Functional response of coniferous trees and stands to commercial thinning in eastern Canada

Forest Ecology and Management 384 (2017) 6–16

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Functional response of coniferous trees and stands to commercial thinning in eastern Canada Simon Boivin-Dompierre, Alexis Achim, David Pothier ⇑ Centre d’étude de la forêt, Département des sciences du bois et de la forêt, Pavillon Abitibi-Price, 2405 rue de la Terrasse, Université Laval, Québec, QC G1V 0A6, Canada

a r t i c l e

i n f o

Article history: Received 20 July 2016 Received in revised form 7 October 2016 Accepted 9 October 2016

Keywords: Leaf area Growth efficiency Inter-tree competition Wood production

a b s t r a c t The overall objectives of commercial thinning are to increase individual stem growth and, arguably, to increase stand yield. Yet few empirical results are available that would confirm the treatment meets such expectations in an industrial context. We studied the response of stands to commercial thinning with a particular focus on variables that were related to processes of stemwood production at the tree and stand levels. We inventoried permanent sample plots established between 1980 and 2000 in naturally regenerated conifer stands of southern Quebec seven to ten years after thinning. We reconstituted tree leaf area and wood production per unit leaf area using field and dendrochronological data for different years following thinning. Mixed linear models showed that tree basal area and leaf area increments following treatment were strongly related to the tree distance from the nearest skid trail. Wood production per unit leaf area was significantly higher for trees located within 5 m of a skid trail compared to control trees as soon as one year following thinning application, while significant differences in tree leaf area required five years. Compared to control stands, thinned stands had higher wood production per unit leaf area, but merchantable volume increment did not differ. These results provided insight into growth processes that are involved in tree responses to mechanized thinning and could aid in the development of decision tools that determine the suitability of stands for receiving the treatment. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Commercial thinning (CT) is a type of partial cut that is applied to dense premature stands to increase the diameter growth, quality and vigour of residual trees (Nyland, 2002). There are three hypothetical productivity outcomes for thinned stands compared to unthinned stands: (1) a convergent volume trajectory over time, which implies that CT can stimulate wood production per hectare; (2) a parallel volume trajectory, implying that thinning only redistributes the growth potential of the site among residual trees; and (3) a divergent volume trajectory that is indicative of underutilized growth potential of the site after thinning. The last outcome may be incurred by insufficient stand densities or caused by the presence of large gaps that decrease wood production over time (Zeide, 2001; Pelletier and Pitt, 2008). In all cases, the stand volume trajectory is the net result of survivor growth, ingrowth and mortality (Beer, 1962) but since CT is applied to premature stands, the survivor growth is usually the most important of these three components.

⇑ Corresponding author. E-mail address: [email protected] (D. Pothier). http://dx.doi.org/10.1016/j.foreco.2016.10.024 0378-1127/Ó 2016 Elsevier B.V. All rights reserved.

Even if parallel volume trajectories are generally obtained from CT that has been applied in conifer stands (Zeide, 2001; Mäkinen and Isomäki, 2004; Pelletier and Pitt, 2008), some studies have reported both convergent (Marshall and Curtis, 2002; Soucy et al., 2012; Barrette and Tremblay, 2015) and divergent (Loftus, 1997) responses. These conflicting results may be associated with differences in thinning types (e.g., low vs high thinning), application methods (e.g., mechanized vs manual), site conditions, or tree species. While controlled experiment designs are useful, they do not generally include the many constraints and sources of variability that are associated with large-scale operational treatments (Benjamin et al., 2013; Guay-Picard et al., 2015). From an operational perspective, thinning must remove a sufficient volume of merchantable wood to generate profits, which in turn may prevent the volume growth trajectory of the thinned stand from converging on that of an unthinned stand. Indeed, gaps that are created by the passage of harvesting machinery may limit resource acquisition to a suboptimal level at the stand scale by introducing spatial heterogeneity in tree distribution and competition (Perry, 1985; Jack and Long, 1996; Binkley et al., 2004). Inter-tree competition is usually quantified by various indices, which have been tested through empirical studies under several conditions where they reasonably describe the interactions

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between a tree and its environment (Contreras et al., 2011). These indices can be classified as distance-dependent or distanceindependent. Distance-dependent indices take into account the distances between trees and are used to represent the spatial heterogeneity of a stand (Contreras et al., 2011). Acquiring tree spatial locations is time-consuming and costly, and the superiority of distance-dependent indices in predicting stemwood growth has yet to be clearly demonstrated (Biging and Dobbertin, 1995; Roberts and Harrington, 2008; Contreras et al., 2011; Bosela et al., 2015). Distance-independent indices are simpler and usually can be computed from forest inventory data. Yet little is known regarding the abilities of the two types of indices to predict tree growth in the context of spatial heterogeneity that is caused by partial cutting (Puettmann et al., 2009; Comfort et al., 2010). This heterogeneity, which is mainly due to the presence of skid trails, could stimulate the growth of only certain trees, thereby creating pronounced spatial patterns of response to thinning (Roberts and Harrington, 2008; Genet and Pothier, 2013). One limitation of the relationships between tree growth and competition indices is that they are only applicable to the conditions under which they have been calibrated. In order to generalize tree growth patterns in relation to their competitive environment, it is necessary to take into account the complex interrelationships between resource availability and acquisition, resource use efficiency, and biomass partitioning between tree parts (Binkley et al., 2004). Indeed, focusing upon the fundamental drivers of stem growth can help predict response patterns that are applicable to a wide range of stand conditions and forest types (Binkley and Reid, 1984). At the stand level, the reduction of competition that is incurred by thinning involves a decrease in leaf area index (LAI) (Jack and Long, 1996), i.e., the amount of leaf area per unit ground area. Since LAI is directly related to light interception and photosynthetic capacity (Vose and Allen, 1988), it strongly influences the wood productivity of a stand (Binkley and Reid, 1984; Perry, 1985), which is thus expected to decrease immediately after CT. Over the short-term, reduction in LAI could be compensated for by an increase in growth efficiency (GE), which is defined as the amount of wood that is produced annually per unit leaf area (Brix, 1983; Oren et al., 1987; Velazquez-Martinez et al., 1992). Over the mid-term, the reduction in LAI due to thinning would progressively diminish due to an increase in leaf area of the remaining individual trees, which gradually fill the canopy gaps (Pretzsch and Mette, 2008). This response should contribute to the recovery of initial LAI and wood productivity and, thus, support the hypothesis of parallel volume trajectories between thinned and unthinned stands. If increases in post-thinning GE or LAI are not sufficient to restore initial stand growth, divergent volume trajectories may occur. The forest products industry often relies upon commercial thinning to increase both stand yield and quality of naturally regenerated stands, even though such effects are not always supported empirically. Thus, it is important to document the effects of this treatment over a wide range of conditions. A better understanding of these effects could be obtained by studying the main processes that are involved in tree and stand responses. Empirical results can be accompanied by recommendations on how to adjust CT applications to maximize their positive effects. Accordingly, the general objective of this study was to examine tree and stand growth responses to CT using variables that were related to resource availability, acquisition and utilization. Our working hypotheses were that: (1) there is a shift over time in tree growth response to CT from better wood production efficiency of each leaf unit to leaf area expansion; (2) tree growth response to CT increases with decreasing distance from skidding trails; and (3) the temporal trajectories of wood volume for control and thinned stands shift from convergent to divergent with increasing canopy gap size that is caused by tree removal and skid trail occupancy.

