included four tree-ring chronologies from the same region taken from the International Tree-ring Databank ( I T R D B ) for comparison purposes. T o explore the impact o f large-scale climate on tree growth, I compare the ( A O ) and tree growth in this region.  41  Specifically, this chapter aims to: (i) evaluate climatic factors correlated with radial growth o f Picea mariana along the latitudinal treeline in the central N . W . T , (ii) reconstruct summer climate in this region o f the N . W . T . , and (iii) examine tree growth i n relation to the Arctic Oscillation.  3.2 M E T H O D S 3.2.1 C h r o n o l o g y development Seven ring-width chronologies were developed from samples taken from Picea mariana trees at treeline northwest and southeast o f Great Slave Lake. These sites were 7 o f the 19 sites selected to reconstruct caribou population abundance in Chapter 2. The sites were located along the treeline between latitudes o f 61°- 65° N and longitudes o f 106°-115° W , and were selected along the transect o f 19 sites, to evenly represent the study area (Fig. 3.1, Table 3.1). The maximum distance between the two furthest sites is approximately 600 k m . A l l sites were dominated b y Picea mariana and had similar site characteristics. The sites were located within the foresttundra with the landscape surrounded b y broad upland and shallow lowland areas, rock outcrops, hummocky and ridged morainal deposits and eskers.  In 2003 and 2004, a combination o f both cores and cross-sections were collected from a minimum o f 40 trees at each site. A n increment borer was used to extract two cores, at least 90° apart at breast height. Cross-sections were collected using a chainsaw, with all samples taken at breast height. Only healthy, undisturbed trees (i.e. without visual evidence o f disturbance) were sampled. Cross-sections and cores were sanded using progressively finer grades o f sandpaper (240, 320, 400 and 600 when necessary). The tree rings were visually and statistically crossdated to the calendar year o f their formation b y employing standard dendrochronological techniques as  42  125°W  120°W  110°W  115°W  105°W  65°N  Elevation (m)  y  13.  10  -100  rZlJ 101-300 K65°N  701 -950 •  .8  Yellowknife _ Mac Kinky  1 ~\  A  » Migratory route  ,, . .,  A  t  Great Slave L.  A  (h  ITRDB  Austin!  60°N-  19.  100  Study sites  - - - Treeline  J6  200 km I  I15°W  I  110°W  105°W  F I G U R E 3.1 M a p showing location o f tree-ring chronology sites (numbered sites), sites from the International Tree-Ring Databank ( I T R D B ) , Yellowknife meteorological  station in the  N . W . T . and the summer migratory route o f barren-ground caribou. T A B L E 3.1 Site characteristics of Picea mariana chronologies along treeline in the N . W . T .  Chronology Site 1 Site 6 Site 8 Site 11 Site 13 Site 16 Site 19  Location  1  64° 27.2 N , 112° 45.6 W 63° 32.7 N , 112° 18.8 W 63° 13.9 N , 110° 55.2 W 64° 47.7 N, 115° 12.2 W 65° 09.8 N , 115° 37.5 W 61° 45.1 N , 106° 29.0 W 61° 36.0 N, 106° 49.3 W  Time span 1685-2003 1736-2003 1758-2003 1830-2003 1711-2003 1916-2004 1889-2004  Mean series length (years) 154 126 151 92 158 60 68  No. trees  No. radii  34 33 34 31 34 39 35  66 64 66 58 68 78 69  Locations are shown in Figure 3.1  43  outlined by Stokes and Smiley (1968). Although there were few missing rings, cross-sections were preferred as they facilitated cross-dating.  The width o f each dated ring was measured using a Quick Check QC-1000 connected to an AcuRite III digital encoder with a precision o f 0.001 m m . These measurements and assigned dates were then verified using the computer program C O F E C H A (Holmes 1983). Once the crossdating errors were resolved using C O F E C H A and by re-examining the dates assigned to the samples, the computer program A R S T A N was used to develop the chronology. Ring-width measurements were standardized to remove growth-related trends while retaining the maximum common signal found i n the tree-rings. Interactive detrending was used i n A R S T A N  to  determine which curve was the best fit for each tree-ring series. A R S T A N plots the curve to the original data, and presents the resulting curve after each ring width is divided by the value o f the growth curve. For the purposes o f this study, a negative exponential curve, a linear regression line o f negative slope or a horizontal line through the mean was fitted to each series to remove the growth trends. The detrended indices from each year for all series are then averaged to obtain a master chronology. The residual chronology (instead o f the Standard or Arstan chronologies) was used for each site because it was developed using autoregressive standardization techniques on the detrended ring-width series, which removes the effects o f autocorrelation (Cook 1985). It is important to remove the effects o f autocorrelation i n a dendroclimatic reconstruction since each tree-ring width must accurately represent the climate o f that particular year, and must not be an artifact o f a previous year's climate conditions.  44  T o evaluate common features among tree-ring chronologies developed i n this study, a statistical analysis was carried out over the common period covered b y all seven chronologies. Pearson's product moment correlations were computed to examine the between tree signal using data from the period 1916-2003. In order to provide additional information on the tree-growth response to climate i n this region, I compared the chronologies developed i n this study to four obtained from the I T R D B (Fig. 3.1)  (Austin Lake, M a c K i n l e y and Pethai Peninsula standard chronologies  from Schweingruber, F . H . ; Coppermine standard chronology from Jacoby, G . C . , D ' A r r i g o , R . D . , Buckley,  B . ; http://www.ncdc.noaa.gov/paleo/webmapper-treering.htmlv)  using  Pearson's  correlations. The Pethai Peninsula and Austin Lake chronologies were derived from Picea glauca trees; the M a c K i n l e y chronology was derived from P. mariana; and the Coppermine chronology was derived from samples o f both Picea species. These I T R D B chronologies were selected based on their proximity to the tree-ring chronologies developed i n this study, and because they were developed using only Picea species.  3.2.2 Multivariate Techniques Cluster analysis, a hierarchical clustering method that uses Euclidean distance as a measure o f dissimilarity, was used to examine the structure o f the relationship among the residual chronologies. The tree-ring chronologies were used as variables, i n this analysis. Principal components analysis ( P C A ) was performed to determine the explained variance among the residual chronologies. To evaluate the temporal stability o f this shared variance, P C A s were performed for successive overlapping 30-year periods (1916-1946, 1926-1956, 1936-1966, 19461976, 1956-1986, and 1966-1996) and for the entire common period (1916-2003) (Tardif et al. 2002; Girardin and Tardif 2005). Overlapping 30-year periods were chosen since this is the  45  standard period (30-year climate normals) that is used by climatologists when analyzing climate patterns through time  (http://climate.weatheroffice.ec.gc.ca/climate_normals/index_e.html).  3.2.3 Comparing meteorological data to the tree-ring chronologies The relationship between tree growth and climate was tested using Pearson's correlations and response function analysis (Fritts 1976), where monthly climate data are used as statistical predictors and ring-width chronologies as predictands (Davi et al. 2003). This analysis was performed using the P R E C O N program (Fritts 1999). The common time span (1943-2002) o f mean monthly temperature and total monthly precipitation and ring-width data from the seven chronologies were selected for this analysis. The growing period for trees near treeline in this region is expected to be between M a y and August, therefore the analysis was performed using meteorological data from the previous year's M a y to the current year's August for each chronology. The previous year's climate was included to account for the lag effect i n tree growth. The lack o f long-term meteorological stations in this region makes tree-growth-climate studies difficult. The closest station was used for this analysis (Yellowknife airport meteorological station  (Fig.  3.1;  62°27'N,  114°26'W,  http://climate.weatheroffice.ec.gc.ca/prods_servs/cdcd_iso_e.html)).  205.70  m  a.s.l.;  The data were truncated to  the period between 1943-2002 to avoid missing data which were present prior to 1943.  3.2.4 Transfer functions To avoid problems o f intercorrelation among the chronologies, a P C A was used to create a set o f orthogonal (uncorrected) eigenvectors, which were then used as candidate predictor variables (Bhattacharyya and Chaudhary 2003; Girardin and Tardif 2005). Only the  five  longest  46  chronologies were used i n order to obtain the longest dendroclimatic reconstruction possible (Table 3.1). If all seven chronologies had been used, the reconstruction would only extend to 1916 since this is the length o f the shortest chronology, which is very short b y dendroclimatic standards. For the purpose o f this study, the chronology needed to extend to at least 1900 to provide a long-enough time-frame for comparison to the caribou abundance cycles. The eigenvectors were then used to represent the signal derived from five chronologies. Based on the results  o f the  response function  analysis, transfer  function models  predicting  summer  temperatures from five eigenvectors were calibrated using stepwise multiple linear regression over the common period o f analysis (1943-2002). A calibration was done for preliminary models to predict temperature for individual months o f the growing season (May-September), and for different combinations o f months (Case and MacDonald 1995; Rayback and Henry 2006). Forward (t +1) and backward (t - 1) lagged indices were added to capture persistence effects in the growth-climate relationships (Fritts 1976; Jacoby et al. 1985; Briffa et al. 1988).  A data-splitting technique was used to examine the temporal stability o f the derived model (Fritts 1976). The entire data set (1943-2002) was split i n two halves: the "early" period (1943-1972) and the "late" period (1973-2002). Following this, a transfer function was calibrated for each period using stepwise regression. Each reconstruction was then verified with the opposite period; for example, the early model was verified on the 1973-2002 period and the late model was verified on the 1943-1972 period. Verification statistics included Pearson's  product-moment  correlation coefficient (r), reduction o f error statistic ( R E ; positive values demonstrate skill in reconstruction),  the  coefficient  o f efficiency ( C E ; positive values  demonstrate skill  in  reconstruction), and the nonparametric sign test o f first differences (Briffa et al. 1988).  