Appl Veg Sci. 2020;23:159–174.  |  159

Applied Vegetation Science

wileyonlinelibrary.com/journal/avsc

Received: 23 February 2018  |  Revised: 7 January 2020  |  Accepted: 15 January 2020

DOI: 10.1111/avsc.12483

R E S E A R C H A R T I C L E

Thresholds in woody and herbaceous component co-existence inform the restoration of a fire-dependent community

Andrew L. Vander Yacht1  | Patrick D. Keyser2 | Charles Kwit2 | Michael C. Stambaugh3 | Wayne K. Clatterbuck2

© 2020 International Association for Vegetation Science

1Department of Forestry, Michigan State University, East Lansing, MI, USA2Department of Forestry, Wildlife, and Fisheries, University of Tennessee, Knoxville, TN, USA3Department of Forestry, University of Missouri, Columbia, MO, USA

CorrespondenceAndrew L. Vander Yacht, Forestry Department, Michigan State University, East Lansing, MI, USA.Email: vandery1@msu.edu

Funding informationResearch funded by USA Department of Interior and US Forest Service's Joint Fire Science Program (Project ID: 13-1-04-14). Additional supporting funds received from the University of Tennessee—Department of Forestry, Wildlife, and Fisheries, and Tennessee Wildlife Resources Agency (TWRA).

Co-ordinating Editor: David Ward

AbstractQuestions: A paradoxical co-existence challenges woodland and savanna restoration worldwide: How are shade-intolerant, flammable herbaceous layers promoted while maintaining the shade-casting, more fire-sensitive woody regeneration that sustains overstorey structure? Where restoration success consisted of robust, diverse her-baceous layers and vigorous, well-stocked Pinus echinata regeneration (hereafter, shortleaf-bluestem response), we asked: are there targetable conditions of oversto-rey, understorey, topography, and proximity to mature Pinus echinata that simultane-ously maximize desired woody and herbaceous understorey components? Do these conditions and dependent responses differ across canopy disturbance level and fire season?Location: Cumberland Plateau, Tennessee, USA (36°04′8.11″ N, 84°50′38.36″ W).Methods: We measured 12 shortleaf-bluestem response and 17 explanatory condi-tion variables at 345 plots spanning an experimental restoration gradient (canopy disturbance level and fire season combinations). We ordinated variation and iden-tified response thresholds using a multivariate regression tree. Differences across tree groupings and splits associated response thresholds with specific explanatory conditions.Results: Pockets of substantial Pinus echinata regeneration (>3,000 stems/ha), C4 grass density (>40,000 ramets/ha), and herbaceous diversity (increase from 22 to 205 species) occurred 7–14 years after canopy disturbance and 3–8 fires. Such shortleaf-bluestem response was maximized at 3 m2/ha residual tree basal area, 11% canopy closure, reduced midstorey density (5,000 small-sapling stems/ha), and southwest-erly aspects within 70 m of mature Pinus echinata. In contrast, shortleaf-bluestem re-sponse was negligible at 11.3 m2/ha basal area and 68% canopy closure. Fire season, snag basal area, slope, and slope position effects were minimal.Conclusions: We identified specific conditions fostering the co-existence of desired herbaceous and woody understorey components, addressing a major woodland and savanna restoration challenge and expanding on previous threshold concept applica-tions by simultaneously considering multiple desired responses. Results can direct the restoration of imperiled shortleaf-bluestem communities east of the Mississippi

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1  | INTRODUC TION

Fire has shaped the distribution, composition, and structure of the world's vegetation communities for nearly 500 million years (Pausas & Keeley, 2009). Where climate otherwise supports con-tinuous closed-canopy forest, fire can insert diverse herbaceous layers under sparse tree cover (Bond & Keeley, 2005; Stevens, Lehmann, Murphy, & Durigan, 2017). A well-studied example includes the globally significant levels of biodiversity found in Pinus palustris (longleaf pine) communities of the North American Coastal Plain (Noss et al., 2015; Guldin, Rosson, & Nelson, 2016). Considerably less attention has been paid just to the north, where Pinus echinata (shortleaf pine) supplants longleaf pine's dominance but helps form comparably diverse communities (Guyette, Spetich, & Stambaugh, 2006; Masters, 2007). Historically frequent fire throughout northern Alabama and Georgia, Tennessee, Kentucky, and North Carolina of the USA (hereafter Mid-South; Lafon, Naito, Grissino-Mayer, Horn, & Waldrop, 2017) maintained layers of warm-season (C4) grasses, other graminoids, and forbs under well-spaced (<20 m2/ha) shortleaf pine overstories (hereafter, short-leaf-bluestem communities; NatureServe 2013). Early European explorers described Cervus canadensis and Bison bison herds graz-ing on the Cumberland Plateau in conditions that match modern shortleaf-bluestem community descriptions (Michaux, 1805).

Fire suppression and exclusion threatens biodiversity glob-ally (O'Connor, Puttick, & Hoffman, 2014; Kelly & Brotons, 2017) and has virtually eliminated shortleaf-bluestem communities east of the Mississippi River in the USA (Anderson et al., 2016). Development and land conversion have contributed, but succes-sion in the absence of disturbance drives current declines (South & Harper, 2016). Dense woody canopies and subcanopies suppress herbaceous plants (Feltrin et al., 2016), including shade-intolerant C4 grasses (Peterson, Reich, & Wrage, 2007). This reduces fuel-bed flammability (Vander Yacht et al., 2019), decreases fire frequency and/or intensity, and subsequently, the control of invading woody plants (Maynard & Brewer, 2013). Nearly half of the >60% loss in shortleaf pine cover types since the 1950s is attributed to hard-wood encroachment (Oswalt, 2012). Fire's absence also threatens community sustainability by eliminating or reducing the compet-itive status of shortleaf pine regeneration as mature trees face senescence (South & Harper, 2016), degraded genetic integrity (Stewart, Tauer, & Nelson, 2012), and overstocking. These factors contributed to unprecedented severity and ≥$1 billion in regional

economic loss during recent Dendroctonus frontalis outbreaks (Nowak, Asaro, Klepzig, & Billings, 2008). Shortleaf-bluestem community decline threatens many plant and wildlife species, in-cluding the federally endangered Picoides borealis (Kabrick, Dey, & Gwaze, 2007; Masters, 2007).