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2. Material and methods 2.1. Study area The study area is located on private woodlots in southern Quebec (45°280 –46°280 N, 70°200 –71°450 W) that are owned by Domtar Corporation (Montreal, Quebec) and encompasses two bioclimatic subdomains: the eastern sugar maple–American basswood and the eastern sugar maple–yellow birch bioclimatic subdomains (Saucier et al., 2009). The first subdomain is characterized by mean annual temperatures between 4 and 5 °C, and mean annual precipitation ranging between 1000 and 1150 mm, while the length of the growing season ranges from 165 to 180 days. The second one is characterized by mean annual temperatures between 2.5 and 4 °C, and mean annual precipitation between 950 and 1100 mm, with a growing season of 145–165 days (Saucier et al., 2009). Topography is similar in both subdomains and is characterized by hills and slopes. The main surface deposits are shallow or deep tills (Grondin et al., 2007). The study stands are mainly composed of balsam fir (Abies balsamea [L.] Miller) and red spruce (Picea rubens Sargent), with minor components of black spruce (Picea mariana (Miller) BSP), white spruce (Picea glauca [Moench] Voss), white pine (Pinus strobus L.), eastern white cedar (Thuja occidentalis L.), tamarack or eastern larch (Larix laricina (Du Roi) K. Koch), birches (Betula papyrifera Marshall, Betula alleghaniensis Britton, Betula populifolia Marshall), trembling aspen (Populus tremuloides Michaux), balsam poplar (Populus balsamifera L.), and red maple (Acer rubrum L.).

2.2. Experimental design All data for this study originated from 16 pairs of 400-m2 permanent sample plots within which all trees with diameter at breast height (DBH) > 9.0 cm were tagged. These plots were established between 1983 and 2006, at least one year prior to CT, in conifer stands that were clear-cut between 1963 and 1966. The two plots from each pair (hereafter block) had been located close to one another to minimize potential differences in vegetation, soil, climate, and topography. Each block was established in a single, relatively uniform stand ranging in size from 2 to 6 ha. Between 2004 and 2007, one randomly selected plot from each block was commercially thinned, while the other was left untouched (control). A buffer strip of 5 m was delineated around each control plot to prevent thinning effects on edge trees. Plots established before 1997 were square-shaped, while the more recent plots were circular. The experiment follows a complete randomized block design with 16 blocks and two treatments in each block (CT or control). Prior to CT, the stands were characterized by a mean density of about 1500 trees ha1, with a mean stem volume of 70 dm3, and mean stand basal area around 27 m2 ha1. About 35% of the basal area was removed with multifunctional or feller-buncher harvesters that were coupled with a forwarder, a cable skidder or a grapple skidder. Although the harvesting equipment varied among blocks, this had little influence on the treatment application because the reach of the harvesters was similar in each case (8 m) while skidding trails had a maximum width of 4 m and were spaced about 20 m apart. In addition, we observed few tree injuries caused by harvesting operations, regardless of the extraction vehicles. With the aim of releasing spruces or other crop trees, the machine operators selected trees to harvest according to the following criteria: (1) senescent trees, (2) balsam fir trees with a DBH over 20 cm, (3) intolerant hardwoods, and (4) any weak tree with DBH over 10 cm.

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2.3. Data collection The permanent sample plots were usually inventoried every 5 years, but this interval was shorter when commercial thinning was applied within this 5-year period (so that plots were surveyed immediately after thinning). Species, DBH and the social status of each tree with a DBH > 9.0 cm were recorded at each survey. Additional measurements were made in all 32 plots during the summer of 2014. First, using a diameter tape, we measured the diameter (±0.1 cm) of all trees with a DBH greater than 9.0 cm in each plot and within a 3-m strip around each plot. Total tree height and height of the lowest live branch (±0.1 m) were measured with a Haglöf Vertex hypsometer on each tree. Crown radius (±0.1 m) in the four cardinal directions was recorded by measuring the distance between the trunk and the point directly under the projection of the crown using a Suunto clinometer and a measuring tape. Cartesian coordinates of each tree were determined using the distance-azimuth method. The distance (±0.1 m) and the azimuth of each tree was measured with a hypsometer and a compass, from the plot centre in circular plots or from the closest corner to the main road in square plots, respectively. Cartesian coordinates for each tree were computed using trigonometric functions. We also located the skidding trails in each area with aerial photography. Tree coordinates were then used to measure the shortest distance to each tree from the nearest skidding trail using a built-in tool of ArcGIS (ESRI, Redlands, CA). Finally, within each plot, we randomly selected a fir or red spruce tree in each of four social status categories (dominant, codominant, intermediate, suppressed) from which an increment core was taken at a height of 1 m oriented towards the plot centre.

