Lodgepole pine growth as a function of competition and canopy light environment within aspen dominated mixedwoods of central interior British Columbia

Forest Ecology and Management 257 (2009) 1829–1838

Contents lists available at ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Lodgepole pine growth as a function of competition and canopy light environment within aspen dominated mixedwoods of central interior British Columbia George Harper a,*, Meghan O’Neill b, Peter Fielder a, Teresa Newsome c, Craig DeLong d a

Research Branch, B.C. Ministry of Forests and Range, PO Box 9519 Stn Prov Govt, Victoria, BC, Canada V8W 9C2 Department of Biology, University of Victoria, Victoria, BC, Canada c Southern Interior Forest Region, B.C. Ministry of Forests and Range, 200-640 Borland Street, Williams Lake, BC, Canada V2G4T1 d Northern Interior Forest Region, B.C. Ministry of Forests and Range, 5th Fl 1011 4th Ave., Prince George, BC, Canada V2L3H9 b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 October 2008 Received in revised form 13 January 2009 Accepted 1 February 2009

Three lodgepole pine and aspen mixedwood sites located in the central interior of British Columbia within the Sub Boreal Spruce (SBS) biogeoclimatic zone were chosen to study the neighbourhood aspen competition and canopy light environment of 14–19-year-old lodgepole pine. All three sites had previously been established as separate research trials designed to explore various silviculture options for controlling aspen competition (aspen brushing, herbicide, thinning and untreated areas). For each site, 33–36 pine trees were selected to represent the observed range of light regimes under the influence of various aspen competition levels. At each sample pine, competition and stand measurements were made and a series of vertical canopy light measurements from the top to the base of the live crown. After an evaluation of a variety of competition indices, the index DRD; sum of the ratio of each of the three nearest neighbour’s DBH to the subject pine divided by their distance and, amount of available light at the top of the crown (DIFNt) were found as the best overall predictors of pine stem volume growth. A site specific exponential relationship of relative pine stem volume growth to DRD was found and minimum growth response competition thresholds were determined, which could provide useful targets where maximizing pine volume is intended. Evaluation of both linear and non-linear models of DIFNt versus height growth indicated the response to be linear across the observed range of available light. Implications for management are discussed. Crown Copyright ß 2009 Published by Elsevier B.V. All rights reserved.

Keywords: Lodgepole pine Trembling aspen Sub-boreal mixedwoods Competition indices Light

1. Introduction In British Columbia, lodgepole pine (Pinus contorta Dougl.) and trembling aspen (Populus tremuloides Michx.) are commonly found in mixtures within young seral stands of the sub-boreal and boreal landscape (Meidinger and Pojar, 1991). Both species have rapid juvenile growth rates however aspen usually overtops lodgepole pine and other conifers such as white spruce (Picea glauca Moench) in this region. Overtopping aspen can influence the growing condition of understorey trees through impacts on light, air and soil temperature, soil moisture and nutrients (Comeau, 2001; McKinnon and Kayahara, 2003; Voicu and Comeau, 2006). Within pine plantations, aspen can be a significant competitor for light, water and nutrients when found at high densities (Newsome et al., 2003). Predicting and managing the impact of aspen competition on pine survival and growth continues to be a

* Corresponding author. Tel.: +1 250 387 8904; fax: +1 250 387 0046. E-mail address: [email protected] (G. Harper).

forest management concern (DeLong, 2007; Newsome et al., 2008). Quantifying pine growth response to changes in light environment has been of particular interest. Describing the light environment is considered crucial to the understanding of forest stand dynamics and individual tree performance (Wright et al., 1998; Comeau, 2001). The light environment has been recognized as one of the key determinants of understorey growth and survival (Kaufmann and Ryan, 1986; Chen et al., 1996; Williams et al., 1999) and much research has focused on understanding and predicting light levels in relation to boreal mixedwood regeneration dynamics and understorey growth (Comeau et al., 1998; Greene et al., 1999; Lieffers et al., 1999, 2002; Messier et al., 1999; Pinno et al., 2001). Several spatial light models have been developed to simulate the light environment within stands (Brunner, 1998; Comeau et al., 1998; Canham et al., 1999; Stadt and Lieffers, 2000; Groot, 2004). Few published studies document the response of lodgepole pine to the range of light environments within aspen-pine mixedwoods (Landhausser and Lieffers, 2001; Claveau et al., 2002, 2005). Most published studies containing pine light and growth information are from young trees <7 m tall found within conifer stands (Eis,

0378-1127/$ – see front matter . Crown Copyright ß 2009 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2009.02.005

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1830

1970; Chen et al., 1996; Kayahara et al., 1996; Wright et al., 1998, 2000; Coates and Burton, 1999; Williams et al., 1999; Burton, 2002; Vyse et al., 2006) or, are from artificial shading or green house experiments (Shirley, 1945; Logan, 1966; Hoddinott and Scott, 1996; Reich et al., 1998; Day et al., 2005). In general the literature indicates lodgepole pine height and diameter growth increases with increasing light availability. Lodgepole pine responds to competition by allocating biomass preferentially to the stem, specifically terminal shoot elongation at the expense of branch and needle growth (Claveau et al., 2002). Lodgepole pine’s vertically oriented crown, high rate of branch mortality and well spaced branches have been found to reduce self-shading (Williams et al., 1999). Also, a decrease in biomass and leaf area with increasing competition levels has been noted (Landhausser and Lieffers, 2001; Claveau et al., 2005; Johnstone, 2005) as has increased allocations to roots under high competition levels (Day et al., 2005). Together, these characteristics provide lodgepole pine with a competitive advantage in dry environments since greater root growth will increase water uptake and minimizing needle growth and leaf area will decrease transpiration water loss (Eis et al., 1982; Williams et al., 1999). Lodgepole pine diameter growth has been found to respond to reductions in competition levels more quickly than height growth (MacIsaac and Navratril, 1996; Wright et al., 1998; Coates, 2000; Newsome et al., 2003). This is apparently due to the high priority of height growth over diameter growth (Chen et al., 1996; MacIsaac and Navratril, 1996; Williams et al., 1999; Claveau et al., 2002). Consequently, lower competition levels have been found to influence diameter increment more than height increment (Coates, 2000; Newsome et al., 2003; Strong and Sidhu, 2005). In this paper, we present the results of a retrospective study of interspecific competition between pine and aspen found in young mixedwood stands of the Sub Boreal Spruce (SBS) zone of British Columbia (Meidinger and Pojar, 1991). Our main objective was to predict pine growth relative to neighbourhood competition and light environment. Our purpose is to establish the level of mixedwood competition required to impact pine height, diameter, crown dimensions and volume growth in several older pine plantations and use this to develop simple field assessment tools to quantify aspen competition levels and the impact on pine growth within the SBS. 2. Methods Three sites were selected, all located within the SBS biogeoclimatic zone of the central interior of British Columbia (Table 1). The