In response, conservation efforts have recently accelerated; however, directing knowledge largely comes from a disconnected portion of the communities' range west of the Mississippi River (Anderson et al., 2016). Comparatively, little research has occurred in the eastern USA (Jenkins, Klein, & McDaniel, 2011; Elliott, Vose, Knoepp, & Clinton, 2012) where wetter climates, dense hardwood regeneration, longer histories of fire suppression, and relative rarity of mature shortleaf pine complicate restoration. Before accepting the high costs of artificial regeneration (i.e., planting, Elliott et al., 2012; South & Harper, 2016), two understudied options should be evaluated: canopy reductions exceeding 40% and repeated, grow-ing-season fire. Heavy canopy disturbance increases the light avail-able to shade-intolerant community components (Peterson et al., 2007; Kabrick, Knapp, Dey, & Larsen, 2015), and growing-season fire could accelerate restoration relative to the traditional use of dor-mant-season fire through greater hardwood control (Knapp, Estes, & Skinner, 2009).

In this context, a broader question facing fire-dependent wood-land and savanna restoration can be considered: how are desirable woody and herbaceous community components simultaneously restored? Known colloquially as “the savanna question” (Sankaran, Ratnam, & Hanan, 2004), this paradox involves the opposition be-tween the defining woodland and savanna components. Increasing woody cover shades out herbaceous layers (Dohn et al., 2013; Feltrin et al., 2016), and herbaceous plants negatively affect tree regener-ation through competition and facilitation of fire (Scholes & Archer, 1997; Mayer & Khalyani, 2011). Co-existence involves the storage of tree reproductive potential during periods of frequent fire that maintain herbaceous layers, and overstorey tree recruitment during infrequent periods of fire's absence (Higgins, Bond, & Winston, 2000). When this balance is achieved, fire-tolerant trees and her-baceous layers work in concert to promote the fires that maintain the communities they form (Mitchell, Hiers, O'Brien, & Starr, 2009). Low interception of light by woodland and savanna-adapted trees facilitates herbaceous layers that control fire-sensitive woody en-croachment through substantial and annual contributions of highly flammable fuels (Maynard & Brewer, 2013; Ratajczak, D'Odorico, & Yu, 2017).

River, USA, where work has been scarce or ineffective, and similar approaches could inform fire-dependent woodland and savanna restoration worldwide.

K E Y W O R D S

canopy disturbance, ecology of fuels, fire exclusion, fire season, Pinus echinata, restoration thresholds, savanna, shortleaf-bluestem, tree–grass co-existence, warm-season grasses, woodland, woody encroachment

     |  161Applied Vegetation Science

VANDER YACHT ET Al.

Such understanding highlights woody and herbaceous compo-nent interdependencies but fails to provide specific recommen-dations for their simultaneous promotion. In shortleaf-bluestem communities, a unique basal crook allows shortleaf pine to re-sprout after fire by sheltering dormant buds beneath an insulating layer of soil (Mattoon, ; Clabo & Clatterbuck, 2019). As a result, regeneration accumulates during the frequent fires (Stambaugh, Guyette, & Dey, 2007) that maintain herbaceous layers (Kabrick et al., 2007). However, little research has explored how to simulta-neously maximize shortleaf pine regeneration and robust, diverse herbaceous layers. Our goal was to relate identified thresholds in shortleaf-bluestem community response (shortleaf pine regenera-tion, native C4 grasses, and additional herbaceous layer develop-ment) to potential explanatory drivers in overstorey, understorey, and site conditions. We hypothesized positive, multivariate rela-tionships with canopy openness, reduced understorey density, and conducive site characteristics (e.g., xeric aspects, proximity to mature shortleaf pine) would regulate the maximization of de-sirable woody and herbaceous components. We explored this on Tennessee's Cumberland Plateau (USA), where a range of canopy disturbance levels and prescribed-fire seasons had promoted positive but variable shortleaf pine regeneration and C4 grass response (Bowers, Clatterbuck, McCloy, Royer, & Peairs, 2016; Vander Yacht et al., 2017). Results should be informative to the restoration of fire-dependent woodlands and savannas across their global distribution.

2  | METHODS

2.1 | Study area

Our study occurred at Catoosa Wildlife Management Area (CWMA), 32,374 ha of the Cumberland Plateau and Mountains physiographic region managed by the Tennessee Wildlife Resources Agency (TWRA). Elevations were 437–521 m and soils were Mesic Typic Hapludults over weathered sandstone and con-glomerate (Soil Survey Staff & NRCS, 22014). Annual (1981 to 2010) mean precipitation and temperature was 140 cm and 13°C for nearby Crossville, TN (NCDC, 2014). Forests were established in the 1920s following agricultural abandonment, and shortleaf pine was common before the 1999–2000 Dendroctonus frontalis outbreak (Nowak et al., 2008). Before management, Quercus spp., Acer rubrum, Oxydendrum arboreum, and Carya spp. dominated the overstorey (Vander Yacht et al., 2017). Mean tree basal area and canopy closure were 17.8 m2/ha and >85%, respectively. Nyssa sylvatica, Amelanchier arborea, Acer rubrum, Oxydendrum arboreum, and Sassafras albidum dominated seedling and sapling size classes. Herbaceous vegetation was sparse (4.4% ± 0.7 SE) and woody plants and leaf litter dominated ground cover. In 2000, TWRA began salvage logging and prescribed burning which promoted shortleaf pine (Bowers et al., 2016), native C4 grasses, and other prairie flora (Vander Yacht et al., 2017).

2.2 | Experimental design and restoration treatments

We ensured data spanned wide response and explanatory gradients by temporally and spatially distributing observations across two 20-ha replicates of six treatments. Each replicate was configured to maximize core area, and treatments included stands thinned to woodland (14 m2/ha) or savanna (7 m2/ha) residual tree basal area paired with repeated spring or fall burning (1: spring-burned wood-lands; 2: fall-burned woodlands; 3: spring-burned savannas; and 4: fall-burned savannas) and unmanaged stands (5: control). We estab-lished these treatments in 2008 to assess oak ecosystem restoration (Vander Yacht et al., 2017). We expanded reference space relative to shortleaf-bluestem research goals by delineating two 20-ha rep-licates in an adjacent area that was burned eight times since 2000 (6: advanced savannas). Commercial logging (winter of 2008–2009) thinned 2008-established treatments to noted basal area targets. Advanced savannas were thinned similar to other savanna treat-ments in June 2000. In all treatments, Quercus spp., Carya spp., and shortleaf pine were retained while fire-intolerant species were re-moved. After thinning, >75% of overstorey trees were Quercus spp. Residual basal area and canopy closure during this study (2014–2015) was comparable by overstorey treatment (controls: 20.1 m2/ha ± 0.7 SE, 97.6% ± 0.2 SE; woodlands: 11.6 m2/ha ± 0.5 SE, 70.2% ± 2.5 SE; savannas: 4.5 m2/ha ± 0.3 SE, 34.8% ± 2.4 SE; and advanced savan-nas: 5.4 m2/ha ± 0.4 SE, 27.4% ± 2.6 SE).

