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Dendrochronological analysis of the growth rate of White Spruce in Whitehorse, Yukon deBruyn, Alexander 2010-04-30

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 1     Dendrochronological Analysis of the Growth Rate of White Spruce in Whitehorse, Yukon                Alexander deBruyn   Science One Program   University of British Columbia   Vancouver, Canada   April 2010  2    Abstract We used an increment bore to take core samples of 15 specimens of White Spruce, Picea glauca, then compared the observed annual growth rates observed in the tree rings to the annual mean temperature, annual precipitation, and the average of July’s temperature highs in order to show the correlation. A difficulty in the procedure eliminated some of the raw data, but enough remained to show that isolated White Spruce specimens will grow more quickly when they are in non-crowded areas.  Introduction This experiment took place in an area of forest located towards the southern end of Whitehorse City limits. The area was a gently-sloping westward-facing hillside at coordinates 60°36’ North and 134°58’ West. The region is a stable forest at the peak of succession. None of the core samples show any evidence of fire scarring over the past 200 years. White Spruce form part of this climax community, alongside Lodgepole Pine and Black Spruce, the other two trees significant in the forest’s structure. 1  Trees in this region are very slow-growing. The Yukon has a cold climate with an average annual temperature hovering around the freezing point of water. Precipitation is light, ranging between 150 and 350 millimetres per year, roughly evenly split between rain and snowfall. Temperatures vary significantly throughout the year, with monthly averages ranging from under -20°C in January to above 20°C in July. The annual average temperature hovers around 0°C. 2  White Spruce is a common species found across northern North America. The specimens examined in this experiment are sometimes referred to as Alaska White Spruce, and are frequently hybridized with the closely-related Engelmann Spruce. The difference is difficult to observe, and potential differences between different strains were outside of the focus of this experiment. This tree plays a significant role in forest structure in this region of the Yukon, and is an economically valuable species. As such, it is worthwhile to understand more about the growth and development of the White Spruce in this environment.  It is expected that increased precipitation, annual temperatures, and peak growing season temperatures will all cause an increased rate of growth in specimens of White Spruce. 3  It is also expected that less crowded conditions will allow White Spruce to better take advantage of these resources, and grow faster still. This experiment intends to support these hypotheses.  The growth of these trees can be measured in the widths of the rings in core samples collected with an increment borer. As a tree advances through the growing season, it builds up a layer of light-coloured ‘spring wood’ before completing its growth with a thinner layer of dark ‘summer wood’. Knowing that one set of light and dark layers is deposited each year, we can use the visible demarcations to determine the age of a tree, and through measuring ring width, learn about its climatological history.  Methods Fifteen specimens of White Spruce, Picea glauca, were measured for the purposes of this experiment. The trees were selected at random from a sample of White Spruce all living within a 100m radius from a central point, all in a common microclimate for consistency. The trees selected had circumferences of at least 60 centimetres, were the tallest trees in their immediate vicinity (15 to 20 metres in height), and to be at least 2 metres away from each other.  We used a Suunto increment borer to take core samples of each tree. Use of the tool is relatively straightforward. An increment borer consists of three components: a handle, an auger and an extractor tray. When stored, the extractor tray fits inside the auger, which can be sealed inside the cylindrical handle for easier transport.  