sites, McKinley Lake, Teardrop, and Tyee Lake were chosen to represent pine plantations 15–20 years of age, in an advanced state of mixedwood competition beyond the seedling establishment phase. This age class also represents the period when the legal obligation to establish a free growing stand must be completed (British Columbia Ministry of Forests and Range, 2008). All three sites had previously been established as separate research trials designed to explore various silviculture options for controlling aspen competition (Newsome et al., 2004, 2006a; DeLong, 2007) and have also been used to evaluate understorey light within aspen stands (Comeau et al., 2006). At each site, a wide range of aspen-pine mixedwood stand conditions existed with aspen density ranging from 0 to 24,800 trees per hectare (tph) and conifer density from 200 to 5600 tph (Table 2). Pine composed approximately 95% of the conifer component with scattered individuals of hybrid white spruce (Picea glauca [Moench] Voss  engelmannii Parry ex Engelm.) and subalpine fir (Abies lasiocarpa [Hook.] Nutt.). Also, in addition to aspen, there were minor occurrences of paper birch (Betula papyrifera Marsh.) and black cottonwood (Populus balsamifera ssp. trichocarpa [T.& G.] Brayshaw). At the McKinley Lake site, aspen thinning treatments had resulted in a range of managed aspen densities from 0 to 2800 stems per hectare (tph) as well as retained untreated areas. At Teardrop, the impact of aspen regeneration on planted pine and spruce was assessed within untreated and glyphosate herbicide (hack and squirt method prior to logging) treated areas. At Tyee Lake, manual cutting of aspen within 50 or 100 cm radii around lodgepole pine was applied as well as broadcast removal treatments and control areas with no removal. During the spring of 2006 at each of the three sites, 33–36 lodgepole pine trees free from damage and disease were selected from the previously studied pine populations using regression sampling (Demaerschalk and Kozak, 1974) which called for uniform sampling across the observed range of light and aspen competition at each site. Time and resources limited the sample size. Pine selection included suppressed and co-dominant as well as dominant trees. At each sample pine, stand level assessments were made within a 3.99 m radius (50 m2), tree centred plot. Diameter at breast height (1.3 m) (DBH), distance and azimuth of the nearest three neighbours (>1.3 m in height) were measured as well as sample pine tree crown class (Walmsley et al., 1980), DBH (assessed in early May and late August), total height, height increments for 2005 and 2006, crown width (E–W, N–S) and height to lowest live branch (bottom of live crown). At every 5th pine sample tree (random start), the DBH of all trees found within a

Table 1 Research site information. Site

Lat (N) Long (W)

Biogeoclimatic subzone/elevation

Silviculture

Aspen treatments

McKinley Lakea

528130 N 1208560 W

SBSdw1 1000 m

Clearcut 1988, pine natural regeneration

Teardropa

548250 N 1238280 W

SBSmk1 760 m

Clearcut 1986, pine planted 1988

Tyee Lakea

528 230 1228 10

SBSdw2 885 m

Clearcut 1980, brushed 1993, pine planted 1994

Thinning during 1999: (1) 0 tph, (2) 500–800 tph, (3) 1000–1500 tph, (4) 2000–2800 sph, (5) untreated control (Newsome et al., 2006a). Treatment plot size approximately 50 m  50 m for a total treatment area of 4.5 ha. Hack and squirt with glyphosate 1 year prior to harvest and untreated control (DeLong, 2007). Treatment areas approximately 25 ha each for a total treatment area of about 50 ha. During 1994–2002: (1) 50 cm radius vegetation removal, (2) 100 cm radius removal, (3) all vegetation removal, (4) untreated control (Newsome et al., 2004). Treatment plot size approximately 30 m  30 m including buffers for a total treatment area of about 1.2 ha.

a

Lodgepole pine age in 2006: McKinley Lake = 18 years, Teardrop = 19 years, Tyee Lake = 14 years.

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1831

Table 2 Pine neighbourhood statistics for aspen density and size, nearest neighbour size and distance, and canopy light (DIFN) by site. Site

n

Aspen density (trees/ha)

Conifer density (trees/ha)

Aspen DBH (cm)

Aspen height (cm)

Neighboura distance (cm)

Neighboura DBH (cm)

Neighbour DBH ratiob

DIFNbc

DIFNmd

DIFNte

McKinley Lake

37

2665  3134f 400–14,400g

2081  1079 200–5600

5.17  2.66 0.3–11.8

845.45  252.89 168–1260

138.7  55.9 22.0–333.0

5.82  1.82 0.6–12.5

0.78  0.32 0.10–2.12

0.41  0.118 0.19–0.67

0.55  0.197 0.20–0.93

0.76  0.166 0.40–1.0

Teardrop

34

10,241  5072 2200–23,600

1700  755 400–4200

4.24  2.34 0.5–12.4

815.84  311.89 200–1375

87.52  36.93 20.0–233.0

4.02  1.63 0.8–12.5

0.45  0.24 0.07–1.95

0.18  0.065 0.06–0.35

0.34  0.177 0.09–0.74

0.58  0.241 0.16–0.97

Tyee Lake

36

12,300  5951 0–24,800

2122  1245 400–5600

2.46  1.46 0.1–7.6

438.77  169.79 150–825

103.98  58.75 15.0–305.0

2.95  1.32 0.5–8.7

0.59  0.32 0.085–2.09

0.47  0.181 0.17–0.92

0.67  0.223 0.28–0.99

0.86  0.185 0.36–1.0

a b c d e f g

Based on nearest three neighbours to sample pine 1.37 m tall. Ratio of nearest neighbour DBH (cm)/sample pine DBH (cm). Transmittance at base of live crown position. Transmittance at mid-crown position. Transmittance at top of crown. Mean and standard deviation. Range.

3.99 m radius plot were recorded and for every 10th stem within the plot, total height, height to live crown, tree crown class, and crown width were recorded. Light measurements were made using a LiCor LAI 2000 Plant Canopy Analyzer (LiCor Inc., Lincoln, Nebraska) fitted with a 1808 view restrictor. Readings were made to the east and to the west to cover the full 3608 view, and each direction was timed to avoid the direct sun. Open-sky readings were also recorded using a second instrument located in a nearby opening. Prior to starting measurements, the LAI 2000 units were calibrated to each other in the open and their clocks synchronized to the nearest second (Gower and Norman, 1991). FV2000 software (LiCor Inc., Lincoln, Nebraska) was used to determine diffuse non-interceptance (DIFN) values. As per Comeau et al. (1998, 2006), readings from the outer rings (4 and 5) of the LAI 2000 sensor were not included in the DIFN determination. At each sample pine, a series of light measurements were made to record the light condition vertically within the canopy. LAI 2000 measurements were taken at various vertical canopy positions relative to each pine tree live crown (near crown edge); base, middle, 3/4 crown and top through attaching the LAI 2000 sensor to an extension cable and an adjustable aluminium measurement pole. Data analysis was completed using SAS version 9.1 (SAS Institute Inc. Cary, NC). Statistical significance was based on P < 0.05. Model fitting and regression analysis followed that of Sit and Poulin-Costello (1994). Appropriate intrinsically linear model transformations were used and adjusted R2 = 1  [(n  1)/(n  p)] SSE/SSTC (R2adj ) was provided for all non-linear equations (Cornell and Berger, 1987). Residual plots were assessed visually for each

fitted equation and models were evaluated using root mean squared error (RMSE) and R2adj . A mixed models approach (Ott, 1997) was used to determine if data from individual sites could be pooled (i.e. besides the continuous regressor, two random effects were added to account for site and the site-by-regressor interaction). A variety of competition indices (CI’s) were calculated to explore the impact of neighbourhood competition on pine growth (Table 3) including aspen density (TPH) (Newsome et al., 2006b), four measures of CI using the nearest three neighbourhood competitors (all tree species >1.3 m height); DBH sum (DBHS), sum of DBH distance ratio (DDR) (Canham et al., 2004), Lorimer competition index (LCI) (Lorimer, 1983), and a version of LCI using only the largest competitor (MLCI) (Navratril and MacIsaac, 1993). The DBH ratio of the competitor to the sample pine weighted by distance was also assessed (DRD) (Daniels, 1976). This CI is very similar to Hegyi’s Index (Hegyi, 1974) which is a commonly assessed distance-dependent competition measure (Biging and Dobbertin, 1995) also known as a distance-weighted size ratio index (Tome and Burkhart, 1989). Also, the various vertical canopy transmittance measurements of DIFN including light transmittance at the top (DIFNt), middle (DIFNm) and base of the live crown (DIFNb) were correlated with pine growth parameters. Correlations between CI’s, vertical canopy DIFN levels and pine growth were examined using linear and non-linear regression following transformations of the dependent and independent variables, as appropriate. Stem volume increment (stem volume August 2006 stem  volume May 2006 where stem volume = p(DBH/ 2)2  height/3 (volume of a cone) was calculated to combine the DBH and height growth response variables.

