Regional tree growth and inferred summer climate in the Winnipeg Riverbasin, Canada, since AD 1783
Scott St. George a,b,c,⁎, David M. Meko b, Michael N. Evans b,c
a GSC Northern Canada, Geological Survey of Canada, Ottawa, Ontario, Canadab Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ 85721, USA
c Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA
Received 22 March 2007Available online 3 July 2008
Keywords: Canada; Dendrochronology; Northwestern Ontario; Vaganov–Shashkin model; Winnipeg River
Introduction
The principle of site selection is one of the main tenets ofdendroclimatology, and states that tree-ring samples should becollected from sites with characteristics that are likely to maxi-mize the desired environmental signal (Fritts, 1976; Schwein-gruber et al., 1990). Researchers have demonstrated repeatedlythat tree-ring records from sites near the limits of the ecologicalrange of a species can be strongly related to a single climatefactor. Themost prominent examples of this phenomenon are therelationship between trees at the altitudinal or latitudinal treelineand surface temperature during the growing season (e.g., Jacobyet al., 1996; Luckman andWilson, 2005) and the strongmoisturesignal recovered from trees in semi-arid environments (e.g.,
⁎ Corresponding author. Laboratory of Tree-Ring Research, University ofArizona, Tucson, Arizona 85721, USA. Fax: +1 520 621 8229.
Cook and Krusic, 2004; Woodhouse et al., 2006). In contrast,ring-width records obtained from trees growing near the centerof their range usually exhibit a more complex relationship withclimate (Hughes, 2002). This complexity does not mitigate theneed to obtain information about past climate conditions in theseareas.
The Winnipeg River in south-central Canada is the largestsingle source of water used to generate hydroelectricity in theCanadian Prairies. Water managers are interested in under-standing how the hydrology of this and other watersheds in theregion has behaved during the last millennium, and have sup-ported a number of independent research projects using severaltypes of environmental proxies (Beriault and Sauchyn, 2006;Laird and Cumming, 2008). This study uses a multi-speciesnetwork of trees at sites within the Winnipeg River region toexamine the nature of the environmental signals recorded inregional tree-ring data. A combination of principal-componentand response-function analyses, and forward modeling are used
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to determine: (1) if trees growing in this cool, moist environmentretain a climate signal within their annual growth increment; (2)what factors might be responsible for creating that signal; and (3)if this information can be used to make inferences about climatic
Table 1Site locations and characteristics of Winnipeg River region tree-ring network
Site Latitude (°N) Longitude (°W) Elevation (m) Specie
Bruno Lake 51.62 95.83 285 PIGLRed Lake 51.08 93.82 354 PIRESnail Lake 50.87 93.38 408 PIBNLake Packwash 50.77 93.43 359 PIBN
PIRECamping Lake 50.58 93.37 352 PISTHighway 105 50.45 93.12 377 PIBNOnaway Lodge 50.43 93.10 396 PIREHigh Stone Lake 50.40 91.45 398 PIGLMaynard Lake 50.38 93.88 321 PISTBall Lake 50.32 94.00 319 PISTLac Seul south 50.27 92.28 361 THOC
PIRESioux Lookout 50.07 91.92 369 PIST
PIREClay Lake 50.06 93.51 376 PIREKenora 49.92 94.12 385 PIREGordon Lake 49.89 93.75 396 PIREEagle Lake 49.77 93.33 374 PIST
PIRELongbow Lake 49.72 94.28 339 PIST
PIRESheila Falls 49.70 93.79 401 THOCGranite Lake 49.69 94.86 395 PIREHillock Lake 49.69 93.88 413 PIRETeggau Lake 49.68 93.67 392 PIREExpulsion Bluff 49.67 93.77 430 PISTSowden Lake 49.53 91.21 450 PISTSowden Lake 49.53 91.17 406 PIST
PIRESandbar Lake 49.45 91.55 431 PIST
PIRESioux Narrows 49.42 94.05 331 PIREStormy Lake 49.35 92.23 413 PIRETurtle River 49.25 92.22 427 PISTMoose Lake 49.20 95.35 362 PIST
PIRETurtle Lake 49.18 94.15 305 PIST
PIREBrim Lake 49.12 91.13 493 PISTCaliper Lake 49.07 93.90 339 PIREDurie Lake 48.97 91.26 355 PISTVolcano Bay 48.93 91.81 450 PISTEye Lake Ridge 48.89 91.70 440 PISTPerch Lake 48.72 91.86 631 PISTEva Lake 48.71 91.17 424 PISTFrench Lake Portage 48.67 91.10 465 THOCWindigostiwan Lake 48.66 91.09 450 PIST“The Pines” at Quetico 48.65 91.21 411 PIREGreenwood Lake 48.39 90.75 503 PISTMud River 48.32 95.70 354 QUMASeagull Lake 48.12 90.92 435 PIRESaganaga Lake 48.22 90.90 435 PIREEd Shave Lake 48.08 91.97 430 PIRE
conditions prior to the onset of instrumental monitoring. Thisproject is part of a larger study investigating the frequency andcauses of hydrological drought in the Winnipeg River basin(St. George, 2007).