In order to use the tool, the auger must be locked into a t-shape with the handle. The auger bit is lubricated with a substance such as WD-40, then pressed into the side of a tree at breast height, a consistent value of approximately 175 cm above the ground. Pressure is applied directly down the auger and into the tree, with the auger pointing directly towards the centre of the tree, while the handle is turned counterclockwise. The borer quickly passes through the bark, but continued pressure is required to advance into the sapwood and gain traction inside the tree. Once this is achieved, no further pressure into the tree will be required, as the turning motion on the auger’s threading will provide all of the force needed to draw the borer further into the tree.   Once the instrument is more than halfway through the tree, the extractor tray was inserted through a hole in the end of the auger opposite to the bit. The tray has a half tubular shape with serrated edges towards its tip which allowed it to pass inwards around the core sample, then dug in when pulled outwards. Once fully inserted, the handle was turned 180° counterclockwise to break off the core, and the tray is pulled out, with the core sample attached. The tree was marked with a small, numbered flag,  3   measured around its diameter at breast height, and subjected to qualitative observation of the number of and distance to trees in its immediate surroundings.  This procedure was repeated for all fifteen trees. Following the collection of all fifteen sets of data, the cores were affixed to a sheet of paper and scanned at high resolution. Using GIMP, an image editing program, the number of pixels per annual growth ring width were counted and recorded. Once these values were calculated, the number of pixels in an item of known length which had been scanned was calculated, to provide a conversion multiplier from pixels to millimetres. With that done, the results of annual tree ring growth are seen, in millimetres.  Understanding the outermost ring immediately beneath the bark to be the product of the most recent growing season, each year of growth can be determined by the width of the subsequent internal band. By connecting years with ring width, it became possible to directly compare climate records, obtained from Environment Canada, with the rate of growth as measured in the core samples, and thus gain an understanding of the relationships between temperature, precipitation and the growth of White Spruce specimens in the measured environment. We used the records from the station ‘Whitehorse A’, which is approximately 12.5 kilometres away from the sampling site, but possesses a very similar elevation and surroundings, and thus climate. 2  Results and Discussion Preliminary Observations of Trees Tree # Diameter at Breast Height (mm) Surroundings Qualitative Observations 1 190 Crowded None. 2 330 Isolated Split 1/3 of the way up. 3 270 Isolated Sappy bark with pitch-outs. 4 250 Isolated Sappy bark. 5 220 Small Neighbours None. 6 220 Crowded Strange black marks on bark. 7 260 Crowded None. 8 250 Isolated None. 9 220 Small Neighbours None. 10 220 Crowded None. 11 200 Small Neighbour None. 12 190 Neighbour None. 13 260 Crowded None. 14 210 Isolated None. 15 250 Small Neighbour None. Table 1. Above are the results of preliminary investigations of the sampled trees. The definitions of the terms in the ‘surroundings’ column are as follows:  Definitions Isolated:   No other trees within a 2 metre radius. Small Neighbour:  One tree of less than 10 cm diameter within 2 metres of the measured tree. Small Neighbours:  More than one tree of less than 10 cm diameter within 2 metres of the measured tree. Neighbour:   One large tree based within 2 metres of the measured tree. Crowded:   More than one large tree based within 2 metres of the measured tree.           4   Core Measurements and Meteorological Records Year Avg