Contribution relative des arbres à la croissance
d'érablières en fonction de leur structure et de
traitements sylvicoles
Mémoire
Caroline Lemire
Maîtrise en sciences forestières - avec mémoire
Maître ès sciences (M. Sc.)
Contribution relative des arbres à la
croissance d’érablières en fonction de leur
structure et de traitements sylvicoles
Mémoire
Caroline Lemire
Sous la direction de :
David Pothier, directeur de recherche
Steve Bédard, codirecteur de recherche
Résumé
Le succès de l’application d’une coupe partielle dans les peuplements feuillus repose sur la contribution des arbres prélevés et résiduels à la croissance globale du peuplement, ce qui peut être capté par un critère à l’échelle du peuplement appelé le coefficient de dominance de croissance (CDC). L’objectif principal de cette étude était donc de tester la pertinence d’utiliser le CDC pour évaluer le succès de coupes partielles appliquées à des peuplements d’érable à sucre (Acer saccharum Marsh.) et de bouleau jaune (Betula alleghaniensis Britt.) de structures équienne et inéquienne. Pour ce faire, le CDC a été calculé de deux façons, c’est-à-dire en utilisant comme base de calculs la biomasse des tiges ou la surface foliaire des arbres. Par la suite, nous avons utilisé des modèles linéaires mixtes pour vérifier si une coupe de jardinage dans les peuplements de structure inéquienne occasionnait un changement de CDC à court et à moyen termes, et pour vérifier si les effets de la vigueur et de la taille de l’arbre avant la coupe influençaient la croissance en diamètre de l’arbre après la coupe. Les résultats indiquent que les CDC calculés à partir de la biomasse des tiges étaient semblables aux CDC calculés à partir de la surface foliaire des arbres dans les peuplements de structure équienne (p = 0,7200), mais qu’ils avaient des valeurs plus basses dans les peuplements de structure inéquienne (p < 0,001). De plus, les coupes partielles n’ont pas significativement modifié les CDC à court (p = 0,7721) et à moyen (p = 0,8363) termes. Cependant, plusieurs arbres qui avaient une croissance rapide avant la coupe ont réagi négativement à la coupe partielle alors que de nombreux arbres qui avaient une croissance lente avant la coupe ont réagi positivement. En somme, l’évaluation du succès d’une coupe partielle semble difficile en utilisant un critère à l’échelle du peuplement comme le CDC. Une telle évaluation, ainsi qu'une meilleure compréhension des effets d'une coupe partielle, pourraient toutefois être obtenues par une analyse de croissance à l’échelle de l'arbre ou de la classe de diamètre.
Abstract
The success of applying a partial cut in northern hardwood stands is based on the contribution of harvested and residual trees to overall stand growth, which can be captured by a stand-level criterion called the growth dominance coefficient (GDC). The main objective of this study was to test the relevance of using the GDC to evaluate the success of partial cuts applied to even-aged and uneven-aged sugar maple (Acer saccharum Marsh.)-yellow birch (Betula alleghaniensis Britt.) stands. To do this, we first determined whether GDC calculated with two indicators of resource acquisition, stem mass and tree leaf area, produce similar results after partial cutting according to stand structure. Second, using mixed linear models, we determined the effect of selection cuts on GDC in uneven-aged stands over the short and medium term, and we quantified the effects of tree vigour and size before cutting on diameter growth response of trees after cutting. Results indicate that GDC calculated based on stem mass were similar to GDC calculated based on tree leaf area in even-aged stands (p = 0.7200) but were significantly lower in uneven-aged stands (p < 0.001), likely because of a limited ability for crown expansion in large trees. Furthermore, selection cutting in uneven-aged stands did not significantly change GDC in the short (p = 0.7721) and medium (p = 0.8363) term. However, several trees that grew rapidly before cutting responded negatively to partial cutting while many trees that grew slowly before cutting responded positively. Overall, assessing the success of a partial cut seems difficult using a stand-level criterion such as the GDC. Such an assessment, as well as a better understanding of the effects of a partial cut, could, however, be obtained by a more detailed growth analysis performed at the tree or diameter class level.
Table des matières
Résumé ... ii
Abstract ... iii
Table des matières ... iv
Liste des figures ... v
Liste des tableaux ... vi
Liste des abréviations, sigles, acronymes ... vii
Remerciements ... viii
Avant-propos ... ix
Introduction ... 1
1. Material and methods ... 4
1.1 Study area ... 4 1.2 Experimental design ... 4 1.2.1 Even-aged stand ... 4 1.2.2 Uneven-aged stand ... 5 1.3 Tree measurements ... 6 1.4 Computed variables ... 6 1.4.1 Leaf area ... 6
1.5 Growth dominance coefficient ... 7
1.6 Statistical analysis ... 8
2. Results ... 10
3. Discussion ... 17
3.1 Comparison of GDC calculated based on stem mass and tree leaf area ... 17
3.2 Effects of stand structure on GDC ... 17
3.3 Tree growth response according to stem size and vigour ... 18
3.4 Change in GDC after selection cutting in the medium term ... 19
3.5 Silvicultural implications ... 20
Conclusion ... 21
Bibliographie ... 22
Liste des figures
Figure 1. Expected changes in growth dominance coefficients immediately after partial cutting applied (A) to an uneven-aged stand with a sound selection of harvested trees, i.e. large trees with low stem mass growth, and (B) after partial cutting for which tree selection was done at random among large trees ... 3 Figure 2. Relationships between tree leaf area and stem mass for sugar maple (SM) and yellow birch (YB) in even-aged and uneven-aged plots ... 12 Figure 3. Trees harvested during selection cutting and residual trees after selection cutting in the four studied uneven-aged plots (A-D) ... 13 Figure 4. Change in stemwood increment as a function of stem mass for all trees remaining after cutting in four uneven-aged plots after selection cutting ... 14 Figure 5. Relationships between the differences in mean annual increment 3 to 5 years following selection cutting and mean annual increment 5 years before selection cutting and stem mass before cutting according to tree vigour before cutting in uneven-aged plots. .. 16
Liste des tableaux
Table 1. Means and standard error (SE) of characteristics of Duchesnay forest stands according to treatments before cut, after cut and in 2018. ... 5 Table 2. Estimated specific leaf area for each species. ... 7 Table 3. Mean growth dominance coefficients (± SE) calculated using stem mass and tree leaf area. Confidence intervals generated by bootstrap-t variance stabilizing method for grouped plots by structure and treatment are in parentheses. ... 10 Table 4. AICs of regression models in uneven-aged stands for sugar maple and yellow birch trees to determine the best model fit. ... 11 Table 5. Results of mixed linear regression relating differences in tree diameter growth between 3-5 years after and before selection cutting to regression residuals of stem mass increment to stem mass before cutting (Fig. 3). ... 15
Liste des abréviations, sigles, acronymes
CDC: Coefficient de dominance de croissance DBH: Diameter at breast height
GDC: Growth dominance coefficient SD: Standard deviation
SM: Sugar maple YB: Yellow Birch
Remerciements
Je souhaite remercier tout d’abord mon directeur David Pothier qui m’a accompagné tout au long de mon cheminement. Son esprit d’analyse et de synthèse est incroyable! Merci aussi à mon codirecteur Steve Bédard et François Guillemette qui m’ont fourni de précieux commentaires.
Un gros merci à mes collègues de bureau, sans qui mon expérience aux études graduées aurait été bien moins sympathique. À Marine, pour m'avoir apporté un soutien moral, autant sur le plan personnel que professionnel; à Guillaume, pour les riches échanges qui ont contribué à approfondir ma réflexion sur ce travail; à Olivier, pour son entraide et partage de la connaissance sur le monde de la science; à Pierre-Yves, pour les bodums de café que tu nous prépares chaque matin.
