Annals of Forest Science

, Volume 73, Issue 4, pp 971–986 | Cite as

Drought response of upland oak (Quercus L.) species in Appalachian hardwood forests of the southeastern USA

Original Paper

Abstract

Key message

In Appalachian hardwood forests, density, stem size, and productivity affected growth during drought for red oak, but not white oak species. Minor effects of density suggest that a single low thinning does little to promote drought resilience for oaks in the region.

Context

Management is increasingly focused on promoting resilience to disturbance. Because stand density can modulate climate-growth relationships, thinning may be an adaptation strategy that promotes resistance/resilience to drought.

Aims

We examined how density, manipulated via thinning, stem size, and site productivity, influences the drought response of northern red, black, chestnut, and white oak.

Methods

We modeled the role of density, stem size, and site productivity on resistance, recovery, and resilience during two drought events.

Results

Chestnut and white oak displayed greater resistance, recovery, and/or resilience than did northern red and black oak. For black oak, density and stem size negatively affected resistance during the first and second drought, respectively. Density, stem size, and site productivity had no effect on chestnut and white oak.

Conclusion

The lack of sensitivity of chestnut and white oak to the ranges of density, stem size, and site productivity observed in this study and generally better resistance, recovery, and resilience suggests that management focused on the maintenance of these species, as opposed to a single silvicultural low thinning, may be a possible strategy for sustaining the growth and productivity of oak species in Appalachian hardwood stands. Drought response as affected by alternative thinning interventions should be evaluated.

Keywords

Resistance Resilience Recovery Climate change Quercus Appalachian Mountains 

1 Introduction

Across the Appalachian-Cumberland region of the southeastern USA, forecasted changes in climate, which include an increase in temperature along with more variable precipitation patterns, are expected to result in an increase in the severity and frequency of extreme weather events, such as drought (McNulty et al. 2013). Drought is a primary disturbance agent affecting forest productivity at local, regional, and global scales (Boisvenue and Running 2006; Zhao and Running 2010; Chen et al. 2012). Drought-related decreases in forest productivity are associated with increased tree mortality (Elliott and Swank 1994; Jenkins and Pallardy 1995; Adams et al. 2009; Allen et al. 2010; Anderegg et al. 2012), reduced growth (Fekedulegn et al. 2003; Klos et al. 2009; D’Amato et al. 2013), and magnification of complex forest-insect-disease interactions which can, in turn, further exacerbate ongoing mortality and growth reductions (Clinton et al. 1993; Desprez-Loustau et al. 2006; Haavik et al. 2015). Drought-related declines in forest growth and productivity—whether caused by reductions in growth or increases in mortality—have the potential to affect the sustained production of a variety of ecosystem services, including timber production (Hanewinkel et al. 2013), wildlife habitat and food resources (Sork et al. 1993), and terrestrial carbon storage (Zhao and Running 2010; Vayreda et al. 2012).

The vulnerability of tree species to drought-related reductions in growth is species- (e.g., Fekedulegn et al. 2003; Pan et al. 1997) or functional group-specific (Anderegg et al. 2015). In a recent study, Elliott et al. (2015) analyzed the variability of drought response of six prominent Appalachian hardwood species and observed the individual tree drought-growth relationships varied according to xylem architecture, with diffuse porous species (e.g., yellow-poplar (Liriodendron tulipifera L.), red maple (Acer rubrum L.), sweet birch (Betula lenta L.)) more susceptible to reduced growth than ring-porous oak (Quercus L.) species. Similarly, utilizing US Forest Inventory and Analysis data, Brzostek et al. (2014) reported that mesophytic species such as the yellow-poplar and sugar maple (Acer saccharum Marsh.), both diffuse porous species, exhibited greater reductions in growth during periods of chronic moisture stress than did xeric species such as chestnut oak (Quercus montana wild.) and white oak (Quercus alba L.). Beyond interspecific differences in response to drought, drought-growth relationships further vary across age (Copenheaver et al. 2011; Martínez-Vilalta et al. 2012), tree size (Zang et al. 2012; Keyser and Brown 2014), and compositional (Lebourgeois et al. 2013) and environmental (Orwig and Abrams 1997; Tardiff et al., 2003; White et al. 2011) gradients.

Forest management efforts are increasingly focused on promoting resistance and/or resilience to the potential effects of climate change, including drought at the tree, stand, and landscape level. Retrospective analyses suggest that individual tree- and stand-level growth during drought events is modulated by stand density and intraspecific and interspecific competitive interactions (Piutti and Cescatti 1997; D’Amato et al. 2013; Lebourgeois et al. 2013; Sánchez-Salguero et al. 2015). As such, the manipulation of stand density and associated competitive interactions via silvicultural thinning is advocated by many as an adaptation strategy that promotes the resilience of individual trees, stands, and forests to future drought-related decreases in growth and productivity (Bréda and Badeau 2008; Klos et al. 2009; Martin-Benito et al. 2010; Gómez-Aparicio et al. 2011).

Knowledge regarding tree- and stand-level resilience to drought across species, functional groups, and forest types is increasing. For many species, increasing levels of competition (i.e., density) is related to a decrease in an individual tree’s drought resistance, recovery, and resilience (Linares et al. 2010; Kohler et al. 2010; Lebourgeois et al. 2014). Other studies suggest that the relationships among overall resilience, competition, and climate are not straightforward. For example, in plantation-origin red pine (Pinus resinosa Ait.) stands in Minnesota, resistance to drought at the stand level was dependent upon an interaction between stand age/tree size and density (D’Amato et al. 2013). The authors observed that thinning to low densities at a young age (49 years) conferred increased drought resistance while the reverse was observed during a drought event occurring at an older age (76 years). Variability in the effects of density or competition on climate-growth relationships and, more specifically, response to drought have been observed in natural and plantation-origin forests in Europe (e.g., Gea-Izquierdo et al. 2009; Lebourgeois et al. 2014; Sánchez-Salguero et al. 2015) and, although to a far lesser extent, North America (e.g., McDowell et al. 2006; D’Amato et al. 2013; Keyser and Brown 2014). Results from these studies suggest that the development of thinning prescriptions intended to increase resiliency to extreme climatic events, including drought, must account for variation across species, emergent stand properties, and environmental conditions.