Fall burns occurred prior to leaf abscission (mid-October) in 2010, 2012, and 2014, and spring burns occurred prior to bud-break (mid-March) in 2011, 2013, and 2015. Advanced savannas were burned late-February to mid-March in 2000, 2003, 2004, 2005, 2006, 2009, 2013, and 2014. TWRA used ring-firing techniques and back-ing-fires rarely burned >50-m into stands. Weather, fuel moisture, and fire behavior were monitored (Vander Yacht et al., 2017), and a two-sample t-test assuming unequal variance determined heading fires were more intense in the spring than fall, likely due to seasonal wind differences (Appendix S1). Burns were otherwise comparable, including fire temperature recorded by ceramic tiles painted with Tempilaq® (LA-CO Industries Inc.) liquids. Fires were not monitored in advanced savannas, but TWRA descriptions aligned with col-lected spring burn data.

2.3 | Sampling design and data collection

We monitored 17 explanatory and 12 response variables (Table 1). Explanatory variables described overstorey and understorey (mid-storey tree, sapling, shrub, and seedling layers) structure, topog-raphy, and proximity to mature shortleaf pine. Response variables described shortleaf pine regeneration, C4 grass density and cover, and additional herbaceous layer attributes. We measured variables in June and July during the second (2014) and first (2015) growing seasons post-fire (i.e., the latest burn in all treatments occurred be-fore the 2015 growing season). Sampling occurred at approximately

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VANDER YACHT ET Al.

TAB

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15 plots (Appendix S2)·stand−1·year−1 (n = 345) along a 70 m × 70 m grid cruised through treatment stand-cores (50-m buffer) indepen-dently by year (i.e., cruises started from alternate stand-core corners each year). Live and dead tree basal area, canopy closure, percent slope, aspect, slope position, and vertical understorey woody cover was measured at three locations within each plot. Basal area and canopy closure were determined using two-factor metric prisms and convex spherical densiometers, respectively. Each plot was assigned a numerical value, 1–6, corresponding to alluvial, cove, toe slope, mid-slope, shoulder, and ridge slope positions. Vertical woody cover in the understorey was the mean percent obstruction estimated across five 50-cm strata on a Nudds (1977) profile board placed 15-m up- and down-slope. Individual C4 grass ramets were tallied in three 1.5 m radius subplots.

Woody undergrowth was monitored in seven nested sets of 1-m2 and 3-m radius subplots. We tallied all seedling (trees ≥ 30.5 cm tall but < 1.4 m tall) and shrubs (multi-stemmed species < 4 m tall at maturity) in 1-m2 subplots. In 3-m radius sub-plots, we tallied small (≥1.4 m tall and < 7.6 cm DBH [diameter at breast height]) and large (≥1.4 m tall and ≥ 7.6 but < 12.7 cm DBH) saplings (tree species), and all shortleaf pine ≥30.5 cm tall but <12.7 cm DBH. For each shortleaf pine regeneration stem, we recorded root collar diameter, height, largest basal sprout diame-ter, number of basal sprouts, midstorey canopy class (dominant, co-dominant, intermediate, or suppressed), and location using a Trimble Recon® GPS (Trimble Inc.) (2–5 m accuracy). We sim-ilarly recorded mature shortleaf pine (>12.7 cm DBH) locations during a winter (2015) census of the study area (150-m buffer) We used ArcMap (v 10.5, ESRI) to calculate distance and direction to the nearest mature shortleaf pine from each regeneration stem. Coordinate accuracy was probably insufficient to distinguish in-dividual stems within tight clumps, but the relatively wide spacing of mature shortleaf pine (0.4 ha−1) across our study area rendered dependently calculated distances useful.

We centered the intersection of two perpendicular 9-m tran-sects at three locations within each plot, and characterized ground cover using the point-intercept method at 1-m intervals. All vegeta-tion below 1.4 m at each interval was identified to species and cate-gorized as C4 grass, other graminoid, forb, fern, or woody vegetation (all trees, vines, and shrubs). Our source for plant taxa scientific no-menclature was the US Department of Agriculture's Plants Database (https ://plants.sc.egov.usda.gov/java/). Data were used to deter-mine herbaceous diversity (Shannon–Wiener's Index; Magurran, 1988). When vegetation was absent, cover was classified as litter or bare ground. We calculated percent ground cover by category, and visually estimated mean height of understorey woody vegetation (<3.2 m tall) along each 9-m transect.

2.4 | Data analysis

We ordinated response and explanatory variation using non-metric multidimensional scaling (NMDS; Kruskal, 1964). We used

observed values for all but shortleaf pine attributes, which were predicted from zero-inflated negative binomial (ZINB) models (Zeileis, Kleiber, & Jackman, 2008). A multivariate regression tree (MRT; De'Ath, 2002) identified response thresholds in NMDS axis scores. We determined multi and univariate differences across MRT groups using permutation-based multivariate analysis of variance (PERMANOVA; Anderson, 2001) and linear mixed-ef-fect (LME) models (Zuur, Ieno, Walker, Saveliev, & Smith, 2009), respectively. We also conducted these analyses on explanatory variables at each tree split to associate response thresholds with explanatory variable values. For all analyses, we used RStudio ver-sion 1.1.463 (2018, RStudio, Inc.).

2.4.1 | Identifying thresholds in shortleaf-bluestem response

We transformed aspect and direction to nearest overstorey shortleaf pine using Beers, Dress, and Wensel (1966). Routine for aspect data, the transformation was justified for the latter variable based on the disparity in wind frequency from southwesterly and northeasterly directions (135°–315° = 79.8%, 315°–135° = 20.2%) in nearby Crossville, TN, from 1981 to 2010 (National Climatic Data Center, 2014) during peak shortleaf pine seed-fall (October–January, Baker, 1992). We averaged data to plot level, which transformed categorical (slope position, midstorey canopy class) into continuous variables.

Shortleaf pine regeneration data were clustered and lacked nor-mality, were overdispersed, and had excessive zeroes. To enable anal-ysis with other responses, shortleaf pine attributes were predicted using ZINB models. This involved a logistic occurrence function and a negative binomial function predicting count variation (Zeileis et al., 2008). Before modeling, we transformed (√[Y × 10,000]) and rounded to the nearest integer. We tested all explanatory variables and year within occurrence and count functions using the package pscl (Zeileis et al., 2008), retaining significant effects (Wald test, α = 0.05) during backward selection based on AIC and χ2 goodness of fit. This may have introduced some circularity in subsequent anal-yses; however, the process was based on real response and explan-atory relationship observations, moderated the influence of plots where shortleaf pine regeneration occurred, considered plots where absence may have been random, and allowed simultaneous analysis of all response variables.

