Competition, size and age affect tree growth response to fuel reduction treatments in mixed-oak forests of Ohio

Forest Ecology and Management 307 (2013) 74–83

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Competition, size and age affect tree growth response to fuel reduction treatments in mixed-oak forests of Ohio Alexander K. Anning ⇑, Brian C. McCarthy Department of Environmental and Plant Biology, Ohio University, 315 Porter Hall, Athens, OH 45701, United States

a r t i c l e

i n f o

Article history: Received 6 February 2013 Received in revised form 4 July 2013 Accepted 5 July 2013

Keywords: Basal area increment Competition Dendroecology Forest disturbance Neighborhood analysis Radial growth

a b s t r a c t Prescribed fire and thinning treatments are increasingly used to mitigate potential wildfire hazards, alter stand structure and restore forest functions. In this study, the effects of competition, tree size and age on tree growth following prescribed fire and thinning treatments were examined to better understand the consequences of these management tools on forest systems. Tree-ring data from 348 trees, comprising five species (white oak, black oak, chestnut oak, hickories, and yellow-poplar) were analyzed following standard dendrochronological protocols. Data were collected from 80 0.1-ha plots in two study sites, each with four treatment units (control, thin-only, burn-only, thin + burn) in mixed-oak forests of Ohio. A neighborhood analysis was used to assess the competitive status of each sampled tree. Basal area increment (BAI) and tree size were positively related, with the strongest correlation found in the burn-only treatment. Age was negatively related to BAI, though weakly. Competition was inversely correlated with BAI, with trees from the thin-only unit showing the strongest correlation. BAI was greater for larger trees when competition was low and declined at a steeper rate as competition increased. Smaller trees grew less in general but decreases in BAI were not as steep as competition increased. Overall, tree size, age, and competition explained 40% of total variance in BAI across all species. Values for individual tree species ranged from 30% to 57%, indicating considerable variation in the responses of species to these factors. Yellow-poplar exhibited greater sensitivity to competition than the other species analyzed. Altogether, competition was more important than size and age for tree growth in these managed stands. Variation in competitive status of trees within treatments supports the view that prescribed fire and thinning influence forest growth by creating heterogeneity among stands, and thus demonstrates the need for individual tree-based analysis to fully understand prescribed fire and thinning impacts on forest ecosystems. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Prescribed fire and thinning treatments are widely used to mitigate wildfire effects, alter stand structure and species composition, and restore forest functions (Hutchinson et al., 2005; Schwilk et al., 2009). Over the past two decades, many studies have examined the effects of these treatments on forest ecosystems; however, the response of large residual trees has received considerably less attention. The few studies focused on residual tree growth following these management disturbances have demonstrated interesting response patterns over space, time, and across species (Boerner et al., 2008; Chiang et al., 2008; Lutz et al., 2012; Anning and McCarthy, in press). While these patterns are becoming apparent, the underlying factors have not been investigated thoroughly at the scale of the individual tree. Understanding these factors is essential for predicting productivity and develop-

⇑ Corresponding author. Tel.: +1 740 590 1743; fax: +1 740 593 1130. E-mail address: [email protected] (A.K. Anning). 0378-1127/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2013.07.008

ment of forest stands following prescribed burning and thinning (Johnson et al., 2002). More fundamentally, knowledge of the relative growth rates of different species in relation to competition, size and age is useful to forest managers interested in determining which species to favor in partial harvesting (Trimble, 1967). Tree growth is regulated by many biotic and abiotic factors (Fritts, 1976; Kozlowski et al., 1991). Among these, competition between individual trees continues to receive greater research attention because of its strong controlling effects on stand structure and development (Kozlowski et al., 1991; McDonald et al., 2002; Weber et al., 2008; Thorpe et al., 2010). Competition affects the availability and acquisition of resources such as light, water, nutrients and physical space, and thus has profound implications for forest ecology and management. In the closed-canopy forest of eastern North America, for example, competition has been identified as a major determinant of plant growth and productivity (Phipps, 1982). Therefore, it is not surprising that release of certain desirable tree species from competition has become synonymous with many prescribed fire and thinning management efforts in these temperate hardwood forests.