The southern Appalachian-Cumberland Region encompasses ∼25.2 million hectares in the southeastern USA. These forests possess some of the highest level of tree diversity outside of the tropics (Keyser et al. 2014), and, although diverse, the broadleaved deciduous forests that characterize the forested landscapes of this area are dominated by ecologically and economically valuable oak species (Fei et al. 2011; Keyser et al. 2014). Sustaining the growth and productivity of oak species, in particular, is critical to the long-term production of critical ecosystem goods and services provided by these mixed-deciduous broadleaved forests (e.g., McShae et al. 2007; Keyser et al. 2014). Although oaks are considered tolerant of drought (Abrams 1990), reduced growth due to drought predisposes oaks to mortality (Jenkins and Pallardy 1995; Pedersen 1998). In this study, we examined how tree density, stem size, and site productivity influence tree-level growth during drought years for the predominant oak species across southern Appalachian mixed-hardwood forests. Specifically, we hypothesized that (1) the ability to resist and recover from drought will be positively associated with stem size; (2) as stand density increases, trees will be increasingly susceptible to drought-related reductions in tree growth (i.e., display lower resistance to drought) and have a lesser ability to recover from drought; and (3) the ability to resist and recover from drought will be positively associated with estimates of site productivity (i.e., site index). Quantitative information regarding the response of tree growth to drought across structural and environmental gradients will provide information as to whether modifying stand structure via silvicultural thinning can mitigate the deleterious effects of drought on tree growth as well as provide information that will aid in the identification of stands most susceptible to drought-induced reductions in growth.

2 Methods

2.1 Study area

This study uses data collected as part of a study examining the growth and yield of mixed-hardwood stands in response to thinning across the southern Appalachians. During the dormant season of 1974/1975, 62 permanent plots ranging in size from 0.06 to 0.1 ha were established in upland hardwood stands throughout the Blue Ridge and northern Ridge and Valley provinces of the southern Appalachian Mountains. Plots were located in naturally regenerated stands that originated as a result of heavy cutting between 1916 and 1955 (Harrison et al. 1986) in the mountains of northern Georgia (n = 3), western North Carolina (n = 41), eastern Tennessee (n = 11), and southwestern Virginia (n = 7). Plots were located in a variety of topographic positions, with slopes between 6 and 65 % and altitudes that ranged from 600 to 1350 m. All plots were established in even-aged stands of mixed-species composition, with oak species generally constituting the greatest proportion of plot basal area (BA; m2/ha). The geographic and altitudinal gradients associated with the study area resulted in variability in climate and soils. Soils were generally typic dystrochrepts and typic and humic hapludults derived from arkose sandstone, granite gneiss, mica gneiss, and mica schist (Beck 1983). Across the study area, 30-year mean annual temperature varied from 11.6 °C in Virginia to 15.1 °C in Georgia (http://www.ncdc.noaa.gov/cag/time-series/us). Precipitation across the study area is generally evenly distributed throughout the year and increases with elevation (McNab 2011). Thirty-year mean annual precipitation varied between 1119 mm in Virginia and 1406 mm in North Carolina (http://www.ncdc.noaa.gov/cag/time-series/us).

2.2 Data collection

At the time of plot establishment, all live trees >2.54 cm in diameter at breast height (DBH (cm); 1.37 m above ground line) within each plot were tagged, and species and DBH were recorded. During the initial inventory, one increment core from 0.3 m above ground line along with total tree height (m) was obtained from six dominant/codominant trees per plot. Using age and height data, an estimate of site index (SI (base-age 50)) for each of the six trees per plot was calculated. Site index for non-oak species was converted to that of white oak using Doolittle’s (1958) conversion equations while SI for oak species was computed according to Olson (1959). Plot-level SI was calculated as the average SI of the six sample trees. Immediately following the initial inventory (dormant season 1974/1975), the 62 plots received a low thinning to a residual BA that was at least 6 m2/ha less than the prethinning BA. To eliminate any potential edge effect, a 20-m buffer was treated in a similar manner around each plot. Remeasurement of all plots occurred during the dormant season every 5 years following thinning through 2005. During each inventory, DBH of all the live trees tagged in the original inventory was recorded. Although unthinned control plots were established as part of the original (1970s) study, sample size was low (n = 13) relative to the thinned plots (n = 62). In addition, unthinned plots were geographically clustered in eastern Tennessee, generally contained older and larger trees, and were remeasured only sporadically throughout the 1975–2005 time period. A limitation of the current study, consequently, is that only data from thinned plots were utilized.

During the fall/winter of 2010, one increment core was collected from three to eight dominant/codominant trees per plot. Increment cores were collected at approximately 1.37 m above ground line and along the contour of the slope. Initially, five dominant/codominant trees per plot were randomly selected for coring. Oak species were the preferred sample species, with other broadleaved deciduous species selected only when oaks were unavailable. During the collection of increment cores, a portion of the preselected sample trees were dead or possessed serious defect/damage. In those cases, alternative dominant/codominant oak trees were selected for sampling. In some cases, no suitable substitute sample tree(s) existed, hence, the reduction in sample trees from the original five to three on a subsample of the plots. When sampling on a given plot was reduced due to lack of adequate sample trees, additional cores were obtained from suitable sample trees on subsequent plot(s). Cores were dried, mounted, and sanded with progressively finer sandpaper until cell structure was clearly visible. Rings were visually crossdated, and radial growth was measured to the nearest 0.001 mm using a linearly controlled stage and microscope attached to a digital encoder (Velmex, Inc.). Accuracy of visual crossdating was supported statistically using the program COFECHA (Holmes 1983). Tight rings, indistinct ring boundaries, and/or broken cores resulted in poor crossdating on a subsample of cores. Consequently, increment cores from 11 plots were eliminated from the dataset resulting in 233 cores from 8 species: white oak (50 cores from 18 plots), chestnut oak (54 cores from 24 plots), scarlet oak (Quercus coccinea Muenchh.; 13 cores from 10 plots), northern red oak (Quercus rubra L.; 91 cores from 34 plots), and black oak (Quercus velutnia Lam.; 25 cores from 17 plots). Crossdated ring-width chronologies were converted to chronologies of annual inside bark basal area increment (BAI; cm2/year) assuming circularity for each sample tree. Specifically, BAI was calculated as

BAI = π(r2t − r2t − 1),

where r is the radius of the tree and t is the year of ring formation.