Plots (n = 345) were then ordinated (NMDS) using metaMDS (vegan package; Oksanen et al., 2017). Regarded as the most ro-bust unconstrained ordination, NMDS is free from assumptions concerning normality and linear relationships (Kruskal, 1964). We requested a three-axis solution (k = 3) after non-convergence with two axes. Stress was not substantially reduced by additional axes (Δ ≤ 0.02), and 20 random iteration starts avoided local op-tima. We then identified shortleaf-bluestem response thresholds using MRT analysis. Cut-off values within NMDS plot axis scores were repeatedly selected to minimize within-group multivariance

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(De'Ath, 2002). Euclidean distances were used, and package mvpart (Therneau, Atkinson, Ripley, Oksanen, & De'ath, 2014) se-lected the final MRT based on minimum cross-validated relative error (CVRE). Forward and backward selection resulted in the same tree, terminal nodes contained a minimum of 10 plots, and we used a complexity parameter of 0.01. MRTs based on NMDS axis scores explained substantially more response multivariance (R2 = 0.56) than MRTs based on raw variables (R2 = 0.31). We used z-score standardization (mean = 0, standard deviation = 1) to dis-play results across many variable scales.

We then explored differences in response across final MRT groupings using restricted, non-parametric PERMANOVA with function adonis in R's vegan package (Oksanen et al., 2017). We used Euclidean distances and compared observed R2 to a distri-bution of R2 values calculated from 4,999 data permutations. We freely exchanged data stratified by replicates and blocked on year to isolate MRT group from year and stand effects. We tested mul-tivariate homogeneity of distance dispersions across groups using function betadisper (vegan package). Such differences contributed to some, but not all, group differences (F4,340 = 15.4, p = 0.0002, Appendix S3). Dispersion in leaf 1 was 1.6-fold greater than in the least dispersed leaf, but large differences in means across MRT groups suggested significance involved both dispersion and location.

After observing multivariate differences, LME models (Zuur et al., 2009) with Tukey mean separation detected univariate differences (α = 0.05). We accounted for non-independent ob-servations by including random year and treatment replicate effects. Significance was determined via likelihood ratio tests comparing null (only random effects) and full (random effects plus a fixed MRT-group effect) LME models. Bonferroni ad-justment protected multiple univariate tests from Type I error inflation. Linearity and homoskedasticity were assessed with re-sidual plots, and all variables were normal (Wilk's test, W > 0.85). Estimated marginal means of responses were calculated by MRT group (Lenth, 2019).

2.4.2 | Drivers of shortleaf-bluestem response thresholds

The analysis chain used to identify response thresholds (NMDS, MRT, PERMANOVA, and LME) was also used to identify underlying drivers of shortleaf-bluestem response. Differences in explanatory variables were apparent in the ordination, and we assessed explana-tory differences across final MRT groupings using the PERMANOVA and LME model protocols described for response variables. In ad-dition, we used these analyses to translate response thresholds identified within the MRT into explanatory variable values by de-termining differences across each tree split. Bonferroni adjustment of likelihood ratio tests occurred within each split and estimated marginal means were calculated by split and for final MRT groupings (Lenth, 2019).

3  | RESULTS

3.1 | Identifying thresholds in shortleaf-bluestem response

Patches of robust shortleaf-bluestem response (Figure 1) occurred mostly within savannas and advanced savannas. We encountered 205 herbaceous species, an increase from 22 species observed across the 10 non-advanced stands prior (2008) to management (Vander Yacht et al., 2017). Across all point-intercept data, Piptochaetium avenaceum accounted for 17.6% of herbaceous encounters, more than triple the next most common species (5.7%, Dichanthelium di-chotomum). Chasmanthium sessiliflorum (4.6%), Carex albicans (3.8%), and Dichanthelium latifolium (2.7%) also contributed to graminoid dominance of herbaceous layers. Solidago odora (4.2%), Lysimachia quadrifolia (3.3%), Lespedeza repens (2.6%), and Coreopsis major (2.2%) were frequently encountered forbs. Ferns were 6.4% of her-baceous encounters. Six species of C4 grass collectively accounted for 8.2% of herbaceous encounters. Andropogon virginicus was 65% of C4 grass ramets counted in density plots, followed in domi-nance by Schizachyrium scoparium (19%), Andropogon gerardii (11%), Sorghastrum nutans (6%), Andropogon gyrans (0.1%), and Andropogon ternarius (0.1%). These grasses were observed at 184 of 345 plots. When present, C4 grasses were more dominant (ground cover, 12.7%; density, 5,033 ramets/ha) than overall means indicated (Table 1).

Shortleaf pine regeneration was clustered; attributes were ≥5× overall means (Table 1) at the 68 of 345 plots where all 512 docu-mented stems were located (density, 378.9 stems/ha ± 90.0 SE; root collar diameter, 5.9 cm ± 0.4 SE; height, 195.0 cm ± 18.2 SE; largest basal sprout diameter< 3.5 cm ± 0.5 SE; sprouts/plant, 6.7 ± 1.0 SE; and midstorey crown class, 2.4 [intermediate to co-dominant] ± 0.2 SE). Canopy closure and site aspect influenced this clustering; ex-cessive absence increased as canopy closure increased, and as as-pects became more northeasterly, for all shortleaf pine attributes (Table 2). Where shortleaf pine occurred, related attributes were often negatively related to woody understorey variables (Table 3). In contrast, density and basal sprout count increased as seedling density and woody ground cover increased. All shortleaf pine attri-butes except basal sprout count were negatively related to vertical woody cover in the understorey. Shortleaf pine regeneration and C4 grass density was absent in controls and generally increased with increasing disturbance (woodlands, 81 stems/ha, 1,199 ra-mets/ha; savannas, 108 stems/ha, 3,170 ramets/ha; and advanced savannas, 70 stems/ha, 8,805 ramets/ha).

The final NMDS ordination explained 99.2% (non-metric R2) of the variation between calculated and observed dissimilarities in explana-tory and response variables among plots (stress = 0.09, Figure 2). Axis one captured a transition in overstorey characteristics, from open to closed canopies, and axis two captured a transition in understorey dominance from herbaceous to woody (Figure 2a). Shortleaf pine re-generation rarely occurred where C4 grasses were absent (Figure 2b). Control plots were ordinated in association with overstorey variables, advanced savannas straddled the centers of herbaceous and shortleaf

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pine response, and woodland and savanna treatments formed a transi-tion between the two in association with woody understorey variables (Figure 2c). Shortleaf pine response appeared more associated with spring-burned savannas than other treatments.