2.4. Competition index Several competition indices were compared to identify which one was most closely related to tree growth in both control and thinned plots. Only live balsam fir and spruce trees were used for these analyses (1169 firs, 232 spruces). Three distance-dependent and 7 distance-independent indices were compared (Table 1). Distance-dependent indices were previously tested to determine the competition radius (from 3 to 11 m) that was best related to tree growth, based on Akaike’s information criterion corrected for small sample sizes (AICc). Since it was not possible to test long radii

Equations

Distance-dependent competition indices Pn DD1 di =ðd  dist i Þ Pi¼1 n DD2 i¼1 ðdi =dÞ  arctanðdi =dist i Þ Pn DD3 i¼1 ðhi =hÞ  arctanðhi =dist i Þ Distance-independent competition indices Dq/d DI1 DI2 Dmax/d DI3 Ddom/d     DI4 1= 10;000  g N

DI5 DI6 DI7

g


(H  HCB)/H Stand basal area Stand basal area at CT time

ln BAI ¼ b0 þ b1 DDx þ b2 DBHo

ð1Þ

where BAI is the mean annual basal area increment after CT (cm2 yr1), DDx is one of the three distance-dependent indices, and DBHo is the DBH measured immediately after thinning. Because DBHo was not measured for trees in the 3-m strip around each plot, we estimated its value using the following equation, which was calibrated with trees that were measured in all plots using a mixed-effects linear model with a plot random effect (R2 = 0.94, RSE = 10.39):

DBH0 ¼ 24:40 þ 9:80DBH  0:17Ht2014  6:48CT  1:52T  0:002CSA2014

ð2Þ

where DBH is the diameter at breast height in 2014, Ht2014 is the total tree height in 2014, CT is the thinning treatment (1 for thinned, 0 for control), T is the number of years since thinning, and CSA2014 is the crown surface area in 2014 (m2). To estimate CSA2014, balsam fir was given a paraboloid-shaped crown and spruce a rocket-shaped crown (Mailly et al., 2003). Total height immediately after thinning (Ht0 ) was also estimated for each tree using a DBH-height relationship (Fortin et al., 2007). Preliminary results indicated that the competition radius that minimized the AICc associated with Eq. (1) was 4 m. Using this optimum radius, we compared the distance-independent and distance-dependent indices, both using the same procedure with the addition of site index (SI) as a covariate. Results were compared for thinned plots only, control plots only, and both plot types combined to determine if heterogeneous (thinned) or homogeneous (control) tree distributions influenced the predictive ability of the competition indices. The presence of outliers, variance inflation factors (VIF), variance homogeneity, normality of residuals, and random effects were tested to ensure that regression assumptions were met. The tested models were of the form:

ln BAI ¼ b0 þ b1 CIx þ b2 DBH0 þ b3 SI

ð3Þ

where CIx is a competition index and SI is the stand site index (m at 50 years) that was computed for each plot using the dominant tree species according to the following species-specific relationships (Pothier and Savard, 1998):


0:8325H0:03259 d Balsam fir SI ¼ 0:9524H0:9626 1  e0:03498Ac d

ð4Þ

Sources


0:634H0:09796 d Spruce SI ¼ 1:0935H0:895 1  e0:033Ac d

ð5Þ

Hegyi (1974) Rouvinen and Kuuluvainen (1997) Rouvinen and Kuuluvainen (1997)

where Hd is the plot dominant height, i.e., mean height of the 100 largest trees per hectare, and Ac is the age at 1 m.

Table 1 Competition indices compared in this study. Index

with trees that were located near the plot boundary, the sample size decreased with increasing competition radius. The optimum radius was defined as the shortest radius with an AICc lower than that of a longer radius. It was identified using mixed-effects linear models with a plot random effect to fit the following equation:

Tomé Tomé Tomé Tomé

and and and and

Burkhart Burkhart Burkhart Burkhart

(1989) (1989) (1989) (1989)

Prévosto (2005) Prévosto (2005) Prévosto (2005)

Note: all values were computed immediately after thinning, unless otherwise stated. di, DBH0 of the ith neighbouring tree (cm); d, DBH0 of the subject tree (cm); disti, the distance between the ith neighbour and the cored tree (dm); Dq, mean quadratic diameter of the stand (cm); Dmax, maximum diameter of the stand (cm); Ddom, mean diameter of the 100 largest trees per hectare (cm); g, basal area of the subject tree (m2 ha1); g
, mean tree basal area (m2 ha1); H, total height of the cored tree (dm); HLB, height of the lowest live branch (dm); N, number of trees per hectare.

2.5. Leaf area To estimate the leaf area (LA) of each cored tree at the time when CT was applied, we used a method based on a speciesspecific relationship between tree age and the number of rings in the sapwood (Pothier et al., 1989; Coyea et al., 1990). To construct such a relationship, we collected data from stands that were younger than those in the permanent plot network. Hence, stands with similar vegetation and site conditions as the sampled plots were identified using a recent forest map (9 stands for each age class: 20, 30 and 40 years). We collected four increment cores (one for each of four social statuses) from these stands with the same procedure that was used in the permanent plot network. Sapwood boundaries were either marked in the field by transparency

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or, if it was not possible, in the laboratory using aqueous potassium iodine, which reacted with the starch that was present in the sapwood (Kutscha and Sachs, 1962). Cores were then air-dried, glued on wood moldings to facilitate manipulation and sanded. They were scanned using WinDendro software (Regent Instruments, Quebec City, QC), which measured the width of all individual rings from pith to bark. A correction of 4% for spruce and 2.7% for fir was applied to ring-widths to compensate for drying shrinkage (Jessome, 2000). A total of 244 trees were cored, 128 of which came from the permanent plots. We excluded ten cores from the analyses because of excessive damage. To estimate leaf area for every year before and after CT, we first calibrated a relationship between tree age and the number of growth rings in the sapwood (Pothier et al., 1989):