Between the fall/winter of 1974/1975 and 2010, two distinct drought events (1985/6 to 1988 and 2006/7 to 2008; Table 1) occurring across the entire study area (all 62 plots) were recorded. Drought events were identified using the Palmer Drought Severity Index (PDSI) values during the months of May, June, and July (PDSIMJJ), the period when moisture deficit most significantly impacts the current year radial increment of red and white oak species (Speer et al. 2009; LeBlanc and Terrell 2011). A drought event was considered moderate when PDSIMJJ values were ≥−3.0 but <−2.0 and severe when PDSIMJJ values were ≥−4.0 but <−3.0. All PDSI values were obtained from the National Climatic Data Center (http://www.ncdc.noaa.gov/cag/time-series/us), with PDSI values specific to each plot’s location within its respective State and Climate Division (plots in Georgia were within the North Central Climate Division (CD) 2, plots in North Carolina were with the Southern Mountains CD1, plots in Tennessee were within the Eastern CD1, and plots in Virginia were within the Southwestern Mountains CD6).
Table 1

Mean Palmer Drought Severity Index values during the months of May, June, and July (PDSIMJJ) during the 2 years preceding a drought (PDSIMJJpre), during the drought (PDSIMJJdrought), and during the 2 years post-drought (PDSIMJJpost)

Location

Pre-drought years

PDSIMJJpre

Drought years

PDSIMJJdrought

Post-drought years

PDSIMJJpost

 

Drought 1

Georgia

1984–1985

1.17

1986–1988

−3.52

1989–1990

2.10

North Carolina

1983–1984

1.28

1985–1988

−3.34

1989–1990

2.03

Tennessee

1983–1984

0.75

1985–1988

−3.59

1989–1990

2.29

Virginia

1984–1985

−0.34

1986–1988

−2.68

1989–1990

1.04

 

Drought 2

Georgia

2004–2005

1.02

2006–2008

−3.52

2009–2010

−1.23

North Carolina

2004–2005

0.85

2006–2008

−3.12

2009–2010

−0.12

Tennessee

2005–2006

−0.44

2007–2008

−3.70

2009–2010

−0.07

Virginia

2005–2006

−0.02

2007–2008

−2.67

2009–2010

0.19

2.3 Data analysis

We calculated three drought indices according to Lloret et al. (2011): (1) resistance (BAIresistance), defined as the quotient of BAI during a drought (BAIdrought) and average BAI 2 years prior to a drought (BAIpre); (2) recovery (BAIrecovery), defined as the quotient of average BAI 2 years following a drought (BAIpost) and BAIdrought; and (3) resilience (BAIresilience), defined as the quotient of BAIpost and BAIpre. When appropriate, BAIdrought values encompassed multiple years (Table 1). BAIresistance values <1.0 indicated that BAI during a drought event was less than the average BAI 2 years prior to the drought event while BAIresistance values >1.0 indicated that BAI during a drought event was greater than the average BAI 2 years prior to the drought. Interpretation of BAIrecovery and BAIresilience values are similar to that of BAIresistance. Due to the limited sample size, scarlet oak, red maple, and hickory were not analyzed.

For each species and drought event, we used a random coefficients model to examine the effects of density, stem size, and site productivity on BAIresistance, BAIrecovery, and BAIresilience. Due to the hierarchical structure of the data (i.e., trees nested within plots), the plot was considered a random factor. Five candidate models were evaluated: (1) null, or intercept only model; (2) a model with only DBH; (3) a model with only a measure of density; (4) a model with only SI, which is an indirect measure of site productivity; and (5) an additive model with DBH and density and SI. Stem size (i.e., DBH) was reconstructed using individual tree BAI values and the initial prethinning DBH value. We chose to use a measure of relative density (RD) in lieu of absolute measures of density (e.g., BA or stems/ha) because plots were of mixed-species composition and variation in species composition often confounds the effects of absolute density on growth (Roach 1977; Stout and Nyland 1986). Relative density was calculated as the quotient of stand density index (SDI) (Reineke 1933) during each inventory period and maximum SDI. Maximum SDI values (Keyser 2008) varied by forest type associated with each plot. An information theoretic approach was used to compare the five aforementioned a priori models per species and drought event. Akaike’s information criterion adjusted for sample size (AICc) was used to determine which models most parsimoniously fit the data (lowest AICc). Parameters associated with top-ranked models and any “competitive” models within two AICc units of the best model were considered informative if the 95 % confidence interval did not include zero. Akaike weights (wi) were calculated to provide information regarding the strength of evidence for each model (Burnham and Anderson 2002). Plot-level density statistics were obtained from the inventory data most closely associated with a particular drought event. Drought indices were ln-transformed to approximate normality and equalize variance of the residuals. Because models were developed to test for the effects of stem size, density, and site productivity on the three drought indices, the models should be considered explanatory as opposed to predictive. All analyses were conducted using SAS version 9.3 (SAS Institute 2011).