A.K. Anning, B.C. McCarthy / Forest Ecology and Management 307 (2013) 74–83

Prescribed fire and thinning treatments may create structurally complex forest stands, with trees varying in age, size, species and spatial arrangement, which require spatially-explicit, individual tree-based models to understand (Lorimer, 1983; Canham et al., 2004; Thorpe et al., 2010). Evidence from several studies suggests considerable differences in the nature and effects of these management operations, with prescribed fire being more variable but removing less basal area than mechanical treatments (e.g., Waldrop et al., 2008). Canham et al. (2004) noted that partial harvesting can lead to considerable changes in the physical and competitive environments of stands. Despite these differences or changes, and the fact that resources are generally spatially heterogeneous across landscapes (Coomes and Allen, 2007), most researchers continue to rely on coarser descriptors (e.g., stand basal area or density) when analyzing tree growth patterns within these complex systems. Consequently, how prescribed fire and thinning manipulations influence the competition status of individual trees, and how this, in turn, mediates residual tree growth remain unclear. Traditionally, the effect of competition on tree growth has been assessed via neighborhood analysis (Lorimer, 1983; Canham et al., 2006). This approach typically requires the demarcation of the spatial extent of a tree’s competitive environment (i.e., its neighborhood), within which tree growth is assumed to be a function of the number, size, species and spatial configuration of neighboring trees. Thus, integrating these neighborhood characteristics, ecologists have developed a variety of competition indices with which to measure the extent of resource limitation by a plant’s growing environment (Shi and Zhang, 2003). The most popular of these are the distance-dependent and the distance-independent models (Wimberly and Bare, 1996; Canham et al., 2006). Weiner (1990) also distinguished between asymmetric competition models, which involve only individuals larger than the target tree and reflect competition for light or effect of shading, and symmetric competition models, which incorporate all neighbors irrespective of size and represent competition for nutrients. These indices are of great value in accounting for the spatial structure in community data (Shi and Zhang, 2003), although the choice of a particular competition index is rather subjective. Tree growth is strongly related to size and age (Wyckoff and Clark, 2005; Macfarlane and Kobe, 2006; Coomes and Allen, 2007). Studies have shown that larger trees usually produce more wood than smaller trees (Kozlowski et al., 1991). For example, McDonald et al. (2002) state that the size of an individual tree has a direct effect on its future growth, while size in relation to competitors indirectly affects growth through competitive effects. The size of an individual tree relative to its neighbors also influences resource supply and tree growth. Smaller trees are often shaded and suppressed by their larger neighbors (Coomes and Allen, 2007); although, some trees naturally lack the capacity to grow into the canopy. Thus, failure to account for initial tree size in tree growth models may lead to residual size bias (Macfarlane and Kobe, 2006), which in turn may obscure the effects of environmental stresses on tree growth. The present study examined the influences of competition, size and age on the growth response of residual trees to fuel reduction treatments. Three specific questions were addressed: (a) Does the size of an individual tree and its age affect its growth response to prescribed fire and thinning treatments? (b) Does competition among individual trees mediate tree growth response to these treatments? and (c) What are the relative contributions of competition, size and age to tree growth following prescribed fire and thinning treatments? These questions were evaluated using treering data from five common tree species in the mixed-oak forests of southeastern Ohio. It was predicted that basal area growth would increase with size and decrease with age of trees following the treatment, with different treatments having varying effects on

75

different-sized trees. If treatments conferred differential competitive advantages on trees or structural changes among stands due to differences in intensity (Waldrop et al., 2008), growth responses of the individual trees would be expected to differ among the treatments. Finally, it was expected that competition would influence tree growth responses to the treatment more than size and age. 2. Materials and methods 2.1. Description of sites and experimental treatments Two replicate blocks within the Ohio Hills site of the national Fire and Fire Surrogate (FFS) study were used for this study. The Raccoon Ecological Management Area (REMA) block is located within the Vinton Furnace State Experimental Forest (39°120 4100 N, 82°230 0900 W), and the Zaleski block is within the Zaleski State Forest (39°210 1700 N, 82°220 0600 W); both are in Vinton County, Ohio. The Ohio Hills site lies within the unglaciated Allegheny Plateau physiographic region. The landscape is dissected into ridges, hills and valleys (Hutchinson et al., 2005), with elevation ranging from 200 to 300 m (Waldrop et al., 2008). Soils are mainly acidic and are derived primarily from sandstone, siltstone and shale (Boerner et al., 2003). Annual precipitation and temperature average 1024 mm and 11.3 °C, respectively (Sutherland et al., 2003). The vegetation is classified as upland mixed-oak (Quercus spp.) hardwood forests (Iverson et al., 2008). Prior to the start of the treatments in 2000, the even-aged stands within both blocks were fully stocked with tree basal area ranging from 25.5 to 29.4 m2 ha1 (Appendix A; Waldrop et al., 2008). At each site, there are four experimental units, each 50 ha in extent and containing ten 20  50 m (0.1 ha) permanent plots (i.e., 2 sites  4 experimental units  10 plots = 80 plots total). The four treatments consist of an un-manipulated control, a mechanical thinning (thin-only), a prescribed burning (burn-only), and a combination of the two (thin + burn). These plots are distributed across the landscape from ridgetops to lower slopes based on the integrated moisture index (IMI) developed by Iverson et al. (1997). The IMI combines four topographic and soil factors (hillshade, curvature, flow accumulation and water holding capacity of the soil) in GIS (geographic information systems) to derive relative moisture ratings for sites. The model has been used successfully to predict site productivity and species composition in the oak-dominated forests of eastern North America (Iverson et al., 1997). Mechanical thinning was conducted in the fall and winter of 2000–2001. This operation was primarily thinning from below with a focus on removing midstory trees (15–30 cm diameter at breast height, DBH), and resulted in 30% reduction in stand basal area, although variations in treatment intensity were discernible (Appendix A). For example, at REMA, the thin-only and the thin + burn treatments reduced stand basal areas by 31.4% and 18.9%, respectively, from their initial values of 27.4 and 27.9 m2 ha1. Prescribed fires were conducted in the spring of 2001, and repeated in 2005 and 2010. The intensity of prescribed fire varied greatly over the years and across landscapes. In 2001, for example, fire intensity was generally low with flame lengths getting to about 1 m. However, higher intensity fires (i.e., flame lengths reaching 4–5 m) were deliberately created in 2005 and 2010, resulting in significant overstory mortality (Iverson et al., 2008; Waldrop et al., 2008). 2.2. Increment core sampling and measurements In the fall and winter of 2011–2012, 696 increment cores were extracted from white oak (Quercus alba), chestnut oak (Q. prinus), black oak (Q. velutina), hickories (Carya spp. primarily C. glabra)