s1 Number of trees Number of cores Span Principal investigator
12 23 1822–1988 Schweingruber20 41 1818–2001 Girardin12 24 1847–2002 Girardin5 10 1852–2001 Girardin20 37 1744–2002 Girardin16 34 1827–2002 Girardin21 40 1815–2001 Girardin15 30 1807–2004 This study11 22 1813–1988 Schweingruber17 34 1801–2004 This study18 36 1784–2004 This study18 38 1762–2002 Girardin19 40 1837–2001 Girardin22 40 1784–2002 Girardin16 29 1766–2002 Girardin23 46 1776–2004 This study22 38 1792–2001 Girardin17 34 1759–2004 This study18 37 1712–2002 Girardin19 39 1808–2001 Girardin18 40 1789–2002 Girardin21 43 1830–2001 Girardin21 37 1853–2003 This study21 38 1775–2004 This study10 19 1875–2003 This study13 27 1750–2003 This study11 21 1885–2003 This study18 36 1767–2004 This study18 36 1816–2002 Girardin18 40 1640–2001 Girardin21 54 1773–2002 Girardin16 35 1828–2000 Girardin15 43 1772–2001 Girardin16 37 1791–2001 Girardin16 34 1810–2001 Girardin14 28 1897–2004 This study11 21 1899–2004 This study15 30 1862–2004 This study117 34 1854–2004 This study10 20 1797–2003 This study24 49 1836–2004 This study/Girardin12 24 1846–2003 This study11 21 1876–2003 This study16 32 1817–2003 This study15 30 1897–2004 This study16 29 1796–2003 This study9 18 1878–2003 This study20 39 1791–2004 This study38 75 1768–2003 This study44 85 1732–2004 This study10 21 1715–1983 Stockton15 29 1625–1971 Fritts66 139 1620–1988 Fritts/Graumlich18 38 1700–1982 Stockton
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Study area
The Winnipeg River region is immediately west of LakeSuperior, and occupies the western edge of the SuperiorProvince of the Canadian Shield. The region can be describedgenerally as heavily forested, with little local relief, frequentbedrock outcrops, and many large lakes. Where they arepresent, surficial deposits are mainly sandy tills derived fromthe surrounding Shield (Dredge and Cowan, 1989). Glaciola-custrine clays deposited in glacial Lake Agassiz are also presentin the westernmost sector of the region, including the Lake ofthe Woods area. The Winnipeg River is a bedrock-channel riverthat begins at the northern end of Lake of the Woods andterminates in the south basin of Lake Winnipeg. The river is thesingle largest source of water to Lake Winnipeg, and influences
Figure 1. Map of tree-ring sites and National Climate Archive stations (daily data)Control Board.
the production of over 4600 MW of hydroelectricity (St.George, 2007).
Trees in this area are a combination of species drawn fromthe Canadian boreal forest and the mixed forests of the northernUnited States. In the south, jack pine (Pinus banksiana Lamb.),eastern white pine (Pinus strobus L.), red pine (Pinus resinosaAit.) are quite common, although logging of red and white pinesin the 20th century has encouraged the expansion of jack pine.White spruce (Picea glauca (Moench) Voss), balsam fir (Abiesbalsamea (L.) Mill.) and paper birch (Betula papyriferaMarsh.)are also present but are primarily found on adequately drainedlowland soils. Stands of eastern white cedar (Thuja occidentalisL.) occupy boggy lowlands and lakeshore sites. Boreal foresttrees become more common toward the north-east, and dry sitesare usually occupied by white spruce, jack pine, balsam fir,
in the Winnipeg River region. Base map provided by the Lake of the Woods
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quaking aspen (Populus tremuloides Michx.) and paper birch.In both southern and northern regions, poorly drained lowlandareas are colonized by black spruce (Picea mariana (Mill.)BSP) and tamarack (Larix laricina (Du Roi) K. Koch). Standmaps for forests in northwestern Ontario show that trees areusually between 40 and 100 yr old, and that stands older than140 yr are extremely uncommon due to the prevalence of stand-replacing forest fires (Suffling, 1992).
Data
Tree-ring network
Sampling concentrated on the three longest-lived tree speciesin the Winnipeg River region — eastern white pine, red pine,and eastern white cedar. Stand-age maps provided by AbitibiConsolidated and the Ontario Ministry of Natural Resourceswere used as a guide to identify stands of older trees as targetsfor collection. In addition to their longer lifespan, the ecologicaloptima for these species (the pines favouring dry sites with thinor sandy soils, and the cedars growing in wet bogs) may createring-width signals related to moisture availability, as treegrowth may limited by either too much or too little water. Weattempted to sample trees growing on steep slopes (therebyfurther strengthening potential moisture signals), but these op-portunities were limited by the region's predominantly lowterrain (local relief is usually less than 60 to 90 m; Dredge andCowan, 1989). Tree-ring samples were collected at 24 sitesduring July and August in 2004 and 2005 (Table 1). At two sites(Moose Lake and Turtle Lake), samples were taken from bothred and white pine, bringing the total number of new tree-ringcollections to 26. Increment borers were used to sample be-tween ten and 45 trees at each site, with two cores taken frommost trees.
This collection was supplemented by ring-width data ob-tained from the International Tree-Ring Databank (http://www.ncdc.noaa.gov/paleo/treering.html) for sites located within thegreater Winnipeg River region (47°–52°N, 90°–96°W). Thissearch area encompassed four sites in Minnesota collected byresearchers affiliated with the Laboratory of Tree-RingResearch, and two that were part of Fritz Schweingruber'sNorth American ring-width and density network (Schweingru-ber et al., 1993). Data from an additional 15 sites were providedbyMartin Girardin (personal communication, 2006). These dataare part of a larger dataset that has been used to reconstruct thebehaviour of the Canadian Drought Code across the borealforest of central and eastern Canada (Girardin et al., 2004,Girardin et al., 2006a).