Table 3 The selected competition indices tested. Competition index

Formula

Value

TPH (total density) DBHS (DBH sum)a DDR (DBH distance ratio)a LCI (Lorimer competition index)a MLCI (modified LCI) DRDa DD DIFNt (diffuse non-interceptance light) DIFNm DIFNb

Number/plot  per hectare multipler S DBHjb S (DBHj/Dijc) S (DBHj/DBHid) Largest tree DBHj/DBHi S [(DBHj/DBHi)/Dij] Largest tree DBHj/Dij Transmittance at top of crown Transmittance at mid-crown position Transmittance at base of live crown position

Trees/ha (tph) (cm) (cm/cm) (cm/cm) Largest DBH tree selected (cm/cm) Diameter ratio divided by distance (cm/cm/cm) Largest DBH tree selected (cm/cm) Average of east and west crown measurements (growing season fractional transmittance as a proportion of open-sky light)

a b c d

Based on nearest three trees to sample pine. DBHj = competitor diameter at breast height (DBH) (cm). Dij = distance between sample pine and competitor. DBHi = diameter breast height of sample pine.

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1832 Table 4 Pine growth parameters by site for 2006. Site

n

DBH (cm)

Height (cm)

HDRa

Diameter increment (cm/year)

Height increment (cm/year)b

Crown width (cm)

Height to base of crown (cm)

McKinley Lake

37

7.9  2.1c 4.8–11.9d

631.2  106.6 440–900

83.2  17.4 50.3–126.0

0.27  0.12 0–0.5

56.3  13.5 31.5–86.5

126.7  31.2 78.0–195.8

137  42.4 64.0–246.0

Teardrop

34

10.3  3.2 4.0–16.2

924.8  197.9 454–1359

94.6  21.3 59.7–150

0.18  0.18 0–0.9

37.3  15.4 9.0–76.0

108.3  34.3 57.5–185.0

249  85.2 80.0–405.0

Tyee Lake

36

5.6  1.8 1.8–9.2

464.8  90.7 250–642

88.3  19.2 60.1–138.9

0.23  0.15 0.2–0.5

56.8  13.7 23.5–86.0

92.7  27.2 37.0–146.5

138.2  27.5 90.0–220.0

a b c d

Height/DBH ratio (cm/cm). Average leader for years 2005 and 2006. Mean and standard deviation. Range.

3. Results Aspen and pine average tree height, DBH, crown width and height to the base of the live crown are provided in Tables 2 and 4 along with the range of DIFN found at the crown assessment heights. At Teardrop several pine exceeded the LAI 2000 maximum measurement height of 9 meters and therefore the number of data points for DIFNt were reduced. All three sites differed in their average aspen density, aspen DBH and height (Table 2). McKinley Lake aspen were on average less dense and of larger DBH than aspen at Teardrop and Tyee Lake. The maximum aspen density found at McKinley Lake was approximately 10,000 tph less than the other two sites. The height/DBH ratio (HDR), a relative measure of tree slenderness related to growth factors such as light availability and competition, indicated a similar range across the three sites (Table 4). However, as shown in Fig. 1, McKinley Lake HDR values are clustered below a density of 6000 tph (only 4 trees were found to have a total density >6000 tph, whereas Teardrop and Tyee Lake had 28 and 31, respectively). HDR plotted over LCI presents a different picture of McKinley Lake competition indicating no clustering and a full range of LCI values (data not shown). The three nearest neighbours were found to be predominantly aspen however, at McKinley Lake and Tyee Lake several pine (6 and 4, respectively) were found to have no aspen recorded as the nearest three neighbours. Pine stem volume annual increment (STV) which integrates both DBH increment and height growth, was used to explore the pine growth response. Linear regressions predicting the natural log transform of STV (LSTV) from various CI’s were significant

Fig. 1. Observed height to diameter ratio (HDR) over stand density (tph) for the sample pine located at the three SBS zone sites.

(Table 5a). The log transform of STV was necessary to create uniform variance across the range of competition data. No significant relationship was found between the LSTV and the DBH sum of the nearest three competitors (DBHS) (site P values  0.3105, results not shown). By incorporating competitor distance resulting in DDR, the relationship with LSTV was significantly improved (based on R2adj and RMSE). However, across all three sites, DRD was found the best predictor of LSTV with DIFNt and LCI almost as good. Modifying LCI through incorporation of nearest neighbour distance (DRD, Table 3) improved the linear predictions for Teardrop and Tyee Lake but not McKinley Lake. Total aspen density (TPH) and largest competitor DBH/distance ratio (DD) were found to be the poorest of the significant CI models (results not shown). Fig. 2 presents the observed values and fitted models of pine LSTV over DIFNt. The mixed model results indicated, except for DIFNb, the individual site model parameters were significantly different (Table 5a). The relationship of relative stem volume increment (RSTV, relative to maximum observed per site) to DRD was explored using non-linear regression to describe the growth impact of increasing mixedwood competition. The exponential model similar to that used by Simard et al. (2005) was found to best describe the relationship (Fig. 3 and Table 5b). Following Wagner et al. (1989) we approximated a minimum tree growth response competition threshold by site, based on 20% of the maximum observed DRD level (Wagner, 2000; Simard et al., 2005). This DRD level was determined to be 0.02, 0.022 and 0.028 for McKinley Lake, Teardrop and Tyee Lake, respectively. The results of simple linear regression of the various pine growth variables against DIFNt are presented in Table 6. As expected, pine DBH, total height, height increment (HINC), relative height growth (RHINC, where RHINC = HINC/maximum observed HINC at each site), percent live crown ratio (LCR) and crown width (CW) significantly increased with increasing DIFNt at all sites (DBH and total height results not shown). However, linear regression results indicated DBH increment (DBHINC) and HDR did not increase with DIFNt at McKinley Lake. Mixed model ANOVA results supported all-sites model development for HINC, RHINC, DBHINC and CW suggesting no significant difference exists between sites for the growth variables linear relationships across DIFNt. In an attempt to improve on the linear models as well as provide results comparable with other published work, a variety of nonlinear models were fit over percent DIFNt for the combined all site data for the HINC, RHINC, DBHINC (Table 7) and CW variables. Except for DBHINC, there was no apparent improvement in model prediction over the simple linear model, based on R2adj and RMSE. For DBHINC, the logistic, Michaelis-Menton type 2, and Gompertz functions marginally improved the prediction from an R2adj of 0.10 for the linear to 0.17 for the non-linear models. The MichaelisMenton type 1 and 2 models were selected for results comparison