3 Results

3.1 Study attributes

Plot- and individual tree-level attributes varied considerably across species (Table 2, Fig. 1). Over the time period examined in this study (1975–2010), individual tree BAI for northern red, black, chestnut, and white oak was variable, with northern red and black oak generally possessing greater BAI than chestnut and white oak (Fig. 2). Tree-level BAI during both drought events (BAIdrought) was greatest for northern red and white oak than for black and chestnut oak (Table 3).
Table 2

Range of plot- and sample tree-level attributes associated with the analyses of northern red, black, chestnut, and white oak, including plot-level basal area (BA), relative density (RD), site index (SI; upland oak, base-age 50), individual tree DBH, and individual tree age

 

Mean

Standard deviation

Minimum

Maximum

Mean

Standard deviation

Minimum

Maximum

Drought 1

Drought 2

Northern red oak

 BA (m2/ha)

19.1

6.0

9.2

35.1

26.9

8.2

13.1

55.1

 RD (%)

0.34

0.11

0.16

0.59

0.44

0.14

0.21

0.83

 DBH (cm)

32.3

7.1

11.6

52.6

41.7

8.8

14.0

65.0

 SI (m)

24.1

2.2

18.0

29.3

24.1

2.2

18.0

29.3

 Age (yrs)

44

12

18

70

64

12

38

90

Black oak

 BA (m2/ha)

17.1

5.2

9.2

25.9

24.3

7.2

13.1

34.7

 RD (%)

0.30

0.10

0.16

0.48

0.39

0.12

0.21

0.58

 DBH (cm)

33.9

4.5

23.3

43.3

40.7

5.4

27.2

51.9

 SI (m)

24.6

1.8

21.3

27.7

24.6

1.8

21.3

27.7

 Age (yrs)

50

9

30

60

70

9

50

80

Chestnut oak

 BA (m2/ha)

18.3

4.6

10.8

28.2

25.3

5.2

15.4

33.1

 RD (%)

0.34

0.09

0.17

0.51

0.43

0.10

0.22

0.56

 DBH (cm)

28.5

5.3

20.2

43.0

35.2

6.4

25.8

55.0

 SI (m)

23.9

2.2

18.0

27.7

23.9

2.2

18.0

27.7

 Age (yrs)

44

10

27

68

64

10

47

88

White oak

 BA (m2/ha)

17.5

4.4

10.8

25.9

24.6

5.3

16.1

34.7

 RD (%)

0.30

0.08

0.17

0.47

0.38

0.09

0.25

0.58

 DBH (cm)

33.1

5.5

22.5

46.3

40.7

6.8

25.9

63.8

 SI (m)

24.7

1.7

21.6

27.7

24.7

1.7

21.6

27.7

 Age (yrs)

56

7

38

70

76

7

58

90

Fig. 1

Distribution of sample trees by relative density class (a), diameter class (b), and site index class (c). Relative density classes were (1) <0.20, (2) ≥0.20 and <0.30, (3) ≥0.30 and <0.40, (4) ≥0.40 and <0.50, (5) ≥0.50 and <0.60, and (6) ≥0.60. Site index classes were (1) ≥18.0 and <20.0, (2) ≥20.0 and <22.0, (3) ≥22.0 and <24.0, (4) ≥24.0 and <26.0, (5) ≥26.0 and <28.0, and (6) ≥28.0 and <30.0

Fig. 2

Basal area increment (cm2/year) for northern red (a), black (b), chestnut (c), and white oak (d). Gray lines represent the individual tree BAI chronologies while the black line represents average BAI chronology for the respective species. Dashed vertical line indicates year of thinning (fall/winter 1974/1975)

Table 3

Mean (standard deviation) annual basal area increment (BAI) calculated during the 2 years preceding drought (BAIpre), during drought (BAIdrought), and during the 2 years following drought (BAIpost)

Species

BAIpre

BAIdrought

BAIpost

BAIpre

BAIdrought

BAIpost

(cm2/year)

(cm2/year)

(cm2/year)

(cm2/year)

(cm2/year)

(cm2/year)

Drought 1

Drought 2

Northern red oak

17.65

17.57

19.44

22.97

22.65

22.03

(9.05)

(9.68)

(10.82)

(14.88)

(14.76)

(14.77)

Black oak

16.11

14.56

14.47

14.94

13.43

13.64

(8.00)

(6.10)

(6.40)

(7.26)

(5.41)

(6.21)

Chestnut oak

11.60

11.31

12.38

15.17

13.14

14.76

(6.94)

(6.63)

(6.77)

(7.44)

(6.03)

(6.85)

White oak

16.21

16.86

17.28

19.63

17.07

19.31

(8.77)

(9.31)

(8.85)

(11.09)

(8.32)

(9.47)

The two drought events recorded between 1975 and 2010 (1985/1986 to 1988 and 2006/2007 to 2008; Table 1) were classified as severe in Georgia, North Carolina, and Tennessee and moderate in Virginia. PDSIMJJdrought values ranged from −3.59 and −2.68 during the first drought and from −3.52 and −2.67 during the second drought. PDSIMJJpre was near normal prior to both the first and second drought, with PDSIMJJpre averaging 0.72 and 0.35, respectively. The 2 years following the first drought were characterized by slightly wet conditions, with PDSIMJJpost averaging 1.87 compared to the 2 years following the second drought when PDSIMJJpost was classified as near normal, with PDSIMJJpost averaging −0.31.