The combined network is made up of 54 ring-width recordsfrom 44 sites in the Winnipeg River area (Fig. 1; Table 1). Thedataset includes measurements of nearly 2000 radii from almost1000 trees. Most collections are developed from red pine (24records) and white pine (21), with a limited number made fromcedar (3), jack pine (3), white spruce (2) and bur oak (1). Themajority of sites are located in the central part of the watershedin northwestern Ontario. There is only a single site in south-eastern Manitoba and none in northwestern Minnesota, largely
because of the increasing prevalence of grassland ecosystems inthe western and southern part of the region.
Climate data
Regional precipitation and temperature data were extractedfrom the CRUTS 2.1 gridded (0.5° resolution) dataset of monthlyclimate observations (Mitchell and Jones, 2005).Monthly climaterecords for grid points within the greater Winnipeg River region(47.5°–51°N, 90°–96.5°W) were averaged to produce summarytime series describing temperature and precipitation over theregion, and expressed as anomalies relative to the 1901–2002mean. Several analyses were repeated using station data down-loaded from the Adjusted Historical Canadian Climate Data(AHCCD) website (Environment Canada, 2005, http://www.cccma.bc.ec.gc.ca/hccd). This data set consists of rehabilitatedand homogenized climate records that have been corrected forinhomogeneities and missing data (Vincent and Gullett, 1999;Mekis and Hogg, 1999).
Daily climate data were downloaded from the National Cli-mate Archive (Environment Canada, 2006, http://www.climate.weatheroffice.ec.gc.ca/climateData). Daily and monthly stream-flow data within the Winnipeg River watershed were obtainedfrom theWater Survey of Canada's HYDAT data archive (WaterSurvey of Canada, 2005, http://www.wsc.ec.gc.ca/hydat/H2O/).Gauges were selected for analysis if they had more than 50 yr ofrecord, and were still operating at the end of 2003 (see St.George, 2007, for a list of stream-flow records).
Time series describing the Canadian Drought Code (CDC)over the “Lac Seul region” (the boreal forest of northwesternOntario, including theWinnipeg River region) were provided byMartin Girardin (personal communication, 2006). The CDC is asoil-moisture index that is one of the inputs used bymanagementagencies tomonitor forest fire risk across Canada (Girardin et al.,2006b).
Methods
Samples were prepared, cross-dated and measured followingstandard dendrochronological techniques (Stokes and Smiley,1968). Principal-components analysis (PCA; North et al., 1982;Wilks, 2005) was used to identify the dominant modes ofvariability present in the ring-width network. The PCA wasrepeated several times to determine the sensitivity of the finalresults to the choice of (1) detrending method, (2) the intervalused for analysis (and associated spatial coverage of the tree-ring network), and (3) the application of the PCA technique.
A ring-width series from an individual tree represents amixture of climatic and non-climatic signals. The latter nearlyalways includes an age- or size-related trend and often includestransient influences of endogenous or exogenous disturbances(Cook, 1990). Because it is not possible to determine a prioriwhat portion of the variability in ring width is controlled byclimate and what portion is due to other factors, a number ofdata-adaptive techniques have been developed to reduce non-climatic noise in ring-width series, especially at longer timescales. To address this issue, individual ring-width series were
16 PIST 0.96 0.58 1877 0.22Brim Lake 10 PIST 0.90 0.46 1846 0.16Caliper Lake 24 PIRE 0.96 0.53 1846 0.22Durie Lake 8 PIST 0.85 0.41 1887 0.12Volcano Bay 9 PIST 0.93 0.61 1881 0.17Eye Lake Ridge 16 PIST 0.95 0.55 1824 0.17Perch Lake 13 PIST 0.90 0.41 1909 0.12Eva Lake 13 PIST 0.88 0.35 1861 0.11French Lake Portage 9 THOC 0.85 0.39 1901 0.04Windigostiwan Lake 17 PIST 0.95 0.50 1823 0.10“The Pines” at Quetico 37 PIRE 0.95 0.32 1777 0.13Greenwood Lake 44 PIST 0.98 0.50 1778 0.13Mud River 10 QUMA 0.92 0.54 1763 NAc
Saganaga Lake 66 PIRE 0.98 0.47 1675 0.14Seagull Lake 15 PIRE 0.94 0.51 1684 NAc
Ed Shave Lake 18 PIRE 0.95 0.51 1797 NAc
Mean 17 0.91 0.45 1838Percent varianceexplained
41.0
a Collected for this study.b Collected by M. Girardin.c This record does not span enough of the 1900–2004 period to be included in
the PCA.
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converted to unitless indices by fitting a smooth curve to thering-width data and calculating the ratio of the observed ringwidth to the curve in each year. The smooth curves were derivedusing a spline with 50% response wavelength equal to aprescribed percentage of the length of each ring-width series(Cook et al., 1990a). The same spline parameters were used toremove any trend in variance in each time series. Experimentswere conducted using a range of reasonable spline parameters todetermine if using more or less flexible splines affected thesignals recovered from the ring-width network. In eachexperiment, the same spline parameters were used to detrendthe ring-width series at all sites, which ensured uniformtreatment of the entire dataset.