47  3.2.5 The Arctic Oscillation (AO) The A O index is primarily evaluated and modeled during winter months (Shindell et al. 1999; Hartmann et al. 2000). However, recent reductions i n atmospheric pressure have been observed during summer months ( A O S ) , reflecting the same nature as the A O winter index ( A O W ) (Serreze et al. 1997, 2000; Aanes et al. 2002; D ' A r r i g o et al. 2002). This may indicate that the A O can have impacts on weather patterns during the summer season. The A O W index is based on the months October-May, while the A O S index reflects the plant growth season from JuneAugust, corresponding to the snow-free months i n the study area.  The phases o f the A O S have not been well documented, therefore I developed a method to distinguish these phase changes through time. I performed a 5  th  order regression (y = 5E-07x  5  -  O.OOOlx + 0.0121x - 0.4543x + 4.9464x + 34.282; R = 0.29) with the A O S index as the 4  3  2  2  dependent variable and year as the independent variable. The change i n the slope o f the regression was then used to determine shifts i n the A O S . Based on the analysis o f the changes i n the slope, the A O S index has been divided i n the following four time periods: 1900-1930, 19311955, 1956-1983 and 1984-2000 (Fig. 3.2).  3.2.6 Relationships between the Arctic Oscillation (AO) and tree-ring chronologies To establish whether there was a link between large-scale climate ( A O ) and tree-growth, a response function analysis was computed between all seven chronologies and the monthly index o f the A O for the entire period o f analysis (1900-2000). Spearman's rank correlations were computed for the four phases o f the A O (1900-1930, 1931-1955, 1956-1983 and 1984-2000), to determine whether the chronologies are more responsive to certain phases o f the A O . Pearson's  48  correlations were used to analyze the relationship between negative (<zero) A O index years and the tree-ring chronologies. The same analysis was performed using only positive (>zero) A O index years. This correlation analysis was used to determine whether tree growth is responding to both phases o f the A O (positive and negative) or i f only specific phases o f the A O are affecting tree growth.  c-I.O  1900  1920  1940  1960  1980  2000  Year F I G U R E 3.2 The standardized Arctic Oscillation ( A O ) anomaly index for June to August for the period between 1900-2002, and a 5 order regression curve (data from http://www.jisao.washington.edU/ao/#monthly). The values o f the A O S have been multiplied by -1 to illustrate the positive and negative phases. th  3.3 R E S U L T S  3.3.1 Descriptive statistics and trends of chronologies The longest chronology was 319 years and covered the period 1685-2003 and the shortest chronology was 88 years and covered the period 1916-2004 (Fig. 3.3; Table 3.1). Descriptive statistics such as standard deviation (s.d.), mean sensitivity, and first order autocorrelation ( r l ) , have been included for each chronology to allow comparisons with other studies (Table 3.2).  49  X  T3  M $ s - r—i  1700  1750  1800  1850  1900  1950  200Q  Year F I G U R E 3.3 Ring-width chronologies from the seven sampled sites (ring-width sample depth is below each chronology curve). The thick line is a five-year moving average.  50  T A B L E 3.2 Descriptive statistics for the seven chronologies developed using Picea mariana. s.d. = standard deviation; r l = first-order autocorrelation; S N R = signal-to-noise ratio; V A R p c l = variance explained by the first component; E P S = expressed population signal.  Standard Chronology Chronology Site 1 Site 6 Site 8 Site 11 Site 13 Site 16 Site 19  Common interval Detrended series  Residual chronology  Mean sensitivity  s.d.  rl  Mean Sensitivity  s.d.  Common interval  SNR  VARpcl  EPS  0.17 0.18 0.16 0.19 0.16 0.15 0.13  0.32 0.28 0.41 0.38 0.35 0.23 0.18  0.78 0.80 0.87 0.91 0.83 0.68 0.66  0.19 0.17 0.18 0.15 0.17 0.17 0.14  0.17 0.14 0.17 0.14 0.18 0.13 0.12  1892-2003 1921-2003 1900-2003 1940-2003 1890-2003 1952-2004 1948-2004  16.00 8.61 21.26 17.58 18.57 11.22 11.03  31.0% 27.0% 32.8% 39.6% . 33.2% 23.0% 28.4%  0.94 0.90 0.96 0.95 0.95 0.92 0.92  Tree-ring chronologies with high s.d., high mean sensitivity and low autocorrelation are considered to be the most suitable for dendroclimatic reconstructions (Fritts 1976).  The signal to noise ratio ( S N R ) was used to determine the strength o f the signal between trees (common variance) (Wigley et al. 1984; Briffa and Jones 1990). The S N R o f the chronologies ranged between 8.61 and 21.26. The variance explained by the first component ( V A R p c l ) indicates the variance represented by the common forcing factor at each site, which could be a climatic signal experienced by all trees in a region. A l l seven chronologies had similar common variance with values ranging from 2 3 % to 39.6% (Table 3.2). Expressed Population Signal (EPS) is used as a measure o f the strength o f the common signal within each chronology (Wigley et al. 1984). The average E P S for all seven chronologies was 0.93 (all are above 0.90), which is well above the accepted threshold value o f 0.85 (Table 3.2; Wigley et al. 1984).  A l l seven chronologies depicted similar trends during the period o f analysis (1916-2003; F i g . 3.3). For those chronologies that include the 1800s (all except Sites 11, 16 and 19), ring-widths  51  were narrow, with less variability, during the early 1800s until approximately 1900. Growth rates were greater in the early 2 0  th  century compared to the 1800s, however trends remained relatively  stable throughout the rest o f the century. T A B L E 3.3 Pearson's correlations among the seven residual chronologies developed from samples taken at treeline, N . W . T . A l l correlations were significant at p<0.05; n = 88.  Site 1 Site 8 Site 11 Site 13 Site 16 Site 19  Site 6 Site 1 Site 8 Site 11 Site 13 Site 16 0.77 0.78 0.75 0.61 0.49 0.67 0.72 0.56 0.71 0.75 0.38 0.35 0.46 0.34 0.39 0.43 0.49 0.56 0.36 0.35 0.47  A l l o f the pairs o f correlations between residual chronologies were significant and positive, with the mean correlation coefficient between pairs o f sites being 0.54 (Table 3.3). The highest correlation occurred between Site 8 and Site 1 (0.78). Sites 16 and 19 showed the lowest correlations which may be due to their distance from the other sites. The fact that all chronologies were highly correlated was especially remarkable considering that the two furthest sites were approximately 600 k m apart.  Correlations among the seven chronologies developed i n this study and the four obtained from the I T R D B ranged between -0.07 and 0.49 (Table 3.4). The M a c K i n l e y chronology was highly correlated to all other chronologies except for Site 16. M a c K i n l e y was the only I T R D B chronology that was correlated to all 3 other I T R D B chronologies. Pethai and Austin were correlated to Sites 1, 6 and to each other.  Pethai was also correlated to the M a c K i n l e y  chronology. Coppermine was correlated to all o f the chronologies developed in this study, except Sites 11 and 19.  52  T A B L E 3.4 Pearson's correlations among the seven residual treeline chronologies and the I T R D B chronologies (Aus. = Austin; Copp. = Coppermine; M a c . = M a c Kinley; Pet. = Pethai.). Marked correlations are significant at p<0.05, n = 62.  Site 6 Site 8 Site 11 Site 13 Site 16 Site 19 Aus. Copp. Mac. Pet.  Site 1  Site 6  Site 8  Site 11  Site 13  Site 16  Site 19  Aus.  0.78 0.74 0.52 0.70 0.29 0.38 0.37 0.29 0.49 0.40  0.73 0.49 0.65 0.33 0.46 0.30 0.31 0.47 0.42  0.63 0.71 0.41 0.52 0.18 0.34 0.49 0.30  0.63 0.25 0.36 0.23 0.25 0.39 0.28  0.31 0.39 0.17 0.34 0.50 0.26  0.42 0.10 0.25 0.04 -0.07  0.21 0.05 0.28 0.13  -0.02 0.30 0.42  Copp. Mac.  0.32 0.17  0.44  Since Coppermine was the furthest site from the other three I T R D B chronologies, it was the least correlated among the I T R D B chronologies, however it was correlated to most o f the other chronologies. The analysis o f the correlations and the trends i n the chronologies demonstrated that there was a significant amount o f variance that was common among most sites used i n this study.  3.3.2 M u l t i v a r i a t e analysis U s i n g cluster analysis I identified two clusters (Fig. 3.4). Sites 16 and 19 formed their own cluster, which was likely due to the fact that they were located further from the other chronologies (Fig. 3.1). The remaining sites formed clusters with the sites to which they were closest i n proximity, for example, Sites 1 and 6 formed a cluster, followed by Sites 8, 11 and 13.  Based on the P C A o f the seven chronologies for the common time period 1916-2003, the cumulative explained variance o f the first two principal components was 74.9% (Figure 3.5). The first component explaind 61.8% o f the variance, which demonstrates the similarity among the  53  chronologies, and it was correlated with all seven chronologies. The second  component  (explained variance = 13.06%) was positively correlated to site 11 and 13, but highly negatively correlated to site 16 and 19 and not correlated to sites 1, 6 and 8.  Site 1 Site 6 Site 8 Site 11 Site .1.3 Site 16 Site 19 0.95  1.00  1.05 1.10 . 1.15 Linkage Distance  1.20  1.25  F I G U R E 3.4 Hierarchical cluster analysis o f seven residual chronologies.  1.0  0.5 :I Sitell • .Site.1  S i t c J  0  o * * -1.0 S i t e  8  s i  e 6  -0.5  Site 19 ••  0.5  1.0  PC I  -0.5 Site 16  -1.0 PC 2 F I G U R E 3.5 Plot o f the loadings for the first two principal components for each chronology. The percentage o f explained variance for P C I and P C 2 is 61.80% and 13.10%, respectively.  54  The results from the P C A s calculated for successive overlapping 30-yr periods indicated that the percentage o f explained variance by the two first principal components was relatively constant through time (Fig. 3.6). The variance o f the first component peaked in 1936-1966, after which period it remained generally constant. The variance explained by the second principal component remained relatively constant, with a slight low between 1926-1956.  