3.2 Drought 1

During the first drought, the majority of northern red and black oak trees experienced a decline in growth relative to growth 2 years preceding the drought. BAIresistance values were <1.0 for 55 and 76 % of the northern red and black oak trees sampled, respectively (Fig. 3). In contrast, only 50 % of the chestnut oak and 36 % of the white oak trees sampled possessed BAIresistance values <1.0 during the first drought event. BAIresistance for northern red, chestnut, and white oak resulted in an uninformative model characterized by the null model being the most supported (chestnut oak) or a competitive model within two AICC of the top-ranked model (northern red and white oak) (Table 4). For these species, top-ranked models as well as models within two AICC of the top-ranked models contained uninformative covariates (i.e., 95 % confidence intervals that contained zero), suggesting that these variables had little effect on BAIresistance. For black oak, the most parsimonious model contained RD, which was 5.3 AICC units from the next best model (Table 4). The parameter estimate associated with RD (95 % confidence interval) was −0.9158 (−1.3812, −0.4504), suggesting that the BAIresistance of black oak during the first drought decreased as RD increased.
Fig. 3

Percent of sample trees by species and drought event by BAIresistance, BAIrecovery, and BAIresilience category

Table 4

Model fit statistics associated with BAIresistance, BAIrecovery, and BAIresilience during the first drought for northern red oak, black oak, chestnut oak, and white oak

 

AICC

ΔAICC

wi

 

AICC

ΔAICC

wi

 

AICC

ΔAICC

wi

Drought 1—BAIresistance

 

Drought 1—BAIrecovery

 

Drought 1—BAIresilience

Northern red oak

 RD

−28.5

0.0

0.29

DBH + RDb + SIb

4.6

0.0

0.88

SI

47.4

0.0

0.34

 Null

−28.2

0.3

0.25

RDb

9.2

4.6

0.09

Null

48.2

0.8

0.23

 DBH

−27.9

0.6

0.22

SIb

11.8

7.2

0.02

RD

48.4

1.0

0.21

 SI

−26.7

1.8

0.12

Null

14.8

10.2

0.01

DBH + RD + SIb

49.1

1.7

0.15

 DBH + RD + SI

−26.7

1.8

0.12

DBH

15.9

11.3

0.00

DBH

50.4

3.0

0.08

Black oak

 RDa

−32.0

0.0

0.93

Null

13.2

0.0

0.50

RD

23.3

0.0

0.43

 DBH + RDa + SI

−26.7

5.3

0.07

SI

15.3

2.1

0.18

Null

23.8

0.5

0.34

 Null

−20.4

11.6

0.00

RD

15.5

2.3

0.16

SI

26.3

3.0

0.10

 DBH

−19.1

12.9

0.00

DBH

15.6

2.4

0.15

DBH

26.4

3.1

0.09

 SI

−17.6

14.4

0.00

DBH + RD + SI

20.2

7.0

0.02

DBH + RDa + SI

27.8

4.5

0.05

Chestnut oak

 Null

10.2

0.0

0.42

Null

5.5

0.0

0.40

Null

58.0

0.0

0.51

 SI

11.6

1.4

0.21

SI

6.3

0.8

0.27

DBH

60.2

2.2

0.17

 DBH

11.9

1.7

0.18

RD

7.1

1.6

0.18

SI

60.3

2.3

0.16

 RD

12.0

1.8

0.17

DBH

7.7

2.2

0.13

RD

60.4

2.4

0.15

 DBH + RD + SI

15.4

5.2

0.03

DBH + RD + SI

10.9

5.4

0.03

DBH + RD + SI

65.1

7.1

0.01

White oak

 RD

−25.1

0.0

0.40

Null

−18.6

0.0

0.57

DBH

−3.3

0.0

0.48

 Null

−24.5

0.6

0.29

RD

−16.7

1.9

0.22

Null

−1.9

1.4

0.24

 SI

−23.1

2.0

0.15

DBH

−16.4

2.2

0.19

RD

−0.2

3.1

0.10

 DBH

−22.6

2.5

0.11

DBH + RD + SI

−12.1

6.5

0.02

SI

−0.2

3.1

0.10

 DBH + RD + SI

−20.8

4.3

0.05

SI

16.2

34.8

0.00

DBH + RD + SI

0.5

3.8

0.07

DBH is tree-level diameter at breast height (cm), RD is plot-level relative density (%), and SI is plot-level site index (m). ΔAICC refers to the change in AICC compared to the best overall model (i.e., the model corresponding to the lowest AICC), and wi is Akaike weight.

aInformative (i.e., 95 % confidence interval that does not contain zero) and negative parameter estimate

bInformative and positive parameter estimate

Substantial variability in BAIrecovery among the oak species was observed after the first drought event. For the majority of northern red, chestnut, and white oak trees sampled, growth during the 2 years after drought had surpassed average growth during the drought (Fig. 3). For black oak, however, BAIrecovery values for 56 % of the trees sampled remained <1.0. For northern red oak, the model best supported by the data (4.6 AICC units from the next best model) was the additive model that included DBH, RD, and SI (Table 4). Although the DBH parameter was uninformative in this model, the SI and RD parameters were informative and estimated to be 0.0424 (0.0147, 0.0701) and 0.9377 (0.3966, 1.4787), respectively. This suggests that the BAIrecovery of northern red oak increased as SI and RD increased. BAIrecovery for black, chestnut, and white oak produced an uninformative model characterized by the null model being most supported by the data (Table 4). For chestnut and white oak, models containing either SI or RD were within two AICc units of the null; however, these models contained uninformative parameters.

With the exception of black oak, the majority of northern red, chestnut, and white oak trees sampled possessed BAIresilience values ≥1.0, indicating that average growth 2 years following the first drought had equaled or surpassed average growth 2 years preceding the drought (Fig. 3). For black oak, however, 68 % of the trees sampled possessed BAIresilience values <1.0. BAIresilience generated an uninformative model for black, chestnut, and white oak, with the null model being the most supported by the data for chestnut oak and within two AICC units of the top-ranked model for black and white oak (Table 4). For black and white oak, the top-ranked models contained uninformative parameters for RD and DBH, respectively. For northern red oak, the model most supported by the data as well as the third ranked model (1.0 AICc units from the top-ranked model) contained uninformative SI and RD parameters. Although the additive model (1.7 AICc units from the top-ranked model) contained uninformative DBH and RD parameters, the SI parameter was informative and estimated to be 0.0335 (0.0005, 0.0665), providing weak support (i.e., low Akaike weight values) that SI may have had a slight positive effect on the BAIresilience of northern red oak during the first drought.