ln GRSA ¼ b0 þ b1 ln Ac

ð6Þ

where GRSA is the number of growth rings in the sapwood at breast height, and Ac is the cambial age at height of 1 m. Logarithmic transformations were applied to ensure homoskedasticity. A correction factor for bias was applied to the estimates when they were backtransformed to the original scale (Sprugel, 1983). Second, with the estimates of GRSA at any tree age, we estimated the sapwood area at 1 m for each year preceding and following thinning by calculating the surface area that was covered by growth rings in the sapwood. These calculations were made with the assumption that tree trunks were perfect circles. Leaf area was then computed using specific relationships between leaf area and sapwood area for balsam fir (Coyea and Margolis, 1992) and red spruce (Maguire et al., 1998). 2.6. Growth efficiency Growth efficiency (GE) corresponds to the amount of wood produced per unit leaf area for a given time period (Waring, 1983). We first computed GE for cored trees by using the annual merchantable volume increment (dm3 yr1) before and after CT and the projected leaf area (m2), which was estimated annually for the same period as previously described. Second, we computed GE at the plot scale for the entire post-thinning period by using the difference in merchantable volume between the last and the first post-thinning inventories with the equations of Fortin et al. (2007), and the leaf area of trees that were present in the last inventory. To assess the leaf area index (LAI, m2 m2) at the plot scale, we developed a generalized least- squares model to estimate leaf area of fir and spruce trees that were not cored. Linear mixed models could not be used in this case because the number of observations per plot (a maximum of 4 cored trees) was too low to detect differences in variance between plots. As proposed by Laubhann et al. (2010), the relationship involves crown surface area as an explanatory variable:

ln LA ¼ b0 þ b1 DBH þ b2 CSA þ b3 sp

ð7Þ

where LA is tree leaf area (m2), CSA is crown surface area (m2), and sp is spruce (0) or balsam fir (1). Logarithmic transformations were applied to meet the assumption of homoskedasticity. No data were available on leaf area for species other than fir or spruce. Therefore, leaf biomass (kg) was predicted with DBH and height using a set of relationships that were available for the relevant species (Lambert et al., 2005). Leaf area was then obtained by multiplying leaf biomass by specific leaf area (m2 kg1) for white spruce (Maguire et al., 1998), black spruce, birches, aspens (Bond-Lamberty et al., 2002), eastern white cedar (Hofmeyer et al., 2010), white pine (Guiterman et al., 2012), tamarack (Sala et al., 2001), and red maple (Reich et al., 1998).

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2.7. Modelling growth response to thinning Response to thinning at the tree level, which was computed as BAI since CT, was modeled in 13 different a priori linear mixed models, including an intercept-only model. The previously identified best competition index was used as the main independent variable and a plot random effect was included. Collinearities between the competition index, LA0 and DBH0 prevented their use in the same model. Other candidate variables were treatment (CT), species (sp), site index (SI), and relative height after thinning (RH0). Although DBH0 could not be included as a covariate because it was strongly correlated with leaf area or competition indices, a model that was based on DBH0 was included to ensure that it did not perform better than models without DBH0. Model selection that was based upon AICc was performed and regression assumptions were verified. If no model was clearly superior, i.e., an AICc weight over 90%, model averaging was performed (Mazerolle, 2006). At the stand level, we compared responses to thinning within each pair of plots by determining whether temporal volume trajectories between control and thinned plots were convergent, parallel or divergent. For each of the 16 pairs of plots, we evaluated the type of volume trajectory using the relative difference in volume between the two plots:

RVD ¼ ½ðV 1:CTL  V 1:THN Þ  ðV 0:CTL  V 0:THN Þ=V 1:CTL

ð8Þ

where RVD is the relative volume difference, V1.CTL is the volume of the control stand in 2014, V1.THN is the volume of the thinned stand in 2014, V0.CTL is the volume of the control stand immediately after CT, and V0.THN is the volume of the thinned stand immediately after CT. Positive values of RVD corresponded to divergent volume trajectories, negative values to convergent trajectories, and null values to parallel trajectories. We then modelled RVD as a function of plotlevel variables including the initial basal area (BA0), the proportion of basal area that was removed in thinned plots (%BAR), the proportion of plot area occupied by skidding trails (%ST), the site index (SI), and the initial stand density (SD0):

RVD ¼ b0 þ b1 BA0 þ b2 %BAR þ b3 %ST þ b4 SI þ b5 SD0

ð9Þ

These explanatory variables were chosen because they are all related to variations in wood production over time (Zeide, 2001; Pelletier and Pitt, 2008). 2.8. Statistical analysis All statistical analyses were performed in the R statistical programming environment (Version 3.2.2, R Development Core Team, 2015). Mixed models were programmed using the nlme package (Pinheiro et al., 2015) and model selection based on AICc was performed using the AICcmodavg package (Mazerolle, 2015). Various linear regressions were tested to verify effect of commercial thinning on growth efficiency, stand attributes, and wood production. 3. Results 3.1. Tree scale BAI that was calculated over a short period prior to thinning was similar between treatments (P = 0.6834, Table 2). For the period after thinning (7–10 years), BAI of thinned trees was significantly higher than that of control trees (P = 0.0283). Intensity of competition, which was evaluated using the mean value of DI4, was significantly lower in thinned plots after CT (P < 0.0001), whereas it was

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Table 2 Mean stand characteristics (±standard error) of control and thinned plots before, immediately after and seven to ten years after thinning. Variable

Tree scale BAI (cm2 yr1) DI4 LA (m2) GE (dm3 m2 yr1) Stand scale N Dq (cm) DBH800 (cm) BA (m2 ha1) MSV (dm3 st1) Volume (m3 ha1) VI (m3 ha1 yr1) BAdead (m2 ha1) BAingowth (m2 ha1) LAI (m2 m2) GE (dm3 m2 yr1)

Description

1–4 years before CT

Year of CT

7–10 years after CT

Control

Thinned

P>T

Control

Thinned

P>T

Control

Thinned

P>T

Mean basal area increment Mean competition index Mean leaf area Growth efficiency

8.8 ± 0.3 0.17 ± 0.01 58 ± 6 0.10 ± 0.01

7.9 ± 0.3 0.18 ± 0.01 58 ± 6 0.11 ± 0.01

0.6834 0.6199 0.9750 0.3180

– 0.25 ± 0.01 62 ± 6 0.11 ± 0.01

– 0.20 ± 0.01 63 ± 7 0.12 ± 0.01

– 0.0129 0.9466 0.5080

8.0 ± 0.4 0.20 ± 0.01 58 ± 5 0.10 ± 0.01

9.4 ± 0.4 0.14 ± 0.01 70 ± 6 0.13 ± 0.01

0.0283 <0.0001 0.1298 0.2130

Density Mean quadratic DBH Mean DBH of the 800 largest trees Mean basal area Mean stem volume of the 800 largest trees Mean merchantable volume Mean merchantable volume increment Mean basal area of dead trees Mean basal area of ingrowth Leaf area index Mean growth efficiency