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1833

Table 5a Regression parameters for the linear model ln(STV) = a + b CI, where STV = pine stem volume increment (cm3/year) and CI = various competition indices. Mixed model ANOVA was used to test for CI, Site and CI  Site differences. CIa

Main model Site

Mixed model R2adj

n

a

b

P value

RMSE

Source of variation

F

P value

DDR

McKinley Lake Teardrop Tyee Lake

33 34 36

0.16 0.25 0.16

7.7922 8.3558 7.0125

3.0448 6.7912 5.0828

0.0125 0.0016 0.0102

0.6115 1.0885 0.9589

DDR Site DDR  Site

25.22 5.79 1.24

<0.0001 0.0042 0.2939

LCI

McKinley Lake Teardrop Tyee Lake

33 34 36

0.33 0.45 0.41

8.2390 8.7645 7.6907

0.4027 1.1613 0.7050

0.0003 <0.0001 <0.0001

0.5454 0.9320 0.8026

LCI Site LCI  Site

71.26 3.79 5.38

<0.0001 0.0261 0.0061

MLCI

McKinley Lake Teardrop Tyee Lake

33 34 36

0.27 0.38 0.50

8.1406 8.5636 7.8076

0.7775 2.1316 1.7130

0.0011 <0.0001 <0.0001

0.5682 0.9869 0.7406

MLCI Site MLCI  Site

67.54 1.96 4.65

<0.0001 0.1468 0.0118

DRD

McKinley Lake Teardrop Tyee Lake

33 34 36

0.32 0.50 0.60

7.7993 8.1386 7.1232

21.4755 43.7564 25.8148

0.0004 <0.0001 <0.0001

0.5509 0.885 0.6635

DRD Site DRD  Site

83.05 9.83 3.92

<0.0001 0.0001 0.0230

DIFNt

McKinley Lake Teardrop Tyee Lake

33 28b 36

0.27 0.50 0.50

5.6500 4.9236 2.9567

2.1616 3.7369 4.0332

0.0012 <0.0001 <0.0001

0.5706 0.9062 0.7409

DIFNl Site DIFNl  Site

69.49 5.61 1.82

<0.0001 0.0051 0.1675

DIFNm

McKinley Lake Teardrop Tyee Lake

33 34 36

0.08 0.35 0.26

6.6801 5.7831 4.7795

1.1197 4.2798 2.4898

0.0595 0.0002 0.0009

0.6390 1.0163 0.8963

DIFNm Site DIFNm  Site

35.23 4.33 3.75

<0.0001 0.0158 0.0270

DIFNb

McKinley Lake Teardrop Tyee Lake All sites

33 34 36 103

0.03 0.10 0.09 0.01

7.1000 6.0339 5.5294 6.9582

0.4706 6.8148 1.9366 0.0501

0.6457 0.0401 0.0447 0.9330

0.6749 1.1922 0.9963 1.0918

DIFNb Site DIFNb  Site

8.61 2.04 2.23

0.0042 0.1353 0.1133

a b

See Table 3 for CI descriptions. At Teardrop, six pine did not contain DIFNt values since their height exceed the maximum possible LAI 2000 measurement height.

with previously published light-growth data modelling attempts (Wright et al., 1998; Coates and Burton, 1999) (Fig. 4). The results of a linear model relating DIFNt, DIFNm and DIFNb to the logarithm of DRD, total stand density and basal area are shown in Table 8 (power and exponential functions were also assessed but did not improve fit). Site data was not combined based on mixed ANOVA results (P values < 0.05). At the Teardrop and Tyee Lake sites, a significant correlation between available light at the top of the crown, mid-crown or base of crown and DRD was evident (R2adj = 0.18 to 0.59). However, at McKinley Lake DIFN at all canopy locations was poorly correlated with DRD, indicating no significant change in DIFN with increasing DRD (P > 0.075, R2adj  0.07). This result was evidence of a significantly different competition and light neighbourhood found at McKinley Lake compared to the other sites. The logarithmic regression model was also found to satisfactorily describe the significant increase in top, mid and base

Fig. 2. Observed values and fitted linear regression lines (Table 5a) of the log transform of stem volume increment (2006) for the three sites with increasing DIFNt.

Fig. 3. Pine relative stem volume increment (RSTV, relative to maximum stem volume increment per site) over the DRD competition index for the three sites. Lines illustrate the fitted exponential models describing each site relationship where RSTV = aebDRD + c (Table 5b).

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1834

Table 5b Equation coefficients for the fitted exponential model describing each site relationship where RSTV = aebDRD + c where RSTV is the stem volume annual increment/maximum stem volume increment per site (P values < 0.0001). Site

n

a

b

c

R2adj

RMSEa

McKinley Lake Teardrop Tyee Lake

33 34 36

0.8812 (0.2151)b 0.7603 (0.1805) 0.8767 (0.2490)

89.6281 (37.3685) 99.1375 (40.8493) 92.5457 (41.5706)

0.1378 (0.0738) 0.0277 (0.0719) 0.1543 (0.0750)

0.50 0.45 0.44

0.1619 0.1758 0.1937

a b

Root mean squared error. Parameter standard error in parenthesis.

Table 6 Regression parameters for the linear model Y = a + b DIFNt. Mixed model ANOVA was used to test for DIFNt, Site and DIFNt  Site differences. Y variable

Main model

Mixed model

Site

n

R2adj

DBHINCa

McKinley Lake Teardrop Tyee Lake All sites

33 28 36 97

0.03 0.23 0.12 0.10

HINCb

McKinley Lake Teardrop Tyee Lake All sites

33 28 36 97

0.16 0.36 0.17 0.38

RHINCc

McKinley Lake Teardrop Tyee Lake All sites

33 28 36 97

LCRd

McKinley Lake Teardrop Tyee Lake

CWe

HDRf

a b c d e f

a

b

0.1885 0.04982 0.04130 0.01885

P value

RMSE

Source of variation

F

P value

0.083 0.3853 0.3118 0.2696

0.7366 0.0061 0.0231 0.0012

0.2298 0.1656 0.1431 0.182

DIFNt Site DIFNt  Site

7.05 0.97 0.80

0.0093 0.3824 0.4536

29.7423 14.3112 28.6841 15.7543

35.9818 35.0292 32.5575 46.5731

0.0125 0.0004 0.0073 <0.0001

12.7471 11.1634 12.4518 13.4224

DIFNt Site DIFNt  Site

27.95 1.31 0.02

<0.0001 0.2751 0.9771

0.16 0.36 0.17 0.37

0.3438 0.1884 0.3335 0.2217

0.416 0.4608 0.3786 0.5084

0.0125 0.0004 0.0073 <0.0001

0.1474 0.147 0.1448 0.1512

DIFNt Site DIFNt  Site

28.56 0.93 0.11

<0.0001 0.3995 0.8951

33 28 36

0.24 0.28 0.41

61.4276 61.4361 39.5770

21.9227 19.3143 34.0572

0.0024 0.0021 <0.0001

6.2356 7.2633 7.4391

DIFNt Site DIFNt  Site

44.89 5.89 1.62

<0.0001 0.0039 0.2030

McKinley Lake Teardrop Tyee Lake All sites

33 28 36 97

0.10 0.29 0.33 0.10

77.9066 59.8695 17.7381 73.0466

64.5009 84.3353 86.9146 48.1015

0.0437 0.0018 0.0002 0.0013

28.8281 31.0845 22.3367 32.5603

DIFNt Site DIFNt  Site

28.92 2.01 0.20

<0.0001 0.1401 0.8185

McKinley Lake Teardrop Tyee Lake

33 28 36

0.01 0.32 0.47

92.7035 125.2626 150.7412

14.1699 54.3952 72.3231

0.4157 0.0010 <0.0001

16.144 18.8871 13.9377

DIFNt Site DIFNt  Site

29.25 4.81 3.34

<0.0001 0.0103 0.0398

Average diameter increment (2005 to 2006) measured at 1.3 m breast height (cm). Average height increment over 2006 and 2005 (cm). Average height increment over 2006 and 2005 (cm)/maximum average height increment (cm) by site. Live crown ratio = (live crown length/tree height)  100. Average crown width (cm). Height/DBH ratio (cm/cm).