The best fit MRT (CVRE = 0.497, SE = 0.071) explained 56% of shortleaf-bluestem response multivariance and identified five dis-tinct response groups (Figures 2d and 3). The first split explained 61% of explained multivariance by using NMDS axis 1 to identify plots where both shortleaf pine and herbaceous response was limited. Within these plots, a second split segregated plots where response across all variables was below average (leaf 1) from plots where herbaceous diversity and other graminoid ground cover was

minimally above average (leaf 2). All control and many woodland plots were assigned to leaf 1, whereas savanna plots dominated leaf 2. On the opposing tree side, a third split isolated plots where either, but not both, herbaceous (leaf 3) or shortleaf pine (leaf 4) response occurred from plots where all response variables except shortleaf pine density and basal sprout count were maximized (leaf 5). The majority (77%) of plots classed into leaves 3 and 5 were from advanced savannas, whereas savanna plots dominated leaf 4. The fourth split separating leaves 3 and 4 was uniquely based on NMDS axis 2. Scatterplots further depict how MRT partitions cap-tured response and explanatory variable relationships (Appendix S4).

Response values across MRT groups (Figure 4a, b) depicted thresholds in shortleaf-bluestem response, and we observed multi-variate (F4,340 = 27.7, p = 0.004, R2 = 0.238) and univariate (Figure 4a, b, Appendix S5) differences. All herbaceous response variables were maximized in leaf 5 (Figure 4a). Herbaceous diversity, other graminoid ground cover, and forb ground cover were comparable in leaves 3 and 5, but C4 grass ramet density and ground cover in leaf 5 was nearly dou-ble that observed in leaf 3. Herbaceous response was minimal in leaves 1 and 2, greater in leaves 3 and 5, and intermediate between these groups in leaf 4. Shortleaf pine regeneration attributes were generally greater in leaves 4 and 5 relative to leaves 1–3 (Figure 4b). Shortleaf pine regeneration density was greatest in leaf 4, but all other shortleaf pine attributes were greater or equal in leaf 5. Both herbaceous and shortleaf pine regeneration response was maximized in MRT leaf 5.

3.2 | Drivers of shortleaf-bluestem response thresholds

Explanatory data fostered inference into the drivers behind iden-tified response thresholds by spanning closed to open canopies,

F I G U R E 1   Shortleaf-bluestem community response at Catoosa Wildlife Management Area (Cumberland County, TN) in the dormant season after overstorey thinning and eight prescribed fires. Note the midstorey dominance of natural Pinus echinata (shortleaf pine) regeneration and abundance of native C4 grasses

TA B L E 2   Occurrence component of zero-inflated negative binomial models for Pinus echinata response variables as modeled by overstorey, understorey, and site variables

Response variable Intercept

Explanatory variablesa

LL df Critical χ2 Model χ2Canopy closure (%) Aspect (º)

Density (stems/ha) 1.57 (0.16) 0.55 (0.15) 0.40 (0.15) −697 8 380.8 337.2

Root collar diameter (cm) 1.57 (0.16) 0.55 (0.15) 0.40 (0.15) −542 7 381.9 366.9

Height (cm) 1.69 (0.16) 0.53 (0.15) 0.41 (0.16) −622 7 381.9 337.0

Largest basal sprout diameter (cm)

1.69 (0.16) 0.53 (0.15) 0.41 (0.16) −504 7 381.9 377.8

Basal sprout count (stems)

1.69 (0.16) 0.53 (0.15) 0.41 (0.16) −507 11 377.6 343.9

Midstorey canopy class 1.69 (0.16) 0.53 (0.15) 0.41 (0.16) −444 9 379.8 330.9

Parameter estimates (SE), log-likelihood, df, and a chi-square goodness-of-fit test are presented. Parameter significance determined by Wald test (α = 0.05). Log-likelihood (LL), df, and chi-square values are for the overall model, including the occurrence and count (Table 3) components.aAll explanatory variables were included, but only those significant within the occurrence component are presented. ZINB coefficients reflect binomial distribution with logit link. Before analysis, explanatory variables were z-standardized and response variables were rounded to integers after a √

y∗10,000 transformation.

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sparse to dense woody subcanopies, mesic to xeric site aspects, and many proximities to overstorey shortleaf pine (Table 1). Explanatory variables differed multivariately across MRT groups (F4,340 = 29.4, p = 0.004, R2 = 0.249), and univariate differences across MRT splits associated non-intuitive NMDS axis score thresholds with specific explanatory variable values (Table 4, Figure 5).

Splits 1 and 3, which isolated the plots where herbaceous and shortleaf pine response was simultaneously maximized, were de-fined by multiple overstorey, understorey, and site variables (Table 4, Figure 5). A positive response (split 1) was related to open overstorey conditions (6.2 m2/ha residual basal area, 30.9% canopy closure, 57.2 large sapling stems/ha, and 11.4% litter ground cover), greater shrub density (62,009 vs 38,298 stems/ha) and bare ground cover (14.8% vs 6.6%), and more southwesterly aspects (301° vs 323°). Subsequently (split 3), even more open overstories (1.3 vs 3.6 m2/ha residual basal area, and 5.3% vs 21.9% canopy closure), more southwesterly aspects (255° vs 307°), and closer (102 m vs 157 m), and more southwesterly (298° vs 323°), proximities to mature shortleaf pine influenced the maximization of herbaceous and shortleaf pine regeneration response.

Comparing mean explanatory variable values across MRT groups further clarified response threshold drivers (Figure 4c–e, Appendix S5). Relative to other groups, aspects were more south-westerly (272°) where response was maximized (leaf 5). Plots in this group were also 67 m closer to mature shortleaf pine (Figure 4c) than plots in groups where response was unbalanced between herbaceous and shortleaf pine components (3 and 4). Live basal area (2.6 m2/ha) and canopy closure (10.6%) in leaf 5 was 4.4× and 6.4× lower, respectively, than that observed in groups where shortleaf-bluestem response was limited (1 and 2, Figure 4d). Generally, woody understorey variables increased from leaf 1 to leaves 2 through 4, and leaf 5 was intermediate between these extremes (Figure 4e). The clearest distinction in woody understo-rey variables between leaf 5 and other groups where a substantial response was observed (3 and 4) was a nearly two-fold reduction in small-sapling density (from 8,948 to 5,237 stems/ha). Shortleaf-bluestem response was not related to snag basal area, slope, or slope position.