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and yellow-poplar (Liriodendron tulipifera). These tree species were selected for analysis because they are the most common overstory species in the study area. To minimize noise in the data, healthylooking co-dominant trees (P25 cm DBH) having no more than 50% branch or twig mortality or foliage discoloration, dieback and defoliation (Yaussy et al., 2004) were sampled using pre-existing FFS overstory data as a guide. Two increment cores were taken from each tree at breast height, and at roughly 180° from each other to account for variation in ring-width around the circumference of the tree. To obtain at least one tree per species per plot, sampling was extended into a 20 m buffer outside the plot, if necessary. In spite of this sampling effort, some species were still absent in some sampling areas. For an estimate of tree age, one of the two cores from each tree was taken to the pith (Speer, 2010). Diameter at breast height (DBH) was measured at the time of coring. Cores were extracted from trees using an increment borer and transported to the laboratory in paper straws kept in a metallic case to prevent mechanical damage. In the laboratory, increment cores were glued to wooden core mounts after they had been air-dried for at least 24 hrs. The cores were then sanded using a belt sander with increasingly finer grades of sandpaper (100–600 grit), and micro-finished with 30, 15 and 9 micron Microfilm Sheets to clearly reveal the ring structure (Speer, 2010). Calendar dates were assigned to each ring with the help of skeleton-plots (Stokes and Smiley, 1968). Ring widths were measured to the nearest 0.01 mm using a Velmex measuring system (Velmex, Boomfield, NY) and the standard Measure J2X program (Voortech Consulting Holderness, NH). The quality control program, COFECHA (Grissino-Mayer, 2001), was used to verify cross-dating and measurement accuracies. The age of each tree was estimated from a ring count in the core that contained the pith. If the pith was absent in a core, a pith indicator (a series of concentric rings) was used to estimate the number of missing rings (Speer, 2010). 2.3. Growth analysis To quantify the radial growth of individual trees, each ring was converted into a basal area increment (BAI), the net increase in the total cross-sectional stem area of a tree. BAI is the preferred metric of growth for many dendrochronologists because it has been found to provide a better approximation of annual tree growth than simple ring width or stem diameter increment (Biondi and Qeadan, 2008). BAIs were computed using the bai.out function in ‘‘dplR’’, which calculates the ring area from the bark to the pith (Bunn et al., 2012). Computations were based on diameter inside bark of trees, derived by first estimating bark thickness of the species using available regression equations (Hilt et al., 1983). The BAIs of the two cores extracted from a tree were averaged to obtain a single value per year for that tree; thus, the individual tree constituted the sampling unit. To accomplish the goal of understanding how tree size, age and competitive status influence post-treatment growth patterns of trees, analysis was restricted to only the posttreatment period (2001–2010). Thus, for each tree, mean periodic annual BAI was computed by averaging the annual BAIs from 2001 to 2010. 2.4. Neighborhood analysis A neighborhood analysis (Lorimer, 1983; Wimberly and Bare, 1996) was used to quantify the effect of competition on tree growth response to the treatments. Considering the large number of competition indices that exist in the ecological literature, it was decided to empirically determine the best model for our tree growth analysis. This was accomplished by evaluating a set of five candidate models within three neighborhood radii – 10 m, 15 m, and a variable radius (Appendix B). The variable radius was a function of

the size of the target tree and was obtained by multiplying the diameter of the target tree by 40 (Lorimer, 1983). All but one of the models were based on the traditional distance-dependent competition model (denoted here as ‘‘basic’’ model; Appendix B), which is derived by summing the ratios of the diameters of a target tree and its neighbors, weighted by the distance from the target tree:

