CHAPTER II: MATERIALS AND METHODS
1. Study sites
CHAPTERII
MATERIALS AND METHODS
Three sites were selected for this research. The first study site is composed of eighteen jack pi ne and black spruce stands located in the vicinity of Beardmore in Northwestem Ontario (49°30' N,88° W) (Fig. 1). Data and samples from these stands were provided by Dr. A.M. Gordon (Univ. Guelph, Ont.) and a complete description of the stands and their management history can be found in Hunt et al. (2003). The study area is located in the Central Plateau and Superior forest regions (Rowe 1972). The mean annual temperature is 0.2°C and precipitation is 784 mm (Environment Canada 1993).
The frost-free period ranges from 75 to 100 days (Environment Canada 1993). The stands are managed plantations, except for three jack pine stands that are of fire origin. The majority of stands are between 31-53 years old, except for two jack pine stands and one black spruce stand that are 10-14 years old. Jack pine stands are found on deep sand or silty sands and are rapidly drained. Black spruce stands are found on silty sand and silty clays and are weIl drained.
The second study site is the Lac Duparquet Teaching and Research Forest, located north of Rouyn-Noranda, Qc (48°30' N, 79°20' W) (Fig. 1). Data from this site was gathered by Doucet (1997) in four unmanaged white spruce stands of fire-origin ranging from 51 to 125 years old (Doucet 1997). This study area is located in the Missinaibi-Cabonga forest region (Rowe 1972) of the southem boreal forest. The mean annual temperature is 0.8 oC and average precipitation is 857mm (Environment Canada 1993).
The mean frost-free period is 64 days (Environment Canada 1993). This area was divided into two sub-sites according the requirements of the initial experimental design (Doucet 1997). Soil at the first sub-site was derived from lacustrine clay deposits while the second one originated from glacial till (Doucet 1997).
The third study area is located on the shore of Cartier lake at the Petawawa Research Forest (45°57' N, 77°34' W), in Ontario (Fig. 1). Data and samples from a 17-year-old white spruce plantation were provided by Dr. A.D. Munson (Laval Univ.). This plantation is located in the Middle Ottawa Section ofthe Great Lakes-St-Lawrence Forest
reglOn (Rowe 1972). The mean annual temperature is 4.3°C and mean annual precipitation is 814 mm (Environment Canada 1993). The soil is a deep well-drained loam to sandy-Ioam (Brand and Janas 1988, Périé and Munson 2000). The plantation (2 x 2 m spacing) was established as a replicated 2x2x2 factorial experiment design with scarification, herbicide and fertilizer treatments (Brand and Janas 1988). Fertilizer (17:16:10 NPK plus micronutrients) was applied annually for six years with increasing amounts ranging from 30 to 200g per seedling.
Figure 1: Location of the study sites: Beardmore (A), Lac Duparquet (B), and Petawawa (C).
2. Foliage sampling and analysis
2.1 Tree selection
Different numbers of trees were selected for foliage sampling and growth . measurements, according to the original experimental design in place in each study area.
In Beardmore, three to eight dominant and co-dominant trees with a diameter at breast height corresponding to the mean plot diameter were sampled from 3-4 plots (15 x 15m)
in each of the 18 stands. A total of 94 jack pi ne and 91 black spruce trees were selected for foliage sampling and growth analysis. In Petawawa, five white spruce trees representative of plot average height and diameter were sampled from each of the thirty-two experimental plots (n = 160), hence comprising aIl treatment combinations. At Lac Duparquet, 52 white spruce trees were sampled from eighteen 10 x 10m plots spread across the four selected stands with 85% percent of the trees sampled in the two oldest stands (72 and 125 years old). Sorne of the trees sampled at these sites were suppressed.
2.2 Sampling proto col
Needles were collected on 3-5 shoots from the upper third of the crown of trees with a telescopic pruner (Petawawa and Beardmore), a shotgun (Lac Duparquet) or hand clipper for felled trees (Beardmore). Sampling took place during mid-October 1999 and 2002 at the Beardmore sites, at the end of August 1995 at Lac Duparquet and on October 2_3rd 2002 at Petawawa. Only the CUITent year foliage was harvested at the Beardmore sites, whereas second year needles were also collected at Lac Duparquet and Petawawa.
Twigs and needles were immediately stored in paper bags after harvest. Samples were oven-dried at 65°C for 48h. After drying, needles were detached from twigs and were sieved and sorted to remove dust, debris and dead or damaged needles. Needles were ground to fine powder with a Tecator Cylotec mill when samples had a larger volume than approximately 30mL. Smaller samples
«
30mL) were ground with a Wiley mill using a Imm mesh screen. Samples were digested in H202-H2S04, according to Allen (1989). N and P concentrations were determined colorimetrically on a Lachat Quick Chem Autoanalyzer. An atomic absorption spectrophotometer (Perkin-Elmer model 2380) was used to determine K, Ca, Mg and Mn concentrations. Manganese was not measured on Lac Duparquet sampi es. Therefore, analysis of Mn nutrition in white spruce was performed on data from Petawawa only.3. Growth measurements
At the Beardmore site, trees sampled in 1999 were felled and disks were taken at 1.3m above ground. Tree cores were taken with an increment borer for trees sampled in 2002. In both cases, annual rings were measured with a tree ring increment measurement (TRIM) system, developed by the Ontario Ministry of Natural Resources. It consists of a
microscope mounted on a rnovmg platform connected to a motion detector and a computer. Tree cores were also taken at Lac Duparquet and were analysed with MacDendro™ V6.1D (Régent Instruments 1996). For each tree, basal area growth was ca1culated for the 2 years preceding sampling of foliage. Growth was averaged to obtain the mean annual basal area increment (BAG). Due to the small diameter of sorne trees at the Petawawa site, it was impossible to take cores. Terminal shoot length measurements were used to assess tree growth.