3.3 Drought 2

During the second drought, the vast majority of trees sampled, regardless of species, experienced a decline in growth relative to that observed during the 2 years preceding drought. Overall, 60, 64, 70, and 73 % of the northern red, black, chestnut, and white oak trees sampled possessed BAIresistance values <1.0, respectively (Fig. 3). For northern red oak, the BAIresistance model most supported by the data included only SI (2.5 AICC units from the next best model) (Table 5). The parameter estimate associated with SI was 0.0216 (0.0019, 0.04133), suggesting that the BAIresistance of northern red oak during the second drought increased as SI increased. For black oak, the most parsimonious BAIresistance model contained DBH (4.1 AICC units from the next best model) (Table 5). The parameter estimate associated with DBH was −0.0156 (−0.0278, −0.0033), suggesting that the BAIresistance of black oak decreased as DBH increased. For chestnut and white oak, BAIresistance produced an uninformative model, with the null model being the most supported (white oak) or within two AICC values of the top-ranked model (chestnut oak) (Table 5). For chestnut and white oak, all models within two ΔAICC of the top-ranked model contained uninformative covariates.
Table 5

Model fit statistics associated with BAIresistance, BAIrecovery, and BAIresilience during the second drought for northern red oak, black oak, chestnut oak, and white oak

 

AICC

ΔAICC

wi

 

AICC

ΔAICC

wi

 

AICC

ΔAICC

wi

Northern red oak

Drought 2—BAIresistance

 

Drought 2—BAIrecovery

 

Drought 2—BAIresilience

 SIb

−27.2

0.0

0.63

SIa

−16.4

0.0

0.76

Null

26.0

0.0

0.44

 Null

−24.7

2.5

0.18

DBH + RD + SIa

−13.0

3.4

0.14

RD

27.2

1.2

0.24

 DBH

−22.9

4.3

0.07

Null

−11.1

5.3

0.05

SI

28.1

2.1

0.15

 RD

−22.6

4.6

0.06

RD

−10.4

6.3

0.03

DBH

28.2

2.2

0.15

 DBH + RD + SI

−22.5

4.7

0.06

DBH

−9.0

7.4

0.02

DBH + RD + SI

31.6

5.6

0.03

Black oak

 DBHa

−10.3

0.0

0.80

Null

−10.2

0.0

0.48

DBH

−2.8

0.0

0.40

 Null

−6.2

4.1

0.10

RD

−8.5

1.7

0.21

Null

−1.6

1.2

0.22

 DBHa + RD + SI

−4.6

5.7

0.05

SI

−8.0

2.2

0.16

DBHa + RD + SIa

−1.3

1.5

0.19

 RD

−3.5

6.8

0.03

DBH

−7.6

2.6

0.13

RD

0.1

2.9

0.09

 SI

−3.3

7.0

0.02

DBH + RD + SI

−3.7

6.5

0.02

SI

0.2

3.0

0.09

Chestnut oak

 DBH

−12.5

0.0

0.45

Null

−15.7

0.0

0.49

Null

27.2

0.0

0.34

 Null

−11.2

1.3

0.23

SI

−13.6

2.1

0.17

DBH

27.2

0.0

0.34

 RD

−10.2

2.3

0.14

RD

−13.5

2.2

0.16

RD

28.5

1.3

0.18

 SI

−9.3

3.2

0.09

DBH

−13.4

2.3

0.16

SI

29.5

2.3

0.11

 DBH + RD + SI

−9.3

3.2

0.09

DBH + RD + SI

−8.8

6.9

0.02

DBH + RD + SI

31.3

4.1

0.04

White oak

 Null

−26.8

0.0

0.42

Null

−22.4

0.0

0.48

Null

−10.6

0.0

0.35

 DBH

−25.6

1.2

0.23

SI

−20.6

1.8

0.20

SI

−10.1

0.5

0.27

 SI

−24.9

1.9

0.16

DBH

−20.2

2.2

0.16

DBH

−9.7

0.9

0.22

 RD

−24.8

2.0

0.16

RD

−20.0

2.4

0.15

RD

−8.5

2.1

0.12

 DBH + RD + SI

−21.0

5.8

0.02

DBH + RD + SI

−15.7

6.7

0.02

DBH + RD + SI

−6.2

4.4

0.04

DBH is tree-level diameter at breast height (cm), RD is plot-level relative density (%), and SI is plot-level site index (m). ΔAICC refers to the change in AICC compared to the best overall model (i.e., the model corresponding to the lowest AICC), and wi is Akaike weight.

aInformative (i.e., 95 % confidence interval that does not contain zero) and negative parameter estimate

bInformative and positive parameter estimate

The ability of BAI to recover from the second drought varied considerably among the four oak species, with a greater percentage of chestnut and white oak possessing BAIrecovery values ≥1.0 than northern red and black oak trees (Fig. 3). Overall, 63, 44, 30, and 18 % of the northern red, black, chestnut, and white oak trees sampled possessed BAIrecovery values <1.0, respectively. For northern red oak, the most parsimonious model for BAIrecovery contained SI (3.4 AICC units from the next best model) (Table 5). The parameter estimate associated with SI was −0.0251 (−0.0461, −0.0042), suggesting that the ability of northern red oak to recover from the second drought decreased as SI increased. For black, chestnut, and white oak, the best supported model for BAIrecovery was the null model (Table 5). Although alternative models were within two AICC units for black and white oak, these models contained uninformative covariates.