The detrended ring-width indices were combined to computesite chronologies that represent annual tree growth at eachlocation. Short series (less than 80 yr) were excluded to preservelower-frequency variability in the final chronology. Autore-gressive modeling was used to remove persistence from eachring-width series, producing pre-whitened “residual” indices(Cook, 1985; Cook et al., 1990b). These residual ring-widthindices were scaled to equal variance and averaged to producethe site chronologies.
Variance stabilization (Osborn et al., 1997) was applied toadjust for changes in variance associated with declining samplesize (number of trees) over time. The ability of each chronologyto represent the ideal population signal was assessed using themean between-tree correlation and the expressed populationsignal (EPS) statistic (Wigley et al., 1984). Because fewer treescontribute to the site chronology at the earlier part of the record,the chronology becomes increasingly noisy back in time andeventually provides an inadequate estimate of the true signal. Toeliminate uncertain portions of its record, each chronology wastruncated at the point where the subsample signal strength (SSS;Wigley et al., 1984) falls below 0.85; years prior to this cutoffare excluded from the final chronology.
The spatial coverage of ring-width data across the region isbest during the most recent period (roughly the last 100 yr), butbecomes progressively poorer back in time as shorterchronologies developed from younger trees are eliminated.Therefore, it is necessary to test whether a reduced network ofring-width chronologies can provide an accurate estimate of thespatiotemporal modes of variability that are resolved from thefull network. The stability of the PCA results over time wasinvestigated by repeating the analysis using four overlappingtime intervals (each with the same end year but a differentbeginning year). For each repetition, chronologies were used asinputs in the analysis if they had observations covering morethan 85% of the interval. Experiments were also conductedusing either the covariance and correlation matrices of the ring-width network, and non-rotated and varimax-rotated compo-nents. The significance of the resulting eigenvectors wasassessed using the Rule N test and a null hypothesis of randomwhite noise (Overland and Preisendorfer, 1982).
Potential relationships between annual tree growth andclimate variables were assessed using correlation and responsefunctions (Fritts, 1976) generated using DENDROCLIM2002(Biondi and Waikul, 2004). In this program, bootstrapped
Figure 2. Results of principal-component analysis (varimax rotated) of the Winnipeg River region ring-width network. Map (a) showing chronology loadings on thefirst eigenvector for the 1900–2004 period, with the magnitude of the loading indicated by the size of the circles and the colour bar. The first principal component since1900 (b) calculated over four different time periods and using various subsets of input chronologies. The components are scaled to have equal variance over the 1900 to2004 period. The inset matrix shows the correlation between the component derived using the full suite of input chronologies (1900–2004), and those derived usingonly those chronologies that span most of the longer interval. The first principal component (c) calculated over the 1783–2004 interval. The heavy line illustratesvariability at time scales above 10 yr.
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confidence intervals are determined by shuffling the matrix ofclimate data 1000 times and calculating the 95th percentilerange of the coefficients obtained from the randomized data.
We used the Vaganov–Shashkin (VS) model of tree-ringformation (Vaganov et al., 2006) to simulate tree growth in theWinnipeg River region. The VS model is a forward model thatcalculates tree-ring growth and the internal characteristics
(density, cell sizes) of annual rings based on equations relatingdaily temperature, precipitation and sunlight to the kinetics ofsecondary xylem development. The VS model does notexplicitly include photosynthesis, and does not account forother environmental forcings that might impact tree growth,such as CO2 fertilization, natural and anthropogenic distur-bance, or nutrient availability (Evans et al., 2006). The model
Table 3Top ten years of high and low growth in the first principal component of theWinnipeg River region ringwidth network
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has recently been applied to simulate tree-ring widths fromnearly 200 sites in North American and Russia (Evans et al.,2006) and to detect and attribute changes in climate/ring-widthresponse in the southeastern United States (Anchukaitis et al.,2006). Model parameters are not tuned for specific tree taxa, butEvans et al. (2006) reported that the model is most effective atsimulating the growth of pines, firs and junipers.
Tree-ring width was simulated at nine climate stations in theWinnipeg River region. Each station has more than 50 yr ofdaily climate data. Model parameters were the same as used byEvans et al. (2006), with two exceptions. First, the minimumtemperature for growth was increased (to 13°C) so that themodeled growth season matched local observations of thetiming of wood formation (Bowling, C., personal communica-tion, 2006). Second, the soil drainage coefficient was raised (to0.009) to account for the more rapid movement of water throughthe dry soils preferred by red and white pine (Sims et al., 1996).Principal-components analysis was applied to the resulting nine“model” ring-width chronologies to produce a summary timeseries that represents simulated tree-growth over the region(after Anchukaitis et al., 2006).
Results
Chronology characteristics
Most ring-width chronologies in the Winnipeg River regionare between 100 and 170 yr long (Table 1). The median lengthof record recovered from a single tree is roughly 155 yr. Theoldest tree in the network is a 353-yr red pine collected by HalFritts at the Seagull Lake site in the Boundary Waters CanoeArea in 1971; if this tree is still alive, it is nearly 400 yr old. Theoldest tree in the Canadian portion of the Winnipeg River regionis a 334-yr red pine sampled by Martin Girardin at Sowden Lakein 2001. Only four sites have median tree ages greater than200 yr, and all are developed from red pines: Clay Lake andSowden Lake in Ontario, and Seagull Lake and Saganaga Lakein Minnesota. The lesser ages of white pine stands are at leastpartially due to that species' tendency to exhibit heart-rot, as theinner-most rings of white pines were often rotten and could notbe sampled with an increment borer. Consequently, the agesshown here should be interpreted as minimum estimates of treeage only, especially for white pines.