Un merci tout spécial à Cédric Gilbert qui m’a aidé à récolter les données sur le terrain. Son organisation et éthique de travail ont grandement contribué à rendre cette partie du projet plus efficace!
Avant-propos
Je suis l’auteure principale de ce mémoire. Il a préalablement été corrigé et bonifié par mon directeur David Pothier et mon codirecteur Steve Bédard. J’ai élaboré le protocole de recherche conjointement avec mon directeur de recherche David Pothier. Mon codirecteur m’a fourni des bases de données de placettes permanentes à partir desquelles j’ai pu réaliser les analyses statistiques des données. J’ai effectué les analyses statistiques, l’interprétation des résultats et la rédaction du manuscrit.
Mon directeur m’a fourni des commentaires et corrections tout au long du projet, notamment sur l’élaboration du protocole, l’interprétation des résultats et la rédaction de ce manuscrit. Mon codirecteur Steve Bédard m’a également donné des commentaires sur le manuscrit.
Introduction
A large portion of northern hardwood forests is dominated by the shade-tolerant sugar maple (Acer saccharum Marsh.) and the mid-shade-tolerant yellow birch (Betula alleghaniensis Britt.) that generally form uneven-aged forest stands. Tough less common in these forest landscapes, even-aged stands consisting of sugar maple and yellow birch are also present because of past management practices such as strip or patch clearcuttings (Hornbeck, 1990; Allison et al., 2003; Doyon et al., 2005). The vertical distribution of trees in a stand influences inter-tree competition and largely determines how resources (light, soil nutrients and water) are partitioned among trees (Battaglia et al., 2002; Coates et al., 2003; Sprugel et al., 2009; Boyden et al., 2012). Even-aged stands are often characterized by an uppermost layer that is composed of large trees forming an almost closed canopy. Because these large trees have a direct access to sunlight, they often grow disproportionately faster than smaller, understory trees (Weiner, 1990; Schwinning and Weiner, 1998). As forest stands develop, tree mortality shifts towards large trees (Lorimer et al., 2001) to create canopy gaps that increase the light availability to understory trees, which progressively leads to an uneven-aged stand structure. Differences in resource availability according to tree size and stand structure, in addition to the capacity of resource uptake and resource use efficiency of individual trees are determining factors in tree growth (Binkley, 2004; Forrester, 2019).
To accelerate the process of stand development, partial cut treatments can be applied to redistribute site resources among trees. In both even-aged and uneven-aged stand structures, partial cuts are often applied to reduce mortality losses (Wilson, 1953; Erdmann and Oberg, 1973; Pothier, 1996; Bédard and Majcen, 2001; Bédard and Majcen, 2003), promote residual tree growth (Wilson, 1953; Erdmann and Oberg, 1973; Ondro and Love, 1979; Bédard and Majcen, 2001; Bédard and Majcen, 2003), and establish regeneration (Bolton and D’Amato, 2011; Gauthier et al., 2016). Current partial cutting practices in northern hardwood forests aim to remove trees with a high probability of mortality (i.e., low vigour) before the end of the next cutting cycle (~25 years), which is determined by classification systems based on stem and crown defects (Guillemette et al., 2008). However, classification systems have recently been found to be poorly related to tree vigour (Moreau et al., 2018b), suggesting that residual trees are not necessarily the strongest contributors to total stand productivity.
The contribution of each tree size to total stemwood production can be characterized by a growth dominance pattern, which is related to a metric named stand growth dominance coefficient (GDC) (Binkley, 2004). In assessing growth dominance patterns, most studies have used stem size (mass, volume, or basal area) as an indicator of resource acquisition (e.g. Binkley and Kashian, 2015; Baret et al., 2017; Fernández-Tschieder and Binkley, 2018; Kuehne et al., 2018). While stem size is a practical measure that is routinely measured in forest inventories, it can diverge from the actual resource acquisition capacity of a tree, which is better represented by its leaf area (Ex and Smith, 2014). Indeed, stem size is an adequate proxy for resource acquisition when it increases proportionately to its leaf area, but it is not always the case particularly for large trees (Lusk et al., 2003) that are commonly found in uneven-aged stands. Therefore, the presence of large trees in a stand can generate differences in stand growth dominance based on the selected resource acquisition indicator (i.e. stem size or tree leaf area).
Stand growth dominance is related to the prevailing mode of tree competition in a stand, which can be determined by characteristics of the relationship between stem mass increment and stem mass of each plot tree (Pothier, 2017; Fernández-Tschieder and Binkley, 2018). This relationship has the advantage of clearly representing the contribution of each tree to stand growth compared to the GDC, which uses cumulative stem mass growth and cumulative mass. Stand growth dominance, and thus tree competition mode, depend on stand structure (Bradford et al., 2010; Forrester et al., 2013; Fernández-Tschieder and Binkley, 2018) and composition (Binkley et al., 2006; Fernández et al., 2011; Pothier, 2017). In addition, GDC can change after a partial cut depending on the stand structure and the size of removed trees (Soares et al., 2017). For example, if a partial cut aims at removing trees among the smallest ones from a stand, GDC is likely to decrease, as is the case for stand structural heterogeneity (Glencross et al., 2016; Soares et al., 2017). However, GDC and stand structural heterogeneity are not always perfectly related (Soares et al., 2017; Fernández-Tschieder and Binkley, 2018) because the resulting GDC will also depend on the contribution of both the removed and residual trees to total stand growth. For example, if large residual trees are poor contributors to total stand growth, the resulting GDC is likely to decrease more than if they were strong contributors. Finally, over the medium term, the resulting stand GDC will also vary according to the growth response of all-sized residual trees (Bradford et al., 2010; Keyser, 2012; Soares et al., 2017).
In this study, we aimed at describing trends in GDC calculated based on stem mass and tree leaf area according to stand structure and partial cut treatments in order to determine the best indicator of resource acquisition. We first hypothesized that GDC calculated with stem mass were similar to those calculated with tree leaf area for even-aged stands but not for uneven-aged stands. Such a difference between stand structures is expected because of the curvilinear relationship between tree leaf area and stem mass when large trees are present (Ex and Smith, 2014), which is usually the case in uneven-aged stands. Second, we hypothesized that a sound selection of trees to be harvested during a partial cut applied to an uneven-aged stand produces an increase in GDC immediately after cutting (Fig. 1A). In contrast, if tree selection is done without specifically targeting low-growth trees, the GDC after partial cutting would remain unchanged (Fig. 1B). Further, we also predicted that the effect of tree selection on GDC would persist in the medium term because the growth of large vigorous trees should increase, while that of small trees should remain unchanged. The results of this study could help target trees to be cut during partial cutting operations in order to maximize post-harvest productivity while considering stand structure dynamics and resource distribution among trees.
Figure 1. Expected changes in growth dominance coefficients immediately after partial cutting applied (A) to an uneven-aged stand with a sound selection of harvested trees, i.e. large trees with low stem mass growth, and (B) after partial cutting for which tree selection was done at random among large trees. Each point represents a tree in a given plot.