19161946  19261956'  19361966  19461976  19561986  19661996  Time span  FIGURE 3.6 Percent variance expressed b y the first and second principal components o f P C A derived from the seven residual chronologies for the subperiods: 1916-1946, 1926-1956, 19361966, 1946-1976, 1956-1986, and 1966-1996. For the whole common period 1916-2003, the variance explained by the first and second principal components is 74.9%.  3.3.3 Relationship between meteorological data and tree-ring chronologies Response function analyses indicated that at all sites, except Site 19, there was a significant relationship between Picea mariana growth and July temperatures o f the current year (Fig. 3.7) Tree growth at Site 19 was positively correlated with July temperatures, but the correlation was not significant. August temperatures were consistently, positively correlated to ring-width, however only one o f the chronologies had significant coefficient values. In addition, there was a  55  significant negative correlation to July temperatures o f the previous year at all sites, except for Site 19.  Mean temperature  Total precipitation Site 1  M.  J i' A S  ON D J  F MA MT 1 A  MJ J A S O N D J F MA M J J A  •Site 6  0.4 f  0.2  0.0  ml  EX  -0.2  C3  cH  M J .1 A S O N D. J F 0.4 rta  U A :M  _  J. J A  _  M  1J  A S O N D J F M A M j;  'j A  Site 8  CD  a  I 0.0  im  I MJ  J A S O N D J F M A 'M .1 J A  0.4,  M J J A. S O N D  Site 11  0.2  0.0  0.0  -0.2  ^0.2  0N  previous year  D J F MAM J  .1  | growth year  A  F MA  M  I  F MA MJ J A  J J A  0.4  0.2  M J J A S  J  J  M J  .1  u  A S O N D  previous year  growth year  56  Mean temperature  Site 13  0.4  L  0.2-1  fl  o.o  o  P  fl  -0.2 MJ J A S ON D j F M A M 1  J A  o &  0.0  1 ^  Pi -0. M J  o.41  M J J A S O N D J F M A M J  Site 16  O fl  0-2  JZL  0.0  0.4  3  0.4 0:2  -0.2  a  Total precipitation  .1 A  S  0N  j A  -1  D J F M A M  -0.2  .1  j A.  —  M J J A, S  0N  D J F M A M J J A  , Site 1.9 0.4 0.2  0.0  -02 M J J A S O N D J F M A M J J A  previous year | growth year  MJ  J A S O N D J F M A M  .1 .1  A  previous year | growth.year  F I G U R E 3.7 Response function coefficients relating mean monthly temperature and total monthly precipitation to ring-width chronologies at treeline sites, N . W . T . Shaded coefficients are significant at p<0.05.  There was also a general tendency for August and September temperatures o f the previous . growth year to be negatively related to tree-ring growth. Interestingly, October temperatures o f the previous year were positively related to ring-widths i n five o f the seven sites. There was poor correlation at all sites between tree-ring growth and total monthly precipitation. There was a  57  tendency for June precipitation o f the previous growth year to be positively correlated to treering growth, although the correlation was significant only at Site 6.  Pearson correlations were also performed for mean monthly temperature (Appendix A : Table A.2) and total monthly precipitation (Appendix A : Table A . 3 ) for the same time period. These results agreed with those yielded by the response temperatures  function analysis. July and August  were most significantly correlated to tree-growth and there was less (non-  significant) correlation with total monthly precipitation.  3.3.4 Summer temperature reconstruction Based on the results o f the response function analyses, mean July to August temperature (averaged for both months) o f the growing season were selected as the variable to be reconstructed. July to August temperatures were used rather than only July temperatures as the inclusion o f August temperatures helped explain a greater amount o f variance in the final model. The P C A o f the five longest chronologies yielded five eigenvectors (Factors) that were then used to represent the chronologies. The results o f the stepwise multiple linear regression resulted i n the maximum variance (40%) being explained by the inclusion o f Factor 1 , F a c t o r 4  h  and  Factor \ . Factor 1 i s the first factor lagged by 1 year, and Factor 4 and Factor 1 were o f the t  current  growth  reconstructions  year.  This  percentage  o f explained variance  is  (Watson and Luckman 2004). Early (1943-1972)  comparable and late  to  other  (1973-2002)  calibration models accounted for 58% and 39% o f the variance i n the July-August temperature data, respectively. For the verification o f the early (1943-1972) and late (1973-2002) periods, the  58  Pearson's correlation coefficients (r = 0.61 and r = 0.50) were significant (Table 3.5), which renders the model adequate for estimating values not contained in the calibration period. T A B L E 3.5 Calibration and verification statistics for predicting July-August temperatures from eigenvectors derived from five chronologies at treeline i n the N . W . T . Calibration 1943-1972 1973-2002  Verification r 0.764 0.621  r 0.584 0.386 2  adjr 0.517 0.341  1973-2002 1943-1972  n 30 30  r 0.501 0.607  ST 20/9 21/8  RE 0.430 0.487  CE 0.370 0.405  Full model July-Aug temp = 15.264+ (0.426 * F 1 , . ) - (0.282 * F4 ) -(0.186 * F l ) 1943-2002 0.635 0.403 0.371 n = sample size F = Factor ST (sign test) = number of agreements/disagreements; nonparametric sign test of first differences (Fritts 1976) R E = reduction of error (Fritts 1976) C E = coefficient of efficiency t  Four  independent  verification tests demonstrated  that the  t  climatic information i n  the  reconstruction is adequate to apply it beyond the calibration period (Table 3.5). The reduction o f error (RE) (0.43; 0.49) and coefficient o f efficiency ( C E ) (0.37; 0.41) values were positive which indicates that the model is stable and has predictive capabilities (Briffa et. al 1988). The reconstruction performed well in the sign test as there were a significantly larger number o f agreements than disagreements, indicating that there was sufficient similarity existing between the actual and estimated data (Fritts 1976). The model passed the verification tests and can therefore be used to estimate July-August temperatures (Fritts 1976). Thus, the full model calibrated over the entire time period (1943-2002) was considered statistically stable. The model was used to reconstruct summer (July-August) temperatures to 1831 (Fig. 3.8).  The record was restricted to the longest o f the five chronologies chosen for this analysis (18312002) and by lags included i n the model. The time series o f the observed and estimated reconstruction for July-August temperatures were significantly correlated (r = 0.63). 2  59  i  j  l  iI  r\ >v» • h iA>A A/w iuU ,k i k i l i 1 lAIlt hfifl III V  S i  • a  3T  ill ^ ft  'i  1850  1900  P 11  .»  1,950  2000  Year F I G U R E 3.8 Observed (dashed line) and reconstructed (solid line) July-August temperatures at treeline, N . W . T . Darker solid line is a 3-year running mean o f the reconstructed July-August temperatures. Reconstruction covers the time span 1831-2002.  In general, the reconstructed July-August temperatures followed closely the trends found i n the observed meteorological data. However, the reconstruction tended to underestimate the warmest and coldest years (1947, 1950, 1952, 1954, 1968, 1978, 1981, 1985, 1989, 1994, and 1998) and was marked by lower variability in the cold years in comparison to the observed time series. Despite these differences, the reconstruction demonstrated below average temperature values until approximately 1900, followed by a slow increase in temperature until the early 1940s, after which summer temperatures became increasingly variable.  3.3.5 Comparing tree-ring chronologies to the AO During the last century, the A O S has undergone four phases: a positive phase from 1900-1930, a negative phase from 1931-1955, a positive phase from 1956-1983 and a negative phase from 1984-2000 (Fig. 3.9; http://tao.atmos.washington.edu/ao/#monthlv). The first two phases o f the A O S have a clear pattern whereby a high index is seen during the positive phase and a l o w index during the negative phase. However, the strength o f the last two phases (1956-1983 and 19842000) is less evident with the index showing more episodic and variable behavior.  60  Arctic Oscillation (JJAH  1900  1920  1940  1960  1980  Year F I G U R E 3.9 The standardized Arctic Oscillation ( A O ) anomaly index for June to August for the period between 1900-2002, and a 5 order regression curve (data from http://tao.atmos.washington.edU/ao/#monthly). The values o f the A O S have been multiplied by 1 to illustrate the positive and negative phases. th  The response function analysis computed between the tree-ring chronologies and the monthly index o f the A O demonstrated that all sites except Sites 1 and 6 were positively correlated to July index o f the A O (Appendix A ; Table A.4). Only Site 11 had a significant positive correlation to June index o f the A O . A l l sites except Site 16 were correlated to the previous November index o f the A O . Since the July index o f the A O had the highest correlations to tree-ring growth, I used this month to determine whether the different phases o f the A O were correlated to tree growth. The chronologies showed no correlations to the first two phases o f the July A O (1900-1930 and 1931-1955), however Sites 8, 11 and 16 were correlated to the July A O for the period from 1956-1983 (Appendix A ; Table A . 5 ) . None o f the sites were correlated to the July index o f the A O for the period from 1984-2000.  61  3.4 D I S C U S S I O N  The seven chronologies derived from Picea mariana provided proxy climate data in a region where climate records are few and short in length. A l l seven chronologies depicted similar trends over the common time period. The fact that the chronologies are all correlated to each other and that there is a significant amount o f correlation to the distantly located I T R D B sites, indicates that common climatic factors are affecting the growth o f these trees in this area. The longer chronologies all demonstrated low growth rates during the earlier part o f the time series until approximately 1900, which suggests that the tree-rings were recording the cooling near the end o f the Little Ice A g e (Gajewski and Atkinson 2003). Cluster analysis and P C A confirmed that the tree-ring chronologies developed in this study were experiencing similar trends across all sites.  The factors most highly correlated to ring-width were July-August mean temperatures, w h i c h were used in the dendroclimatic reconstruction. It has been generally found that growth o f trees in northern regions is mainly limited by summer temperatures (Tranquillini 1979; Black and Bliss 1980). The geographic position o f the treeline, where trees grow at the limit o f their ecological tolerance, is controlled by climate, and is associated with the position o f the 10°C mean July isotherm in North America (Sirois 1992).  