of crown DIFN levels with decreasing stand density and basal area at all sites (all results not shown, P < 0.05). The fit of DIFNb against the log of stand basal area was strong (R2adj = 0.61) albeit for a limited number of samples (n = 7 per site). Mixed ANOVA model results indicated all sites could be combined. Fig. 5 provides a comparison of the observed and fitted data with the Comeau et al. (2006) model for the SBS zone (based on a canopy DIFN measurement at 1.5 m height within pure aspen stands). The scatter plot of DRD versus total density provided in Fig. 6 suggests the level of competition (as defined by DRD > 0.02) are positively correlated with stand density for Tyee Lake and Teardrop (P = 0.0164 and 0.0012, respectively). However at the McKinley Lake site, DRD is not correlated with density (P = 0.7811) and high DRD levels occurred in a clumped distribution at lower stand densities than at the other two sites likely due to the impact of aspen thinning treatments on aspen size. 4. Discussion 4.1. The competition neighbourhood At the three SBS sites, the variety of aspen management treatments (thinning, herbicide and brushing) provided a variety of

stand conditions from which to select lodgepole pine for light and competition environment study. An assessment of the various CI’s (Table 3) that were used suggested annual pine stem volume growth (LSTV) was, in general, predicted best by DRD or DIFNt (R2adj between 0.27 and 0.60). However, the results varied by site suggesting that both available light at the top of the crown (DIFNt) and the relative size and proximity of competitors are equally important determinants of pine growth. In comparison, Strand et al. (2006) found distance to the nearest neighbour to be more strongly correlated to growth than light availability in lodgepole pine. As a simple assessment of competition, the DRD index offers the advantage of requiring the measurement of only the nearest three neighbours’s DBH and distance. MacIsaac and Navratril (1996) found a simple basal diameter ratio using the tallest aspen within 1.8 m of the target pine as the best index. Newsome et al. (2008) found tall aspen density in stands 15–19 years (aspen density  pine height within a 1.8 m plot radius) to be the best indicator of subsequent 5-year lodgepole pine diameter and height growth. In this study, we did not measure the height of the nearest neighbours in part because of the good correlation between diameter and height growth which is routinely used to predict height within plot data where only a sub-sample of heights are

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1835

Table 7 Parameter estimates for various predicted pine average growth parameters using several non-linear equations relating to growth to percent DIFNt for each sample pine (n = 97, P values  0.0004). Variable

NLIN model

DBHINCa

Michaelis-Menton 1b Logisticc Powerd Michaelis-Menton 2e Gompertzf

HINCg

Michaelis-Menton 1b Logisticc Powerd Michaelis-Menton 2e Gompertzf

126.5 71.215 3.379 171.3 79.058

RHINCh

Michaelis-Menton 1b Logisticc Powerd Michaelis-Menton 2e Gompertzf

1.179 0.83 0.068 1.669 0.899

a b c d e f g h

a 1.456 0.3203 0.0051 1.0727 0.3591

b

c

R2adj

0.00353 2.665 0.875 0.00457 1.288

na 0.0511 na 8.576 0.0295

0.10 0.17 0.10 0.17 0.17

1.175 1.362 0.097 0.877 0.621

na 0.0314 na 9.087 0.0198

0.39 0.38 0.39 0.38 0.38

0.0184 1.0167 0.513 0.0111 0.411

na 0.0291 na 15.94 0.0193

0.34 0.34 0.34 0.34 0.34

RMSE 0.1819 0.1456 0.1819 0.1463 0.1460 13.402 13.450 13.394 13.457 13.454 0.1513 0.1517 0.1510 0.1517 0.1517

Diameter increment (2005 to 2006) measured at 1.3 m breast height (cm). Y = {aD/((a/b) + D)} where D is percent DIFNt (Wright et al., 1998). D Y ¼ a=ð1 þ ebc Þ where D is percent DIFNt (Sit and Poulin-Costello, 1994). Y = aDb where D is percent DIFNt (Sit and Poulin-Costello, 1994). Y = {a(D  c)/((a/b) + (D  c))} where D is percent DIFNt (Coates and Burton, 1999). ðbc DÞ

Y ¼ a½ee  where D is percent DIFNt (Sit and Poulin-Costello, 1994). Average height increment over 2006 and 2005 (cm). Average height increment over 2006 and 2005 (cm)/maximum average height increment (cm) by site.

measured (Temesgen and Gadow, 2004). Our results using the largest DBH neighbour (MLCI) did not rank as high as DRD although the R2adj range indicated the largest neighbour described 27–50% of pine growth (Table 5a). The advantage of using DRD over the CI’s proposed by MacIsaac and Navratril (1996) and Newsome et al. (2008) is that height measurement is avoided which can be problematic in older plantations where the surveyor may be overtopped by dense, closed canopy conditions. The relationship of pine growth (RSTV) to competition level (DRD) explored using the hypothetical response threshold concepts developed by Wagner et al. (1989) indicated a minimum growth response level could be determined by site based on 20% of the maximum competition (DRD) level. The minimum growth response level was 0.02–0.028 DRD across the three sites. Since DRD is the sum of the closest three neighbour/pine DBH ratios divided by their distances to the select pine, one can easily calculate an average threshold distance (ATD) dependent on the average neighbour/pine DBH ratio (ATD = neighbour/pine DBH

Fig. 4. Observed values and fitted regression lines of relative height growth versus percent DIFNt from pine located at the three SBS sites. All sites model parameter values provided in Tables 6 and 7.

ratio/(minimum response level/3)). Similarly, we can determine an ATD for maximum growth (5–10% of maximum DRD) (Wagner, 2000; Simard et al., 2005). Minimum and maximum growth response ATD are then available to apply at the individual pine level as a site specific tool to assess growth potential and guide mixedwood management treatments. At these three sites, the range of neighbour DBH ratio (Table 2) indicates that brushing radius treatments need only be applied to select trees to meet pine management objectives and growth expectations. Taking the McKinley Lake site minimum growth response level as an example (DRD = 0.02), if a pine has an average neighbour DBH ratio = 1.0 (neighbours are of similar DBH to the pine) then the ATD for minimum growth response (RSTV) is 150 cm, and the ATD for maximum growth response is 303 cm. If the neighbours are only a third the size then ATD for minimum growth response drops to 41 cm, and the ATD for maximum response is 91 cm. 4.2. Light and growth relationships The results of relating pine stem volume growth (LSTV) to the various canopy DIFN levels (DIFNt, DIFNm, and DIFNb) consistently found each site to have unique linear model parameters except for DIFNb (Table 5a). This was due in part by the relative pine size differences between the sites resulting in significantly different model intercepts. Observing model R2adj for volume growth versus DIFN across our sites we found pine growth at McKinley Lake to be poorly predicted by all DIFN measurements. At all sites, R2adj was found to become progressively smaller moving from the top of the crown (DIFNt) to the base of the crown (DIFNb) where DIFNb was found to be a very poor predictor of stem volume growth. However, relating HINC, RHINC, DBHINC and CW to DIFNt did indicate data could be combined into single all sites models (Tables 6 and 7). Others such as Chen et al. (1996) combined lodgepole pine height growth and light data from 6 BC sites after finding no significant difference between sites. Wright et al. (1998) also combined lodgepole pine diameter and height growth data from a range of sites within 6 separate climatic regions of BC however, no discussion of between site variation was provided. Comeau et al. (2006) found aspen data could be pooled from multiple SBS sites (and other biogeoclimatic zones) after testing for site differences.