Cl ¼

 n  X DBHj =DBHi distanceij j¼1

ð1Þ

where CI denotes the competition index, DBHj and DBHi are the diameters of the target tree and the neighbor, respectively. All trees with DBH equal to 10 cm or larger were considered as neighbors. The distance between a target tree and its neighbor was measured using an Optic-Logic InSight™ 800X L Laser Rangefinder (Optic-Logic, Tullahoma, TN). A tape measure was used to measure distances shorter than the minimum range of the rangefinder (3.6 m). Measurement of the neighbor DBH and distance from the target tree were done at the time of coring. Using the simple linear regression model, the five models were fitted to the pre-treatment BAI (i.e., BAI for the period 1996–2000), and their predictive capacities for BAI compared using the resulting R2 values. Model parameters were estimated using the restricted maximum likelihood method (REML), whilst model validation was performed by plotting the residuals against each of the explanatory variables (Zuur et al., 2009). The selected model (the model used in subsequent analysis) included only neighbors larger than the target tree with the neighborhood radius of 15 m (Appendix B). 2.5. Assessing size, age and competition effects on tree growth All analyses were performed using R (R Development Core Team, 2012). The effects of size, age and competition on BAI responses of trees to the fuel reduction treatments were evaluated using analysis of covariance (ANCOVA). ANCOVA is a general linear model commonly used to evaluate the covariance of a dependent variable (e.g., growth metric) and a categorical predictor (main effect) while "controlling" for the effects of other continuous predictors, known as covariates. Treatment was specified as the main effect, and tree size, age, and competition status as covariates. The goal of this analysis was to determine whether significant interactions existed between the levels of the treatment variable and the covariates. Data were analyzed separately for each species and were also pooled together for the general effect across treatments. The R2 value was used to assess the goodness of model fit. Linear regression models were used to assess the effect of species on BAI-competition relations, whilst ANOVA was used to evaluate variation in competitive status of trees among the four treatments. Further, BAI-competition relations for different species were analyzed by IMI (which stratified the landscape into mesic, intermediate, and xeric sites). USDA staff at the Northern Research Station, Delaware, OH, supplied the IMI scores. To investigate whether competition effect was dependent on size of target tree, all cored trees were sorted into three size classes according to the definition of Schwilk et al. (2009). The three classes were medium (25–39.9 cm DBH), large (40–54.9 cm DBH), and very large (55 cm or greater). However, considering the small number of trees in the very large size class, trees belonging to the last two size classes were lumped together as a single ‘‘large’’ category. A simple linear regression analysis was then used to evaluate the relationship between BAI and competition for each of the two size classes (medium and large). Further, trees were divided into two competition classes: those that experienced lesser competition – competitively advantaged (index value < 0.4313, mean BAI = 28.81 cm2 yr1), and those that received greater competition – competitively disadvantaged (index value P 0.4313, mean

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3. Results

100

(a)

BAI (cm2 yr−1)

80 60 40 20 0 20

30

40

50

60

DBH (cm) 100

(b)

Control Thin Thin+Burn Burn

80

BAI (cm2 yr−1)

BAI = 15.64 cm2 yr1) using the ‘rpart’ function in R (Therneau et al., 2012). In this analysis, BAI was modeled as a function of competition using the ‘‘anova’’ method for continuous endpoint. The splitting criterion was chosen in order to maximize the between-group sum of squares as in simple ANOVA. Variations in BAI between these two competition classes across treatments, and the different size classes were then compared with ANOVA. Finally, the relative importances of size, age and competition for BAI following the treatments were quantified by decomposing the total variance explained (R2) in a multiple linear regression by averaging sequential sum of squares over all orderings of the explanatory variables. The results were then normalized to sum to 100%. This analysis was implemented using the ‘relaimpo’ package in R (Gromping, 2006). In addition to the relative importance values, the package contains functions for estimating 95% bootstrap confidence intervals using 1000 replications generated from the original data. This analytical approach overcomes the usual problem of correlation among regressors, and thus has advantage over the use of R2 from univariate regressions (Gromping, 2006). To simplify the analysis and better understand the roles of competition, size and age on tree growth in the managed stands, data from the control stands were excluded from this analysis.

60 40 20

3.1. Characteristics of trees sampled Characteristics of the trees sampled from the four treatment units, including the number of trees cored, the mean DBH, age, and periodic annual BAI before and after the treatment are summarized in Table 1. Tree size and age did not differ among the four treatments (P > 0.05). Average size of trees ranged from 42.12 cm in the control to 44.24 cm in the thin + burn stand, whilst mean age ranged between 109.49 (control) and 110.50 (for both thin-only and burnonly) years. Pre-treatment BAI was quite comparable among all treatments (P = 0.084), averaging between 15.13 and 17.15 cm2 yr1. However, post-treatment BAI was higher (P < 0.05) for trees growing in the thin-only (23.14 cm2 yr1) and the thin + burn (23.55 cm2 yr1) treatments than for those from the control (16.33 cm2 yr1) and the burn-only (20.52 cm2 yr1) stands. 3.2. Size and age effects on tree growth When data for all species were pooled, tree growth responded strongly to tree size and age (P < 0.001; Fig. 1 and Table 2). As expected, BAI showed a positive correlation with tree size, and also responded strongly to the treatments (P < 0.001). Trees from the thin-only and thin + burn treatments grew more relative to those from the control. No significant treatment  size effect on BAI was found (P > 0.05). However, growth response to prescribed fire appeared to vary among trees of different sizes. Prescribed burning generated much greater BAI response among smaller trees compared to larger trees (Fig. 1A). Together, treatment and size explained 25.5% of the variations in BAI. In contrast with size, tree

0 50

100

150

200

Age (years) Fig. 1. Relationships of tree size (a) and age (b) with basal area increment (BAI; 2001–2010) responses of residual trees to prescribed fire and thinning treatments in southeastern Ohio. For clarity, the age axis has been scaled such that two oldest trees are not shown.