Its geographic position is similar to the  summer position o f the arctic front (Beringer et al. 2001), which is the zone o f transition between dry, cold arctic air masses and warm, moist air masses from the south.  Therefore, it is not  surprising that July temperatures influence tree growth i n this region. One reason for the lack o f correlation between total monthly precipitation and tree-growth is that precipitation is an intermittent phenomenon and is more regionally variable than average temperature. In addition, the short climate record and the long distance to the nearest climate station (Yellowknife)  62  contribute to the low correlations observed i n the response function. Tree-growth could be limited by precipitation i n this region but the aforementioned scale-dependent factors affect the results o f the response function analysis.  This study provided the first dendroclimatic reconstruction o f summer temperatures in this region o f central N . W . T . The reconstruction was correlated to the observed meteorological data, however, it tended to underestimate variability in the climate data. It is possible that the effect o f variability i n temperature which was recorded i n the instrumental data was only partly captured i n the tree-ring record since the effects o f extreme temperatures is often reflected i n tree rings over two consecutive years. Despite the underestimation o f extreme temperatures, reconstruction captured long-term trends including the below average temperatures  the until  approximately 1900, followed by a slow increase i n temperature until the early 1930s, after which summer temperatures became increasingly variable until the end o f the record. The lower temperatures in the early part o f the reconstruction correspond with the end o f Little Ice Age, where temperatures were lower than average (Cropper and Fritts 1981; Szeicz and Macdonald 1995; Ruddiman 2001; D a v i et al. 2003). Although the reconstruction demonstrated lower variability in the common period (1943-2002) compared to the meteorological record, this period was generally more variable than the rest o f the reconstruction.  There was a strong relationship between the July index o f the A O and tree-growth at most sites, but the relationship has not remained constant through time. Tree-ring chronologies were not correlated to the first two phases o f the A O , however the positive phase from 1956-1983 had an influence on tree growth at 3 o f the 7 sites. The change i n the response o f tree-growth to the A O  63  after 1950 corresponds well with the increased variability as was seen in both the instrumental data and the July-August reconstruction (Fig. 3.8). A similar variability in both tree-ring chronologies and summer temperature reconstructions was found in the Northwestern region o f the N . W . T . (Szeicz and Macdonald 1995).  In summary, summer temperatures strongly affected ring-width growth i n Picea mariana at the sites i n the central N W T . Both the tree-ring chronologies and the temperature reconstruction demonstrated similar trends to other chronologies developed in the Northwestern region o f the N . W . T . (Jacoby et al. 1985; Szeicz and MacDonald 1995). In Chapter 2,1 demonstrated that two groups o f caribou herds were experiencing similar population abundance cycles since 1900. These simultaneous changes in abundance and the similar patterns in ring-width chronologies are an indication that the region is influenced by a major climate signal. The next objective is to investigate the relationship between long-term caribou abundance and the A O .  64  3.5 REFERENCES Aanes, R., Saether, B . , Smith, F . M . , Cooper, E . J . , Wookey, P . A . , and 0ritsland, N . A . 2002. The Arctic Oscillation predicts effects o f climate change in two trophic levels in a high-arctic ecosystem. Ecology Letters, 5: 445-453. Beringer, J., Tapper, N . J . , M c H u g h , I., Chapin III, F.S, Lynch, A . H . , Serreze, M . C . , and Slater, A . 2001. Impact o f Arctic treeline on synoptic climate. Geophysical Research Letters, 28: 4247-4250. Bhattacharyya, A . and Chaudhary, V . 2003. 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T H E RELATION BETWEEN LONG-TERM ABUNDANCE CYCLES IN BARREN-GROUND CARIBOU AND T H E ARCTIC OSCILLATION  4.1 INTRODUCTION Climate is one o f the most influential factors driving the population dynamics o f ungulates through various direct and indirect effects on forage quality and availability, predation, and abundance o f biting insects. In the winter, deep snow can prevent caribou from finding highly digestible lichen (Turner et al. 1994). The quality and availability o f forage directly influences female caribou body condition, which then affects calf survival (Skogland 1985; Portier et al. 1998). Conversely, a year with reduced snow pack can help decrease predation rates since the caribou are able to run through the snow easier, thereby avoiding predation (Telfer and Kelsall 1984; Nelson and M e c h 1986; Huggard 1993; Post et al. 1999; Hebblewhite et al. 2002; Hebblewhite 2005). The influence o f mosquitoes can increase dramatically during wet, and cloudy summers (Downes et al. 1986; Noel et al. 1998), which can result i n lower calf survival and reduced caribou numbers.  Recent studies reveal widespread effects o f large-scale climatic patterns or oscillations on the population dynamics o f ungulates (Post and Stenseth 1999; Patterson and Power 2001; Aanes et al. 2002; Griffith et al. 2002; Post and Forchhammer 2002; Hebblewhite 2005). The index o f the Arctic Oscillation ( A O ) , a large-scale climate oscillation, is based on the mean deviation from the average sea level pressure throughout the Northern Hemisphere at latitudes poleward o f 20°N (Thompson and Wallace 2001). The A O has been shown to explain a considerable portion o f the variation i n mean annual temperatures recorded at different stations in the Arctic (Chapter 3; Overpeck et al. 1997). Several studies have demonstrated the indirect impacts o f such large-scale  71  climate oscillations on the population dynamics o f different mammals. For example, positive values o f the A O were associated with a decline i n reindeer population growth rate i n Svalbard (Aanes et al. 2002). The positive phase o f the North Pacific Oscillation (NPO) had an indirect effect on elk population growth rate by increasing winter severity in Banff National Park (Hebblewhite 2005). A similar indirect effect o f the positive phase o f the North Atlantic Oscillation ( N A O ) was experienced with population abundances o f muskoxen and caribou, which were affected b y changes i n winter climate (Forchhammer et al. 2002).  A recent synthesis o f research has shown that the Arctic is experiencing unprecedented increases in temperature ( A C I A 2004). Climate change predictions have implied that caribou/reindeer are likely to be stressed as their access to food sources, breeding grounds and migration routes are altered ( A C I A 2004). One o f the major challenges for biologists is to predict the future impact o f global warming on animal populations. However, to differentiate natural variation from the impacts o f global warming, we must first understand how climate has impacted caribou abundance in the past.  A t present, the data describing caribou population abundance is limited to the last three or four decades when relatively accurate estimates o f population size have been available from aerial surveys (Caughley and Gunn 1993; Gunn 2005). In addition, qualitative data on the population dynamics o f caribou since at least the 1920s has been collected through narratives o f aboriginal community members i n the form o f traditional knowledge (Dogrib Treaty 11 Council 2001).  72  A lack o f annually resolved archival data on demographic trends for the past century makes it difficult to support any hypotheses about the causal factors associated with changes in caribou abundance through time. Recently, a new method was developed to reconstruct past caribou abundance cycles using dendroecology (Morneau and Payette 1998, 2000; Boudreau et al. 2003; Zalatan et al. 2006). Trampling scars formed b y caribou on superficial roots and low branches remove part o f the bark, leaving a scar. The scars are then accurately dated and their changes i n frequency used as a proxy for caribou abundance through time. Dating a number o f scars distributed along caribou trails over the herd's annual range can provide an index o f caribou activity over a longer time period than other methods. Using a targeted approach to find the oldest scars possible is essential to develop a long proxy record o f caribou abundance (Chapter 2). However, the nature o f how the scars are formed on the roots results i n a dataset that is grouped into 5-year age-classes. Although this method is limited because it does not provide annually resolved estimates o f caribou abundance, it does provide a much longer record o f caribou abundance cycles than other methods.  This long term record o f caribou abundance is essential i f researchers are interested in obtaining further insight into the complex influences that climate imposes on this ungulate species. The two main objectives o f this study were to: (1) provide evidence for changing barren-ground caribou abundance during the last century, using dendroecological evidence, and (2) evaluate the correlation between large-scale climate patterns, such as the Arctic Oscillation, on the cycles i n population abundance o f these caribou.  73  4.2 METHODS 4.2.1 Study Area and Site Selection The study sites were situated along caribou migration trails near the treeline in the Northwest Territories between 61° N , 106° W and 65° N , 115° W (Fig. 2.1). The sites were located i n the forest-tundra transition zone, with black spruce {Picea mariana [Mill.] B S P ) being the dominant tree species (Matthews et al. 2001). Caribou tend to follow the same migration routes to and from their calving grounds, and their trails are imprinted on the landscape (Gunn et al. 2001). A total o f nineteen sites were sampled for barren-ground caribou activity; sites 1-15 were selected based on information from Dogrib elders who identified areas across the treeline that were frequented by the Bathurst caribou herd (Dogrib Treaty 11 Council 2001). The remaining four sites (16-19) were selected on the late summer range o f the Beverly herd. Details o f the sampling and analysis procedures were presented i n Chapter 2 and i n Zalatan et al. (2006).  