1485 ± 69 16.4 ± 0.1 16.9 ± 1.1

1550 ± 101 15.0 ± 0.1 16.9 ± 0.5

0.7260 0.0480 0.9980

1575 ± 90 14.7 ± 0.1 16.4 ± 0.5

1092 ± 72 14.8 ± 0.1 15.2 ± 0.4

0.0003 1.0000 0.0713

1666 ± 85 16.3 ± 0.6 19.1 ± 0.6

1152 ± 84 17.0 ± 0.4 18.2 ± 0.5

0.0008 0.3560 0.1726

31.3 ± 0.9 88 ± 5

27.2 ± 1.7 86 ± 5

0.2060 0.8910

26.9 ± 2.2 110 ± 9

18.8 ± 1.5 92 ± 7

<0.0001 0.1361

36.7 ± 2.1 173 ± 15

27.0 ± 1.8 162 ± 12

<0.0001 0.5547

176 ± 7 8.8 ± 1.6

141 ± 11 6.4 ± 1.1

0.7810 0.3660

146 ± 15 –

91 ± 8 –

0.0003 –

203 ± 19 6.5 ± 0.9

151 ± 14 6.9 ± 0.7

0.0061 0.7230

0.5 ± 0.2 5.5 ± 1.5 5.1 ± 0.3 0.17 ± 0.02

0.4 ± 0.2 3.5 ± 0.9 4.3 ± 0.1 0.15 ± 0.02

0.5685 0.7992 0.3470 0.7769

– – 3.8 ± 0.6 –

– – 2.5 ± 0.4 –

– – 0.0652 –

1.7 ± 0.4 1.7 ± 0.3 5.2 ± 0.4 0.13 ± 0.01

0.9 ± 0.3 1.6 ± 0.3 3.7 ± 0.2 0.19 ± 0.01

0.1520 0.7150 0.0330 0.0195

Note: All values were estimated using information from 16 plots per treatment, except for the period prior to CT, for which only 7 plots per treatment were available. LA and GE at the tree scale were estimated using 64-cored trees per treatment for each of the three periods.

similar between thinned and control plots prior to treatment (P = 0.6199). Post-thinning tree BAI decreased with increasing distance from the nearest skid trail (Eq. (10), R2 = 0.12, RMSE = 0.6106). For trees that were located more than 5 m away from a trail, this effect became negligible as mean tree BAI approached that of trees in control plots (Fig. 1).

BAI ¼ 12:81  1:37 log Dist

ð10Þ

where Dist is the shortest distance between a tree and a skid trail (m). To evaluate whether tree LA was affected by the distance from the nearest skid trail, we first calibrated relationships between the number of growth rings in the sapwood and tree age using an age range covering 11–65 years for balsam fir (Eq. (11), n = 168, R2 = 0.33, RMSE = 0.3208) and 12–57 years for red spruce (Eq. (12), n = 66, R2 = 0.79, RMSE = 0.2407). Site index and tree social status were tested as covariates in these models, but they

did not have a significant effect on the number of growth rings in sapwood (GRSA):

Balsam fir Red spruce

ln GRSA ¼ 0:652 þ 0:470 ln Ac ln GRSA ¼ 0:642 þ 0:530 ln Ac

ð11Þ ð12Þ

Even if a fair part of the variation of GRSA remained unexplained, equations 11 and 12 were associated with unbiased residuals and were thus used to estimate past leaf area of both tree species using specific relationships between leaf area and sapwood area. Tree LA at any year after CT was best explained by LA at time of thinning (LA0), tree social status (SS), and the interaction between the time that had elapsed since thinning (T) and distance from the nearest skid trail (Eq. (13), R2 = 0.85, RMSE = 0.3610).

ln LA ¼ 3:46 þ 0:01LA0 þ ða T  DistÞ þ b SS

ð13Þ

where a and b are regression coefficients that are indicated in Table 3.

Fig. 1. Tree basal area increment (BAI) as a function of distance from the nearest skid trail. BAI was calculated for a period of 7–10 years after thinning. Each point is the mean increment of 10–40 trees and the bars correspond to standard errors.

S. Boivin-Dompierre et al. / Forest Ecology and Management 384 (2017) 6–16 Table 3 Coefficient values for Eq. 13. Coefficient values Time  Distance T Ctl (Control) T Near (Distance < 5 m) T Far (Distance > 5 m)

a 0.003 0.022 0.005

P>T 0.651 0.003 0.529

Social status (SS) Dominant and codominant (D-C) Intermediate (I) Suppressed (S)

b 0.00 0.31 0.83

P>T 0.000 0.000 0.000

Note: For SS, the coefficients of dominant and codominant trees, which were statistically similar, were the reference value.

According to the observed effect of distance from the nearest skid trail on tree BAI (Fig. 1), we used Eq. (13) for three categories of tree: control trees and thinned trees that were located within and outside a 5-m strip along skidding trails. Codominant trees had the largest LA, which was similar to that of dominant trees, but significantly higher than that of intermediate and suppressed trees (Table 3). LA of trees that were located near skid trails increased linearly after CT, and based on confidence intervals, was significantly larger than that of control trees immediately after the fourth year following treatment (Fig. 2A). This period corresponded to the maximum

11

GE values for all thinned trees, whether they were within or outside a 5-m strip along skid trails (Fig. 2B). GE of thinned trees that were located less than 5 m from skid trails was higher than that of thinned trees located further away (Fig. 2B). The preceding results suggest that competition indices that take into account the distance from skid trails should be more efficient in explaining tree growth than distance-independent competition indices. Using the optimum competition radius of 4 m, we compared distance-dependent and distance-independent indices on a subset of trees, for which the 4-m radius could be computed (575 trees), i.e., by excluding trees closer than 4 m from the inventoried zone boundaries. When we considered only trees that were located in the 5-m strip along a skid trail, we found that the distance-dependent index DD1 performed better than all other competition indices for predicting tree basal area increment (Table 4). When we considered all trees in the sampling plots, we found that distance-independent competition indices generally outperformed distance-dependent indices, even in thinned plots where tree removal created heterogeneous tree distributions (Table 4). The best overall index was DI4, which represents a measure of the area that is potentially available for a tree considering the proportional contribution of this tree to plot basal area (Tomé and Burkhart, 1989). Using the competition index that was most closely related to tree growth (DI4), we found that tree BAI decreased with increasing

Fig. 2. Temporal changes of tree leaf area (A) and growth efficiency (B) in control plots and for two distance classes from the nearest skidding trail in thinned plots. Shaded areas correspond to the confidence interval of each tree category.