At nearly all sites, signal strength statistics indicate that treesshare a strong ring-width signal that reflects synchronizedgrowth across the entire stand (Table 21). Most chronologieshave between-tree correlations (Rbar; Briffa and Jones, 1990)above 0.4; nine chronologies have Rbar values greater than0.55. The EPS, which is a function of sample size and thebetween-tree correlation, exceeds 0.85 at nearly all sites (atmaximum sample size) and 0.9 at most sites. The onlychronologies with EPS values below 0.85 (Lake Packwash-Jack pine, Expulsion Bluff, Snail Lake, Sioux Lookout-Red
1 The number of trees reported for some chronologies in Table 2 does notmatch the values given in Table 1. This discrepancy reflects the exclusion ofshort series (less than 80 yr) during the chronology-building procedure.
Pine, Stormy Lake) are developed from few (b10) trees andmost exhibit a relatively weak shared signal (Rbar values of0.32 or lower). There are no apparent differences in the qualityof chronologies developed from red or white pine. The SSS,which measures a chronology's ability to resolve its maximum-sample-size signal with fewer trees contributing to the earlierpart of its record, indicates that most records are reliable back tothe early- to mid-1800s. Ten chronologies retain an adequatesignal prior to 1800. By 1776, only four chronologies remain,and the final Canadian chronology (Sowden Lake) is eliminatedin 1748.
Principal components analysis
The PCA produces two patterns that can be distinguishedfrom random noise. For the period 1900–2004, 41% of the totalvariance in the ring-width network is described by the firstprincipal component (PC1). Applying rotation to the eigen-vectors changes the spatial loading patterns of PC1, butregardless of the choice of rotation (no rotation or varimax) ormatrix (correlation or covariance), the resultant component isnearly identical (the minimum correlation between any pair oftime series is 0.994). The leading PC is also almost indis-tinguishable from a simple average of all chronologies in thenetwork. Under varimax rotation, the chronology loadings(Fig. 2a; Table 2) indicate that all records load on the EOF1with the same sign, but the pattern gives more weight to sites inthe Lake of the Woods region (the southwestern sector). Map-ping the correlation between individual ring-width records andPC1 does not show this southwestern bias; with the exceptionof sites on the periphery of the network, the correlation betweenthe leading pattern and individual sites is between 0.6 and 0.75.From these observations, we conclude that it is reasonable tointerpret this pattern as a measure of tree growth across theentire region.
The second eigenvector (PC2) describes roughly 10% of thetotal variance, and appears to reflect differences in growthbetween the network's two main species of pine (loadings areopposite for red and white pine chronologies, with only a fewexceptions). Choices in the application of the PCA techniquehas a greater effect on this pattern, but still produce time seriesthat are highly correlated (0.9 and above). In all cases, PC2 is
Figure 3. Correlation (left) and response (right) functions comparing the first principal component of ring width against monthly (a) temperature and (b) precipitation from the Winnipeg River region. Coefficients that aredifferent from zero (at the 95% significance level) are plotted with heavy lines. Confidence limits represent the 95th percentile range of 1000 bootstrapped iterations (Biondi and Waikul, 2004).
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dominated by a single year (1966) where growth at red pinesites is much greater than at white pine sites. If data from 1966are excluded from the analysis, this pattern cannot be resolvedfrom white noise. We therefore conclude that the variabilitydescribed by PC2 reflects the influence of this unique event anddoes not represent a consistent difference in growth betweenthese two species. Although this mode describes a relativelylarge fraction of the variance in regional ring width, we do notinclude it in our subsequent analyses.
The choice of spline used to detrend the raw ring-width didnot affect the patterns recovered by PCA. Detrending withsplines varying from 50 to 200% of the length of each ring-width series produced nearly identical principal components(with the same proportion of variance in PC1).
The PCA results are also largely insensitive to changes in theperiod of analysis or the number of chronologies used as inputs.We assume that the best estimate of the true regional signal isobtained by conducting the analysis over the most recent period(1900–2004), when the number of input chronologies isgreatest. If the same patterns are identified using a reducedsubset of longer chronologies, the reduced network is also likelyto be able to resolve those patterns accurately in earlier periods.The leading component is largely unchanged if the analysis isrepeated using longer time intervals and fewer chronologies
Figure 4. Scatterplots comparing regional ring width with precipitation (left side) andwidth and the climate parameter are selected based on the state of the other climate varusing only those years where summer temperatures were in the top tercile of their distrlow summer rainfall (precipitation in the bottom tercile).
(Fig. 2b). For example, conducting the analysis using only thosechronologies that cover most of the 1800–2004 interval (n=37)produces a principal component that is nearly identical to thatgenerated using the full network (r=0.972). Beginning theanalysis in 1800 and 1783 also makes very little difference toPC1. Further restricting the analysis using only the ninechronologies that extend completely back to 1783 (not shown)also yields a very similar component (r=0.93).