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

1836

Table 8 The logarithmic relationship between the light level at various crown heights versus the nearest neighbour competition index DRD, density and basal area. Dependent variable (Y)

Independent variable (X)

Main model Site

Mixed model n

a

b

R2adj

RMSE

P value

Source of variation

F

P value

DIFNt

DRD

McKinley Lake Teardrop Tyee Lake

33 28 36

0.4641 0.3314 0.1932

0.0743 0.2194 0.1654

0.07 0.57 0.59

0.1603 0.1613 0.1187

0.0749 <0.0001 <0.0001

X Site X  Site

65.07 8.23 4.39

<0.0001 0.0005 0.0151

DIFNm

DRD

McKinley Lake Teardrop Tyee Lake

33 34 36

0.3231 0.3234 0.1228

0.0563 0.1568 0.1947

0.01 0.52 0.56

0.1965 0.1227 0.148

0.2632 <0.0001 <0.0001

X Site X  Site

45.62 4.63 3.84

<0.0001 0.012 0.0248

DIFNb

DRD

McKinley Lake Teardrop Tyee Lake All sites

33 34 36 103

0.3599 0.00066 0.09 0.206

0.0126 0.0416 0.0935 0.0361

0.03 0.26 0.18 0.02

0.1192 0.0562 0.1646 0.1806

0.6776 0.0014 0.0065 0.1089

X Site X  Site

9.90 2.44 2.36

0.0022 0.0922 0.1001

DIFNb

Total density (sph)

McKinley Lake Teardrop Tyee Lake

33 34 36

1.4907 0.7981 2.3536

0.1308 0.0666 0.20

0.32 0.18 0.56

0.09731 0.05916 0.1201

0.0004 0.0080 <0.0001

X Site X  Site

51.32 7.65 4.75

<0.0001 0.0008 0.0107

DIFNb

Basal area (m2/ha)

McKinley Lake Teardrop Tyee Lake All sites

7 7 7 21

0.8881 1.0069 1.2071 1.0791

0.1896 0.2403 0.3231 0.2637

0.05 0.37 0.46 0.61

0.1277 0.0605 0.1685 0.1153

0.3060 0.0860 0.0566 <0.0001

X Site X  Site

6.10 0.22 0.26

0.0260 0.8055 0.7756

Model used was: Y = a + b ln(X).

Even though DIFNt predicted volume growth better than for the single growth parameters HINC, RHINC, DBHINC and CW, statistical results did not support the combining of site data as we and others found using the diameter and height growth parameters. The relationship of white spruce stem volume growth to aspen competition has also been found to be site specific (Filipescu and Comeau, 2007). Our attempts to improve the simple linear prediction of HINC, RHINC, DBHINC, and CW by DIFNt using a variety of published nonlinear light-growth models (Table 7) only resulted in a small improvement with DBHINC. Comparing our light-growth model results with published linear models (Claveau et al., 2002; Vyse et al., 2006), Michaelis-Menton equations (Wright et al., 1998, 2000; Coates and Burton, 1999; Kobe, 2006), and logistic models (Kayahara et al., 1996; Chen et al., 1996) highlighted the influence of low light level data on model choice. Significant differences in height growth prediction can result from model response assumptions to low light levels <20% (Fig. 4). However, low light data for undamaged trees can be difficult to acquire in older mixedwood plantations since these areas of intense aspen competition are preferred by wildlife for cover and habitat and

trees are often browsed. Animals can damage trees and accelerate suppressed pine mortality and as Kobe and Coates (1997) have shown, lodgepole pine mortality increases significantly with increased tree growth suppression. To ensure adequate low-light data for light-growth modelling needs, it may be necessary to establish research plantings directly into a range of controlled light environments protected from wildlife such as attempted by Logan (1966), Reich et al. (1998), and Coates and Burton (1999). Our modelling results verified the results published by others of increased pine diameter and height growth with increasing light availability. However, few other published studies have explored light and growth within a broadleaf-conifer mixedwoods. Differences in light transmission characteristics between conifer and broadleaf canopies suggest light-growth models developed from conifer stands may not be directly transferable to broadleaf mixedwood stands (Lieffers et al., 1999).

Fig. 5. Observed and fitted light level measured at the base of the live crown (DIFNb) over stand basal area compared to the Comeau et al. (2006) model for the SBS zone based on a canopy DIFN measurement at a height of 1.5 m within pure aspen stands.

Fig. 6. Scatter plot showing the range of observed DRD competition index and total stand density (tph) by site. Dashed line represents the calculated minimum response threshold level for pine at McKinley Lake (see Fig. 3).

4.3. Light availability and neighbourhood competition Comparison of our DIFNb to stand basal area model with the Comeau et al. (2006) SBS model indicated the relationship of DIFN

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

measured in the understorey of aspen stands (at 1.5 m height for Comeau et al., 2006) was very similar to our DIFNb (measured at the base of the pine live crown) within mixedwood stands dominated by aspen canopies (Fig. 5). A good correlation was found between pine canopy DIFN levels (measured at top and mid crown) and DRD at Teardrop and Tyee Lake but not McKinley Lake (Table 8). This was evidence of a significantly different competition and light environment at McKinley Lake. Also noted only at McKinley Lake, were poor correlations between pine DBHINC, HDR and CW and DIFNt further supporting the conclusion that canopy light availability was not well correlated with competition and pine crown and diameter growth. This is contrary to the correlations observed at the other two sites and may be related to the aspen thinning treatments at McKinley Lake which resulted in less dense aspen of regular, well spaced distribution (except for the untreated areas). The aspen thinning treatments resulted in a range of lower densities of large diameter aspen which may have modified the relationship between available canopy light, aspen size and proximity found at the other two sites. For some pine, it appeared aspen thinning replaced high competition (DRD) from small diameter aspen at close proximity with high competition from large diameter aspen at greater proximity. High competition levels at Teardrop and Tyee Lake were well aligned with increasing stand density but not at McKinley Lake where the minimum growth response DRD level (0.02) was found at densities as low as 1800 tph. With one exception, all pine with DRD levels > the minimum growth response level were found with stand densities > 9000 tph at Teardrop and Tyee Lake. Additional studies are needed to explore the confounding influence of aspen relative size and proximity on pine growth and provide incite into the tree level dynamics of aspen-pine mixedwoods. 4.4. Management implications The findings of this paper lead to three important management implications: (1) The exponential response of lodgepole pine volume growth to increasing DRD competition levels suggested that DRD may have value as a survey index in determining site specific average threshold distances needed to maintain minimum lodgepole pine growth response where aspen is the dominant competitor. Managing pine growth response at the tree level may be possible in similar SBS aspen-pine mixedwood plantations through defining site specific average threshold distances. (2) Lodgepole pine height growth response to increasing light availability is linear in nature for SBS aspen dominated mixedwood stands. Modelling assumptions of asymptotic response must carefully consider low light level conditions and the confounding interactions of increased stem damage and stand mortality with highly suppressed trees. (3) The correlation of pine volume growth with neighbour relative size and proximity suggests thinning treatments may not always improve pine growth in SBS mixedwood stands since the resulting well spaced, larger diameter aspen may have as high an impact on pine growth as more dense (but smaller) untreated aspen.