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Table 4 Statistics of competition index models fitted according to Eq. 3. Index

All R

2

Control AICc

2

Thinned Rank

R2

AICc

Rank

0.19 0.32 0.27

904.2 814.1 845.9

8 2 6

0.41 0.38 0.31

241.8 256.6 275.7

1 5 8

0.33 0.32 0.33 0.37 0.32 0.08 0.08

851.5 845.5 829.7 797.8 820.2 967.1 967.0

7 5 4 1 3 9 10

0.41 0.42 0.43 0.43 0.37 0.21 0.21

274.7 255.0 253.1 245.3 264.5 305.9 306.1

7 4 3 2 6 9 10

R

AICc

Rank

R

Distance-dependent competition indices DD1 0.32 2476.5 DD2 0.34 2435.5 DD3 0.29 2520.8

6 5 7

0.35 0.27 0.22

1493.5 1565.3 1616.9

5 6 8

Distance-independent competition indices DI1 0.49 2192.3 DI2 0.47 2231.0 DI3 0.48 2207.4 DI4 0.50 2126.5 DI5 0.32 2569.4 DI6 0.18 2696.1 DI7 0.18 2694.6

2 4 3 1 8 10 9

0.51 0.48 0.49 0.51 0.23 0.13 0.13

1330.2 1367.8 1353.8 1314.1 1593.1 1691.0 1691.4

2 4 3 1 7 9 10

values of the competition index, but this decrease was less pronounced for thinned than for unthinned trees (Fig. 3). In addition, spruce trees had a slightly lower BAI compared to fir trees (Eq. (14); P = 0.0442, R2 = 0.50, RMSE = 0.5719).

ln BAI ¼ 2:77  0:10sp  4:81ðDI4  THNÞ  3:85ðDI4  CTLÞ

ð14Þ

where THN is for thinned trees and CTL for control trees. 3.2. Stand scale To compute growth efficiency at the stand scale, LA for spruce and fir trees that had not been cored was estimated with the following equation (R2 = 0.65, RMSE = 0.42):

ln LA ¼ 0:580 þ 0:05DBH þ 0:530 ln CSA þ 0:470sp

ð15Þ

Thinning significantly increased the periodic amount of wood that was produced per unit leaf area, i.e., GE, by 45% (Fig. 4). GE tended to increase with increasing basal area that was measured in 2014 for both control and thinned plots, but this trend was not statistically significant. Leaf area index (LAI) decreased by 2 m2 m2 about 7–10 years after thinning (Table 2). Yet mean DBH of the 800 largest trees per hectare (DBH800), which corresponds approximately to the number of trees that were harvested at maturity, did not differ between treatments, either immediately following thinning, or 7–10 years afterwards (Table 2). Accordingly, no significant differences between treatments were detected for mean stem volume and stand merchantable volume increment.

Thinned (Dist < 5 m) AICc

Rank

2

Thinning slightly reduced tree mortality compared to control plots, but this difference was not statistically different. There was also no evidence that CT increased the basal area of ingrowth (Table 2). Temporal changes in merchantable stand volume for each pair of plots followed statistically parallel trajectories, with volume increments averaging of 6.7 m3 ha1 yr1 for both thinned and unthinned plots. Merchantable standing volume of these stands was best predicted by time-since-thinning (T), the basal area prior to thinning (BA0), and the leaf area index before thinning (LAI0), whereas other stand and site variables did not improve the model (Eq. (16); R2 = 0.81, RMSE = 29.79):

V ¼ 6:7T þ 4:2BA0 þ 12:1LAI0  39:7CT

ð16Þ

Accordingly, the relative difference in volume (RVD) between thinned and control plots was not statistically different from zero (P = 0.2580) when all trees from the treated plots were used in the calculations, indicating parallel volume trajectories. However, when RVD was evaluated using only the trees located within 5-m strips along the skid trails, volume trajectories were found to be divergent (P = 0.0092). Despite the overall parallel volume trajectories of thinned and control plots, some of the 16 pairs of study plots showed slightly divergent or convergent volume trajectories (Fig. 5). Using the RVD between the control and thinned plots in each pair, we related these differences with other variables, such as the proportion of basal area removed and the proportion of the thinned plot area that was covered by a skid trail. Since none of these variables was significantly related to RVD, the deviations from parallelism appeared to be incidental or at least unrelated

Fig. 3. Predicted tree basal area increment as a function of the competition index DI4; thinning is for balsam fir trees.

S. Boivin-Dompierre et al. / Forest Ecology and Management 384 (2017) 6–16

13

Fig. 4. Growth efficiency as a function of basal area in 2014 for thinned and unthinned stands. Each point corresponds to GE computed at the plot level.