Most of our subsequent analyses were (1) conducted on a ring-width matrix detrended using spines with a 50% response wave-length equal to 70% of the length of each ring-width series and (2)were derived from PCAusing the covariancematrix and a varimaxrotation. The first principal component produced for the 1783–2004 period shows persistently low tree growth across the Winni-peg River region during the early 1840s, the 1860s and around1910 (Fig. 2c). High growth periods include the 1850s, the late1890s, and the 1950s and 1960s. Themost recent part of the recordis notable for having high growth over three consecutive years(1999–2001). The last century also includes both the lowest (1910)and highest (1965) growth year in the record (Table 3). The firstcomponent is quite similar (r=0.67) to the reconstructed CanadianDrought Code record for the Lac Seul region produced byGirardinet al. (2004), which was based on a subset of the chronologies inthe Winnipeg River network. These records are most alike with
temperature (right side) during summer. In each frame, the observations of ringiable. For example, in the “WARM” case, ring width is compared to precipitationibution. The “DRY” case plots ring width and temperature data during years with
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respect to long-term changes (≥20 yr), but give different emphasisto single-year extremes. The Lac Seul CDC record identifiesseveral years with severe drought conditions, whereas theWinnipeg River PC shows that regional tree growth was muchlower in 1910 than during any other year since the late 1700s.
Climate vs. ring-width relationships
In this section, we describe comparisons between ring widthand the regional climate series derived from the CRU TS 2.1gridded dataset. Very similar results were obtained using re-gional climate data produced by averaging the AHCCD stationdata. Correlation and response function analyses (Fig. 3) suggestthat tree growth in the Winnipeg River region (as represented byPC1) is primarily influenced by summer moisture stress andgrowing-season length. Ring width has a positive relationshipwith precipitation in early summer (May to July) and is nega-tively related to June temperature. These results indicate thattrees grow best when summers are cool and wet, presumably dueto increased soil moisture and reduced evaporative losses. Ringwidth has a strongly negative response to the Canadian DroughtCode in July and August (not shown), which provides furtherevidence that summer moisture stress is a key factor influencingtree growth. Significant positive relationships are observedfor temperature in spring (April and May) and late summer(August). Temperatures during these months may be acting asproxies for total growing-season length, so that tree growth isenhanced in years with warm springs, long summers and ex-
Figure 5. Summer climate over the Winnipeg River area stratified by regional tree grobserved in years of low (bottom tercile), moderate (middle tercile) and high (uppequartile values, and the whiskers encompass one-and-a-half times the interquartile ranomalies in temperature–precipitation space. Each circle represents one year, and thcircles represent years that have high or low growth, while small circles represent yeaon whether they fall within the bottom (black), middle (white) or upper (grey) tercile o
tended growth seasons. Warm weather near the end of thegrowing season has a negative influence on ring width in thefollowing year, possibly by reducing food stores and limiting thesubsequent development of needles, buds and roots (Fritts,1976). Although statistically significant, the correlationsbetween tree growth and any single climate parameter are rela-tively modest. Significant correlation coefficients range between0.2 and 0.4, and are not increased by aggregating monthlyobservations over longer intervals (2–12 month averages).
There is some indication that trees in this region responddifferently to summer (June–July) temperature and precipita-tion conditions depending on the state of the other variable(Fig. 4). For example, regional tree growth is more stronglyinfluenced by summer precipitation when summer temperaturesare warm (in the upper tercile of its distribution). The cor-relation between precipitation and ring width is highest duringwarm summers, lower when summer temperatures are moder-ate, and non-significant for cool summers. Similarly, ring widthhas a significant negative correlation with temperature onlywhen summer precipitation is low (“DRY”) and is not signi-ficantly correlated when precipitation is either moderate or high.However, the correlation coefficients from the WARM andCOLD cases are not significantly different from each other, andthe coefficients for the DRY and WET cases are significantlydifferent only at the p=0.08 level.
Box plots confirm that high tree growth across the region isassociated with cool, wet summers, and that low growth typi-cally occurs during years with warm temperatures and low
owth. Box plots (a) describe the range of summer temperature and precipitationr tercile) ring width. The boxes illustrate the lower quartile, median and upperange. Outliers are marked by crosses. The bubble diagram (b) plots ring-widthe area of the circle is proportional to that year's ring-width anomaly (i.e., largers where growth was close to the mean). The circles are colour-coded dependingf ring width. The dashed lines mark the medians of temperature and precipitation.
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rainfall (Fig. 5a). Using t-tests to evaluate differences in themean of each subset indicates that (1) summer precipitation ishigher in high-growth years than in moderate or low-growthyears (p=0.00002 and 0.001, respectively); and (2) summertemperature is higher in low-growth years than in moderate- orhigh-growth years (p=0.007 for both cases). With only fourexceptions, all low-growth years (shown in black; Fig. 5b)occurred when mean June–July temperatures were above16.5°C. Although low-growth years are observed when summerprecipitation was above-average, nearly all high-growth yearshave summer precipitation greater than 80 mm. Finally, the yearwith the lowest regional tree-growth (1910) was coincident withthe least summer precipitation in the instrumental record (June/July precipitation=37.4 mm).
Correlations between PC1 and monthly or seasonalisedstream flow at gauges across the Winnipeg River watershed (notshown) were generally not significant. Although regional treegrowth is most closely tied to summer climate, summer
Figure 6. Simulated ring width for the Winnipeg River region and its relationship witline) and real ring width (grey line). Box plots (b) illustrating the range of summerdifferences in mean temperature and precipitation in low and high growth years areanomalies in temperature–precipitation space.
conditions have only a moderate influence on runoff in theWinnipeg River system. Annual discharge in the WinnipegRiver is more strongly correlated with precipitation duringwinter and the prior autumn (St. George, 2007), and even at themonthly scale, rainfall during June and July has only a modestcorrelation with river flow. Because regional ring width is onlyweakly correlated with summer precipitation, and summerprecipitation itself has only a moderate influence on summer orannual discharge, the absence of a robust association betweentree growth and stream flow is perhaps not surprising.