age was only weakly associated with BAI (Fig. 1B), with younger trees showing slightly higher growth rates than older ones. When combined in regression analysis, tree age and treatment only explained 7.51% of the variations in BAI (Table 2). No interactive effect of age and treatment on BAI was found (P > 0.05). Analysis of BAI by species suggested a strong positive effect of size across all five tree species (P < 0.001; Table 2). After accounting for the effect of size, treatment effect on BAI was significant only for white oak, black oak, and hickory (P < 0.05; Table 2). There was no significant treatment  size effect on BAI for any of the species. The importance of tree size and treatment for BAI (as indicated by R2) varied greatly among species. These factors were more strongly associated with growth of white oak (R2 = 43.15%), hickory (35.8%) and yellow-poplar (35.2%) than with those of black oak (23.17%) and chestnut oak (15.73%). With the exception of chestnut oak, tree age had no significant effect on BAI of trees

Table 1 Mean (±standard error) basal area increment (BAI), size (i.e., diameter at breast height, DBH) and age of residual trees sampled from the prescribed fire and thinning treatments. BAIb and BAIa indicate annual BAIs before (1991–2000) and after (2001–2010) treatment, respectively. Variable

Control

Thin

Thin + burn

Burn

P value

Number of trees Age (yrs) Size (cm) BAIb (cm2 yr1) BAIa (cm2 yr1)

84 109.49 (2.53) 42.12 (0.71) 15.13 (0.58) 16.33 (0.76)

87 110.50 (3.22) 43.27 (0.74) 17.00 (0.62) 23.14 (1.05)

94 109.50 (1.66) 44.24 (0.63) 17.15 (0.87) 23.55 (0.98)

89 110.50 (2.01) 42.45 (0.66) 16.72 (0.90) 20.52 (0.89)

– 0.983 0.119 0.084 <0.001

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Table 2 Summary of analysis of covariance showing the effects of size (DBH) and age on basal area increment (BAI) responses of tree species to prescribed fire and thinning treatments in southeastern Ohio. Species/variable (covariate)

d.f.

R2 (%)

P Treatment

Covariate

Treatment  Covariate

1, 340 1, 315

<0.001 0.001

<0.001 0.023

0.143 0.172

25.50 7.51

White oak Size Age

1, 86 1, 81

<0.001 0.001

<0.001 0.185

0.086 0.744

43.15 12.57

Chestnut oak Size Age

1, 73 1, 63

0.377 0.108

<0.001 <0.001

0.944 0.367

15.73 14.05

Black oak Size Age

1, 74 1, 67

0.014 0.016

<0.001 0.276

0.843 0.504

23.17 8.18

Hickory Size Age

1, 36 1, 35

0.035 0.082

<0.001 0.178

0.788 0.423

35.80 16.60

Yellow poplar Size Age

1, 39 1, 37

0.071 0.302

<0.001 0.253

0.531 0.516

35.2 4.16

All species Size Age

(P > 0.05; Table 2). As with size, no significant treatment  age effect on BAI (P > 0.05) was found.

3.3. Competition effects on tree growth As predicted, competitive status of individual trees differed among the four treatments (P < 0.001; Fig. 2). Trees from the control plots experienced the most intense competition while those from the thin-only and the thin + burn units had the least competition. Competition had a strong negative effect on BAI across all treatments (P < 0.001, Table 3 and Fig. 3). A strong competition  treatment effect on BAI was also noticeable when results for all species were pooled (P = 0.012). At low competition intensity, trees from all three active treatments (particularly thin-only and thin + burn) exhibited higher BAI rates than those from the control. Conversely, only trees growing in the thin + burn plots exhibited higher BAI rates than the control trees at high competi-

tion intensity. Interestingly, tree growth in the thin-only stands was more sensitive to competition than in the other treatments. Together, competition and treatment explained 31.37% of the variance in BAI. The strong negative effect of competition on BAI was also evident at the species level (Table 3 and Fig. 4), with growth rates declining rapidly as competition intensity increased across species. After "controlling" for the effect of competition, treatment was only significantly related to BAI of white oak (P = 0.020) and hickory (P = 0.014). The five species studied responded differently to competition and treatment, as indicated by variation in the R2 (26.86–47.86%; Table 3). Yellow-poplar was more sensitive to competition than the other species (Fig. 4). Hickory was the least responsive, while oaks were intermediate in their reaction to competition. Among the oaks, black oak and chestnut oak were more sensitivity to competition than white oak. Besides their independent effects, competitive and species also showed strong interactive effects on basal area growth in the manipulated stands (P < 0.05; Fig. 4). The two variables accounted for 53.34% and 40.53% of the variance in BAI for the control and the active treatments, respectively. Site quality index (represented by the IMI categories), somewhat mediated the BAI-competition relation (Table 4). This interactive effect was exemplified by white oak, chestnut oak and yellow-poplar, which showed concordance between competition importance (indicated by R2 values) and the optimal ‘‘physiographic’’ range of the species across the landscape (indicated by the intercepts estimated from the regression analysis). Competition effect on BAI following the treatment was size-dependent (Fig. 5). Although larger trees generally grew more (intercept = 32.95 cm yr1) than medium-sized trees (intercept = 18.44 cm yr1), the latter were less sensitive (slope = 4.8 cm yr1, R2 = 12.72%) to changes in competition compared to the former (slope = 17.55 cm yr1, R2 = 24.96%). 3.4. Relative importances of competition, size and age for tree growth

Fig. 2. Variation in competitive status of individual trees across prescribed fire and thinning treatments in southeastern Ohio. Different letters indicate statistical difference at a = 0.001.