During the 2 years following the second drought, the growth of the majority of trees sampled, regardless of species, remained lower relative to growth during the 2 years preceding drought. However, a greater percentage of chestnut and white oak trees possessed BAIresilience values ≥1.0 than of northern red and black oak trees (Fig. 3). Overall, 61, 64, 55, and 55 % of the northern red, black, chestnut, and white oak trees sampled possessed BAIresilience values <1.0, respectively. BAIresilience generated an uninformative model for northern red, chestnut, and white oak, with the null model being the best supported model (Table 5). Although models were within two AICC units of the null for these species, the parameters associated with these models were uninformative. For black oak, the top-ranked model contained DBH; however, DBH was an uninformative parameter in this singular model. The third ranked model for black oak (1.5 AICc units from the top-ranked model and 0.3 units from the null), although possessing low strength of evidence (i.e., low Akaike weight values), contained informative parameters for DBH and SI, but not RD. Parameter estimates associated with DBH and SI in this model were −0.0177 (−0.0321, −0.0033) and −0.0537 (−0.1013, −0.0062), respectively, providing weak evidence that increasing DBH and SI negatively affected the BAIresilience of black oak.

4 Discussion

We found that resistance, recovery, and resilience to drought varied greatly among the four oak species examined as well as between the two drought events. Previous research suggests that species in the white oak (Leucobalanus) group (e.g., chestnut and white oak) are more resistant and resilient to drought than species in the red oak (Erythrobalanus) group (e.g., northern red and black oak) (LeBlanc and Foster 1992; LeBlanc 1998; Abrams 2003). In this study, the white oak group displayed greater drought resistance during the first drought, but not during the second drought. During the first drought event, 57 % of the trees in the white oak group possessed BAIresistance values ≥1.0 compared to 41 % of the trees in the red oak group (Fig. 3). During the second drought, however, only 28 % of the trees in the white oak group possessed BAIresistance values ≥1.0 compared to 39 % of the trees in the red oak group. Across both drought events, the proportion of trees possessing BAIresilience ≥1.0 was consistently greater in the white oak group than in the red oak group.

We hypothesized that stem size would be positively associated with resistance, recovery, and resilience to drought. In the current study, we found that DBH affected the drought response of black oak during the second drought where, contrary to our hypothesis, DBH had a negative effect on BAIresistance and BAIresilience. Although size-modulating effects on drought response are less abundant for deciduous broadleaved tree species than for coniferous species, the seemingly minor and inconsistent influence of stem size on drought tolerance aligns with previous research results. For example, the effect of increasing stem size on growth during and after drought has been shown to be insignificant (Mérian and Lebourgeois 2011; Zang et al. 2012), positive (Orwig and Abrams 1997; Merlin et al. 2015), and negative (Merlin et al. 2015) for North American and European oak species, including black, white, sessile (Quercus petraea (Matt.) Liebl.)), and pedunculate (Quercus robur L.) oak. A positive relationship between stem size and drought response is thought to arise from the positive relationship between stem size and the size of an individual tree’s root system (Bond-Lamberty et al. 2002; Bolte et al. 2004). As the size and density of an individual’s root system increases, so does drought tolerance, as large root systems are able to access soil moisture and nutrients while smaller individuals are not (Bréda et al. 2006). In contrast, the negative effect of stem size on drought tolerance documented for black oak in this study suggests that hydraulic limitations associated with tree architectural characteristics that increase with stem size (e.g., leaf area) (Martin et al. 1998) may negate any positive effects of stem size on drought-related growth (Mencuccini et al. 2005; McDowell et al. 2006; Zang et al. 2012; D’Amato et al. 2013).

In this study, we found some evidence that SI affected drought response, but the influence varied across species and between the two drought events. For northern red oak, there was strong evidence that SI positively influenced BAIrecovery and BAIresistance during the first and second drought, respectively. Although a low-ranked model suggested that SI positively affected BAIresilience during the first drought, the strength of evidence was relatively low. In contrast, we found strong support that SI negatively affected the BAIrecovery of northern red oak during the second drought. Black oak was the only other species to display a relationship between SI and drought response. Although the strength of evidence was low, we did identify a negative effect of SI (in the context of the additive model) on BAIresilience during the second drought. Positive relationships between BAIrecovery and BAIresilience and SI during the first drought and BAIresistance and SI during the second drought confirms that northern red oak growth during and after drought is reduced to a greater degree on poorly versus highly productive sites (Tainter et al. 1990). These positive relationships between drought indices and SI are likely related to the fact that SI is negatively correlated with soil moisture deficit (Klinka and Carter 1990; Kayahara et al. 1997; Chen et al. 2002). Consequently, on highly productive sites (i.e., high SI values), soil moisture availability, even during the two drought events, may not have been reduced to levels that would have limited individual tree growth. Changes in biomass allocation patterns, such as lower allocation to fine-root biomass as stands/trees age and site productivity increases (Keyes and Grier 1981; Vogt et al. 1987), may be, in part, responsible for the negative relationships observed between SI and drought indices for northern red and black oak during the second drought; an event that occurred 19–20 years after the first drought.

There is a growing body of literature that describes and quantifies the interacting effects of competition and climate, and drought, in particular, on individual tree growth (e.g., Kohler et al. 2010; Martínez-Vilalta et al. 2012; Magruder et al. 2013; Keyser and Brown 2014; Sánchez-Salguero et al. 2015). Thinning increases individual tree growing space and reduces competition for soil moisture (Martín-Benito et al. 2010). Additionally, soil moisture availability often increases postthinning due to reduced stand-level transpiration and increased canopy throughfall (Aussenac and Granier 1988; Bréda and Granier 1996; Bréda et al. 1996). Unlike previous studies quantifying the impact of density on drought response of coniferous (e.g., Mission et al. 2003; D’Amato et al. 2013) and deciduous broadleaved forest tree species (e.g., Merlin et al. 2015), we found a very limited effect of density on various drought indices. The decreased resistance of black oak at progressively greater RD observed during the first drought partially supports our hypothesis regarding density and tree growth during drought. The fact the that effect of RD was drought-specific has been observed in Norway spruce (Picea abies L. (Karst.)) plantations in Europe and led authors to suggest that the positive effects of reduced density via thinning on resistance to drought may be short-lived (Mission et al. 2003; Kohler et al. 2010; Sohn et al. 2013). The first and second drought occurred 10–11 years and 31–32 years postthinning, respectively. In the case of the second drought, recovery of biomass and reoccupation of growing space in plots of progressively lower densities may have ameliorated any increase in soil moisture, nutrient, and/or light availability that often follows thinning operations. Our results suggest that the effects of density may be confounded by the length of time between thinning interventions and individual drought events (Sohn et al. 2013). Contrary to our hypothesis, density had a positive effect on the BAIrecovery of northern red oak during the first drought event. Like Sohn et al. (2013), we found the positive relationship between density and recovery to be ambiguous. The lack of an effect of RD on the drought indices of the two species in the white oak group is consistent with the suggestion by LeBlanc and Terrell (2011) that the growth of species in the white oak group is less affected by stand dynamics and competition than of species in the red oak group.