Vaganov–Shashkin model results
In general, the synthetic ring-width data produced by theVaganov–Shashkin model are more similar to chronologiesdeveloped from red and white pine than records developed fromjack pine or other tree species. Maximum correlations betweenmodeled and real ring-width records range between 0.3 and 0.55.
h regional summer climate. The first principal component (a) of modeled (blackclimate observed in years of low, moderate and high modeled ring width. Thesignificant at the 98% level. The bubble diagram (c) plots modeled ring-width
Figure 7. Simulated mean daily tree-growth at Kenora, Ontario for the 1938–2006 period. The daily growth rate (G) is calculated as the product of the growth rate dueto solar radiation (Ge) and the minimum of the growth rate due to either surface air temperature (Gt) or soil moisture (Gw).
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Because climate data are one of the primary inputs used to drivethe VS model, the synthetic chronologies should be expected tohave some correlation with temperature and precipitation. It isnotable, however, that the model PC exhibits similar correlationand response functions as the real data (not shown), with sig-nificant negative correlations with July temperature and sig-nificant positive correlations with summer precipitation. Thesimulated ring-width series produced by the VS model issignificantly correlated (r=0.42, p=0.00003) with PC1 of thereal Winnipeg River ring-width network (Fig. 6a). The simulatedring width does not duplicate the significant positive correlationbetween the real ring-width data and April andMay temperatures.However, aswith the real data, correlations between synthetic ringwidth and climate variables are relatively weak, with none above0.36. The terciles of the simulated ring width show the samerelationships with summer climate as the real data, with lowgrowth being associated with warm and dry conditions and highgrowth with cool and wet summers (Fig. 6b). The model alsoduplicates the general pattern of ring-width anomalies with re-spect to temperature and precipitation, with nearly all high growthoccurring in years where June–July precipitation is greater than80 mm, and most low-growth years coinciding with summertemperatures above roughly 16.5°C (Fig. 6c).
Figure 8. Summer climate conditions in the Winnipeg River region inferred from regisame state for three consecutive years. The lower plot is smoothed to highlight decadatree-ring record at those time scales.
The seasonal dynamics of modeled growth rates indicate thattrees in this area should be sensitive to a mixture of temperatureand moisture signals during the growth season. The VS modelcalculates the daily growth rate (G) as the product of the growthrate due to solar radiation (Ge) and the minimum of the growthrate due to either surface air temperature (Gt) or soil moisture(Gw). Model results suggest that temperature is the principalfactor that triggers the beginning of growth season (Fig. 7), aswater from snowmelt keeps soil moisture levels high throughthe spring and early summer. Tree growth continues to beconstrained by temperature until the end of June; at that point,decreasing soil water in the heat of summer causes growth toswitch to being moisture-limited.
Discussion
Ring-width records from across the Winnipeg River regionshare a common signal that is present in more than 50 chro-nologies developed from six different tree species. This signal iscoherent over an area of more than 160,000 km2, and is strongenough that it can be resolved from only the nine longestchronologies. These characteristics lead us to interpret thissignal, as represented by the first PC, as a robust measure of
onal tree growth, AD 1783 to 2004. Asterisks mark cases where climate is in thel variability, with the height of the bars scaled to the proportion of variance in the
Table 4Archival accounts of wet summers in northwestern Ontario during the early 19thcentury
Year Selected archival accounts1 Tree-ringclassification
1824 David Thompson canoed from Fort William (ThunderBay, Ontario) and Lake of the Woods and reported highwater throughout the trip. J.D. Cameron reported that theWinnipeg River was “exceptionally high” and “amazinglyhigh” at Pinewa, Manitoba (Lac la Pluie Journal).
Cool/wet
1825 Reports of high and rising water on the Rainy river fromAugust to early December (Lac la Pluie Journal).
Cool/wet
1827 Heavy rains and high water at Rainy Lake in May (Lac laPluie Journal).
Cool/wet
1828 “Incessant” summer rains and high waters in theWinnipegRiver region, especially in the Boundary Waters area (Lacla Pluie Journal).
Cool/wet
1830 High water at Rainy Lake in May, with reports of water“higher than the high-water year” (Lac la Pluie Journal).
Warm/dry
1832 Torrential frequent rain and dangerously high water atRainy Lake in May and June (Lac la Pluie Journal).
Cool/wet
1834 “Uncommonly high” water on June 13 and heavy rain atRainy Lake throughout June and most of July (Lac laPluie Journal).
Cool/wet
1Archival accounts are abstracted from Rannie (2006).
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regional tree growth. A second signal can also be identified thatrelates to differences between white and red pine, but thispattern seems to reflect a single case of extremely high red pinegrowth relative to white pine growth.