4.2.2 Relationships between meteorological data and the AO I tested the relationship between the summer index o f the A O ( A O S ) and the winter index o f the A O ( A O W ) and the meteorological data (mean monthly temperature, total monthly precipitation and total monthly snow depth; Meteorological Service o f Canada monthly climate data from Yellowknife  airport,  62°27'N,  114°26'W,  205.70  m  http://climate.weatheroffice.ec.gc.ca/prods_servs/cdcd_iso_e.html) using Pearson's  a.s.l.; correlations.  The data were truncated to the period between 1943-2001 to ensure that there were no missing data in the record. The purpose o f this analysis was to determine i f the A O W and A O S have an impact on the local climate, and hence to better understand how the A O may influence caribou abundance cycles.  74  To evaluate the temporal stability o f the shared variance among the monthly index o f the A O S , Principal Components Analyses were performed for successive overlapping 30-year periods (1900-1930, 1910-1940, 1920-1950, 1930-1960, 1940-1970, 1950-1980, 1960-1990, and 19702000) and for the entire common period (1900-2000) (Tardif et al. 2002). Since the standard period for studying climate trends is 30 years (30-year climate normals), I chose this same time period to evaluate the temporal stability o f the A O . A n a l y z i n g the change i n the variance o f the A O may provide some insight to help further decipher the temporal dynamics between caribou and the A O .  4.2.3 Barren-ground caribou abundance estimates A total o f 1991 trampling scars were found i n Picea mariana root samples and included in the scar frequency distribution which represent a proxy-record for caribou abundance (Zalatan et al. 2006; Chapter 2). This reconstruction o f caribou abundance covers the period from 1900-2002, which is a much longer period that has been previously presented (Post and Stenseth 1999; Aanes et al. 2002; Griffith et al. 2002; Post and Forchhammer 2002; Hebblewhite 2005). These other studies obtained population data for caribou and reindeer for periods from 13 and 43 years. The shorter period o f data for these caribou/reindeer populations is due to the fact that the data were derived from aerial photography or animal counts and can therefore be only as long as the data collection method permitted. Conversely, the benefit o f using dendroecology to reconstruct caribou abundance is that the record can be much longer.  Most o f the scars are formed during late summer/early fall, when the ground is snow-free. A s a consequence o f the difficulty associated with dating these scars (Chapter 2), the data have been  75  grouped into 5-year age-classes, resulting i n only 21 data points. There is an increasing underestimation o f caribou abundance with increasing age o f the roots. Various  factors  contribute to the loss o f scars through time such as the death o f scar-bearing roots, fading o f scars by weathering, decomposition, and repeated caribou trampling activity (Morneau and Payette 1998, 2000). This underestimation was addressed b y applying a log-linear regression and using the residuals to obtain a more accurate depiction o f the abundance pattern o f these herds (Morneau and Payette 1998, 2000). Departures from the negative exponential model were used to demonstrate the years o f high and low caribou abundance (Chapter 2; Zalatan et al. 2006).  4.2.3.1 Determining the relation between the AO and caribou abundance cycles I tested for the relationship between the A O S and the scar frequencies as a proxy for shifts i n caribou abundance for the last two phases o f the A O S separately (1955-1985 and 1990-2000) i n 5-year age-classes, for these two periods combined (1955-2000) and for the entire study period (1900-2000) using Spearman's rank correlations. The dataset for the A O was grouped into fiveyear age-classes to perform equivalent correlations with the caribou residual data. Although the time series o f the A O W did not follow any trends i n caribou abundance, the last decade (19892002)  of  the  AOW  corresponded  strongly  to  the  scar  frequencies  (www.arctic.noaa.gov/detect/climate-ao.shtml). Thus, Spearman's rank correlations were also performed between the A O W and the caribou abundance cycles for the period between 1990 and 2000 (in 5-year age-classes). Correlations were also performed using annual data (1-year ageclasses) between the caribou abundance cycles and the annual data o f the A O W for the equivalent period (1989-2002) to determine whether higher resolution data would provide  76  different results. I used annual data for this analysis since the time period is too short to justify using a 5-year age-class, and therefore the results would be insignificant.  4.2.3.2 Cross wavelet transform and wavelet coherence analysis Another approach that has been used to examine the variability i n the signal o f time series data is wavelet analysis (Jevrejeva et al. 2003). Wavelet transforms are used to expand time series into time frequency space and are therefore useful for finding localized intermittent periodicities (Foufoula-Georgiou and Kumar 1995; Grinsted et al. 2004). The Continuous Wavelet Transform ( C W T ) is a common tool for analyzing oscillations i n a time series, and is often used on two time series that are expected to be linked in some way. More specifically, the C W T examines whether there is a consistent phase relationship and, therefore, a suggestive causality between two time series. Wavelet transforms are often used to analyze time series that contain nonstationary power at many different frequencies (Jevrejeva et al. 2003). The Wavelet Coherence ( W T C ) is calculated from two C W T s , and represents the local correlation between two time series i n time frequency space. Another way to define the W T C is that it helps determine how two time series are coherent even i f the common power (correlation) is low, and shows how confidence levels against random noise backgrounds are calculated. The advantage o f these methods is their ability to detect amplitude- and phase-modulated oscillations (Jevrejeva et al. 2003).  Here I examined the connection between the A O S and caribou abundance cycles, specifically the phase relationships between the two time series using both C W T and W T C . The A O W was not examined in this analysis as it was not showing any relation to the caribou abundance cycles (see  77  Results). Wavelet transforms require as many data points as possible therefore I used annual caribou residual data rather than grouping the scars in 5-year age-classes (Chapter 2).  4.3  4.3.1  R E S U L T S  Relationships  b e t w e e n m e t e o r o l o g i c a l d a t a a n d the  A O  A positive correlation was found between the A O S and mean monthly summer temperatures, June-August (Fig. 4.1 A , r = 0.43, n = 58, /K0.05). The A O W was also positively correlated to 2  summer temperatures, June-August (Fig. 4.IB, r = 0.34, n = 58,/J<0.05). 2  A.  -60  -.10 40 Arctic Oscillation Index (Jun-Aug)  B.  -160  -110 -60 -10 40 90 Arctic Oscillation Index (Oct-May)  140  (A) Relationship between the summer index of the standardized Arctic Oscillation (AOS) and mean monthly summer temperatures for the period 1943-2001; ( B ) Relationship between the winter index of the standardized Arctic Oscillation (AOW) and mean monthly summer temperatures for the period 1943-2001. F I G U R E  4.1  78  However, the AOS and A O W were statistically unrelated to both total monthly precipitation and total monthly snow depth.  From this analysis, I concluded that the strongest relationship was found between the A O S / A O W and summer temperatures. As the highest correlation was found between the A O S and mean monthly temperatures for June to August, I proceeded by using the AOS only. The curves of the AOS and mean monthly summer temperatures show similar trends for the period 1943-2001, thus indicating the influence of the A O S on mean monthly summer temperatures in this region (Fig. 4.2)  17  M e a n M o n t h l y Temperature (°C; Jun-Aug) A r c t i c Oscillation (Jun-Aug)  < c 16 3  u 15 P.  >-.  3 13 12 -1.0 1950  1960  1970  1980  1990  2000  Year F I G U R E 4.2 The standardized Arctic Oscillation (AO) anomaly index for June to August and mean monthly temperatures for June to August for the comparative period of 1943-2001.  Pearson's correlations were used to analyze the relationship between only negative (<0) A O S index years and mean monthly temperature. This analysis was also performed using only positive (>0) AOS index years. The purpose of this analysis was to determine whether the local climate is  79  responding to both phases o f the A O S (positive and negative) or only one o f the phases o f the A O S . Correlations computed over the entire period o f analysis (1943-2001) with only positive and negative years o f the A O S showed no significant correlations. However, negative values o f the July monthly index o f the A O were significantly correlated to July temperatures (r = 0.43; n 2  = 58; p O . 0 5 ) .  The results from the P C A calculated for successive overlapping 30-yr periods indicated that the percentage o f explained variance by the two first principal components was relatively constant until 1950 (Fig. 4.3). The explained variance o f the first principal component remained between 18%-21% until 1980, however it increased to 24% between 1960-1990. The explained variance o f the second principal component remained between 14%-16% until 1980, however it decreased to 13% between 1960-1990 These results demonstrated that the explained variance o f the A O did not fluctuate a great deal until approximately 1960, after which it became more variable.  4.3.2 Barren-ground caribou abundance cycles The scar frequency distribution from both groups o f sites showed similar abundance cycles through time (Chapter 2; Fig. 4.4; r = 0.55, n = 21, j?<0.05). Caribou abundance was high during s  the mid-1940s, and 1990s, and was very low during the 1920s, 1950s-70s and in the 2000 ageclass. The results from the scar frequency distribution correlate strongly with trends observed in the data from aerial surveys and traditional knowledge (Dogrib Treaty 11 Council 2001; Zalatan et al 2006).  