1. Material and methods
1.1 Study area
The experiment was carried out in even-aged and uneven-aged northern hardwood stands in the Duchesnay Forest (46° 52’ 30”, 71° 39’ 0”), which is located northwest of Quebec City, Canada. These stands are in the balsam fir (Abies balsamea [L.] Mill.) – yellow birch bioclimatic domain (Robitaille and Saucier, 1998), which is characterized by mean annual temperatures of 2.5 °C, mean annual precipitation of 1360 mm, and a growing season that typically ranges from 150 to 170 days. Topography is hilly with altitude varying between 200 and 300 m. The main surface deposits are composed of marine tills while soil is well to moderately-well drained (Blouin and Berger, 2003). Sugar maple, yellow birch and American beech (Fagus grandifolia Ehrh.) are the dominant tree species in the studied stands, together with minor components of balsam fir, red maple (Acer rubrum L.), white spruce (Picea glauca Moench Voss), red spruce (Picea rubens Sarg.) and eastern hemlock (Tsuga
canadensis (L.) Carr.). Tree species present in even-aged stands also included trembling
aspen (Populus tremuloides Michx.), largetooth aspen (Populus grandidentata Michx.), striped maple (Acer pensylvanicum L.), white birch (Betula papyrifera Marsh.), grey birch (Betula populifolia Marsh.) and pin cherry (Prunus pensylvanica L.f.). The even-aged and uneven-aged stands were 1 km apart from each other.
1.2 Experimental design
1.2.1 Even-aged standThe studied even-aged stand originated from a clear-cut that was applied to a 50-ha northern hardwood forest in 1971. A precommercial thinning was applied ten years later (Robitaille et al., 1990). In 2012, a commercial thinning experiment was established in the stand according to a completely randomized block design. For the purpose of this study, we selected four blocks within each of which two treatments were applied: an uncut control with a mean initial basal area of 24.2 m2 ha-1 and a neutral thinning treatment that left an average basal area (BA) of 17.4 m2 ha-1 (Table 1). The neutral thinning treatment consisted of removing trees from all size classes, leading to similar tree size distributions before and after cutting. Each treatment was applied on 0.1-ha, rectangular- or square-shape plots that were surrounded by buffer zones ranging in widths from 8 to 25 m.
1.2.2 Uneven-aged stand
The uneven-aged stand has a history of diameter-limit cuts, but no intervention was applied at least 15 years before the establishment of the experiment (Bérard, 1990). A completely randomized block design was established in 1990, 1993, 1994 and 1995, each year corresponding to the establishment of a new block. Each block consisted of two treatments: a selection cut that aimed to leave a BA of 18 m2 ha-1 in 2-ha, rectangular-shape plots, and an uncut control within 1-ha, squared-shape plots. Twenty years following these first selection cuts, the same treatment was applied again in all treated plots. All plots had a buffer zone varying from 20 to 25 m in width. For the purpose of this study, we performed forest inventories on one 0.25-ha subplot that was located within each treatment. The stand characteristics before, immediately after cutting and in 2018 are presented in Table 1.
Table 1. Means and standard error (SE) of characteristics of Duchesnay forest stands according to treatments before cut, after cut and in 2018.
Even-aged plots Uneven-aged plots
Thinned Control Selection cut Control
Years since last cut 6 - 3,4,5 or 8 - DBH (cm) Before cutting 15.6 ± 0.8 15.1 ± 0.5 21.1 ± 1.2 23.4 ± 1.7 After cutting 16.0 ± 1.2 - 21.2 ± 1.2 - 2018 16.7 ± 0.9 16.3 ± 0.7 21.7 ± 1.5 23.6 ± 2.2 DBH (cm) (min-max) Before cutting 9.1 – 37.1 9.1 – 27.5 9.1 – 71.2 9.1 – 79.8 After cutting 9.1 - 31.8 - 9.1 – 71.2 - 2018 9.1- 36.0 9.1 – 31.6 9.6 - 65.3 9.4 – 82.2
Total basal area (m2 ha-1)
Before cutting 24.9 ± 1.4 23.3 ± 2.2 24.0 ± 1.3 22.6 ± 3.8
After cutting 13.4 ± 0.6 - 17.9 ± 0.4 -
2018 14.9 ± 0.8 24.6 ± 3.3 19.8 ± 0.6 24.2 ± 3.9
Yellow birch basal area (m2 ha-1) Before cutting 14.8 ± 3.6 14.0 ± 3.0 6.9 ± 4.8 9.1 ± 6.3 After cutting 9.3 ± 1.8 - 5.6 ± 3.1 - 2018 10.9 ± 1.7 15.5 ± 3.5 7.3 ± 3.6 9.9 ± 6.4 Sugar maple basal area (m2 ha -1) Before cutting 1.6 ± 1.0 1.3 ± 0.1 10.6 ± 7.0 10.0 ± 4.2 After cutting 1.0 ± 0.6 - 8.1 ± 5.1 - 2018 0.9 ± 0.8 1.2 ± 0.6 8.8 ± 5.8 11.4 ± 6.4
Trees per hectare Before cutting 1472 ± 98 1495 ± 143 573 ± 106 448 ± 70
After cutting 802 ± 122 - 443 ± 48 - 2018 738 ± 124 1238 ± 130 446 ± 49 445 ± 64 Quadratic mean diameter (cm) Before cutting 14.7 ± 0.9 14.1 ± 0.4 23.3 ± 1.8 25.5 ± 3.2 After cutting 14.7 ± 1.0 - 22.7 ± 1.0 - 2018 16.1 ± 1.0 15.9 ± 0.5 23.8 ± 1.3 26.4 ± 3.2
1.3 Tree measurements
In each plot, the diameter at breast height (DBH) of each tree with a DBH > 9.0 cm was measured before cutting, at each post-cut 5-year period, and in 2018. During the 2018 data collection, we also measured the total height and the height of the lowest living branch using a Haglöf Vertex hypsometer. We also measured tree crown radii from the edge of the trunk to the point underneath the edge of the crown along the four cardinal directions using a Suunto clinometer. In total, these measurements were performed on 1540 trees distributed in 16 plots. We purposefully removed trees with bent crowns or damaged trunks because these trees were associated with data outliers.
1.4 Computed variables
1.4.1 Leaf areaWe calculated the crown surface area (CSA) of sugar maple and yellow birch trees according to the equation developed by Moreau et al. (2018a):
CSA = πrq/6clup2 [(4clup2 + rq2) 2
3 ⁄ − r
q3] + πrq√cllow2 + rq2 (1)
where 𝑟𝑞 is the quadratic mean radius of the crown, 𝑐𝑙𝑢𝑝 is the upper part of the crown (40
% of the total crown length), and 𝑐𝑙𝑙𝑜𝑤 corresponds to the lower part of the crown (60 % of
the total crown length). Tree leaf area of sugar maple and yellow birch was then obtained using equation 2:
𝐿𝐴 = 𝑏1𝐶𝑆𝐴𝑏2 (2)
where 𝑏1 and 𝑏2 are species-specific parameters calculated by Moreau et al. (2018a). For
species other than sugar maple and yellow birch, we used allometric DBH-based equations (Lambert et al., 2005) to obtain dry foliage biomass. For species not considered by Lambert et al. (2005), such as striped maple and pin cherry, we substituted DBH-based equations with those of species having similar wood densities (Miles and Smith, 2009). Tree leaf area was computed by multiplying dry foliage biomass with specific leaf area of each species (Table 2).
Table 2. Estimated specific leaf area for each species.