Fig. 5. Temporal changes in merchantable stand volume for each pair of plots.

to site conditions and to the tested thinning patterns. Finally, some of the plot pairs showed small differences in merchantable volume immediately after application of the thinning treatment (e.g. blocks 2, 4, 6 and 13 in Fig. 5). This was not attributable to a lower thinning intensity, but rather to higher pre-treatment merchantable volume in the thinned plots relative to their respective controls. 4. Discussion The spatial coordinates of trees and skid trails allowed us to detect clear tree growth responses to the treatment seven to ten years after commercial thinning. Indeed, because the application of CT mostly consisted of removing trees in skid trails and at short distances on either side of these trails, significant tree basal area and leaf area responses to thinning were limited to the trees that were located within 5-m wide strips on either side of the trails. The quantity of wood that was produced per unit leaf area at the overall plot level was significantly higher in thinned than in control plots, but this seemed insufficient to increase the stand volume, as evidenced by the parallel trajectories of merchantable volume over time between most pairs of thinned and control plots. 4.1. Spatial effects We detected a spatial response pattern among trees in thinned plots, which was related to the creation of gaps that were formed by skid trails and by nearby tree removal. This was consistent with

the observations of Roberts and Harrington (2008) and Genet and Pothier (2013). Consistent with our second hypothesis, BAI of thinned trees increased with decreasing distance from the nearest skid trail (Fig. 1). Genet and Pothier (2013) also found that relative diameter increment of trees with a DBH < 14 cm was stimulated near logging trails after partial cutting in coniferous stands. In our study, growth improvement was mainly present in the first 5 m from a trail, regardless of tree size. Beyond this threshold distance, BAI of trees appeared to stabilize around its lowest value plateau, which was similar to that of control trees (8 cm2 yr1). Therefore, we can conclude that restricting tree removal to only few meters from skid trails prevented the extension of positive effects of thinning to the entire treated stand. Our results support our first hypothesis stating that the causes of tree growth response to CT shift over time from wood production efficiency to resource acquisition. Indeed, we observed an increase in GE as soon as the year of thinning for trees that were located near skid trails (Fig. 2B), suggesting that acclimation of trees to CT first increased the efficiency of a given leaf area to produce wood in response to higher light availability. This difference in GE between thinned and unthinned trees continued to increase until the fourth year after thinning, at which time it began to decrease as a significant difference in LA first appeared (Fig. 2A). Accordingly, increased access to solar radiation is an important determinant of leaf area expansion (Binkley et al., 2010; Omari et al., 2016). Creation of gaps in and beside skid trails resulted in higher light availability for nearby residual trees, which stimulated wood production per unit leaf area over the short-term and crown

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expansion over the mid-term. This overall acclimation of trees to improved light availability explains improved stemwood growth at the tree level (Brix, 1983; Vose and Allen, 1988; DeRose and Seymour, 2010). This effect is especially noticeable for dominant and codominant trees, which have the largest increase in LA. 4.2. Non-spatial effects In spite of the spatial effects of skid trails on tree growth, distance-dependent competition indices did not perform better than distance-independent indices in predicting BAI after thinning for thinned and unthinned trees altogether (Table 4), as was observed by Roberts and Harrington (2008). Indeed, for a distance-dependent index to be more efficient than distanceindependent indices, only trees within 5-m strips along the skid trails needed to be considered in the analysis. Overall better performance of distance-independent indices is likely related to the relatively low mass of trees within 5-m strips along skid trails, which corresponds to 35% of the remaining trees in all thinned plots (185 of 575 trees). This result has practical importance since distanceindependent indices have the advantage of being computable from usual inventory data, which is not the case for distance-dependent indices. The distance-independent competition index DI4 was the overall best predictor of stemwood growth. Thinning significantly reduced overall stand competition, which in turn significantly reduced DI4 values (Table 2) and increased tree growth (Fig. 3). Reduction in competition decreased conflicts between trees for the acquisition of the same pool of available resources (Larocque et al., 2013). Furthermore, DI4 seems to be an adequate index for evaluating the effect of silvicultural treatments at the tree- and stand-levels, given that it refers to stand density and to a dominance ratio that was based on BA (Jack and Long, 1996). It is the only index that including both a tree- and a stand-level variable, which seems necessary for adequately describing the competition in thinned and unthinned stands. Yet the relationship between DI4 and tree BAI resulted from interaction with the thinning treatment (Fig. 3). While tree growth between thinned and unthinned plots was similar at low competition values, trees had increasingly larger BAI in thinned than in control plots with increasing DI4 values. This growth advantage of thinned plots for a given level of competition is likely related to the greater GE of trees near skid trails. Thus, the interaction between DI4 and treatment suggests that effects of spatial heterogeneity in thinned stands were strong enough to influence average tree growth (Fig. 3). Last, the BAI prediction model revealed that for the same competition intensity, growth of balsam fir was slightly greater than that of red spruce, which accords with the generally observed faster development of balsam fir in coniferous stands of northeastern North America (Day, 2000; Dumais and Prévost, 2014). Commercial thinning significantly decreased LAI at the stand level (Table 2), but this response was compensated by an increase of about 45% in annual stemwood production per unit leaf area (Fig. 4). This increase in GE after thinning could have been larger, if we were capable of evaluating the response of all tree biomass compartments. For example, Vincent et al. (2009) found that root biomass growth of black spruce was improved in the first four years following thinning, whereas stem growth response was delayed and still ongoing after 10 years. Apart from effects on tree growth, GE also provided an index of stand vigour or its sensitivity to environmental stresses (Waring, 1983). For example, a detectable benefit of CT on GE is the increased resistance of balsam fir to defoliation, especially by the spruce budworm (Choristoneura fumiferana [Clemens]) (Bauce, 1996). A frequent assumption is that CT can increase the total stand production (the combined volume of thinning and final harvest)