4  | DISCUSSION

Average explanatory values at plots where shortleaf pine re-generation and herbaceous layer response was maximized (MRT group 5) suggest restoration should target 3 m2/ha residual tree basal area, 11% canopy closure, midstorey density at or below 5,000 small-sapling stems/ha, and more southwesterly aspects (mean = 272°) within 70 m of mature shortleaf pine. These practi-cal and specific recommendations for restoration can direct the restoration of imperiled shortleaf-bluestem communities east of the Mississippi River, where work has been scarce, ineffective (Elliott et al., 2012), or focused on more montane pine communi-ties (Jenkins et al., 2011). Closed-canopy forest conditions, which TA

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     |  167Applied Vegetation Science

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dominate much of the eastern US, had strong, negative effects. Shortleaf-bluestem response was negligible where canopy closure and tree basal area averaged 68% and 11.3 m2/ha, respectively. In contrast, robust shortleaf pine regeneration and C4 grass density response (>3,000 stems and > 40,000 ramets/ha, respectively) decades after canopy closure demonstrate community resilience and the combination of heavy canopy disturbance and repeated fire as an effective restoration alternative to expensive plantings (Anderson et al., 2016) in stands with similar histories throughout the Mid-South.

Our identification of specific levels of factors maximizing multiple desired responses expands on previous threshold con-cept applications and adds details to general herbaceous and woody plant co-existence models. Twidwell, Fuhlendorf, Taylor, and Rogers (2013) established a specific fire intensity threshold

for woody encroachment control, but failed to consider how this threshold influenced desired herbaceous response. Thresholds are more useful when they more comprehensively capture restoration goals. In temperate climates, woodlands and savannas are often restored from degraded remnants that lack herbaceous layers (Vander Yacht et al., 2017) and fire-tolerant woody regeneration capable of sustaining overstorey structure (Vander Yacht et al., 2018). Simultaneously promoting woody and herbaceous com-ponents is challenging given strong competitive and inhibitory effects between the two functional groups (Dohn et al., 2013; Mayer & Khalyani, 2011; Scholes & Archer, 1997). The “enemy of my enemy hypothesis” argues woodland and savanna-adapted trees gain an advantage over shade-tolerant, fire-sensitive, for-est trees by facilitating flammable herbaceous layers (Ratajczak et al., 2017). Unlike such conceptual models (Higgins et al., 2000;

F I G U R E 2   Non-metric multidimensional scaling ordinations of variation in shortleaf-bluestem response and explanatory variables across plots (n = 345) on the Cumberland Plateau, TN. Panels present the ordinated positions of (a) variables (see Table 1 for abbreviations), (b) the absence or presence of native C4 grasses, shortleaf pine regeneration, or both, (c) management treatments, including unmanaged stands (Control), spring (Sp) or fall (Fa) fire paired with woodland (14 m2/ha, W) or savanna (7 m2/ha, S) residual basal area, and advanced savannas (AS), and (d) assigned multivariate regression tree (MRT) groupings

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Sankaran et al., 2004), we identified specific overstorey, under-storey or subcanopy, and site conditions that fostered woody and herbaceous component co-existence. Similar approaches could benefit the restoration of fire-dependent woodlands and savan-nas throughout the world by recognizing the complex feedbacks between vegetation, fuels, and fire effects (Mitchell et al., 2009; Vander Yacht et al., 2019) that are often overlooked in favor of more immediate restoration goals (e.g. fuel reduction or increased diversity; O'Connor et al., 2014; Kelly & Brotons, 2017).

Forest densification is a global phenomenon (Mölder, Streit, & Schmidt, 2014; O'Connor et al., 2014; Stevens et al., 2017), and dis-turbance-dependent community restoration must often begin with addressing the negative effects of canopy closure. Related variables defined the initial MRT split that captured 61% of explained vari-ation in shortleaf-bluestem response, and always predicted short-leaf pine's absence (ZINB models). In the southern Appalachian Mountains, Jenkins et al. (2011) reported yellow pine seedlings were absent until basal area was <15 m2/ha (estimated from presented data). In southeastern Oklahoma, Feltrin et al. (2016) reported an upward shift in graminoid and forb productivity per unit increase in available photosynthetically active radiation at approximately 10 m2/ha of tree basal area. Both values compare well to our obser-vation of negligible shortleaf-bluestem response at 11.3 m2/ha re-sidual tree basal area. Naturally regenerating shortleaf pine often requires meeting or exceeding such overstorey reductions (Baker, 1992; Shelton & Cain, 2000). Achieving this with fire alone can re-quire >60 years of repeated burning (Knapp, Stephan, & Hubbart,

2015). Greater overstorey retainment, which may negatively affect hardwood competition more so than shortleaf pine (Kabrick et al., 2015; Shelton & Cain, 2000), could be possible where repeated fires effectively control woody subcanopies. However, herbaceous com-ponents can be even more shade-intolerant (Peterson et al., 2007; Feltrin et al., 2016), and this has important implications for resto-ration that aims to promote both life forms.

Overstorey metrics were comparable across MRT groups 2 through 5, but response in groups 2 through 4 appeared sup-pressed by robust woody subcanopies. Woody encroachment threatens open woodlands and savannas worldwide (Mölder et al., 2014; O'Connor et al., 2014), and potential shortleaf-bluestem res-toration sites throughout the Mid-South are often characterized by abundant and well-established hardwood saplings and seed-lings (Oswalt, 2012). Midstorey and co-dominant tree thinning in shortleaf pine stands of western Arkansas increased the standing biomass of grasses, sedges, legumes, and non-legume forbs by 5-, 7-, 2-, and 10- fold, respectively, relative to unmanaged controls (Masters, Wilson, Bukenhofer, & Payton, 1996). Controlling hard-wood competition with frequent fire (at least once every four years) can also enhance herbaceous layers (Peterson et al., 2007) and im-prove shortleaf pine survival (Stambaugh et al., 2007). Repeated burning, without gaps ≥3 years which replenish below-ground re-sources, can limit the sprouting capacity of hardwood competition (Arthur, Blankenship, Schorgendorfer, Loftis, & Alexander, 2015) perhaps more so than shortleaf pine (Clabo & Clatterbuck, 2019). In our study, 85% of plots where shortleaf-bluestem response

F I G U R E 3   Multivariate regression tree for shortleaf-bluestem community response to thinning and burning on the Cumberland Plateau, TN. Thresholds in NMDS axes are presented above each tree split, and the number of classified plots is presented by node and leaf. Final tree was selected by minimum cross-validated relative error. Response characteristics within tree leaves are presented using standardized z-scores (mean = 0, σ = 1), and composition related to treatments is summarized within pie charts. Abbreviations in the list of variables include GC, ground cover; NWSG, native warm-season grass; and D, diameter (see Table 1 for details). Treatments included unmanaged stands (Control), spring (Sp) or fall (Fa) fire paired with woodland (14 m2/ha, W) or savanna (7 m2/ha, S) residual basal area, and advanced savannas (AS)

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was the greatest (MRT group 5) were burned eight times over the last 15 years. We also observed shortleaf pine regeneration height, root collar and largest basal sprout diameter, and midsto-rey canopy class were more negatively affected by midstorey den-sity variables than attributes less indicative of vigor (density and basal sprout count). Historical fire regimes can inform restoration

(Guyette et al., 2006), but our results and others (Jenkins et al., 2011) support fire management that targets reducing small-sap-ling density to around 5,000 stems/ha.