Species Estimated specific
leaf area (m2 kg-1)
Reference
Balsam fir 6.6 (Dumais et al., 2014)
Beech 32.0 (Fahey et al., 1998)
Black cherry 27.4 (Loranger and Shipley, 2010)
Eastern hemlock 5.8 (Kenefic and Seymour, 1999)
European larch 11.7 (Fellner et al., 2016)
Grey Birch 18.7 (Nelson et al., 2013)
Largetooth aspen 15.7 (Nelson et al., 2013)
Pin cherry 17.0 (Fahey et al., 1998)
Red maple 23.8 (Loranger and Shipley, 2010)
Red spruce 3.8 (Fahey et al., 1998; Dumais et al., 2014)
Striped maple 30.5 (Fahey et al., 1998)
Trembling aspen 17.4 (Bond-Lamberty et al., 2002)
White birch 18.5 (Bond-Lamberty et al., 2002)
1.5 Growth dominance coefficient
The past measurements of tree DBH in each plot allowed us to assess the stem mass and stem mass increment for all trees. Current DBH increments were calculated by subtracting the DBH value of the last survey (3 to 8 years before 2018) to the DBH measured in 2018. For each measurement, stem mass was derived from the calculation of stem volume. First, we used DBH measurements and plot characteristics to estimate tree height 3 to 8 years before 2018 according to species-specific equations developed by Auger (2016). Second, we used both tree DBH and height to determine net merchantable stem volume according to volume equations developed by Fortin et al. (2007). Third, we calculated stem mass by multiplying stem volume by wood specific gravity of each species (Miles and Smith, 2009). In each plot, trees were then arranged in ascending order of stem mass and the GDC was calculated according to West (2014):
𝐺𝐷𝐶 = 1 − ∑𝑛𝑖=1(𝑠𝑖− 𝑠𝑖−1)(∆𝑖+ ∆𝑖−1) (3)
where 𝑛 is the number of live merchantable trees in each plot during both measurements, 𝑠𝑖 is cumulative proportional stem mass of tree 𝑖 , and ∆𝑖 is cumulative proportional stem
mass increment of tree 𝑖. The same procedure was used to calculate GDC based on tree leaf area instead of stem mass. Because GDC calculations imply relationships between cumulative stem mass growth and cumulative stem mass (or tree leaf area), it is somewhat
difficult to visualize the effect of harvesting a certain group of trees from a plot and determine the resulting GDC. We thus took advantage of the close relationship between patterns of growth dominance and tree competition mode (Pothier, 2017) to illustrate such effects. Tree competition mode can be deduced by regressing stem mass increment on stem mass using each tree of a given plot. When the intercept of such a regression is positive while the slope value is low (partial size-symmetric or completely symmetric competition mode), the stand is generally characterized by a reverse growth dominance (negative GDC value). However, if the regression parameters indicate a negative intercept together with a higher slope value (size-asymmetric competition mode), a positive growth dominance generally characterizes the stand growth dynamics (Pothier, 2017).
1.6 Statistical analysis
All statistical analyses were performed in the R statistical programming environment (version 3.4.1, R Development Core Team 2017, Vienna, Austria). To test the first hypothesis, we generated confidence intervals of GDC for each combination of stand structure and treatment using the variance stabilizing bootstrap-t method to account for small sample size (n=4) (Polansky, 2000). When a confidence interval overlapped the null value of GDC, we concluded that GDC was not statistically different from zero. For each stand structure, we performed a linear model using the lm function with GDC as the response variable and a categorical variable indicating if GDC was calculated based on stem mass or tree leaf area. In complement, we also performed a linear model with the difference between GDC calculated based on tree leaf area and GDC calculated based on stem mass as the response variable and stand structure as the categorical variable.
In addition, to help explain potential differences between GDC based on stem mass and leaf area, we described the relationship between tree leaf area and stem mass for trees present in even-aged and uneven-aged plots. Preliminary analyses indicated that relationships between tree leaf area and stem mass were linear in even-aged stands and nonlinear in uneven-aged stands. We determined if sugar maple and yellow birch could be grouped together by testing the statistical difference in the relationships. To do so, we used the lme function to perform a mixed linear model with tree leaf area as the response variable, stem mass and tree species as explanatory variables, and plot as a random effect when relationships were linear. When relationships were nonlinear, we calculated the F ratio adjusted for mixed nonlinear curves described by Potvin et al. (1990). This method consists
in calculating the ratio between the variance of both groups (sugar maple and yellow birch) and that of both groups combined. We excluded species other than sugar maple and yellow birch because their abundances were relatively low, and the application of different methods to compute leaf area would have resulted in discrepancies in its relationship with stem mass. There was a significant difference in the relationships between sugar maple and yellow birch in uneven-aged stands, but not in even-aged stands. We tested different relationships (linear, logarithmic, power, asymptotic, exponential) using the lme function and the nlme package with tree leaf area as the response variable, stem mass as the explanatory variable and plot as a random effect. For uneven-aged stands, we computed the AIC for each model which respected model assumptions (power, asymptotic, exponential) and selected the model with the lowest AIC.
Because we could not estimate tree leaf area for years other than 2018, the second hypothesis was tested by calculating GDC based only on stem mass before and after selection cutting in uneven-aged stands. We performed a linear model using the lm function with GDC as the response variable and an explanatory categorical variable indicating if GDC was calculated before or after cutting. We then related stem mass increment (over a 5-year period before cutting) to stem mass of all trees immediately before cutting and of all residual trees immediately after cutting in each plot. Furthermore, we calculated GDC for an interval of time after cutting (3 to 5 years) for each plot. We performed a mixed linear model using the lme function to test if GDC immediately after cutting was significantly different from GDC 3 to 5 years after cutting, with plot as a random effect. In addition, we determined the diameter growth response of trees according to tree vigour and tree size. To do so, we first calculated differences between mean annual increment 3 to 5 years after cutting and mean annual increment 5 years before cutting. Positive differences mean that selection cutting improved tree growth whereas negative values imply that tree increment decreased after selection cutting. Second, we calculated the residuals around a linear regression line linking stem mass increment immediately after cutting to stemwood mass. Trees with negative residual values were referred to as non-vigorous while those with positive residuals were considered as vigorous. Third, we performed a mixed linear model using the lme function on selection cut uneven-aged plots to test if these residuals affected tree response to cutting. In all cases, we set the significance threshold at 0.05.
2. Results
The confidence intervals of GDC excluded the zero value for all uneven-aged stands, whether GDC was calculated based on stem mass and tree leaf area, or in selection cut and uncut stands (Table 3). In contrast, all confidence intervals of GDC that were calculated in even-aged stands included the zero value (Table 3).
Table 3. Mean growth dominance coefficients (± SE) calculated using stem mass and tree leaf area. Confidence intervals generated by bootstrap-t variance stabilizing method for grouped plots by structure and treatment are in parentheses.
Stand structure and treatment Stem mass Tree leaf area
Uneven-aged stand Selection cut -0.23 ± 0.03 (-0.40 to -0.15) -0.07 ± 0.04 (-0.12 to -0.01) Control -0.21 ± 0.04 (-0.26 to -0.14) -0.07 ± 0.02 (-0.09 to -0.04) Even-aged stand Thinned -0.05 ± 0.03 (-0.13 to 0.02) -0.01 ± 0.04 (-0.17 to 0.02) Control 0.02 ± 0.01 (-0.02 to 0.02) -0.01 ± 0.02 (-0.05 to 0.02)
As expected, negative GDCs (i.e., reverse growth dominance) characterized the growth dynamics of uneven-aged stands regardless of treatment application. However, even-aged stands were associated with neutral growth dominances (i.e., GDC near a zero value), rather than strong growth dominances (i.e., positive GDCs). Furthermore, GDCs calculated with tree leaf area was significantly higher than GDCs calculated with stem mass in uneven-aged stands (p < 0.001), but not in even-aged stands (p = 0.7200). The difference between GDCs based on stem mass and tree leaf area was significantly greater in uneven-aged stands compared to even-aged stands (p = 0.0020). Because the relationship between tree leaf area and stem mass was not statistically different (p = 0.3730) between sugar maple and yellow birch in even-aged stands, we grouped the data of both species to describe the relationship. The relationship between tree leaf area and stem mass was linear in even-aged stands (𝑅2 = 0.80), which can explain the relatively small differences between GDC
calculated based on stem mass and tree leaf area (Table 3). In contrast, according to the F ratio test described by Potvin et al. (1990), the relationships between tree leaf area and stem mass were statistically different for both species in uneven-aged stands. These relationships
were curvilinear for both species (𝑅2 = 0.52 for yellow birch; 0.44 for sugar maple), indicating
that leaf area of large trees tended to reach a plateau while stem size continued to increase in uneven-aged stands (Fig. 2). For both sugar maple and yellow birch, the power model provided the best fit between leaf area and stem mass (Table 4).