Acknowledgements Financial support was provided by the British Columbia provincial government Forest Investment Account, Forest Science Program.

1837

References Biging, G.S., Dobbertin, M., 1995. Evaluation of competition indices in individual tree growth models. For. Sci. 41, 360–377. British Columbia Ministry of Forests and Range, 2008. Forest planning and practices regulation. Forest and Range Practices Act. Victoria, BC. URL: http://www.for.gov.bc.ca/tasb/legsregs/frpa/frparegs/forplanprac/fppr.htm#section16. Brunner, A., 1998. A light model for spatially explicit forest stand models. For. Ecol. Manage. 107, 19–46. Burton, P.J., 2002. Effects of clearcut edges on trees in the sub-boreal spruce zone of northwest-central British Columbia. Silva Fennica 36, 329–352. Canham, C.D., Coates, K.D., Bartemucci, P., Quaglia, S., 1999. Measurement and modeling of spatially explicit variation in light transmission through interior cedar-hemlock forests of British Columbia. Can. J. For. Res. 29, 1775–1783. Canham, C.D., LePage, P.T., Coates, K.D., 2004. A neighbourhood analysis of canopy tree competition: effects of shading versus crowding. Can. J. For. Res. 34, 778– 787. Chen, H.Y.H., Klinka, K., Kayahara, G.J., 1996. Effects of light on growth, crown architecture, and specific leaf area for naturally established Pinus contorta var. latifolia and Pseudotsuga menziesii var. glauca saplings. Can. J. For. Res. 26, 1149– 1157. Claveau, Y., Comeau, P.G., Messier, C., Coates, K.D., 2002. Growth and crown morphological responses of boreal conifer seedlings and saplings with contrasting shade tolerance to a gradient of light and height. Can. J. For. Res. 32, 458–468. Claveau, Y., Messier, C., Comeau, P.G., 2005. Interacting influence of light and size on above ground biomass distribution in sub-boreal conifer saplings with contrasting shade tolerance. Tree Physiol. 25, 373–384. Coates, K.D., 2000. Conifer seedling response to northern temperate forest gaps. For. Ecol. Manage. 127, 249–269. Coates, K.D., Burton, P.J., 1999. Growth of planted tree seedlings in response to ambient light levels in northwestern interior cedar-hemlock forests of British Columbia. Can. J. For. Res. 29, 1374–1382. Comeau, P.G., 2001. Relationships between stand parameters and understorey light in boreal aspen stands. B.C. J. Ecosyst. Manage. 1 (2). Comeau, P.G., Gendron, F., Letchford, T., 1998. A comparison of several methods for estimating light under a paper birch mixedwood stand. Can. J. For. Res. 28, 1843–1850. Comeau, P.G., Heineman, J.L., Newsome, T., 2006. Evaluation of relationships between understorey light and aspen basal area in the British Columbia central interior. For. Ecol. Manage. 226, 80–87. Cornell, J.A., Berger, R.D., 1987. Factors that influence the value of the coefficient of determination in simple linear and nonlinear regression models. Phytopathology 77, 63–70. Daniels, R., 1976. Simple competition indices and their correlation with annual loblolly pine tree growth. For. Sci. 22, 454–457. Day, M.E., Schedlbauer, J.L., Livingston, W.H., Greenwood, M.S., White, A.S., Brissette, J.C., 2005. Influence of seedbed, light environment, and elevated night temperature on growth and carbon allocation in pitch pine Pinus rigida and jack pine Pinus banksiana seedlings. For. Ecol. Manage. 205, 59–71. DeLong, C.S., 2007. Relative impact of aspen competition and soil factors on the performance of lodgepole pine and hybrid white spruce in north-central British Columbia. B.C. Min. For. Range, Res. Br., Victoria, B.C. Tech. Rep. 039. Demaerschalk, J.P., Kozak, A., 1974. Suggestions and criteria for more effective regression sampling. Can. J. For. Res. 4, 341–348. Eis, S., 1970. Root-growth relationships of juvenile white spruce, alpine fir, and lodgepole pine on three soils in the interior of British Columbia. Department of Fisheries and Forestry, Canadian Forestry Service. Publication No. 1276, 10 pp. Eis, S., Craigdallie, D., Simmons, C., 1982. Growth of lodgepole pine and white spruce in the central interior of British Columbia. Can. J. For. Res. 12, 567–575. Filipescu, C.N., Comeau, P.G., 2007. Aspen competition affects light and white spruce growth across several boreal sites in western Canada. Can. J. For. Res. 37, 1701– 1713. Gower, S.T., Norman, J.M., 1991. Rapid estimation of leaf area index in conifer and broad-leaf plantations. Ecology 72 (5), 1896–1900. Greene, D.F., Zasada, J.C., Sirois, L., Kneeshaw, D., Morin, H., Charron, I., Simard, M.-J., 1999. A review of the regeneration dynamics of North American boreal forest tree species. Can. J. For. Res. 29, 824–839. Groot, A., 2004. A model to estimate light interception by tree crowns, applied to black spruce. Can. J. For. Res. 34, 788–799. Hegyi, F., 1974. A simulation model for managing jack-pine stands. In: Fries, J. (Ed.), Growth Models for Tree and Stand Simulation. Royal College of Forestry, Stockholm, Sweden. Res. Note 30, pp. 74–90. Hoddinott, J., Scott, R., 1996. The influence of light quality and carbon dioxide enrichment on the growth and physiology of seedlings of three conifer species. I. Growth responses. Can. J. Bot. 74, 383–390. Johnstone, J.F., 2005. Effects of aspen (Populus tremuloides) sucker removal on postfire conifer regeneration in central Alaska. Can J. For. Res. 35, 483–486. Kaufmann, M.R., Ryan, M.G., 1986. Physiographic, stand, and environmental effects on individual tree growth and growth efficiency in subalpine forests. Tree Physiol. 2, 47–59. Kayahara, G.J., Chen, H.Y.H., Klinka, K., Coates, K.D., 1996. Relations of terminal growth and specific leaf area to available light in naturally regenerated seedlings of logdepole pine and interior spruce in central British Columbia. B.C. Min. For. Research Report 09, 32 pp.