Competition, size and age together explained 40% of the total variance in BAI when data for all five species were combined (Fig. 6). Of this value, competition alone accounted for an average of 46.44%, while tree size and age comprised 39.17% and 14.40%, respectively. The magnitudes of overall variance in BAI explained

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Table 3 Analysis of covariance (ANCOVA) results (P value and R2) showing the influence of competition on BAI responses of some common overstory species to prescribed fire and thinning treatments in southeastern Ohio. Species

d.f.

All species White oak Chestnut oak Black oak Hickory Yellow poplar

1, 1, 1, 1, 1, 1,

331 84 73 71 35 36

R2 (%)

P Treatment

Competition

Treatment  competition

0.291 0.020 0.484 0.474 0.014 0.660

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.012 0.016 0.315 0.581 0.111 0.148

31.37 44.42 38.25 26.86 47.86 40.79

(a) Control 40

Basal area increment (cm2 yr−1)

30

Species *** Competition *** Species × Competition .NS R 2 = 53.34%

20 10 0 70

(b) Active treatments

Black oak Chestnut oak White oak Hickory Yellow-poplar

60 50

Species *** Competition *** Species × Competition * R 2 = 40.53%

40 30 20 10 0

Fig. 3. Effect of competition on basal area increment of residual trees after prescribed fire and thinning treatments in mixed-oak forest of southeastern Ohio.

by competition, size and age varied among species. The R2 values were also generally greater for the individual species (except for black oak) than for the pooled data. Chestnut oak BAI was most strongly correlated with competition, size and age (R2 = 56.52%), whereas black oak BAI was least responsive to them (R2 = 30.33%). Decomposition of the total explained variance indicated that competition (R2 = 42–74%) was generally more important than tree size (R2 = 23–42%) and age (R2 = 3–29%) for predicting BAI of the oaks. On the other hand, among the non-oaks, a greater proportion of the variance in BAI (47–49%) was attributable to tree size. Interestingly, age (R2 = 29%) predicted chestnut oak growth better than size, though it was only weakly (R2 < 9%) associated with BAI of the other species.

4. Discussion The importance of tree size for predicting future tree growth and forest productivity has been emphasized in previous studies (Lorimer, 1983; McDonald et al., 2002; Macfarlane and Kobe, 2006). In the current study, BAI correlated positively with tree size, indicating that larger trees indeed produced more wood than smaller ones. The higher rate of wood production by larger trees likely reflects a greater capacity to capture light for photosynthesis due to their larger crown size (Kozlowski et al., 1991; Wyckoff and Clark, 2005). Mechanical treatments generated the greatest BAI re-

0.0

0.5

1.0

1.5

2.0

2.5

Competition intensity Fig. 4. BAI-competition relations compared for five species in control and active treatment (prescribed fire and thinning) stands in mixed-oak forests of southeastern Ohio. *** indicates significant difference at a = 0.001, ** at a = 0.01, * at a = 0.05, and NS is non-significant.

sponse among larger trees as they reduced stand basal area to a greater extent than prescribed fire. These growth responses greatly depended on species, which tend to vary in their adaptations to fire. On the other hand, growth of suppressed trees with small crowns is reported to cease sooner in the growing season than in dominant trees with large crowns (Fritts, 1976). This difference in growth period partly explains the observed disparity in growth rates of different sized trees. Contrary to the mechanical thinning, the prescribed fire treatment seemed to have benefitted the smaller trees more, judging from their relatively greater growth in the burn-only stand. Fire intensity in our study was generally low to moderate (Waldrop et al., 2008), which induced significant positive growth responses among smaller trees. This positive growth response may seem unusual due to the potential loss of vigor reported for some species after fire. However, enhanced growth of an individual tree following prescribed fire is conceivable if the treatment removes some neighboring trees, and frees up more resources for the surviving trees. Indeed, earlier analysis of 5-yr periodic annual BAI of trees in relation to fire severity in our sites showed a positive response for some species (e.g., black oak; Anning and McCarthy, in press).

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Table 4 Effect of site quality index on growth-competition relationship of tree species following prescribed fire and thinning management in mixed-oak forests of southeastern Ohio. Site quality classification was based on the integrated moisture index (IMI; Iverson et al. (1997)). Species/site quality class

d.f.