A unique databank for white spruce was created to cover a greater range of nutrient concentrations. Preliminary graphical analysis showed that nutrient concentrations of the Petawawa and Lac Duparquet sites were complementary to each other. Pooling the white spruce data was hence considered appropriate since it would improve the re1iability of the results by increasing the sample size and nutrient ranges.
Therefore, white spruce growth data from the Petawawa and Lac Duparquet sites were separately standardized to a mean of zero and a standard deviation of one. Nutritional data and the new growth variable (standardized growth) from the two sites were then merged and the resulting dataset was used for data analysis.
The presence of two younger jack pine stands with very high growth rates in comparison with oIder jack pi ne stands that were sampled was suspected to have an important effect on the establishment of the boundary-line. Plonski's (1974) normal yie1d tables for jack pine were therefore used to verify whether variations in growth rates were caused by differences in site quality or by age. Based on height growth curves, all stands were c1assified as being of site quality l, except one stand that was c1assified as site quality II. Therefore, a growth index was computed to correct for the negative effect of age on tree growth for this species only. The basal area of each stand was obtained by computing the mean basal area of the three 15 x 15m plots that were used for foliage sampling. Stand basal area (BA, in m2/ha) was used as a measure of stand density to correct for its effect on tree basal area growth. This index was developed according to the work ofVizcayno-Soto (2003), who used the live crown ratio oftrees to account for stand density effects on basal area increment. A modified basal area growth index (BAGI) was computed as follows:
BAGI = BAG
*
BA [1]4. Computation of multivariate nutrient ratios
Multivariate nutrient ratios or CND scores were calculated from nutrient concentrations following Parent and Dafir (1992) methodology, as described in the introduction of this thesis. For black spruce and jack pine, a first set of CND scores was generated from the nutrient concentration data (N, P, K, Ca, Mg and Mn). These CND scores are thereafter referred to as CND6 in the following chapters of this thesis. As many earlier nutritional studies only measured foliar concentrations of the five macronutrients (N, P, K, Ca and Mg), a second set of CND scores was calculated without Mn to allow a broader applicability of the results. The latter scores will be referred to as CNDs in the following chapters of this thesis. Since Mn values were lacking for part of the white spruce databank, CND scores were calculated with the five macronutrients only.
5. Data analysis
5.1 The boundary line approach
A boundary line approach (Webb 1972) was used in combination with 2nd degree polynomial regression equations (Eq. 2) in order to estimate the growth-nutrition relationships when exempted from the negative effect of environmental factors other than nutrient supply.
y = ax2 + bx + C [2]
The methodology used in the present study has been modified from Vizcayno-Soto and Côté (2004) to provide greater reliability of the nutritional standards produced. Foliar nutrient concentrations and CND scores were used as independent variables (x) and tree growth (BAG, BAGI or standardized growth) as the dependent variable (Y). Scatterplots for each nutrient were drawn for preliminary examination (Fig. 2a). Data was then screened in order to identify possible outliers that could affect the analysis. Observations that met criterion [3] were rejected.
IfBAGi> BAG + 3.29*SDBAG [3]
Where BAGi is the basal area growth of the ith observation, and BAG and SDBAG are the mean and standard deviation of basal area growth of sampled trees. This criterion was included to remove (one-tail t-test) the most extreme 0.05% ofthe observations. This step was made necessary to exclude trees with such high growth rates that they may be
suspected of having benefited from outstanding environrnental conditions or were the result of manipulation errors. This is the justification for the use of a very small probability level.
Trees with maXImum growth were selected over the entire range of the independent variables. To achieve the selection, the range was divided into Il intervals with the first interval being centered on the smallest observed nutrient concentration or CND score. The tree with the highest growth in each interval was selected for regression analysis (Fig. 2b). The latter operation was perforrned with a computerized routine written in Q-basic and operating in MS-DOS. The routine is presented in Fig Al. The number of selected boundary points represent a compromise between the need to have enough observations to obtain significant regression models and to minimize the probability of selecting trees that grow at sub-optimum rates, which would reduce the accuracy of the models. Centering the first interval around the smallest concentration value was required to increase the selection efficiency at the lowest and highest part of the range because extreme values have a very significant effect on the slope of the boundary line. Boundary points that were located outside the interval bounded by the mean nutrient concentration or CND score plus or minus its standard deviation were excluded from further analysis (Fig. 2c). This step was perforrned to avoid the selection of boundary points from the lowest and highest part of the range of nutrient concentrations or ratios where the number of observations was low. This operation excluded on average 2-3 boundary points.