80  24 o o  t 20  16  12 19001930  19.101940  19201950  1930- 19401960 1970 Time span  19501980  19601990  19702000  4.3 Percent variance expressed by the first and second principal components of P C A based on a matrix with the monthly index of the Arctic Oscillation for the subperiods: 1900-1930, 1910-1940, 1920-1950, 1930-1960, 1940-1970, 1950-1980, 1960-1990, and 1970-2000. For the whole period 1900-2000, the variance explained by the first and second principal components is 50.3%. FIGURE  1940 1960 1980 Year (Five-year age-classes)  2000  Standardized distribution of the number of trampling scars (residuals of the loglinear regression) from 1900-2000 for the northwest and southeast sites. F I G U R E 4.4  81  4.3.3 Determining the relation between the A O and caribou abundance cycles There was no correlation found between the A O S and the standardized residuals o f the northwest and southeast sites for the entire study period between 1900 and 2000 (r = -0.07, n = 21,/?>0.05; s  r - -0.21, n = 21, p>§.§5, s  respectively). During the first positive phase o f the A O S , caribou  abundance was low (Fig. 4.5). A s the A O S began the switch to a negative phase (1931-1955), caribou abundance began to increase. When the A O S was i n a slightly negative phase, caribou abundance was high. During the following positive phase o f the A O S (1956-1983), caribou abundance began to decrease once again, however not as much as during the first positive phase. During the final negative phase o f the A O S (1984-2000), the caribou abundance became more variable with little or no trend at all i n response to the A O S .  Caribou abundance at northwest sites Caribou abundance at southeast sites 5th order curve of the A O (Jun-Au'g)  Positive Phase (1900-1930)  1.0  Negative Phase 0.5  (1984-2000)  rt 3  o  N  •*= 0.0  -5 nt TD C  &  -0.5  'Negative Phase! Positive Phase (1931-1955)  (1956-1983)  F-2  -1.0  1900  1920  1940  1960  1980  2000  Year F I G U R E 4.5 Residuals o f the log-linear regression on the frequency distribution o f trampling scars for both sets o f sites grouped into five-year age-classes and a 5 order regression curve o f the summer index o f the Arctic Oscillation (June-August) for the period 1900-2002. t h  82  Although they were not significant, the positive correlations between the A O S and the caribou residuals for the last positive phase (1955-85) were high (northwest sites: r = 0.71, n = 7, p > s  0.05; southeast sites: r = 0.54, n = l,p> s  0.05). The correlations between the caribou residuals  and the A O S for the last negative phase (1990-2000) were high, but were not significant (northwest sites: r = 0.50, n = 3, /»>0.05; southeast sites: r = -0.50, n = 3, p> 0.05). The s  s  correlations between the A O S and the caribou residuals for the period from 1955-2000 were relatively high but not significant (northwest sites: r = 0.39, n = 10,/?>0.05; southeast sites: r = s  s  0.49, n = \Q,p> 0.05). The last positive and negative phases o f the A O S did not have an inverse relation to the caribou abundance as was seen in the first two phases. Both the caribou abundance and the A O S were more variable and followed roughly similar trends from 1955-2000.  The last decade (1989-2002) o f the A O W time series was strongly positive (Fig. 4.6).  1900  1920  1940  1960  1980  2000  F I G U R E 4.6 The standardized Arctic Oscillation ( A O ) anomaly index for October to M a y for the period between 1900-2002, (data from http://tao.atmos.washington.edu/ao/#monthly).  83  Correlations were not significant between the A O W and caribou abundance (using 5-year ageclasses) at either the northwest (r = -0.5, n = 3; /?>0.05) or southeast sites (r = 0.5, n - 3; s  s  p>0.05). Using annual data, correlations were also not significant between the A O W and northwest sites (r = -0.12, n = 13; p>0.05) or the southeast sites (r = 0.39, n = 13; p>0.05). s  s  4.3.3.1 Wavelet Transform Analysis The Cross Wavelet Transform ( C W T ) between the two groups o f caribou abundance cycles and the A O , as well as between the two groups o f caribou abundance are presented i n Figures 4.7, 4.8, and 4.9. The thick black contour designates the 5% significance level against random noise and the cone o f influence (COI) where edge effects might distort the picture is shown as a lighter shade. The y-axis is the frequency o f the dominant cycle, the x-axis represents the years i n which that cycle dominates and the color represents the strength o f the relationship. The C W T s are not a reliable indication o f causality, but rather depict similarities i n periodicity through time. They are useful to illustrate patterns i n time series curves in comparison to other time series. For all remaining wavelet transform figures, the area above the 5% significance level is not a reliable indication o f causality and therefore the results outside o f these areas should be interpreted with caution.  The time-frequency patterns for the northwest caribou abundance and the A O S (Fig. 4.7) were not perfectly coherent, however, a 16-year oscillation was detected between around 1930 and 1970 i n both time series. The northwest caribou abundance had a periodicity o f 2.5 years at 1930 and mid-1950s. The A O S had a 2.5-year periodicity in the 1930s, a 4-year periodicity i n the early 1900s, 1970s and again 1990s, and an 8-year periodicity i n the 1990s. These findings are  84  similar to those found i n the winter index o f the A O (2.2-2.4-, 7.8-, and 12.8-year periodicities; Jevrejeva et al. 2003). The C W T o f the northwest caribou abundance and the A O S depicted some similarities in periodicity through time.  nwr  1900  1920  1940  1960  1993  2000  F I G U R E 4.7 The cross wavelet transform ( C W T ) o f the northwest caribou abundance (top panel) and the A O S (bottom panel).  Similar to Fig. 4.7, there was a hint o f a 16-year period between approximately 1930 and 1970 in the southeast caribou abundance wavelet transform (Fig. 4.8). This time series also experienced a 2.5-5- year periodicity between 1920 and 1935. Although this is within the cone o f influence (COI), where edge effect might distort the results, the C W T o f the two caribou populations demonstrated common features between the two time series o f 2.5-5-year periodicity around 2000 (Fig. 4.9).  85  ser  1900  1920  1 940  1 993  1 980  1 960  1 980  2000  AO  ~-  I  i  1900  1 920  i  1 940  i_J  2000  F I G U R E 4.8 The cross wavelet transform (CWT) of the southeast caribou abundance (top panel) and the AOS (bottom panel). ser  1900  1920  1940  1960  1980  2000  F I G U R E 4.9 The  cross wavelet transform (CWT) of the northwest caribou abundance (top panel) and the southeast caribou abundance (bottom panel).  86  The figures o f the wavelet coherence ( W C T ) can be interpreted in a similar way as the C W T s , with directional vectors that depict in-phase (vectors pointing up or to the right) and anti-phase (vectors pointing down or to the left) relationships between two time series.  In the W T C o f both caribou abundance time series, several sections are significant and these areas are mostly in-phase (Fig. 4.10). There was a 2, 4-5, 2-5, and 14 year periodicity found between 1930 and 1980. The benefit of the W T C is that it finds not only high common power but also similar patterns in behavior of time series during specific time periods (or phases), and clearly the W T C of both caribou abundance cycles demonstrated the similarity between the two time series. This is not surprising based on the results from Chapter 2 which showed that the residuals of the two groups o f sites (northwest and southeast) were positively correlated (r = s  0.55).  WTC: ser-nwr  i_i  1900  )  v  *•  * *• *• *  1 920  .  *  I  1 940  ^  S  I  I  I  i  1 980  1 993  2000  F I G U R E 4.10 The wavelet coherence ( W T C ) between the southeast caribou abundance and the northwest caribou abundance time series.  87  The W T C between southeast caribou abundance and northwest caribou abundance demonstrated 3-5 year periodicity during the 1930s, followed by a periodicity o f between 2-5 year during the 1940s until 1950. There was a 15-16 year periodicity during the 1960s and following this there was strong coherence from 1980 until 2000 (although most o f it is within the COI).  The northwest caribou abundance and the A O showed high coherency in the 2-2.5- and 8-12year bands, with rather low coherency between 1900-1930 and 1960-1980 (Fig. 4.11).  WTC: nwr-AO  J  1900  1  I  1 920  1 940  ^^^^r  i  I  1 960  1 930  1  1  2000  F I G U R E 4.11 The wavelet coherence ( W T C ) between the northwest caribou abundance and the A O S time series. Although it is not significant (it is outside the 5% significance level (black contour line) and the power is low), there was once again a hint o f a 16-year periodicity between 1930 and 1960. The vectors indicated an anti-phase relationship (vectors were pointing down and to the left) in the early 1900s, and from 1930-1960, after this period the relationship became in phase (vectors  88  were pointing up and to the right). The southeast caribou abundance and the A O S showed high coherency in the 2-2.5-, 8-12-, and 16-year bands, with low coherency i n the 1920s, and between 1970-1990 (Fig. 4.12).  WTC: ser-AO  1900  1920  1940  1980  1980  2000  F I G U R E 4.12 The wavelet coherence ( W T C ) between the southeast caribou abundance and the A O S time series. The strong anti-phase periodicity in the 8-12-year band was consistent with the W T C o f the northwest caribou abundance and the A O S . Other similarities can be seen such as the anti-phase 4-year band in the early 1900s, the in-phase 2-year band (although not significant) i n the 1990s. The 16-year periodicity between 1930 and 1960 was much stronger in this W T C . Again, the periodicities were anti-phase i n the early 1900s, and during the 1960s (8-12-year periodicity), however the most recent periodicity, between 1990 and 2000, (although it is i n the COI) was i n phase.  89  4.4 DISCUSSION This study demonstrates important correlations between decadal climatic patterns and abundance o f barren-ground caribou. Specifically, it illustrates the complexity associated with relating large-scale Arctic climate variation with abundance cycles o f barren-ground caribou at multidecadal time scales. Unlike other studies that have shown a positive relation between large-scale climate and an ungulate population (Post and Stenseth 1998, 1999; Post et al. 1999; Patterson and Power 2001; Aanes et al. 2002; Griffith et al. 2002; Hebblewhite 2005), m y study demonstrates the variability and uncertainty associated with relating mammals to climate without having a dataset that covers multi-decadal time frames. These other studies have compared climate to ungulate population abundance within a much shorter time frame (between 13 and 43 years). M y research is the first to compare long-term (100 years) ungulate population data with a large-scale climate pattern. M y analyses showed that the relation between the A O S and caribou abundance has not remained constant.  .  The wavelet analysis provided a means to follow gradual changes in the natural frequency o f the A O and caribou abundance time series. The wavelet coherence ( W T C ) demonstrated that from about 1900-1930 (first positive phase o f the A O S ) , the A O S and caribou abundance cycles were in anti-phase. Similarly, from 1930-60 (negative phase o f the A O S ) , the A O S and the caribou abundance cycles were also in anti-phase. Thus, for the first two phases there was an inverse relationship between the caribou abundance cycles and the A O S . The A O S and caribou abundance cycles changed to a significant in-phase relationship from approximately 1970-2000 (the most recent positive and negative phases). Thus, it was evident that a phase switch i n the A O S may have been a factor i n the increase or decrease i n the abundance o f these barren-ground  90  caribou over time. The W T C demonstrated  that the relation between A O S and caribou  abundance cycles is not linear and has not remained constant during the last 100 years.  There was a strong relationship between the July index o f the A O and tree-growth at most sites (Chapter 3), which has important indirect implications for caribou. This relationship can be explained by the influence o f the A O S on local climate as the mean temperatures during July and August were correlated to the A O S for the period between 1943-2001. These relationships are not surprising since many studies have been able to model the relationship between large-scale climate and temperature (e.g. Thompson et al. 2000). The positive phase o f the A O S results i n decreased temperatures and increased precipitation during the summer months. It is possible that a positive A O S may be associated with unfavorable forage growth conditions (cooler and wet weather) during the growing season, which could help explain the low abundance o f caribou during the first positive phase. Forage quality on caribou ranges has been shown to be influenced by climate (Chapin et al. 1995). Caribou are selective i n their foraging habits during spring and summer, choosing plants that are high i n nutrients and low in secondary compounds (Klein 1970; Bryant et al. 1983; White 1983). Changes in climate conditions can alter nutrient concentrations, anti-herbivore defenses, and forage availability (Chapin et al. 1995). Field experiments have simulated environmental changes (e.g. increased temperatures, reduced irradiance, reduced precipitation) to determine the effect on growth and nutrient content in several plant species that are important forage for caribou (Chapin and Shaver 1985; Shaver et al. 1986; Chapin et al. 1995).  Forage quality and availability directly influence body condition o f female caribou,  affecting the unborn calf (Skogland 1985). Thus, poor nutrition from low quality and availability o f forage can result i n reduced numbers o f caribou (Valkenburg et al. 1996).  91  Following the first positive phase, caribou abundance was high (during the negative phase o f the A O S ; 1931-1955), when winters tend to be cold and dry. These conditions are conducive to reduced snow pack, which has been linked to reduced rates o f predation (Nelson and M e c h 1986; Huggard 1993; Post et al. 1999; Hebblewhite et al. 2002; Hebblewhite 2005). Reduced snow pack can also provide easier access to forage, which is likely more important (Turner et al. 1994).  During the following two phases o f the A O S (1956-1983 and 1984-2000), the time series o f both the caribou abundance and the A O S were no longer out o f phase but were more coherent. The inverse synchrony that was seen between 1900 and 1955 was no longer present. The caribou abundance was affected by this change since the abundance series also lacked any major fluctuations, with the exception o f the dramatic drop at the turn o f the 2 1 century. The trends i n st  the winter index o f the A O demonstrated highly positive values during the last phase, however correlations between the A O W and caribou abundance were not significant during this period. Thus, the A O W was not likely having an important impact on caribou abundance at the turn o f this century.  There are other factors which could help explain changes i n caribou abundance. In areas without a major influence o f predation, density-dependence and environmental stochasticity become major determinants for ungulate population dynamics (Saether 1997; Gaillard et al. 1998). H i g h animal densities and adverse weather have been shown to influence the population numbers o f ungulates (Clutton-Brock et al. 1987; Singer et al. 1997). Increased mosquito abundance (Downes et al. 1986; N o e l et al. 1998) and a change i n the pattern o f winter predation (Nelson  92  and M e c h 1986; Huggard 1993; Post et al. 1999) have also been linked to decreases i n caribou numbers. Further research would be needed to determine the interaction among these factors, which is beyond the scope o f this research.  The P C A o f overlapping periods on the monthly A O index demonstrated that the A O underwent a distinctive change in the 1950s, during the most recent positive and negative phases. A recent study has shown that the A O has had a different, more episodic behavior, including multi-year runs o f positive or negative anomalies, during the last decade (1996-2004) with values being more negative or neutral than positive (Overland and W a n g 2005). These trends correspond to the most recent phase o f the A O S which was negative (1984-2000). Stratospheric temperature anomalies, another index o f the strength o f the polar vortex, have also shown this episodic character over the Arctic (Rind et al. 2005). This topic remains a central research question i n the field o f synoptic climatology and renders predictions about the direction o f the A O relatively uncertain.  One could put forth the argument that the inconsistent relation between climate and these caribou abundance patterns over longer time scales indicates that caribou are not responding to the A O in this region. However, I would reject this suggestion because the data show that the first two phase-shifts o f the Arctic Oscillation are negatively, although weakly, correlated to increases or decreases in caribou abundance. This indicates that there is a large-scale process influencing these herds but that this relation appears to have changed during the most recent positive and negative phases (1955-1983 and 1984-2000). This simply means that the relationship has changed through time rather than there being no relationship at all. This illustrates the most  93  important aspect o f this study, which is that the relationship between the A O and caribou abundance has not remained consistent when longer time periods are analyzed. Although there was not a constant relationship through time, the A O must be driving changes i n the abundance cycles o f these barren-ground caribou because o f its important influence on weather patterns and climate in the Arctic. The difficulty i n this type o f study is that there is a trade-off between higher resolution short-term data and lower resolution long-term data. Without the long-term data generated i n this study, I could not conclude analyses at multiple time scales and we would be making incorrect interpretations about the relation between the A O and abundance o f barrenground caribou.  4.4.1 Predicting the impacts of climate change This study is the first to demonstrate the long-term relation between the A O and cycles i n barrenground caribou abundance. This relationship is not linear over long time scales and may therefore be very difficult to predict i n the future. Mysterud et al. (2001) also noted the difficulty in predicting the ecological impacts o f large-scale climate fluctuations, and emphasized the need to consider non-linear relationships. Recent studies are now quantifying nonlinear ecological effects o f climate variability (e.g. Stenseth et al. 2002). M o d e l projections o f Arctic temperatures have suggested  large region-to-region variability i n the future  response  o f atmospheric  circulation to external forcing (Overland and W a n g 2005).  The use o f teleconnections as a forecast tool is only useful i f it provides insight into the interaction between prevailing meteorological conditions i n a consistent manner over time (Creilson et al. 2005). If the phases o f the A O remain stable and prominent, the impact on the  94  local meteorological conditions w i l l be more predictable. Thus, the interaction between climate and barren-ground caribou could simultaneously be more predictable. However, a recent study has noted an increase i n the positive phase o f the North Atlantic Oscillation ( N A O ) during the second half o f the 2 0  th  century, which has resulted i n a greater number o f winter warm spells  over most o f Canada (Shabbar and Bonsai 2003). W i t h the onset o f global warming, the A O has already demonstrated  more variable and episodic behavior ( A C I A 2004), thus rendering  predictions about the future impact on caribou populations even more difficult.  The change i n the response o f caribou to climate during the most recent phases o f the A O may also be compounded b y other factors such as predation and hunting. In addition, the recent change i n the A O , which had been linked to global warming, may result i n caribou responding differently to changes i n climate during a decline i n abundance compared to an increase i n abundance. Thus, these factors need to be addressed through further research. The experience o f the last decade suggests that researchers should exercise considerable caution i n addressing Arctic change and the response o f caribou to climate.  95  4.5 R E F E R E N C E S  Aanes, R., Sasther, B . , Smith, F . M , Cooper, E.J., Wookey, P . A . , and 0ritsland, N . A . 2002. 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Using dendroecology, caribou abundance cycles o f two groups o f barren-ground caribou were reconstructed from 1900 to 2000. There were synchronous trends in the cycles o f these two groups o f herds, which strongly correlated to data obtained from traditional knowledge o f Dogrib elders i n the region and animal counts based on aerial photography. The strength o f this reconstruction provided robust information for investigating the spatio-temporal patterns in caribou abundance cycles throughout the range o f the barren-ground herds.  