Table 4. AICs of regression models in uneven-aged stands for sugar maple and yellow birch trees to determine the best model fit.
Species Type of regression model AIC
Sugar maple Power 3342.94
Asymptotic 3452.84
Exponential 3431.04
Yellow birch Power 2015.84
Asymptotic 2102.76
Figure 2. Relationships between tree leaf area and stem mass for sugar maple (SM) and yellow birch (YB) in even-aged and uneven-aged plots. The fitted line of yellow birch trees present in uneven-aged stands is 𝐿𝐴𝑈𝑛𝑒𝑣𝑒𝑛−𝑎𝑔𝑒𝑑1= 11.6330548 · 𝑠𝑡𝑒𝑚 𝑚𝑎𝑠𝑠0.3685849 (𝑅2=
0.52, 𝑑𝑓 = 201). SE of estimated parameters are respectively 1.50587143 and 0.02448742. The fitted line of sugar maple trees present in uneven-aged plots is 𝐿𝐴𝑈𝑛𝑒𝑣𝑒𝑛−𝑎𝑔𝑒𝑑2=
10.5712000 · 𝑠𝑡𝑒𝑚 𝑚𝑎𝑠𝑠0.3197873 (𝑅2= 0.44, 𝑑𝑓 = 375). SE of estimated parameters are respectively 0.9495312 and 0.0193436. The fitted line of trees in even-aged plots is 𝐿𝐴𝐸𝑣𝑒𝑛−𝑎𝑔𝑒𝑑 = 0.56573 · 𝑠𝑡𝑒𝑚 𝑚𝑎𝑠𝑠 (𝑅2= 0.80, 𝑑𝑓 = 441). SE of estimated parameter is
0.01363.
Since DBH measurements before selection cutting were available for uneven-aged stands, we investigated whether the growth level of trees harvested at time of selection cuts can explain the absence of changes in GDC between uncut and selection cut stands (Table 3). For three out of four plots, trees selected for cutting only induced slight or no changes in GDC, because selected trees consisted of both vigorous (i.e., positive residuals before cutting) and non-vigorous (i.e., negative residuals before cutting) trees (Figs. 3B, C, D).
in a post-cut increase in GDC (Fig. 3A). Overall, there were no statistical differences in GDC before and after selection cutting (p = 0.7721).
Figure 3. Trees harvested during selection cutting and residual trees after selection cutting in the four studied uneven-aged plots (A-D). Solid lines correspond to simple linear regressions between stem mass increment and stem mass before selection cutting while black dashed lines correspond to simple linear regressions after selection cutting, i.e. after the removal of trees represented by red circles.
Three to 5 years after cutting, GDCs calculated based on stem mass only slightly increased compared to GDCs calculated immediately after cutting (Fig. 4), but these differences were globally not statistically significant (p = 0.8363). These slight differences can be explained by an overall positive diameter increment response of large trees to selection cutting. However, contrary to our prediction, large non-vigorous trees (i.e., negative residuals before cutting) responded better to treatment than vigorous trees (i.e., positive residuals before cutting) as indicated by the significantly negative parameter (p < 0.001) associated with
residuals of stem mass increment to stem mass before cutting (Table 5). In addition, the post-cut diameter growth of trees that vigorously grew before cutting (i.e., positive residuals) significantly decreased compared to their diameter growth before cutting (Fig. 5). The post-cut growth response of small trees, however, tended to be less affected by their growth prior to selection cutting (Fig. 5).
Figure 4. Change in stemwood increment as a function of stem mass for all trees remaining after cutting in four uneven-aged plots after selection cutting. The dashed line corresponds to a regression performed with trees immediately after cutting (closed circles) and the dotted line corresponds to a regression performed with the same trees 3 to 5 years after cutting (open circles). The panels A, B, C and D correspond to trees within the four plots.
Table 5. Results of mixed linear regression relating differences in tree diameter growth between 3-5 years after and before selection cutting to regression residuals of stem mass increment to stem mass before cutting (Fig. 3).
Variable Estimate Standard error p-value
(Intercept) 0.0794 0.0273 0.0567
Residuals -0.0161 0.0027 <0.001
(Stem mass before cutting/1000)
-0.0187 0.0283 0.0509
Residuals* (Stem mass before cutting/1000)
Figure 5. Relationships between the differences in mean annual increment 3 to 5 years following selection cutting and mean annual increment 5 years before selection cutting and stem mass before cutting according to tree vigour before cutting in uneven-aged plots. Vigorous trees correspond to positive residuals of the regression lines between stem mass increment to stem mass before cutting (Fig. 3) while non-vigorous trees correspond to negative residuals. Confidence intervals are represented by the grey areas.
3. Discussion
3.1 Comparison of GDC calculated based on stem mass and tree leaf area
Our results support the first hypothesis, which postulated that GDCs calculated based on stem mass and tree leaf area are similar in even-aged stands but not in uneven-aged stands. This means that in northern hardwood stands younger than 50 years, stem mass can be considered as a good proxy of leaf area to represent the ability of trees to acquire resources. However, when large trees are present in a stand, such as in uneven-aged stands, GDC values based on stem mass were lower than those based on tree leaf area, suggesting that stem mass is no longer a reliable indicator of resource acquisition.
During the first stages of stand development after a major disturbance, trees generally invest in height and leaf area to maximise light acquisition. At the stand level, this tree allocation is associated with canopy closure, followed by crown overlap (Ray et al., 2011) and mortality of small trees through self-thinning. During these first stages of stand development, crown expansion leads to a proportional increase in both tree leaf area and wood production. In later stages, mortality will eventually reach some dominant trees because of senescence or tree-scale disturbances, creating gaps that increase the light availability to understory trees and lead to uneven-aged stands. In such stands, dominant trees can partially fill the gaps by expanding their shoots laterally, but because branch growth is faster for smaller trees, crown expansion opportunities for large trees are limited (Brisson, 2001). In addition, crown expansion of dominant trees is also limited by wind-induced interactions with neighbouring trees causing crown abrasion (Rudnicki et al., 2001; Meng et al., 2006; Hajek et al., 2015) or crown displacement (Young and Hubbell, 1991; Muth and Bazzaz, 2003). While crown expansion is almost inhibited, stemwood production continues to increase at reduced rates (Lusk et al., 2003), which leads to the curvilinear relationships between tree leaf area and stem mass (Fig. 2).
3.2 Effects of stand structure on GDC
Stand structure caused differences in GDC calculated based on stem mass in the studied hardwood stands. However, treatments did not significantly affect GDC in both even-aged and uneven-aged stands, which suggests that they weakly affected stand structural heterogeneity (Bradford et al., 2010; Fernández Tschieder et al., 2012; Soares et al., 2017). The GDC near zero in even-aged stands indicates that the growth contribution of trees to the overall stand growth was still proportional to their size, suggesting that they have access
to a proportionate amount of resources (Pothier, 2017). Indeed, a narrow range of tree size characterises even-aged stands, which could minimize the differences in the ability of the smallest and largest trees to acquire resources (Fernández and Gyenge, 2009). In contrast, reverse growth dominances were associated with uneven-aged stands, meaning that the relative contribution of smaller trees to total stand growth was higher than that of larger trees, despite the latter having greater access to resources (Doi et al., 2010; Pothier, 2017). Such differences have been attributed to the declining growth of large trees, as opposed to the growth improvement of small trees (Baret et al., 2017). Indeed, in uneven-aged stands, tree mortality typically affects large individuals (Lorimer et al., 2001), which is often preceded by their growth decline (Moreau et al., 2019).