Robust herbaceous response where shortleaf pine response was minimal (MRT group 3) suggests fire can exceed a balance between herbaceous promotion and tree component survival. The general

F I G U R E 4   Linear mixed-effect regression determined differences in shortleaf-bluestem response and explanatory variables across multivariate regression tree identified groups on the Cumberland Plateau, TN. Bars within each multivariate regression tree grouping correspond from left to right to axis label numbers. Only variables differing (α = 0.05) across groups are presented (Appendix S5). (a) Herbaceous response variables, (b) Pinus echinata regeneration response variables, (c) site characteristics, (d) overstorey variables, and (e) understorey variables. Zero-inflated negative binomial model predictions were used for Pinus echinata variables. Lowercase letters represent within-variable differences across groups (Tukey mean separation). Error bars ± 1 standard error of the mean

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disparity in fire tolerance between woody and herbaceous plants complicates woodland and savanna restoration worldwide (Higgins et al., 2000). Fire-induced mortality of shortleaf pine seedlings can be substantial (up to 55%, Clabo & Clatterbuck, 2019) and recruit-ment may require an 8- to 15-year absence of fire (Stambaugh et al., 2007). Locally intense fire resulting from ring-firing, or perhaps edaphic conditions, could explain herbaceous dominated microsites. Moderating fire intensity with strip-head firing could enhance retain-ment of desirable tree regeneration, but fires should still be intense enough to kill fire-sensitive woody species (Michaletz & Johnson,

2007) and remove germination-inhibiting layers of thatch and litter (Jenkins et al., 2011). In addition, intense fire can create patches of bare mineral soil that promote shortleaf pine (Baker, 1992) and her-baceous establishment (Hutchinson et al., 2005). Such effects were identified as important within our analysis. Despite cautionary ad-vice from drier regions (Shelton & Cain, 2000), the wetter climate and greater hardwood competition of the eastern USA may increase the utility of moderately-intense fire. At CWMA, shortleaf pine re-cruitment appears to be occurring during a biennial regime of such fire (Figure 1).

TA B L E 4   Differences in explanatory variables across splits within a multivariate regression tree based on NMDS axis scores for observations of shortleaf-bluestem response and explanatory variables on the Cumberland Plateau, TN

MRT split Category Variablea Adjusted p

Split

Left Right

Estimate SE Estimate SE

1 Overstorey LBA <0.001 11.3 1.7 6.2 1.7

CC <0.001 68.2 5.9 30.9 6.1

LgSapDen <0.001 148.4 50.4 57.2 53.3

pLitter <0.001 19.4 5.5 11.4 5.6

Understorey ShrubDen <0.001 38,297.6 6,182.0 62,008.5 6,845.2

pBare <0.001 4.0 6.6 14.8 6.7

Site Aspect <0.001 1.14 0.08 0.76 0.09

2 Overstorey LBA <0.001 14.6 1.5 7.5 1.5

CC <0.001 84.1 4.2 48.6 4.3

LgSapDen <0.001 237.0 63.9 52.1 64.5

pLitter <0.001 29.8 4.9 9.6 4.9

Understorey SmSapDen <0.001 4,712.7 1,629.0 10,615.2 1,646.9

ShrubDen <0.001 23,498.7 3,802.2 59,525.3 4,114.3

MidHT <0.001 49.1 19.6 99.8 19.8

Nudd <0.001 60.5 3.9 80.9 4.1

pWoody <0.001 55.9 5.5 78.1 5.6

Site DtoSLP <0.001 107.7 21.5 153.3 21.5

3 Overstorey LBA 0.002 3.6 0.8 1.3 0.9

CC <0.001 21.9 4.1 5.3 4.7

Understorey SmSapDen 0.021 8,962.6 2,645.4 4,613.7 2,966.8

pWoody 0.004 82.1 3.5 68.2 4.6

Site Aspect <0.001 0.86 0.12 0.13 0.16

DtoSLP 0.049 157.0 20.3 102.3 24.7

AztoSLP 0.036 1.14 0.18 0.71 0.22

4 Understorey SeedDen 0.030 27,358.3 5,259.1 45,493.9 4,221.1

Site Aspect <0.001 1.40 0.10 0.62 0.08

Likelihood ratio tests (df = 1) compared null (random effects of stand and year) and full (random effects plus a fixed split effect) linear regression models of explanatory variables to determine significant differences. All explanatory variables were tested at each split, but presentation is restricted to identified differences. Bonferroni adjustment was used within split tests, and estimated marginal means of models are presented.MRT, multivariate regression tree.aSee Table 1 for variable abbreviations.

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A transition from dormant to a late growing-season fire could enhance hardwood control (Knapp et al., 2009). Top-kill during the growing season may disrupt carbohydrate translocation to the roots more so than top-kill during dormancy, resulting in less reserves to fuel resprouting. In our study, spring-burned and fall-burned woodlands differed little. In contrast, fall-burned savannas were ordinated in closer association with herbaceous variables than spring-burned savannas and the reverse was true for woody subcanopy variables. Spring-burned savannas were also more closely associated with shortleaf pine attributes than fall-burned savannas, and dominated MRT groups 4 and 5 where shortleaf pine response was substantial. This suggests fall burns in open environ-ments had greater negative effects on understorey woody stems, including shortleaf pine, than spring fire. In agreement, Clabo and Clatterbuck (2019) observed greater negative impacts on subse-quent shortleaf pine sprout height after fall relative to spring burns. Such results are confusing given root carbohydrate content is usu-ally maximized in the late fall (Loescher, McCamant, & Keller, 1990). Perhaps October fires were early enough to cut off a portion of resource translocation to the roots, or longer fire residence times in the fall relative to the spring (Appendix S1) — a factor influencing woody stem mortality (Michaletz & Johnson, 2007) — contributed. Clabo and Clatterbuck (2019) noted similar but variable patterns in fire residence times across seasons. Fire season effects were not obvious in this study, but potential advantages of growing-sea-son fire in reducing hardwood competition (Vander Yacht et al., 2017, 2018) without negatively affecting the herbaceous layers of

shortleaf-bluestem communities (Sparks, Masters, Engle, Palmer, & Bukenhofer, 1998) deserves greater research attention.