The lack of a consistent effect of RD, SI, and/or DBH across species and drought events in this study may be the result of our sampling procedures. In this study, we limited sampling to those individuals in dominant and codominant canopy positions. In regard to stem size, these individuals often possessed the largest DBH values within their respective plots. Additionally, regardless of the competitive environment (i.e., density), these relatively large individuals in the upper canopy layers experience less competitive pressure for aboveground and belowground resources than do smaller individuals. The thinning prescription carried out in 1974/1975 was a low thinning, removing individuals in the suppressed and intermediate canopy positions until the target residual density was achieved. Therefore, growing space within the upper canopy layers remained, for the most part, unchanged by thinning. In oak forests, competition between dominant/codominant oak trees and smaller individuals in the lower canopy layers is minimal and does not affect the survival (Ward and Stephens 1994) or growth of canopy oaks (Kelty et al. 1987; Kittredge 1988). Previous research has demonstrated a differential response to drought of some eastern US oak species between the lower and upper canopy layers (Orwig and Abrams 1997). Sampling of suppressed and intermediate individuals within the context of this study would have been limited due to (1) the low thinning that removed individuals in the lower canopy layers and (2) the low abundance of oak in the lower canopy layers (i.e., sapling and pole-sized individuals) (Nowaki et al. 1990; McWilliams et al. 2002) in southern Appalachian hardwood stands. In addition, this study did not address the potential effects of stand composition (e.g., diversity measures) on tree growth response to drought. Recent evidence suggests that stand composition can modify tree response to drought through niche complementarity (e.g., differences in rooting depth) and various facilitative processes (Lebourgeois et al. 2013; Grossiord et al. 2014). For example, Pretzsch et al. (2013) documented that growth of European beech (Fagus sylvatica (L.)) during drought decreased less in mixed-species stands than in pure stands presumably due to the hydraulic lift of water associated with deep-rooted co-occurring oak species. Because of our sampling procedures and the potential for stand composition (singularly or via interactions with SI, DBH, and/or RD) to alter tree-level drought response, caution should be used when generalizing the results beyond the relatively limited scope of this study.

5 Conclusion

The direct and indirect effects of drought, including decreased tree- and stand-level productivity and increased tree mortality, have the potential to affect ecosystem structure, function, and composition (e.g., Dale et al. 2001; Zhao and Running 2010) making the development of adaptation strategies that promote the resistance or resilience of forest stands and landscapes to the negative effects of drought of ecological and economic importance (Vose and Klepzig 2013). For tree growth and productivity, an increase in frequency and severity of drought events will likely manifest in decreased tree- and stand-level growth and increased tree mortality (Allen et al. 2010).

In these even-aged mixed deciduous broadleaved forests, adaptation strategies for climate change are focused on increasing resistance or resilience to perturbations and stressors, including drought, as species composition is already dominated by relatively drought-tolerant oak species. Oak species vary in their relative tolerance and response to drought (Abrams 1990), which is consistent with the variability in resistance, recovery, and resilience indices we observed in this study. Species in the white oak group are generally longer lived, more tolerant of shade, slower growing, and more drought-tolerant than are species in the red oak group (Abrams 1990; Abrams 2003; Johnson and Abrams 2009). In general, stem size, density, and site productivity failed to consistently explain the growth response of four of the predominant oak species to drought in Appalachian hardwood stands. Studies have forecasted that future climate will result in an increase in the abundance and potential habitat for species in the white oak group in much of the eastern USA (Iverson and Prasad 1998; Iverson and Prasad 2002; McKenney-Easterling et al. 2000). The lack of sensitivity of the two white oak species—chestnut and white oak—to the range of density, stem size, and site productivity observed in this study as well as generally better resistance, recovery, and resilience indices suggests that management activities that focus on the regeneration, establishment, and/or retention of white oak versus red oak species, as opposed to a single silvicultural low thinning (to the levels of density observed in this study), may be one of the many possible strategies for sustaining the growth and productivity of oak species in Appalachian hardwood stands under a changing climate. To make more definitive conclusions regarding the ability of thinning to increase the overall drought resilience of these four prominent oak species, alternative thinning interventions (e.g., high thinning, free thinning, variable density thinning, etc.) that result in greater structural heterogeneity than did the single low thinning conducted in this study as well as the potential influence of species composition on drought response should be evaluated.

Notes

Acknowledgments

We thank the many scientists and technicians who worked on the original study throughout the years. We extend a special thanks to Charlie Possee who collected and prepared all the increment cores. Stan Zarnoch and two anonymous reviewers provided useful suggestions for the improvements of earlier versions of this manuscript. This paper was written and prepared, in part, by a US Government employee on official time, and therefore, it is in the public domain and not subject to copyright.

Compliance with ethical standards

Funding

This study was funded by the Upland Hardwood Ecology and Management Work Unit (RWU-4157) of the USDA Forest Service, Southern Research Station.

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Copyright information

© INRA and Springer-Verlag France (outside the USA) 2016

Authors and Affiliations

  1. 1.USDA Forest Service, Southern Research Station, Bent Creek Experimental ForestAshevilleUSA
  2. 2.Rocky Mountain Tree Ring Research, Inc.Fort CollinsUSA

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