Baret et al. (2015; 2017, 2018) studied possible factors that could explain age-related growth decline in boreal forest stands along a 1067-year-long chronosequence. They found that the declining contribution of large trees to total stemwood growth with increasing stand age, despite their greater access to sunlight, was mainly explained by increased hydraulic limitations of large trees. In addition, large trees increased their carbon allocation to belowground compartments and decreased biomass partitioning to leaf area (Baret et al., 2017). In the case of sugar maple and yellow birch, although the preceding mechanisms may also be involved, the decline in growth of large trees could also be attributed to a decreasing photosynthetic capacity caused by an increasing investment in defense compounds against herbivory and pathogens (Thomas, 2010).
3.3 Tree growth response according to stem size and vigour
In support of our second hypothesis, GDC remained unchanged after selection cutting in uneven-aged stands. However, the prediction related to this hypothesis was not supported by the results. Contrary to our expectations, the diameter growth of large vigorous trees, i.e. trees with above-average growth before treatment, decreased after selection cutting whereas it increased for large and small non-vigorous trees. The reduction in competition after partial cutting implies a redistribution of resources among residual trees. Considering that large vigorous trees likely had sufficient access to light before cutting because of low competition, the removal of direct competitors probably did not increase their light availability (Singer and Lorimer, 1997; Boyden et al., 2012) to trigger a positive growth response. In contrast, large non-vigorous trees, i.e. trees with below-average growth before treatment,
cutting was not representative of their growth potential. This could be the case if these trees underwent strong inter-tree competition for resources before cutting. In this case, a reduction in competition through partial cutting could increase light capture by large non-vigorous trees (Jones and Thomas, 2007) if, for example, a higher proportion of their crown was more exposed to light (Wyckoff and Clark, 2005; Hartmann et al., 2009). This applies especially to sugar maple and yellow birch considering that shade- and mid-shade-tolerant species often have deep crowns of which a large proportion may be in the shade (Canham et al., 1994; Jones et al., 2009). In agreement, tree growth response to partial cutting was observed to be linearly related to the intensity of crown release in northern hardwood stands (Smith and Miller, 1991; Singer and Lorimer, 1997).
The mean annual growth of small trees, whether vigorous or not, increased during the first years following selection cutting, though the response of formerly non-vigorous trees was slightly larger (Fig. 5). Similarly, Jones et al. (2009) observed a larger growth response of small (8-15 cm DBH) slow-growing sugar maple and yellow birch trees compared to fast, growing ones in selected cut stands. Pronounced differences in growth rates of understory trees before cutting were likely attributable to differences in light availability (Wyckoff and Clark, 2005; Hartmann et al., 2008). Thus, small formerly slow-growing trees were likely affected by a higher degree of competition than small formerly fast-growing trees before cutting. Selection cutting might have reduced a higher proportion of competition around small formerly slow-growing trees, thereby supplying a relatively higher increase of light availability (Goldberg, 1987; Coates et al., 2003; Boyden et al., 2012), which would explain the slight difference in growth response between formerly vigorous and non-vigorous small trees. Our results are also in line with the observed ability of suppressed trees to respond positively to gap creation (Bédard and Majcen, 2001; Bédard and Majcen, 2003; Moreau et al., 2019), indicating that selection cutting can induce an improved contribution of small trees to the overall stand wood production.
3.4 Change in GDC after selection cutting in the medium term
Overall, the applied selection cuts did not significantly affect GDC values over the medium term, in agreement with our expectations. To observe an increase in GDC over time, the growth of large trees after cutting should have increased considerably, while that of small trees should have decreased or remained about the same. However, the positive diameter response of small trees, coupled with the positive response of large trees that had below-average growth before cutting, resulted in stable values of GDC over time. Therefore, it
seems that the evaluation of the success of a selection cut applied in an uneven-aged stand cannot be done solely based on stand growth dominance, as opposed to findings for young thinned stands (Bradford et al., 2010; Keyser, 2012; Soares et al., 2017). Such an assessment, as well as a better understanding of the effects of a partial cut, can be achieved by a finer-scale growth analysis, at either the tree or the diameter-class level.
3.5 Silvicultural implications
Our study underlines the fact that many slow-growing trees responded positively to selection cutting, indicating that these trees should be left standing after treatment to promote stand productivity. Conversely, some large fast-growing trees could be harvested because they were unable to respond positively and might have begun a process of gradual growth decline ending by tree mortality (Moreau et al., 2019). However, if their growth is unknown, as is the case for usual forest inventories, the visual identification of such trees is difficult, and further research should be undertaken to determine visible defects that are related to the growth decline of large trees. For example, crown characteristics (size, leaf density, percent of dieback and of crown exposed to light, etc.), as well as stem defects (bark aspect, deformations, cracks, conks, cambial necrosis, etc.), could be tested as potential indicators (Roy et al., 2006; Tominaga et al., 2008; Havreljuk et al., 2014; Cecil-Cockwell and Caspersen, 2015).
Conclusion
The objective of this study was to test the relevance of using stand growth dominance to evaluate the success of partial cuts applied in even-aged and uneven-aged northern hardwood stands. First, we compared two variables representing the acquisition of resources in the calculation of GDC, i.e. stem mass and tree leaf area. Our results indicate that in northern hardwood stands composed of relatively small trees (i.e. relatively young even-aged stands), both stem mass and tree leaf area can be used to describe GDC patterns interchangeably. In contrast, when large trees are present in the stands (i.e. uneven-aged stands), stem mass is no longer a proper indicator of resource acquisition, suggesting that GDC calculated based on stem mass must be interpreted carefully. The second part of our study tested whether GDC can be used to evaluate the success (i.e. an increase in the relative contribution of residual trees after harvest) of a selection cut applied in an uneven-aged stand. We found that the relative invariability of GDC in both the short and medium term after selection cutting – despite the positive growth response of small and large slow-growing trees prior to cutting – undermines the use of GDC to determine if trees harvested during selection cutting were adequately selected. These results indicated some limitations on the use of a stand-level criterion such as GDC to evaluate the success of a partial cut, particularly in the case of uneven-aged stands. For this purpose, a tree-level criterion should better identify trees to be harvested and predict the response of residual trees to a partial cut treatment.
Bibliographie
Allison, T. D., Art, H. W., Cunningham, F. E., Teed, R., 2003. Forty-two years of succession following strip clearcutting in a northern hardwoods forest in northwestern Massachusetts. For. Ecol. Manage. 182, 285-301.
Auger, I., 2016. Une nouvelle relation hauteur-diamètre tenant compte de l'influence de la station et du climat pour 27 essences commerciales du Québec. Technical report, Gouvernement du Québec, Ministère des Forêts, de la Faune et des Parcs, Direction de la recherche forestière. Note de recherche forestière no 146.
Baret, M., Pepin, S., Pothier, D., 2018. Hydraulic limitations in dominant trees as a contributing mechanism to the age-related growth decline of boreal forest stands. For. Ecol. Manage. 427, 135-142.
Baret, M., Pepin, S., Ward, C., Pothier, D., 2015. Long-term changes in belowground and aboveground resource allocation of boreal forest stands. For. Ecol. Manage. 350, 62-69.
Baret, M., Pepin, S., Ward, C., Pothier, D., 2017. Long-term changes in stand growth dominance as related to resource acquisition and utilization in the boreal forest. For. Ecol. Manage. 400, 408-416.