Southwesterly aspects and proximity in a downwind posi-tion from mature shortleaf pine positively influenced response. Shortleaf-bluestem communities are often associated with south-westerly aspects where solar radiation, fire frequency, and fire intensity is greatest (Kabrick et al., 2007). When competition is controlled, shortleaf pine is adaptable to a variety of aspects, soils, annual temperatures (9–21°C), total precipitation (102–152 cm) and elevations (up to 915 m; Lawson, 1990). However, seeds only disperse 60–90 m downwind, remain viable for a single sea-son (Baker, 1992), and production can be erratic and infrequent (Lawson, 1990). The scheduling of woodland and savanna man-agement should coincide with adequate seed production and weather that favors desired tree regeneration. This can be diffi-cult, as illustrated by the failure of appropriate management after overstorey seed sources were killed by drought, severe fire, and a Dendroctonus frontalis outbreak (Elliott et al., 2012). Shortleaf pine was common at CWMA prior to the 1999–2000 Dendroctonus frontalis outbreak, but only 131 mature stems (0.4 ha−1) were cen-sused across our study area. This may have contributed to short-leaf pine regeneration levels (378.9 stems/ha ± 33.5 SE) below that recommended for successful cohort establishment (2,472 stems/ha; Baker, 1992).

Fire exclusion can virtually eliminate C4 grasses (Peterson et al., 2007), and seedbank persistence is not guaranteed (Leck & Leck, 1998). At CWMA, a robust C4 grass response was stimulated

F I G U R E 5   Standardized z-scores (mean = 0, σ = 1) for explanatory variables that differed across each split in a multivariate regression tree describing shortleaf-bluestem response to thinning and burning on the Cumberland Plateau, TN. Differences in explanatory variables were determined by linear mixed-effect regression (Table 4). Each variable was categorized as overstorey, understorey, or site-related and labeled with numbers along each axis that correspond to the centrally located key

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decades after canopy closure. The growth of C4 grasses to >2 m tall in the growing season following canopy disturbance also suggests the presence of dormant rhizomes. Early response was dominated by Andropogon virginicus. Seeding or planting could di-versify composition, and may even be necessary where seedbanks are depauperate (Elliott et al., 2012; Maynard & Brewer, 2013). In such situations, our results could inform site prep to increase planting success, but we also observed succession of C4 grass species. In non-advanced savanna stands, Andropogon virginicus increased from 0.2% to 6.8% of herbaceous encounters (2008 to 2016). In advanced savanna stands (2014–2015), the reasonable future of non-advanced savanna stands with continued burning, Andropogon virginicus was only 0.3% of herbaceous encounters and other C4 species were relatively more common (Schizachyrium scoparium: 8.7% vs 0.7%; Andropogon gerardii: 3.7% vs 0.9%; Sorghastrum nutans: 2.3% vs 0.2%). Characteristics of old-growth grassland plants describe all but Andropogon virginicus (Veldman et al., 2015). This suggests reducing Andropogon virginicus domi-nance, and increasing more conservative C4 grasses, may only re-quire continued burning.

5  | CONCLUSIONS

Promoting herbaceous layers and tree regeneration capable of sustaining overstorey structure challenges fire-dependent wood-land and savanna restoration worldwide, but our results provide guidance for doing so in imperiled shortleaf-bluestem commu-nities of the eastern USA. Restoration begins by selecting sites with adequate seedbanks on southwesterly aspects within 70 m of mature shortleaf pine. Overstorey reductions should then tar-get 3 m2/ha residual tree basal area or 11% canopy closure, and repeated burning can be used to reduce small-sapling density to <5,000 stems/ha. Moderate fire intensities will improve shortleaf pine regeneration retainment. Late growing-season fire may re-duce shortleaf pine regeneration vigor, but this should be weighed against the potential for increased hardwood competition control and greater herbaceous layer development. Response was negligi-ble under closed canopies, but thinning and fire promoted a robust shortleaf pine and herbaceous response decades after canopy clo-sure. This demonstrates resiliency, but also suggests continued shortleaf-bluestem community decline without active manage-ment. Similarly identifying explicit levels of multiple influential factors contributing to the co-existence of desired herbaceous and woody ecosystem components could advance woodland and savanna restoration worldwide.

ACKNOWLEDG EMENTSIn addition to funding sources, we thank the Tennessee Wildlife Resources Agency (TWRA) for implementing management and providing research technician housing. We specifically thank TWRA staff M. Lipner, K. Kilmer, and C. Coffey (retired). We also acknowledge field technicians for assistance with data collection;

C. Ault, K. Dreyer, K. Goostree, C. Gresham, J. Lippert, J. Myers, and J. Redden.

AUTHOR CONTRIBUTIONSALV, PDK, CK, and MCS. conceived and designed the research; ALV led data collection, analysis, and drafting the initial manuscript; PDK, CK, MCS, and WK contributed substantial edits to the final text, analyses, and interpretation.

DATA AVAIL ABILIT Y S TATEMENTAll data associated with this work are stored on the MSU Forest Ecology Laboratory internal server, in A. Vander Yacht's personal databases, and will soon be available within the USDA Forest Service Research Data Archive (https ://www.fs.usda.gov/rds/archi ve/). Datasets and original program source codes are available upon request from the lead author.

ORCIDAndrew L. Vander Yacht https://orcid.org/0000-0002-3296-6163

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SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section.

Appendix S1. Seasonal comparison of fires conducted at Catoosa Wildlife Management AreaAppendix S2. Sampling plot designAppendix S3. Multivariate dispersion of variance across MRT leaf groupsAppendix S4. Scatterplots of variable relationships and MRT group assignmentsAppendix S5. Linear mixed-effect regression analysis of variables across MRT groups

How to cite this article: Vander Yacht AL, Keyser PD, Kwit C, Stambaugh MC, Clatterbuck WK. Thresholds in woody and herbaceous component co-existence inform the restoration of a fire-dependent community. Appl Veg Sci. 2020;23:159–174. https ://doi.org/10.1111/avsc.12483