Intercept

Adjusted R2 (%)

P

All species Mesic Intermediate Dry

1, 71 1, 130 1, 132

30.71 29.86 28.35

18.62 30.75 35.37

<0.0001 <0.0001 <0.0001

Black oak Mesic Intermediate Dry

1, 10 1, 30 1, 33

33.36 31.75 29.07

7.58 40.79 34.84

0.1979 <0.0001 0.0001

Chestnut oak Mesic Intermediate Dry

1, 10 1, 33 1, 32

31.90 32.03 30.33

28.94 42.89 37.38

0.0413 <0.0001 <0.0001

White oak Mesic Intermediate Dry

1, 16 1, 36 1, 34

23.02 23.68 26.66

35.30 27.96 44.28

0.0051 0.0004 <0.0001

Hickory Mesic Intermediate Dry

1, 13 1, 12 1, 12

22.45 17.25 14.46

21.59 25.47 19.27

0.0462 0.0379 0.0656

Yellow-poplar Mesic Intermediate Dry

1, 14 1, 11 1, 13

50.18 44.95 37.42

46.44 20.48 40.38

0.0022 0.0681 0.0068

Other studies have demonstrated the variable effect of fire on forest communities (which may be positive, neutral, or negative; e.g., Hutchinson et al., 2005; Brose et al., 2006). Resistance to lowintensity fire due to increased bark thickness, and reduced vigor might partially explain the slower growth response of larger trees to the prescribed burning. Our results also revealed higher BAI rates among younger trees compared to older trees, agreeing with the well-known phenome-

Basal area increment (cm2 yr-1)

80

non of tree growth decline with age (Kozlowski et al., 1991; Yoder et al., 1994; Ryan et al., 2006; Fiedler et al., 2010). Potential explanations for this growth decline are found in several hypotheses including reduction in photosynthetic rates due to hydraulic limitation (Ryan et al., 2006), decrease in leaf area to sapwood ratio (McDowell et al., 2002) and increase in sapwood respiration (Ryan and Waring, 1992). Age had a significant negative effect on growth rates of chestnut oak trees, but the exact physiological mechanisms are not immediately clear. Differences in treatment intensity resulted in substantial variations in the competitive status of individual trees, largely explaining the variation in BAI among the treatments. Prescribed fire decreased competition between trees but to a much lesser degree than was achieved with thinning alone, or the combined treatment. The relatively higher sensitivity of trees to mechanical thinning suggests a more direct response to release from competition in the thin-only stand. By contrast, the slow decline in growth with increasing competition in the burn-only and the thin + burn stands suggests alteration of the growth environment in some beneficial ways other than simply releasing trees from competition. Such benefits may be a function of increased nutrient availability (e.g., total inorganic nitrogen, calcium, phosphorus) and mineral soil exposure and permeability following fire as documented by previous investigators (Boerner et al., 2009). Albrecht and McCarthy (2006) reported ‘‘sustained reduction in the densities of saplings (3–10 cm DBH)’’ following prescribed fire, whilst Hutchinson et al. (2005) reported significant fire  year effect on small tree (10–15 cm DBH) density. Thus, while our neighborhood analysis did not include these smaller plants, it is important to emphasize that their removal as a result of the prescribed fire treatment may stimulate growth of the larger residual trees. In general, the close relation between BAI and competitive status of trees in these closed-canopy forests is ecologically meaningful and consistent with previous studies, which have highlighted strong negative effects of competition on tree growth and forest structure (Canham et al., 2004; Coomes and Allen, 2007; Thorpe et al., 2010). Competition effect on tree growth following the treatment varied with species. Yellow-poplar, known for its vigorous growth in open canopies (Trimble, 1967), benefited most from competition

80

(a) Medium

(b) Large

y=18.44−4.8x

y=32.95−17.55x

R 2 = 12.72%

R 2 =24.96%

P < 0.001

60

P < 0.001

60

40

40

20

20

0

0 0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.5

1.0

1.5

2.0

2.5

Competition intensity Fig. 5. Size-dependent competition effect on basal area increment following prescribed fire and thinning treatments in forests of southeastern Ohio. Panel (a) medium-size trees (25 to <40 cm DBH); panel (b) large trees (P40 cm DBH).

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100

R 2 = 39.51 % (n = 234)

(a) All species

80

60

40

40

20

20 0 Competition

100

Size

Competition

Age

R 2 = 56.52 % (n = 52)

(c) Chestnut oak

80

100

Size

60

40

40

20

20

Age

R 2 = 42.14 % (n = 64)

(d) White oak

80

60

0

0 Competition

100

R 2 = 30.33 % (n = 49)

(b) Black oak

80

60

0

Proportion of R2

100

Size

R 2 = 46.74 % (n = 26)

(e) Hickory

80

Competition

Age

100

Size

R 2 = 47.05 % (n = 27)

(f) Yellow-poplar

80

60

60

40

40

20

20

Age

0

0 Competition

Size

Age

Competition

Size

Age

Explanatory variable Fig. 6. Relative importances of competition, size, and age for basal area growth of residual tree species following prescribed fire and thinning treatments (data from control plots excluded from analysis) in southeastern Ohio. Bars represent means + 95% bootstrap confidence intervals with 1000 replications (Gromping, 2006). All multiple regressions were significant at a = 0.001.