Battaglia, M. A., Mou, P., Palik, B., Mitchell, R. J., 2002. The effect of spatially variable overstory on the understory light environment of an open-canopied longleaf pine forest. Can. J. For. Res. 32, 1984-1991.
Bédard, S., Majcen, Z., 2001. Ten-year response of sugar maple-yellow birch-beech stands to selection cutting in Quebec. Northern Journal of Applied Forestry. 18, 119-126. Bédard, S., Majcen, Z., 2003. Growth following single-tree selection cutting in Quebec
northern hardwoods. For. Chron. 79, 898-905.
Bérard, L., 1990. Plan d’aménagement intégré des ressources forestières du centre éducatif forestier de Duchesnay (1990-1991 à 1999-2000). Ministère de l’Énergie et des Ressources, Gouvernement du Québec.
Binkley, D., 2004. A hypothesis about the interaction of tree dominance and stand production through stand development. For. Ecol. Manage. 190, 265-271.
Binkley, D., Kashian, D., 2015. Tree-level patterns of lodgepole pine growth and leaf area in Yellowstone National Park: explaining anomalous patterns of growth dominance within stands. Ecosystems. 18, 251-259.
Binkley, D., Kashian, D. M., Boyden, S., Kaye, M. W., Bradford, J. B., Arthur, M. A., Fornwalt, P. J., Ryan, M. G., 2006. Patterns of growth dominance in forests of the Rocky Mountains, USA. For. Ecol. Manage. 236, 193-201.
Blouin, J., Berger, J., 2003. Guide de reconnaissance des types écologiques des régions écologiques 4d–Hautes collines de Charlevoix et du Saguenay et 4e–Plaine du lac Saint-Jean et du Saguenay. Ministère des Ressources naturelles, de la Faune et des Parcs, Gouvernement du Québec.
Bolton, N. W., D’Amato, A. W., 2011. Regeneration responses to gap size and coarse woody debris within natural disturbance-based silvicultural systems in northeastern Minnesota, USA. For. Ecol. Manage. 262, 1215-1222.
Bond-Lamberty, B., Wang, C., Gower, S. T., Norman, J., 2002. Leaf area dynamics of a boreal black spruce fire chronosequence. Tree Physiol. 22, 993-1001.
Boyden, S., Montgomery, R., Reich, P. B., Palik, B., 2012. Seeing the forest for the heterogeneous trees: stand‐scale resource distributions emerge from tree‐scale structure. Ecol. Appl. 22, 1578-1588.
Bradford, J. B., D'Amato, A. W., Palik, B. J., Fraver, S., 2010. A new method for evaluating forest thinning: growth dominance in managed Pinus resinosa stands. Can. J. For. Res. 40, 843-849.
Brisson, J., 2001. Neighborhood competition and crown asymmetry in Acer saccharum. Can. J. For. Res. 31, 2151-2159.
Canham, C. D., Finzi, A. C., Pacala, S. W., Burbank, D. H., 1994. Causes and consequences of resource heterogeneity in forests: interspecific variation in light transmission by canopy trees. Can. J. For. Res. 24, 337-349.
Cecil-Cockwell, M. J. L., Caspersen, J. P., 2015. A simple system for classifying sugar maple vigour and quality. Can. J. For. Res. 45, 900-909.
Coates, K. D., Canham, C. D., Beaudet, M., Sachs, D. L., Messier, C., 2003. Use of a spatially explicit individual-tree model (SORTIE/BC) to explore the implications of patchiness in structurally complex forests. For. Ecol. Manage. 186, 297-310. Doi, B. T., Binkley, D., Stape, J. L., 2010. Does reverse growth dominance develop in old
plantations of Eucalyptus saligna? For. Ecol. Manage. 259, 1815-1818.
Doyon, F., Gagnon, D., Giroux, J.-F., 2005. Effects of strip and single-tree selection cutting on birds and their habitat in a southwestern Quebec northern hardwood forest. For. Ecol. Manage. 209, 101-116.
Dumais, D., Prévost, M., Thomas, S., 2014. Physiology and growth of advance Picea rubens and Abies balsamea regeneration following different canopy openings. Tree Physiol. 34, 194-204.
Erdmann, G. G., Oberg, R. R., 1973. Fifteen-year results from six cutting methods in second-growth northern hardwoods. Research Paper NC-100. St-Paul, MN: U.S. Dept of Agriculture, Forest Service, North Central Forest Experiment Station. 12 p.
Ex, S. A., Smith, F. W., 2014. Wood production efficiency and growth dominance in multiaged and even-aged ponderosa pine stands. Forest Sci. 60, 149-156.
Fahey, T. J., Battles, J. J., Wilson, G. F., 1998. Responses of early successional northern hardwood forests to changes in nutrient availability. Ecol. Monogr. 68, 183-212. Fellner, H., Dirnberger, G., Sterba, H., 2016. Specific leaf area of European Larch (Larix
decidua Mill.). Trees-Struct. Funct. 30, 1237-1244.
Fernández-Tschieder, E., Binkley, D., 2018. Linking competition with Growth Dominance and production ecology. For. Ecol. Manage. 414, 99-107.
Fernández, M. E., Gyenge, J., 2009. Testing Binkley's hypothesis about the interaction of individual tree water use efficiency and growth efficiency with dominance patterns in open and close canopy stands. For. Ecol. Manage. 257, 1859-1865.
Fernández, M. E., Tschieder, E. F., Letourneau, F., Gyenge, J. E., 2011. Why do Pinus species have different growth dominance patterns than Eucalyptus species? A hypothesis based on differential physiological plasticity. For. Ecol. Manage. 261, 1061-1068.
Fernández Tschieder, E., Fernández, M. E., Schlichter, T. M., Pinazo, M. A., Crechi, E. H., 2012. Influence of growth dominance and individual tree growth efficiency on Pinus
taeda stand growth. A contribution to the debate about why stands productivity
declines. For. Ecol. Manage. 277, 116-123.
Forrester, D., Elms, S., Baker, T., 2013. Tree growth-competition relationships in thinned Eucalyptus plantations vary with stand structure and site quality. European Journal of Forest Research. 132, 241-252.
Forrester, D. I., 2019. Linking forest growth with stand structure: Tree size inequality, tree growth or resource partitioning and the asymmetry of competition. For. Ecol. Manage. 447, 139-157.
Fortin, M., DeBlois, J., Bernier, S., Blais, G., 2007. Mise au point d'un tarif de cubage général pour les forêts québécoises: une approche pour mieux évaluer l'incertitude associée aux prévisions. For. Chron. 83, 754-765.
Gauthier, M. M., Lambert, M. C., Bedard, S., 2016. Effects of harvest gap size, soil scarification, and vegetation control on regeneration dynamics in sugar maple-yellow birch stands. Forest Sci. 62, 237-246.
Glencross, K., West, P. W., Nichols, J. D., 2016. Species shade tolerance affects tree basal area growth behaviour in two eucalypt species in thinned and unthinned even-aged monoculture. Aust. For. 79, 157-167.
Goldberg, D. E., 1987. Neighborhood competition in an old‐field plant community. Ecology. 68, 1211-1223.
Guillemette, F., Bedard, S., Fortin, M., 2008. Evaluation of a tree classification system in relation to mortality risk in Quebec northern hardwoods. For. Chron. 84, 886-899. Hajek, P., Seidel, D., Leuschner, C., 2015. Mechanical abrasion, and not competition for
light, is the dominant canopy interaction in a temperate mixed forest. For. Ecol. Manage. 348, 108-116.