Temp (°C) Precipitation (mm) Mean July High Temp (°C) Tree 1 Tree 2 Tree 3 Tree 4 Tree 5 Tree 6 Tree 7 Tree 8 Tree 9 Tree 10 Tree 11 Tree 12 Tree 13 Tree 14 Tree 15 2009 -0.5 300.6 24.0 0.51 1.10 0.42 0.59 0.93 0.76 0.34 0.76 0.51 0.42 0.76 0.76 0.76 0.85 0.42 2008 -1.6 319.6 17.9 0.85 0.85 0.34 0.85 1.18 0.76 0.59 0.59 0.34 0.42 0.42 0.85 0.85 0.76 0.42 2007 -0.6 296.8 20.3 0.51 1.27 0.34 0.68 1.10 0.93 0.42 0.51 0.51 0.51 0.51 0.59 0.59 0.68 0.51 2006 -1.2 284.8 20.8 0.68 1.52 0.25 0.34 0.59 0.51 0.42 0.51 0.68 0.42 0.68 0.68 0.85 0.59 0.59 2005 1.2 333.7 19.5 0.76 1.44 0.34 0.42 1.10 0.76 0.25 0.34 0.59 0.51 0.59 0.93 0.68 0.51 0.76 2004 1.1 263.7 22.0 0.42 1.44 0.34 0.68 1.61 1.18 0.59 0.59 0.59 0.51 0.59 0.68 0.68 0.93 0.68 2003 0.1 224.3 22.0 0.59 1.02 0.25 0.34 0.93 0.42 0.42 0.42 0.42 0.59 0.59 0.93 0.68 0.85 0.59 2002 0.5 174.7 20.3 0.59 1.02 0.34 0.42 1.10 0.85 0.42 0.85 0.42 0.59 0.68 1.10 1.02 0.93 0.59 2001 0.1 265.3 18.9 0.59 0.51 0.34 0.42 1.35 0.68 0.51 0.68 0.68 0.68 0.76 0.68 1.02 1.02 0.42 2000 0.3 303.8 19.2 0.42 0.93 0.25 0.34 1.44 0.34 0.68 0.68 0.42 0.76 0.42 0.93 0.85 1.52 0.59 1999 -0.3 250.4 21.3 0.42 1.86 0.51 0.34 0.93 0.51 0.59 0.59 0.42 0.42 0.51 1.02 0.85 1.27 0.85 1998   No Values 21.0 0.51 1.86 0.42 0.51 1.18 0.42 0.42 0.59 0.42 0.34 0.34 0.93 1.10 1.35 0.68 1997 No Values 0.25 1.61 0.59 0.34 1.02 0.51 0.34 0.34 0.59 0.34 0.34 0.68 0.68 0.76 0.76 1996 0.59 1.27 0.51 0.25 1.78 0.42 0.34 0.68 0.59 0.34 0.34 0.51 0.76 1.02 0.59 1995 -0.1 230.5 20.6 0.51 1.52 0.34 0.34 0.76 0.34 0.25 0.51 0.51 0.42 0.34 0.76 1.27 1.27 0.25 1994 -0.5 271.6 22.4 0.34 1.35 0.51 0.34 0.93 0.34 0.34 0.51 0.42 0.85 0.85 0.85 1.10 1.27 0.34 1993 1.2 266.1 20.3 0.42 1.44 0.51 0.42 1.02 0.59 0.42 0.42 0.59 0.68 0.42 0.76 1.18 1.18 0.51 1992 -0.1 263.7 19.9 0.51 1.86 0.51 0.34 1.52 0.51 0.34 0.59 0.51 1.52 0.51 0.59 1.02 1.44 0.51 1991 0.1 371.3 18.7 0.51 2.20 0.51 0.42 1.44 0.42 0.59 0.34 0.51 1.78 0.34 1.10 1.02 1.78 0.76 1990 -1.7 239.2 21.9 0.34 2.03 0.42 0.51 1.02 0.51 0.42 0.59 0.42 1.44 0.51 1.02 0.93 2.03 0.42 1989 -0.5 257.1 23.0 0.34 1.27 0.42 0.42 0.93 0.42 0.34 0.42 0.34 0.93 0.51 1.02 0.68 1.69 0.51 1988 0.2 336.1 17.7 0.42 1.44 0.51 0.51 0.68 0.51 0.42 0.51 0.51 0.93 0.42 1.44 0.68 1.95 0.59 1987 1.4 210.6 21.3 0.51 1.35 0.42 0.42 0.68 0.34 0.42 0.34 0.51 1.18 0.51 1.10 0.93 2.28 0.68 1986 0.2 350.7 20.6 0.59 1.10 0.51 0.51 0.59 0.42 0.42 0.42 0.34 0.85 0.42 0.76 0.68 1.86 0.68 1985 -0.8 262.8 20.3 0.68 1.10 0.42 0.59 0.68 0.51 0.34 0.68 0.51 1.18 0.59 1.10 0.76 1.95 0.85                   Table 2. Useful information can be collected by comparing the width measurements of each growth ring with annual average temperatures, the average of daily high temperatures throughout the July, the warmest and most growth-friendly month, and annual precipitation.   The following pixel measurements of objects of known length were taken to determine the relationship between millimetres and pixels: 11.5 cm = 115 mm = 1365 pixels → 0.0842 mm/pixel. 10 cm = 100 mm = 1181 pixels → 0.0847 mm/pixel. 8.7 cm = 87 mm = 1025 pixels → 0.0849 mm/pixel. The mean value, 0.0846 mm/pixel, was used to convert each pixel value measured from the scan of the core samples to millimetres.  We could now create scatter plots of growth rate against precipitation, growth rate against average annual temperature, and growth rate against average high July temperature, making use of the values provided by Table 2. However, in doing so, an unexpected trend appeared. Most results were haphazard and seemingly arbitrary, with only a small number of results appeared to form any sort of pattern. Curiously, it was the same trees who displayed this behaviour each time: Trees 2, 10, 12 and 14. In all cases, they provided positive slopes and showed increased growth with greater precipitation, hotter summers, and warmer years overall, just as we had expected. These trees were then graphed in direct comparison to one other.  5    Figure 1. Tree 2 is followed by Trees 14, 12 and 10 in terms of growth rate, while all rates increase with rising temperature. Standard error bars are used to account for the variations of growth and temperature.  