1838

G. Harper et al. / Forest Ecology and Management 257 (2009) 1829–1838

Kobe, R.K., 2006. Sapling growth as a function of light and landscape-level variation in soil water and foliar nitrogen in northern Michigan. Oecologia 147, 119–133. Kobe, R.K., Coates, K.D., 1997. Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia. Can. J. For. Res. 27, 227–236. Landhausser, S.M., Lieffers, V.J., 2001. Photosynthesis and carbon allocation of six boreal tree species grown in understory and open conditions. Tree Physiol. 21, 243–250. Lieffers, V.J., Pinno, B.D., Stadt, K.J., 2002. Light dynamics and free-to-grow standards in aspen-dominated mixedwood forests. For. Chron. 78 (1), 137–145. Lieffers, V.J., Messier, C., Stadt, K.J., Gendron, F., Comeau, P.G., 1999. Predicting and managing light in the understory of boreal forests. Can. J. For. Res. 29, 796–811. Logan, K.T., 1966. Growth of tree seedlings as affected by light intensity. II. Red Pine, White Pine, Jack Pine, and Eastern Larch. Department of Forestry Publication No. 1160, 19 pp. Lorimer, C.G., 1983. Tests of age-dependent competition indices for individual trees in natural hardwood stands. For. Ecol. Manage. 6, 343–360. MacIsaac, D.A., Navratril, S., 1996. Competition dynamics in juvenile boreal hardwood-conifer mixtures. In: Comeau, P.G., Thomas, K.D. (Eds.), Silviculture of Temperate and Boreal Broadleaf-Conifer Mixtures, 1996. Land Management Handbook 36. B.C. Min. For., Victoria, B.C. McKinnon, L.M., Kayahara, G.J., 2003. Ecology and management of microclimate in boreal mixedwoods. Northeast Science and Information, Ont. Min. Natural Resour., South Porcupine, Ont. Boreal Mixedwood Notes 28, 13 pp. Meidinger, D., Pojar, J., 1991. Ecosystems of British Columbia. B.C. Min. For., Res. Br., Victoria, B.C. Spec. Rep. Ser. 6. Messier, C., Doucet, R., Ruel, J.C., Claveau, Y., Kelly, C., Lechowicz, M., 1999. Functional ecology of advance regeneration in relation to light in boreal forests. Can. J. For. Res. 29, 812–823. Navratril, S., MacIsaac, D.A., 1993. Competition index for juvenile mixed stands of lodgepole pine and aspen in west-central Alberta. For. Can. North. For. Cent. Edmonton, Alta. For. Manage. Note 57. Newsome, T.A., Heineman, J.L., Nemec, A.F.L., 2003. Competitive effects of trembling aspen on lodgepole pine performance in the SBS and IDF zones of the CaribooChilcotin region of south-central British Columbia. B.C. Min. For., Res. Br., Victoria, B.C. Tech. Rep. 005. Newsome, T.A., Heineman, J.L., Nemec, A.F.L., 2004. Lodgepole pine response to aspen removal in variable radii in the SBSdw2 variant near Williams Lake, B.C. B.C. Min. For., Res. Br., Victoria, B.C. Tech. Rep. 014. Newsome, T.A., Heineman, J.L., Nemec, A.F.L., 2006a. Effects of variable aspen retention on stand development, aspen sucker production, and growth of lodgepole pine in the SBSdw1 variant of south-central British Columbia. B.C. Min. For. Range, Res. Br., Victoria, B.C. Tech. Rep. 032. Newsome, T.A., Heineman, J.L., Nemec, A.F.L., 2006b. Early effects of manipulating aspen density and spatial arrangement on lodgepole pine performance, aspen sucker production, and stand development in an 11-year-old stand in the SBPSxc subzone of south-central British Columbia. B.C. Min. For., Res. Br., Victoria, B.C. Tech. Rep. 029. Newsome, T.A., Heineman, J.L., Nemec, A.F.L., 2008. Competitive interactions between juvenile trembling aspen and lodgepole pine: a comparison of two interior British Columbia ecosystems. For. Ecol. Manage. 255, 2950– 2962. Ott, P., 1997. The use of indicator variables in non-linear regression. BC Min. For., Victoria, BC Biometrics Information Pamphlet No. 56, 6 pp.

Pinno, B.D., Lieffers, V.J., Stadt, K.J., 2001. Measuring and modelling the crown and light transmission characteristics of juvenile aspen. Can. J. For. Res. 31, 1930– 1939. Reich, P.B., Tjoelker, M.G., Walters, M.B., Vanderklein, D.W., Buschena, C., 1998. Close association of RGR, leaf and root morphology, seed mass and shade tolerance in seedlings of nine boreal tree species grown in high and low light. Funct. Ecol. 12, 327–338. SAS Institute Inc., 2004. SAS OnlineDoc1 9.1.3. SAS Institute Inc., Cary, NC. Shirley, H.L., 1945. Reproduction of upland conifers in the lake states as affected by root competition and light. Am. Midland Nat. 33, 537–612. Simard, S.W., Hagerman, S.M., Sachs, D.L., Heineman, J.L., Mather, W.J., 2005. Conifer growth, Armillaria ostoyae root disease, and plant diversity responses to broadleaf competition reduction in mixed forests of southern interior British Columbia. Can. J. For. Res. 35, 843–859. Sit, V., Poulin-Costello, M., 1994. Catalog of curves for curve fitting. Biometrics information handbook series. Handbook No. 4. March 1994. Res. Br., B.C. Min. For., Victoria, B.C. URL: http://www.for.gov.bc.ca/hfd/pubs/Docs/Bio/ Bio04.htm. Stadt, K.J., Lieffers, V.J., 2000. MIXLIGHT: a flexible light transmission model for mixed-species forest stands. Agric. For. Meteorol. 102, 235–252. Strand, M., Lofvenius, M.O., Bergsten, U., Lundmark, T., Rosvall, O., 2006. Height growth of planted conifer seedlings in relation to solar radiation and position in Scots pine shelterwood. For. Ecol. Manage. 224, 258–265. Strong, W.L., Sidhu, S.S., 2005. Prolonged herbicide-induced vegetation changes in a regenerating boreal aspen clearcut. J. Environ. Manage. 77, 194–204. Temesgen, H., Gadow, K.v., 2004. Generalized height–diameter models—an application for major tree species in complex stands of interior British Columbia. Eur. J. For. Res. 123, 45–51. Tome, M., Burkhart, H.E., 1989. Distance-dependent competition measures for predicting growth of individual trees. For. Sci. 35, 816–831. Voicu, M.F., Comeau, P.G., 2006. Microclimate and spruce growth gradients adjacent to young aspen stands. For. Ecol. Manage. 221, 13–26. Vyse, A., Ferguson, C., Simard, S.W., Kano, T., Puttonen, P., 2006. Growth of Douglasfir, lodgepole pine, and ponderosa pine seedlings under-planted in a partiallycut, dry Douglas-fir stand in south-central British Columbia. For. Chron. 82 (5), 723–732. Wagner, R.G., 2000. Competition and critical-period thresholds for vegetation management decisions in young conifer stands. For. Chron. 76, 961–968. Wagner, R.G., Petersen, T.D., Ross, D.W., Radosevich, S.R., 1989. Competition thresholds for the survival and growth of ponderosa pine seedlings associated with woody and herbaceous vegetation. New Forests 3, 151–170. Walmsley, M., Utzig, G., Vold, T., Moon, D., van Barneveld, J., 1980. Describing ecosystems in the field. RAB technical paper 2. Land Management Report No. 7. Province of BC, Victoria, BC. http://www.llbc.leg.bc.ca/public/PubDocs/bcdocs/ 264741/Lmr007.pdf. Williams, H., Messier, C., Kneeshaw, D.D., 1999. Effects of light availability and sapling size on the growth and crown morphology of understory Douglas-fir and lodgepole pine. Can. J. For. Res. 29, 222–231. Wright, E.F., Canham, C.D., Coates, K.D., 2000. Effects of suppression and release on sapling growth for 11 tree species of northern, interior British Columbia. Can. J. For. Res. 30, 1571–1580. Wright, E.F., Coates, K.D., Canham, C.D., Bartemucci, P., 1998. Species variability in growth response to light across climatic regions in northwestern British Columbia. Can. J. For. Res. 28, 871–886.