by offsetting potential mortality (Nyland, 2002). This would mainly be the result of the removal of weak trees, which are susceptible to attacks by pests and pathogens, while retaining vigorous trees (Marshall and Curtis, 2002; Mäkinen and Isomäki, 2004). Thinning did not significantly reduce losses due to mortality (Table 2), which likely contributed to similar yields between thinned and unthinned plots (Fig. 5). The inability of thinning to significantly reduce tree mortality compared to control plots could be related to the relatively large proportion of thinned plots that was not effectively thinned, as suggested by the tree growth improvement that was limited to the 5-m strips along the trails. These findings are consistent with the most accepted outcome of thinning, which predicts a parallel response to thinning (Zeide, 2001). A major explanation of parallel trajectories of merchantable volume over time that were observed in this case study is that the passage of machinery requires relatively wide trails where every tree is harvested. These trails lead to a reduction of about 20% of the basal area in a treatment that targets an overall removal of 35%. This leaves only 15% for which a true stem selection procedure was possible, and results show that this was concentrated near the trails. Accordingly, mechanized thinning treatments rarely lead to gains in merchantable volume over a full rotation (Smith et al., 1997; Marshall and Curtis, 2002). Our third hypothesis stated that departures from parallelism (divergence) in volume trajectories between control and thinned plots would increase with increasing thinning intensity or area occupied by skid trails. This hypothesis received little support from our analysis, most likely because of our limited sample size, range of thinning intensity or proportion of the area that was effectively thinned. Indeed, when we considered only the part of the stand that was effectively thinned, i.e. the 5-m strips along the skid trails, the volume trajectories were found to diverge. Another assumed effect of thinning that is also used to justify its application, is the improvement of tree size and quality (Mäkinen and Isomäki, 2004; Karlsson, 2006; Pelletier and Pitt, 2008). The study stands did not produce this expected outcome, as CT did not significantly stimulate increases in either DBH800, or mean stem volume. This suggests that 7–10 years after treatment, thinned and unthinned stands should have a similar level of product recovery and, therefore, value (Auty et al., 2014). In addition to the relatively short monitoring period, these results can again be explained by the fact that tree harvesting was mainly done in and around trails, which resulted in a similar level of removal within each size class and a growth response that was limited to a small proportion of trees located near the trails. 4.3. Silvicultural implications Maximizing timber production by CT typically involves the application of light but frequent thinning (Marshall and Curtis, 2002; Mäkinen and Isomäki, 2004). This strategy can be applied in small woodlots or in experimental trials where manual harvesting and careful forwarding are possible (Soucy et al., 2012; Barrette and Tremblay, 2015). However, the operational deployment of thinning over large areas requires mechanized operations with which this strategy is not compatible. Indeed, mechanized operations require trails for machinery passage, which may form unproductive areas if they are too large (Long and Smith, 1992). Also, our observations of localized tree growth improvement along skid trails suggest that mechanized applications of CT should be adjusted to expand its positive effects to a larger proportion of the stand. The first adjustment would be to reduce trail width in order to accelerate canopy closure and, thus, ensure full site occupancy by trees. This would be possible using small harvesters and forwarders. Second, we suggest that the maximum boom reach of the harvester be used to remove trees as far as possible from skid trails to increase the proportion of released trees. If the reach of the

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harvester is limited, we suggest considering alternative harvesting systems such as ‘‘ghost” trails within which only the harvester is allowed to circulate to efficiently access the interior of strips between skid trails. 5. Conclusion This study showed evidence that conifer trees can respond positively to CT with observed increases in growth efficiency, leaf area and basal area increment of trees located near skid trails. When we compared all trees in the thinned plots with those of control plots, no significant differences were detected in the usual indicators of tree growth response, such as DBH800 or mean stem volume. Improvement in growing conditions was likely constrained to a limited proportion of trees, i.e., those located near skid trails. Nevertheless, control and thinned plots showed parallel volume trajectories over time, suggesting that adjustments to the application of thinning could produce advantages in stand-level growth and yield. These adjustments include reduction in the width of skid trails and the removal of trees further than a few meters from both sides of these trails. In addition, since the study only covered a period of 7–10 years following thinning, it is possible that prolonged effects of the treatment on tree leaf area and growth efficiency would produce better results when the stands were ready for another thinning, i.e., 15–20 years after CT. Acknowledgements This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de recherche du Québec–Nature et technologies (FRQNT), and Domtar Corporation. We are grateful to Philippe Leduc for his help with the fieldwork and to the staff from Domtar for their support on the project. We also thank William F.J. Parsons (CEF) and two anonymous reviewers for making valuable comments on the manuscript. References Auty, D., Achim, A., Bédard, P., Pothier, D., 2014. StatSAW: modelling lumber product assortment using zero-inflated Poisson regression. Can. J. For. Res. 44, 638–647. Barrette, M., Tremblay, S., 2015. Réaction convergente du volume marchand 10 ans après l’éclaircie d’une sapinière très dense. For. Chron. 91, 252–259. Bauce, É., 1996. One and two years impact of commercial thinning on spruce budworm feeding ecology and host tree foliage production and chemistry. For. Chron. 72, 393–398. Beer, T.W., 1962. Components of forest growth. J. For. 60, 245–248. Benjamin, J.G., Seymour, R.S., Meacham, E., Wilson, J., 2013. Impact of whole-tree and cut-to-length harvesting on postharvest condition and logging costs for early commercial thinning in Maine. North. J. Appl. For. 30, 149–155. Biging, G.S., Dobbertin, M., 1995. Evaluation of competition indices in individual tree growth models. For. Sci. 41, 360–377. Binkley, D., Reid, P., 1984. Long-term responses of stem growth and leaf area to thinning and fertilization in a Douglas-fir plantation. Can. J. For. Res. 14, 656– 660. Binkley, D., Stape, J.L., Bauerle, W.L., Ryan, M.G., 2010. Explaining growth of individual trees: Light interception and efficiency of light use by Eucalyptus at four sites in Brazil. For. Ecol. Manage. 259, 1704–1713. Binkley, D., Stape, J.L., Ryan, M.G., 2004. Thinking about efficiency of resource use in forests. For. Ecol. Manage. 193, 5–16. Bond-Lamberty, B., Wang, C., Gower, S.T., 2002. Leaf area dynamics of a boreal black spruce fire chronosequence. Tree Physiol. 22, 993–1001. ˇ , V., Petráš, R., Larocque, G.R., 2015. Different mixtures of Bosela, M., Tobin, B., Šeben Norway spruce, silver fir, and European beech modify competitive interactions in central European mature mixed forests. Can. J. For. Res. 45, 1577–1586. Brix, H., 1983. Effects of thinning and nitrogen fertilization on growth of Douglasfir: relative contribution of foliage quantity and efficiency. Can. J. For. Res. 13, 167–175. Comfort, E.J., Roberts, S.D., Harrington, C.A., 2010. Midcanopy growth following thinning in young-growth conifer forests on the Olympic Peninsula, Western Washington. For. Ecol. Manage. 259, 1606–1614. Contreras, M.A., Affleck, D., Chung, W., 2011. Evaluating tree competition indices as predictors of basal area increment in western Montana forests. For. Ecol. Manage. 262, 1939–1949.

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