Hartmann, H., Beaudet, M., Mazerolle, M. J., Messier, C., 2009. Sugar maple (Acer
saccharum Marsh.) growth is influenced by close conspecifics and skid trail proximity
following selection harvest. For. Ecol. Manage. 258, 823-831.
Hartmann, H., Wirth, C., Messier, C., Berninger, F., 2008. Effects of above- and belowground partial harvest disturbance on growth and water status of residual sugar maple. Tree Physiol. 28, 1851-1862.
Havreljuk, F., Achim, A., Auty, D., Bédard, S., Pothier, D., 2014. Integrating standing value estimations into tree marking guidelines to meet wood supply objectives. Can. J. For. Res. 44, 750-759.
Hornbeck, J. W., 1990. Regeneration after strip cutting and block clearcutting in Northern Hardwoods. Northern Journal of Applied Forestry. 7, 65-68.
Jones, T. A., Domke, G. M., Thomas, S. C., 2009. Canopy tree growth responses following selection harvest in seven species varying in shade tolerance. Can. J. For. Res. 39, 430-440.
Jones, T. A., Thomas, S. C., 2007. Leaf-level acclimation to gap creation in mature Acer
saccharum trees. Tree Physiol. 27, 281-290.
Kenefic, L. S., Seymour, R. S., 1999. Leaf area prediction models for Tsuga canadensis in Maine. Can. J. For. Res. 29, 1574-1582.
Keyser, T. L., 2012. Patterns of growth dominance in thinned yellow-poplar stands in the southern Appalachian Mountains, USA. Can. J. For. Res. 42, 406-412.
Kuehne, C., Weiskittel, A., Pommerening, A., Wagner, R., 2018. Evaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA. Ann. For. Sci. 75, 1-12.
Lambert, M. C., Ung, C. H., Raulier, F., 2005. Canadian national tree aboveground biomass equations. Can. J. For. Res. 35, 1996-2018.
Loranger, J., Shipley, B., 2010. Interspecific covariation between stomatal density and other functional leaf traits in a local flora. Botany. 88, 30-38.
Lorimer, C. G., Dahir, S. E., Nordheim, E. V., 2001. Tree mortality rates and longevity in mature and old‐growth hemlock‐hardwood forests. J. Ecol. 89, 960-971.
Lusk, C. H., Jara, C., Parada, T., 2003. Influence of canopy tree size on stand basal area may reflect uncoupling of crown expansion and trunk diameter growth. Austral Ecol. 28, 216-218.
Meng, S. X., Rudnicki, M., Lieffers, V. J., Reid, D. E. B., Silins, U., 2006. Preventing crown collisions increases the crown cover and leaf area of maturing lodgepole pine. J. Ecol. 94, 681-686.
Miles, P. D., Smith, W. B., 2009. Specific gravity and other properties of wood and bark for 156 tree species found in North America. Research Note NRS-38. U.S. Department of Agriculture, Forest Service, Northern Research Station.
Moreau, E., Bédard, S., Baral, S. K., Pothier, D., 2018a. Evaluating electrical resistivity tomography and crown surface area to estimate leaf area of sugar maple and yellow birch. Ecohydrology. 11, 1-10.
Moreau, E., Bédard, S., Moreau, G., Pothier, D., 2018b. Relationships between tree vigor indices and a tree classification system based upon apparent stem defects in northern hardwood stands. Forests. 9, 588.
Moreau, G., Achim, A., Pothier, D., 2019. A dendrochronological reconstruction of sugar maple growth and mortality dynamics in partially cut northern hardwood forests. For. Ecol. Manage. 437, 17-26.
Muth, C. C., Bazzaz, F. A., 2003. Tree canopy displacement and neighborhood interactions. Can. J. For. Res. 33, 1323-1330.
Nelson, A. S., Wagner, R. G., Saunders, M. R., Weiskittel, A. R., 2013. Influence of management intensity on the productivity of early successional Acadian stands in eastern Maine. Forestry. 86, 79-90.
Ondro, W. J., Love, D. V., 1979. Growth response of northern hardwoods to partial cutting in southern Ontario. For. Chron. 55, 13-16.
Polansky, A. M., 2000. Stabilizing bootstrap‐ t confidence intervals for small samples. Can. J. Stat. 28, 501-516.
Pothier, D., 1996. Accroissement d'une érablière à la suite de coupes d'éclaircie: résultats de 20 ans. Can. J. For. Res. 26, 543-549.
Pothier, D., 2017. Relationships between patterns of stand growth dominance and tree competition mode for species of various shade tolerances. For. Ecol. Manage. 406, 155-162.
Potvin, C., Lechowicz, M. J., Tardif, S., 1990. The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology. 71, 1389-1400.
Ray, D., Yanai, R. D., Nyland, R. D., McConnell, T. R., 2011. Growing-space relationships in young even-aged northern hardwood stands based on individual-tree and plot-level measurements. Northern Journal of Applied Forestry. 28, 27-35.
Robitaille, A., Saucier, J.-P. 1998. Paysages régionaux du Québec méridional. Sainte-Foy: Publications du Québec.
Robitaille, L., Sheedy, G., Richard, Y., 1990. Effets de l'éclaircie précommerciale et de la fertilisation sur un gaulis de 10 ans à dominance de bouleau jaune. For. Chron. 66, 487-493.
Roy, G., Larocque, G. R., Ansseau, C., 2006. Prediction of mortality in Appalachian sugar maple stands affected by dieback in southeastern Quebec, Canada. For. Ecol. Manage. 228, 115-123.
Rudnicki, M., Silins, U., Lieffers, V. J., Josi, G., 2001. Measure of simultaneous tree sways and estimation of crown interactions among a group of trees. Trees. 15, 83-90. Schwinning, S., Weiner, J., 1998. Mechanisms determining the degree of size asymmetry
in competition among plants. Oecologia. 113, 447-455.
Singer, M. T., Lorimer, C. G., 1997. Crown release as a potential old-growth restoration approach in northern hardwoods. Can. J. For. Res. 27, 1222-1232.
Smith, H. C., Miller, G. W., 1991. Releasing 75-to 80-year-old Appalachian hardwood sawtimber trees--5-year dbh response.
Soares, A. A. V., Leite, H. G., Cruz, J. P., Forrester, D. I., 2017. Development of stand structural heterogeneity and growth dominance in thinned Eucalyptus stands in Brazil. For. Ecol. Manage. 384, 339-346.
Sprugel, D. G., Rascher, K. G., Gersonde, R., Dovčiak, M., Lutz, J. A., Halpern, C. B., 2009. Spatially explicit modeling of overstory manipulations in young forests: Effects on stand structure and light. Ecol. Model. 220, 3565-3575.
Thomas, S. C., 2010. Photosynthetic capacity peaks at intermediate size in temperate deciduous trees. Tree Physiol. 30, 555-573.
Tominaga, K., Watmough, S. A., Aherne, J., 2008. Predicting tree survival in Ontario sugar maple (Acer saccharum) forests based on crown condition. Can. J. For. Res. 38, 1730-1741.
Weiner, J., 1990. Asymmetric competition in plant populations. Trends Ecol. Evol. 5, 360-364.
West, P., 2014. Calculation of a growth dominance statistic for forest stands. Forest Sci. 60, 1021-1023.
Wilson, R. W., 1953. How second-growth northern hardwoods develop after thinning. Station Paper NE-62. Upper Darby, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 12 p.
Wyckoff, P. H., Clark, J. S., 2005. Tree growth prediction using size and exposed crown area. Can. J. For. Res. 35, 13-20.
Young, T. P., Hubbell, S. P., 1991. Crown asymmetry, treefalls, and repeat disturbance of broad‐leaved forest gaps. Ecology. 72, 1464-1471.