Figure 2. Tree 2 is followed by Trees 14, 12 and 10 in terms of growth rate, while all rates increase along with precipitation. Standard error bars are used to account for the variations of growth and precipitation.  Figure 3. Tree 2 is followed by Trees 14, 12 and 10 in terms of growth rate, while all rates increase as the peak growing season temperatures rise. Standard error bars are used to account for the variations of growth and temperature. 0.00 0.50 1.00 1.50 2.00 2.50 -2 -1 0 1 2 G ro w th  R at e  ( m m /y e ar ) Average Annual Temperature (°C) Growth Rate Against Average Annual Temperature Tree 2 Tree 10 Tree 12 Tree 14 0.00 0.50 1.00 1.50 2.00 2.50 150 200 250 300 350 400 G ro w th  R at e  ( m m /y e ar ) Precipitation (mm/year) Growth Rate Against Precipitation Tree 2 Tree 10 Tree 12 Tree 14 0.00 0.50 1.00 1.50 2.00 2.50 17 19 21 23 25 G ro w th  Rat e  ( m m /yea r) Average High July Temperature (°C) Growth Rate Against Average July High Temperature Tree 2 Tree 10 Tree 12 Tree 14 6   Looking back over the core samples and procedure, one can come to realize how the arbitrary and apparently patternless results may have appeared. One qualitative similarity between the four cores which provided clear results was that their rings were particularly clear and legible, especially near the bark. In many cases, the outermost regions of the sapwood were nearly impossible to make out. Treatments such as varnishing or staining did not make the extremely fine lines any more visible, and thus it is quite likely that the dates predicted by the rings would be shifted by one or more years. Such a change could turn a valid data set into meaningless noise, by corresponding growth rates to unrelated levels of precipitation and temperature.  This problem could be solved through the use of a more elabourate apparatus. A proper sander and microscope system could flatten off the tops of the cores and allow for increasingly accurate, but time-consuming measurements to be performed. The cores collected in this experiment could still be used for this second round of analysis, as scanning them and performing the measurements on a computer was a non-destructive process.  Conclusions   As we can observe in Figures 1, 2 and 3, all four core measurements which produced a pattern showed clear increases in growth rate with increased annual average temperature, July average temperature, and precipitation. However, we do not have as much data as we would have preferred, due to the unfortunate invalidation of a large portion of our collected information. Solutions and improvements to this problem have already been suggested, but also worth consideration is the variety of alternative experiments which could be performed with the data collected. Much more free from difficult age-determining issues would have been measuring the age approximately, outwards from the centre instead of inwards from the bark. Investigations more quantitatively towards the immediate surroundings of the trees would be worthwhile, to help determine more specifically the impacts of crowding on White Spruce Growth.   As we stand now, the data set is ready for another round of investigation.       References  1. Jacoby, G. And Cook, R. Past Temperature Variations Inferred From A 400-Year Tree-Ring Chronology from Yukon Territory, Canada. Arct. Alp. Res. 13 (4), 409-418 (1981).  2. National Climate Data and Information Archive. Canadian Climate Normals 1971-2000.  http://climat.meteo.gc.ca/climate_normals/results_e.html?Province=ALL&StationName=whitehorse%20a&Sear chType=BeginsWith&LocateBy=Province&Proximity=25&ProximityFrom=City&StationNumber=&IDType=MSC&C ityName=&ParkName=&LatitudeDegrees=&LatitudeMinutes=&LongitudeDegrees=&LongitudeMinutes=&Norma lsClass=A&SelNormals=&StnId=1617&&autofwd=1 [Accessed 17 March 2010]  3. Oswalt, W. The Growing Season Of Alaskan Spruce. Tree Ring Soc. 23 (1-4), 3-9 (1954) 

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