Then, a preliminary quadratic regression was computed with the boundary points (Fig 2d). In sorne cases, sorne of the selected trees fit poody the theoretical growthlnutrient model and were suspected to be growing at sub-optimum rates or to have a growth rate that was unusually high in comparison with their neighboring boundary points. The presence of trees with sub-optimum growth is a result of the limited sarnple size since optimum growth is rare1y observed. However, the presence of trees with unusually high growth is likely a result of manipulation error. In order to eliminate such trees from further analysis, the criteria developed by Vizcayno-Soto and Côté (2004) were applied. Trees with a high growth rate and for which equation [5] is true were removed from further analysis.
[4]
Where Y, Y-l and Y+l are respectively the growth rates of the fast growing tree and ofits immediate neighbors. Similarly, trees with sub-optimum growth and for which equation [5] is true were rejected (Fig 2d). Equation [5] was modified from Vizcayno-Soto and Côté (2004) to make the criterion less restrictive.
y < Y-l and Y+l and Y / [(Y-l + Y+d/2] < 90% [5]
However, we frequently observed the presence of 2 to 3 consecutive sub-optimum trees and criterion [5] was modified to account for these cases:
y < Yon and Y+n and Y / [(Y-n + Y+n)l2] < 90% [6]
Where n = 1, 2. Thus, Yon and Y+n are the non-immediate neighbors of trees with sub-optimum growth. This allowed the rejection of consecutive trees showing sub-sub-optimum growth rates. Finally, a 2nd degree polynomial regression was fitted on the remaining boundary points (Fig. 2e). Statistical analysis was performed with STATISTICATM v. 4.1 (StatSoft 1994). Regressions were considered to be statistically significant if both the model and the quadratic coefficient had a probability level below 0.1.
5.2 Determination of nutritional standards
Statistically significant regression mode1s were kept for further analysis.
Calculation of optimum nutritional ranges was not performed in cases where models had to be extrapolated to determine the ranges. Critical and toxicity levels were accepted only if predicted growth at the boundary line minima was equal to or lower than 80% of the predicted maximum growth rate (Fig 2f). Failure to observe a 20% decrease in growth on the deficiency side of a curve also prevented the calculation of a nutritional optimum.
However, failure to observe a growth decrease of 20% on the toxicity si de of a curve did not prevent the calculation of critical and optimum concentrations of CND scores.
Optimum nutrient concentrations or CND scores were computed by solving the first derivative of the quadratic regression when it is set to zero:
Optimum = -b / 2a [7]
Optimum nutritional ranges, corresponding to the two points of the curve where growth is 90% of its maximum value were also calculated. This was done by solving the regression equation for:
y = 0.9
*
max. growth [8]and finding the two nutrient concentrations or CND scores values that can solve this equation using:
-b ± -V (b2 - 4ac) /2a
for which a, band c are the coefficients of the quadratic equation.
6. Nutrient diagnosis
[9]
Foliar nutrient concentration data for the three species under study were gleaned from published articles and governmental reports to establish nutrient diagnoses of forest stands across Canada. Data was selected only if the sampling proto col was similar to ours with needles being sampled from the upper crown and in autumn. For studies reporting nutrient concentrations for multiple years, the mean concentration was computed and used. In cases where data was reported from fertilization studies, only control plots were used. Studies reporting concentrations for at least N, P, K, Ca and Mg were kept for nutrient diagnosis to enable CND score calculation. In a few cases, data was derived from graphs. Data from each stand is presented in table Al. The sites are also identified on maps (Figs. A2, A3, A4).
Nutrient diagnosis based on nutrient concentration data was performed according to the CV A approach, i.e. by comparing the data of each site against the computed nutritional standards. Nutrient indices computed from CND scores were used to identify nutritional imbalances. In addition, nutrient indices were ranked to determine the order of nutrient limitations. The use of a regression approach rather than a conventional high yielding sub-population to determine nutritional optima prevented the calculation of standard deviation values for the optimum nutritional ranges. The methodology used for CND index calculation was therefore modified from Parent and Dafir (1992), CND as follows:
IN = (VNj - VN*) / 1 VN* - VNcrit 1 [la]
Where VNj is the CND score for N for a given tree or stand and VN* is the optimum CND score for nitrogen. The term VNcrit represents the CND score at the lower limit of the optimum range. The denominator of equation [la] is a measure of the width of the optimum CND range, therefore replacing the standard deviation term of Parent and Dafir (1992) equation. This modified formula is comparable to the original one, as it transforms
CND scores into index values centered on zero. Moreover, values comprised between -1 and 1 are corresponding to the optimum nutrition range. An index value smaller than -1 denotes deficiency whereas an index values greater than + 1 is identified as an excess of a given nutrient. These indices represent the deviation of a tree or stand from the optimum CND scores.
Figure 2: Use of the boundary hne approach on nitrogen concentrations from white spruce current year needles.
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