release, although a positive growth response was also evident in all other species. The higher sensitivity of yellow-poplar and, to some extent, chestnut and black oaks implies that intense and periodic thinning may be more beneficial to these species. In contrast, white oak and hickories were less reactive to changes in competition, though they appeared more likely to respond to prescribed fire. Site quality somewhat influenced the growth-competition relation of trees as indicated by the increase in competition importance for BAI within the optimum ‘‘physiographic’’ range (as stratified by the IMI) of species (Table 4). For example, competition effect on BAI of white oak was strongest on xeric sites, yellow-poplar and hickories on mesic sites, and both black oak and chestnut oak on intermediate sites. This pattern agrees with the prediction that ‘‘future stands dominated by oaks would be concentrated in low-site index areas where competition from more mesic and shade-tolerant species would be minimized . . .’’ (Iverson et al., 1997). However, it suggests more intense intraspecific competition within these optimal ‘‘physiographic’’ areas of the landscape. It also indicates that

competition importance may vary in distinct ways along environmental gradients as noted by Kunstler et al. (2011). The size dependence of competition observed in this study is in keeping with the findings of several previous studies (Lorimer, 1983; McDonald et al., 2002; Easdale et al., 2012). As noted by McDonald et al. (2002), tree size affects resource acquisition, and larger trees are less susceptible to competition because they have more stored resources, which give them a greater competitive edge over smaller trees. Variation in the strength of the competitiongrowth relation between smaller and larger trees reflects differences in sensitivity to crowding and shading (Coomes and Allen, 2007). As these authors argued, larger trees with well-developed and exposed canopy are more often limited by nutrient availability (as a result of crowding), while small trees are mainly limited by light availability (as a result of shading). Thus, while partial harvesting of stems may free up nutrients, which can substantially enhance growth of larger residual trees, smaller trees may respond rather slowly due to persistent shading by their larger neighbors. This explanation seems plausible, though our competition model

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did not take into account the angular dispersion of neighbors around the target trees. Tree growth in natural populations is a complex process and integrates multiple factors (Fritts, 1976; Kozlowski et al., 1991). In this light, the 40% of variance in BAI explained by competition, size and age of trees after the treatments is quite striking, and demonstrates the importance of these factors within the managed stands. Despite the difficulty in disentangling their relative effects, the present results demonstrate with reasonable confidence that competition is a more important determinant of residual tree growth than size and age. Among the non-oaks, tree size exerted equal or even greater controls on BAI than competition, probably due to a slight size differential between them and the oaks; yellow-poplar and hickories likely experienced continued shading relative to their larger oak neighbors. The weak growth response to age could be due to the minimal variation in age of trees observed in these even-aged stands. Variance in BAI not explained by competition, size and age might be related to treatment manipulations, species, or site quality as earlier discussed, or they might be a function of climate and some biotic stresses (Fritts, 1976).

5. Conclusions and management implications We observed substantial variation in competitive status of trees among the treatments—this supports the view that prescribed fire and thinning influence forest growth and development by creating heterogeneity among stands (Lorimer, 1983; Thorpe et al., 2010). Moreover, competition, size and treatment interacted in a complex fashion to influence BAI of residual trees. The higher sensitivity of trees to competition in the thin-only stands implies that mechanical treatments influence tree growth mainly through release from competition. However, prescribed fire may provide additional benefits via nutrient release, increased surface temperature and moisture availability (Kozlowski et al., 1991; Peterson et al., 1994; Boerner et al., 2009). These results also agree with the notion that mechanical treatment may not be a complete surrogate of prescribed fire (Schwilk et al., 2009). Overall, competition appeared to be a more important driver of residual tree growth than size and age, though its effect varied among species. Prescribed fire and thinning treatments are frequently used to alter stand structure (Hutchinson et al., 2005) and to help promote tree growth and productivity. In this respect, mechanical treatments will be most effective as they generate greater BAI response. The current results, consistent with previous findings (e.g., Hutchinson et al., 2005; Waldrop et al., 2008), demonstrate that prescribed burning have minimal effect on large trees, probably due to its low intensity. Thus, in forests where mechanical thinning is not a management option or where there is the need to promote growth of fire-adapted species such as the oaks, repeated or more intense burns may be the best strategy to eliciting the desired responses among large trees. Variation in species response to competition in relation to site quality calls for careful consideration, not only ofwhich species to favor during thinning operations, but also, of where and how the treatments are applied. For example, the high sensitivity of yellow-poplar to competition suggests the need for a higher treatment intensity to sustain the growth of this species. However, ‘‘wholesale’’ intense fire and thinning will only give yellow-poplar and other fast-growing species a competitive advantage over the oaks. If the management goal is to enhance growth and productivity of a particular species, reduction in intraspecific competition within the optimal "physiographic" range of the species may be necessary.

Acknowledgements We thank the staff of USDA Forest Service, Northern Research Station, Delaware, OH, for permission to use the experimental sites. Financial support for the study came to AKA via a Donald Clippinger Fellowship, a Student Enhancement Award, a Graduate Student Senate Original Work Grant, and funding from the Ohio Center for Ecology and Evolutionary Study—all of Ohio University, and he is grateful to them all.

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