Although macro-scale climate variability is the most likelycause of the dominant regional growth pattern, trees in theWinnipeg River region are influenced by several differentclimatic factors and, as a consequence, the relationship betweenregional tree growth and any single climate variable is relativelyweak. High growth is associated with long growth seasons andcool, wet summers, but the correlations between ring width andtemperature or precipitation are not high enough to develop atraditional, regression-based dendroclimatic reconstruction(Fritts, 1976, Bradley, 1999). Moreover, the Vaganov–Shashkinmodel predicts that a weak, multivariate climate signal shouldbe expected for trees in this region, as simulated ring-widthchronologies show the same relationships with summer climatevariables as the real chronologies. Because of these limitations,we focus our interpretation on the general relationship betweensummer climate conditions and the terciles of tree growth. Asshown previously, high growth is commonly associated withcool temperatures and high rainfall, while low growth usuallycoincides with hot, dry summers. Therefore, we argue that thefirst principal component of ring width can be interpreted as aproxy record of past summer climate conditions in theWinnipegRiver region, and classify years as either warm and dry (bottomtercile of ring width), cool and wet (upper tercile) or indeter-minate (middle tercile).
This classification scheme provides some indication thatsummer drought may have been more persistent in the Winni-peg River region during the 19th century (Fig. 8a). The 20thcentury has only one case (1946–48) where the region was inthe “Warm/Dry” state for three consecutive years. This condi-tion is more common in the earlier part of the record, as ithappened four times in the 1800s (1829–31, 1839–41, 1862–64, and 1888–90) and once near the beginning of the record(1790–93). Persistent cool and wet conditions occur much lessfrequently, with only two cases in the entire record (1951–53and 1999–2001). Applying the same classification to PC1 afterfiltering to emphasize variations at decadal time scales indicatesthat the 1880s and 1890s had the longest run of warm, drysummers in the last 220 yr (Fig. 8b). This dry interval fallswithin an extended period of frequent forest fires from the1870s to the 1920s identified by Suffling and Speller (1998)from the Hudson's Bay Company journal at Osnaburgh House(located immediately northeast of the Winnipeg River basin).The 1950s and 1960s had the longest-lasting period of cool andwet summers, with similarly persistent conditions observed inthe 1850s. Determining the magnitude of climatic extremes inthe pre-instrumental period is difficult because of the weaknessof the relationship between tree growth and summer climate.However, because the years of both highest (1965) and lowest(1910) growth since AD 1783 occurred during the 20th century,we conclude that there is no evidence that summer climate wasmore extreme in the 19th or late 18th century.
In general, archival evidence from northwestern Ontariocorroborates the tree-ring based classification of summer
climate conditions during the early 19th century (Rannie,2006; Table 4). The Lac la Pluie post (near present-day FortFrances, Ontario) was established by the North West Companyin 1791, but its archives do not contain any entries that mentionclimate conditions until after the post was taken over by theHudson's Bay Company in 1821. Although the post remainedin operation until 1903, its archives were not maintained after1841. Rannie (2006) identified seven “wet summers” withinthat twenty-year period based on entries from the Lac la Pluiejournal: 1824, 1825, 1827, 1828, 1830, 1832 and 1834. Withone exception (1830), these years are classified as “cool/wet”based on the regional tree-ring record. The historical and tree-ring records also agree that there were no notably cool/wetsummers between 1835 and 1841. However, there are nohistorical accounts that support the inference based on tree ringsthat the summers of 1822 or 1833 were cool and wet.
Conclusion
Although the cool, moist climate of the Winnipeg River areamake it an important generating region for runoff, these samecharacteristics cause regional tree-ring records to contain arelatively weak, multivariate climate signal. As predicted by theVaganov–Shashkin model, ring-width data for this regioncontain a mixture of (sometimes competing) influences oftemperature and precipitation during the growth season.However, the absence of a strong linear correlation with asingle climate variable does not mean that tree-growth in thisregion is not sensitive to climate. Instead, the climatic factorsthat limit growth are multivariate and likely change inimportance from year-to year. A common climatic influenceremains the most likely factor responsible for the strongcoherence in tree growth in multiple tree species across theregion since the late 18th century. High and low growth across
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the region is associated with significantly different summerweather during the instrumental period, and this general re-lationship is supported by comparisons with archival recordsfrom early 19th century fur-trading posts. Therefore, even giventhe above limitations, this data set still provides usefulinformation about summer climate in this area since AD 1783.
The tree-ring record suggests that summer droughts weremore persistent in the 19th and late 18th century, with severalinstances of warm and dry conditions lasting for three con-secutive years. Variability over longer timescales is also moreprominent in the 19th century, with the longest interval of warm,dry summers in the last 220 yr occurring in the 1880s and early1890s. Estimating the magnitude of droughts inferred from thetree-ring record is more difficult, but because regional treegrowth was at its lowest in 1910, we conclude that there is noevidence that drought was more extreme prior to the onset ofdirect monitoring.
Acknowledgments
We thank J. Balmat, E. Nielsen and the staff at the ManitobaGeological Survey for field support, and J. Rowland and E.Margolis for laboratory assistance. Manitoba Conservation, theOntario Ministry of Natural Resources and the Greenwood LakeNature Reserve granted permission to collect samples within theirrespective jurisdictions.We thankM.Girardin for contributing hisdata from northwestern Ontario, T. Ault for computing assistance,E. Vaganov for providing access to the Vaganov Shashkin model,and K. Anchukaitis for his guidance in employing the modelwithin Matlab. We are grateful to D. Fisher, P. Bartlein and twoanonymous reviewers for reviewing this manuscript prior topublication. Financial support for this research was provided byManitoba Hydro, the Manitoba Geological Survey, the PrairieAdaptation Research Collaborative and the Natural Sciences andEngineering ResearchCouncil of Canada. Thismanuscript is ESScontribution number 20060728.
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