Once the long-term abundance cycles o f caribou were reconstructed, the next objective was to determine whether climate was influencing these cycles. However, the only annually-resolved climate data in the region is the short record available from Yellowknife. To increase the knowledge o f climate variability i n this region, I developed a series o f tree-ring chronologies and a reconstruction o f summer temperatures at seven o f the 19 study sites where I had reconstructed caribou abundance. The radial growth o f trees at the seven sites in this region demonstrated a common climate-growth signal. The July-August temperature  reconstruction  (1831-2002)  explained 40% o f the climatic variance in the instrumental data and was positively correlated to the meteorological records o f July-August temperatures. The reconstruction demonstrated belowaverage temperatures until approximately 1900, followed by a slow increase in temperature until the early 1930s, after which summer temperatures became increasingly variable until the turn o f  103  this century. L o w temperatures at the end o f the Little Ice A g e were seen i n both the tree-ring chronologies and the July-August reconstruction. This reconstruction is an invaluable source o f climate information since it is the first dendroclimatic study developed i n this region o f central N.W.T.  Knowledge o f the response o f tree-growth to climate at these sites provided a more local-scale perspective o f climate variability i n this region. I then explored the impact o f large-scale climate, Arctic Oscillation ( A O ) , on caribou abundance cycles. Most studies that aim to explain the relation between large-scale climate oscillations and mammal populations are limited i n the length o f their data because it is often derived from aerial surveys, which in most cases do not predate the 1970s. Thus, the results o f most o f these studies are that large-scale climate oscillations are linearly correlated to mammal population abundance cycles. M y study has demonstrated that a long-term perspective o f the relation between caribou abundance cycles and climate is quite different than what can be obtained from short-term records.  July-August temperatures had the greatest consistent effect on tree-ring growth at all sites. Similarly, the A O S was most highly correlated to summer temperatures (June-August). Thus, the A O S is having the greatest impact on summer temperatures, and the summer temperatures are driving tree-ring growth i n this region. Caribou abundance cycles were closely, but inversely, related to the first two phases o f the A O S (1900-1930 and 1931-1955). In contrast, caribou abundance was not inversely related to the most recent positive and negatives phases o f the A O S (1955-1983 and 1984-2000), but rather weakly followed trends in the A O S . The first and second principal components derived from the P C A o f the monthly A O index confirmed that there was a  104  change i n the A O record during the last two phases. This was also seen through the changed response o f the caribou abundance cycle during the most recent phases.  Since the summer index o f the A O seems to be most influential to the vegetation and to the local climate, it is most probable that any changes i n this summer index w i l l affect the vegetation, which w i l l directly and indirectly affect caribou populations. Changes in the A O S could potentially affect the interaction between climate and fire frequency, which could potentially change the migration routes o f caribou i n autumn, thus exposing them to increased predation. Understanding the impacts o f such a change in climate dynamics is essential i f caribou populations in the N . W . T are to be properly managed. This dissertation was unique i n that it demonstrated the nonlinear relationship between long-term caribou abundance cycles and largescale climate. Without a long-term record o f caribou abundance, I would not have been able to illustrate these variable, non-linear trends that were clearly illustrated i n the data. Therefore, researchers should exercise caution when studying short-term relations between large-scale climate and mammal populations.  105  APPENDIX A The residuals o f the log-linear regression for both groups o f sites were plotted using different age-classes (Fig. A . l to A . 4 ) . The trends i n the residuals at the different age-classes all demonstrate l o w caribou abundance i n the 1920s, followed b y a period o f higher abundance i n the 1940-60s. A t the annual age-class there is little change in the abundance cycles o f caribou during the 1960s to the 1990s, whereas as the age-class is increased, there is more o f a trend towards low caribou abundance during this period. The drop i n caribou abundance i n the 2000 time period is seen i n the residuals at all age-classes. The residuals from both groups o f sites i n all age-classes are positively correlated to each other, however the highest correlation was found using the largest age-class (Table 5). A t the 5-year age-class, the correlation between the residuals at both groups o f sites is the greatest (Chapter 2; r = 0.55; /?<0.05). s  T A B L E A . l Spearman's rank correlations between scar frequency distribution for both groups o f sites (northwest and southeast) annually, i n 2-year age-classes, 3-year age-classes and 4-year age-classes. Correlations are significant at p<0.05. Annual data r  s  0.33  2-yr age-class  3-yr age-class  0.37  0.40  4-yr age-class 0.47  106  'Northwest sites  -1.0  -1.5 1900  1920  ,1940  I960  1980  2000  Year o f sear formation (1 -year age-classes) F I G U R E A . l Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites i n 1-year age-classes.  -.- Northwest sites • Southeast sites 0.5 I  w———*  0.0  i ^ f w  • i  c3 '55  -0.5  -1,0  -1,5-  -2.0  -H  1900  :  r  1920  >  1  [  1940  1960  :  •  1980  1—  1  2000  Year o f scar formation (2-year age-classes) F I G U R E A . 2 Residuals o f the log-linear regression on the scar-frequency distribution o f trampling scars for both groups o f sites i n 2-year age-classes.  107  — Northwest sites • -»-,--, Southeast:sites  0,8  03 \ \ VV,  •  03  -.» "FT  • -i;  -0.7  1900  1920  1940  1960  1980  2000  Year of sear .formation (3-year age-classes)  A.3 Residuals of the log-linear regression on the scar-frequency distribution of trampling scars for both groups of sites in 3-year age-classes. FIGURE  1900  1920  1940  .1960  1980  •  2000  Year of scar formation (4-year age-classes) F I G U R E A.4 Residuals of the log-linear regression on the scar-frequency distribution of trampling scars for both groups of sites in 4-year age-classes.  108  T A B L E A.2 Pearson's correlations among the seven tree-ring chronologies and mean monthly temperature. A l l marked correlations are significant atp<0.05; n-59. Jan Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual site 6 site 1 site 8 site 11 site 13 site 16 site 19  Feb  Mar  Apr  0.13 0.10 -0.08 -0.19 -0.02 -0.03 -0.01 0.08 -0.02  0.23 0.03 0.02 0.14 0.15 0.13 0.05  0.41 0.26  0.44  0.04  0.13 0.25 0.21 0.24 -0.02 0.07 0.16 0.17 0.14 0.05  0.12 0.16 0.22 -0.01 -0.03  0.17 0.01 0.05 -0.11 -0.18  0.51  0.56  0.34  0.54  0.48  0.26  0.10 0.00 -0.07  0.00 0.10 0.11 0.21 0.22 0.07 0.09  -0.12 -0.24  -0.08 -0.07 0.03 -0.09 -0.17 0.03 -0.07  0.13 0.03 0.11 0.07 0.07 0.14 0.06  -0.04 -0.03 0.10 0.11 0.00 0.04 -0.05  0.12 0.16 0.10 0.08 0.02 -0.03 0.07 0.07 0.13 0.26  •0.28 -0.26  0.00 0.05  -0.28  -0.15 -0.12 -0.11 -0.18  May  Jun  Jul  Aug  Sep  Oct  Nov  0.20 0.05 0.17  0.29 0.11  0.30  0.34  0.45  0.57  0.37  -0.09 -0.03 -0.10 -0.09 0.00 -0.01 -0.05  -0.13 0.09 0.10 0.06 0.08 0.16 -0.05  0.01 0.08 -0.04 -0.05 -0.12 0.13 0.07  0.13 0.12 0.00 0.03 -0.03 0.09 -0.03  Dec  Annual  0.41  0.30  0.16 0.01 0.09 -0.07 0.18 0.19 0.24  0.17 0.11 0.16 -0.16 0.23 0.18 0.25  0.36 0.34 0.26 0.31  0.31 0.30 0.33  0.19  0.22  0.21  0.04 0.07 0.04 -0.01 -0.04 0.17 0.02  T A B L E A . 3 Pearson's correlations among the seven tree-ring chronologies and total monthly precipitation. A l l marked correlations are significant atp<0.05; n = 59.  PJan PFeb PMar PApr PMay PJun PJul PAug PSep POct PNov PDec PAnnual site 6 site 1 site 8 site 11 site 13 site 16 site 19  PJan  PFeb  PMar  PApr  PMay  PJun  0.13 0.07 0.02 0.09 0.02 0.03 0.01 -0.08 0.06 0.19 -0.02 0.24 -0.33 -0.35 -0.18 -0.33 •0.30 -0.18 -0.11  -0.02 0.11 0.05 0.08 0.09 -0.15 0.12 0.07 0.01 0.05 0.19 -0.02 0.00 0.00 -0.05 -0.02 -0.11 0.02  0.22 -0.02 -0.15 0.16 -0.01 0.10 0.28 0.08 0.34 0.36 -0.14 -0.27 -0.23 -0.27 -0.27 -0.22 -0.13  -0.13 0.04 0.04 0.09 0.06 0.11 -0.08 0.03 0.25 -0.10 -0.22 -0.14 -0.17 -0.17 -0.06 -0.07  -0.06 0.01 0.00 0.19 -0.07 0.00 0.14 0.26 0.04 -0.03 -0.01 -0.17 0.05 -0.08 -0.19  0.14 -0.12 -0.03 0.02 0.10 -0.21 0.25 0.32 0.22 0.13 0.16 0.14 0.01 0.19  PJul  -0.09 0.02 -0.07 0.23 0.26 0.49 0.14 0.12 0.01 -0.13 -0.03 -0.16 0.02  PAug  PSep  POct  PNov  PDec  PAnnual  -0.07 0.01 -0.09 0.20 0.45 0.18 0.14 0.24 0.08 0.03 0.05 0.00  -0.11 0.08 0.18 0.29 0.00 0.00 -0.01 -0.04 -0.01 0.11 0.09  0.22 0.13 0.34 -0.16 -0.22 •0.29 -0.16 -0.22 -0.25 -0.09  -0.06 0.33 -0.12 -0.09 -0.11 -0.01 0.01 -0.15 -0.08  0.46 0.04 -0.14 -0.07 -0.26 -0.14 -0.01 -0.08  0.11 -0.04 -0.02 -0.21 -0.13 -0.20 -0.05  T A B L E A.4 Response function analysis between the monthly index o f the A O and the seven tree-ring chronologies over the entire study period from M a y o f the previous growth year to August o f the current growth year. Marked correlations are significant at p <0.05.  Pmay Pjun Pjul Paug Psep Poet Pnov Pdec Jan Feb Mar Apr May Jun Jul Aug  Site 1 0.11 -0.02 0.02 -0.02 0.01 0.00 -0.27 0.07 0.08 -0.11 0.00 -0.11 -0.01 0.14 0.18 -0.07  Site 6 0.14 -0.04 -0.04 0.06 0.03 0.08 -0.21 0.05 0.13 -0.18 -0.03 -0.15 0.05 0.12 0.15 -0.15  Site 8 0.04 . -0.06 0.00 0.01 0.03 0.09 -0.24 0.05 0.12 -0.09 -0.01 -0.13 0.10 0.02 0.22 -0.09  Site 11 -0.14 0.05 0.12 -0.13 0.01 -0.07 -0.17 0.06 0.04 -0.12 0.02 -0.05 0.09 0.17 0.26 -0.08  Site 13 0.01 -0.03 0.03 0.01 -0.04 -0.04 -0.18 0.04 0.01 -0.13 0.06 -0.13 0.02 0.11 0.21 -0.13  Site 16 -0.04 -0.06 0.01 0.13 0.04 0.03 -0.02 0.04 -0.05 -0.11 0.00 -0.08 0.22 -0.02. 0.28 -0.09  Site 19 0.09 -0.05 0.05 0.02 0.07 0.04 -0.19 0.05 0.11 0.00 0.04 -0.15 0.13 -0.07 0.21 -0.17  T A B L E A . 5 Spearman's rank correlation between the July index o f the A O and the seven chronologies for each phase. Marked correlations are significant at p <0.05.  1900-1930 1931-1955 1956-1983 1984-2000  site 6  site 1  site 8  site 11  site 13  site 16  site 19  -0.06 -0.09 -0.24 -0.07  -0.04 0.02 -0.34 -0.36  0.04 -0.27 -0.40 -0.20  -0.12 -0.32 -0.48 -0.35  -0.10 -0.21 -0.19 -0.45  -0.17 -0.10 -0.44 -0.37  -0.12 0.14 -0.25 -0.31  111  

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