Ecology, 94(2), 2013, pp. 424–434 Ó 2013 by the Ecological Society of America

Understory plant communities and the functional distinction between savanna trees, forest trees, and pines JOSEPH W. VELDMAN,1,3 W. BRETT MATTINGLY,1,4

AND

LARS A. BRUDVIG2

1 Department of Zoology, University of Wisconsin, Madison, Wisconsin 53706 USA Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824 USA

2

Abstract. Although savanna trees and forest trees are thought to represent distinct functional groups with different effects on ecosystem processes, few empirical studies have examined these effects. In particular, it remains unclear if savanna and forest trees differ in their ability to coexist with understory plants, which comprise the majority of plant diversity in most savannas. We used structural equation modeling (SEM) and data from 157 sites across three locations in the southeastern United States to understand the effects of broadleaf savanna trees, broadleaf forest trees, and pine trees on savanna understory plant communities. After accounting for underlying gradients in fire frequency and soil moisture, abundances (i.e., basal area and stem density) of forest trees and pines, but not savanna trees, were negatively correlated with the cover and density (i.e., local-scale species richness) of C4 graminoid species, a defining savanna understory functional group that is linked to ecosystem flammability. In analyses of the full understory community, abundances of trees from all functional groups were negatively correlated with species density and cover. For both the C4 and full communities, fire frequency promoted understory plants directly, and indirectly by limiting forest tree abundance. There was little indirect influence of fire on the understory mediated through savanna trees and pines, which are more fire tolerant than forest trees. We conclude that tree functional identity is an important factor that influences overstory tree relationships with savanna understory plant communities. In particular, distinct relationships between trees and C4 graminoids have implications for grass–tree coexistence and vegetation–fire feedbacks that maintain savanna environments and their associated understory plant diversity. Key words: fire suppression; flammability; functional group; longleaf pine; Pinus palustris; plant diversity; prescribed fire; Quercus spp.; southeastern United States; species coexistence; woodland.

INTRODUCTION In savannas, the relationship between trees and understory vegetation is often framed as an antagonistic interaction mediated by fire (e.g., van Langevelde et al. 2003). Indeed, tree canopy cover is often negatively correlated with grass abundance (Scholes 2003), and surface fires fueled by the herbaceous understory can kill trees (Prior et al. 2010). Recently, the conceptual framework of two contrasting life forms (i.e., grasses and trees) has been replaced by models that recognize that tree species adapted to savanna environments are functionally distinct from species adapted to forests (Hoffmann et al. 2005b, Ratnam et al. 2011). In particular, savanna trees differ from forest trees in ways related to fire tolerance (e.g., bark thickness; Hoffmann et al. 2003), light capture (e.g., specific leaf area; Prior et al. 2003), fire facilitation (e.g., leaf litter flammability; Kane et al. 2008), and architecture (e.g., crown area; Manuscript received 18 June 2012; accepted 29 August 2012. Corresponding Editor: B. D. Inouye. 3 E-mail: [email protected] 4 Present address: Department of Biology, Eastern Connecticut State University, Willimantic, Connecticut 06226 USA. 424

Rossatto et al. 2009). Evaluating the functional differences between savanna and forest trees has improved our understanding of how tree demographics influence savanna–forest boundaries (Hoffmann et al. 2009), and how savanna trees might contribute to ecosystem flammability via positive feedbacks with fire (Beckage et al. 2009). Yet, it remains unclear if savanna trees and forest trees differ in their effects on savanna understory plant communities, which are often species-diverse assemblages of C4 graminoids, herbs, and shrubs of high conservation value (Bond and Parr 2010). Functional differences between savanna trees and forest trees likely influence ecosystem processes in complex ways that, in turn, influence understory plant communities. For example, savanna trees might facilitate understory vegetation by producing highly flammable leaf litter that helps to carry surface fires, whereas forest trees produce litter that impedes fire (Fonda 2001, Kane et al. 2008, Beckage et al. 2009). The crown architecture of savanna trees allows relatively large amounts of light to reach plants in the understory, whereas forest trees intercept more light and thereby limit understory productivity (Hoffmann et al. 2005a, Rossatto et al. 2009). Because direct sunlight helps to dry fuels (and shade can permit fuels to stay too damp to

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burn, but see Hoffmann et al. 2012b), light capture and litter characteristics interact to influence the accumulation of leaf litter, a factor that can limit understory vegetation (Hiers et al. 2007). Whether these differences are relevant for understory plant communities has yet to be clarified, because most studies that investigate factors influencing savanna understory plants consider all trees collectively in canopy cover measurements (e.g., Peterson and Reich 2008). In this study, we distinguish between tree functional groups, and ask: Do savanna trees, forest trees, and pines differ in their effects on savanna understory plant cover and diversity? To answer this question, we used structural equation modeling (SEM) to evaluate the influences of broadleaf savanna trees, broadleaf forest trees, and pine trees on understory plant communities at 157 sites from three locations in the longleaf pine (Pinus palustris) ecosystem of the southeastern United States. Like many of the world’s savannas (e.g., Bond and Parr 2010), fire suppression in longleaf pine savannas results in increased abundances of forest trees, the accumulation of litter, and a decline in understory plant species (Hartnett and Krofta 1989, Hiers et al. 2007). Most studies that assess the relationship between tree cover and understory plants in this ecosystem either do not distinguish between savanna and forest trees (e.g., Brudvig and Damschen 2011), exclude pines from the analyses (e.g., Hiers et al. 2007), or are focused on pines alone (e.g., Platt et al. 2006). For this study, we distinguish broadleaf savanna trees from forest tree species and consider pines as a distinct functional group. SEM is an ideal approach for understanding complex multivariate problems (Grace 2006), and allows us to explicitly test hypothesized relationships between tree functional groups and understory plant communities, while also incorporating the direct and indirect effects of underlying environmental gradients (i.e., fire frequency and soil moisture; Fig. 1). Increased abundance of forest trees is typically concurrent with fire suppression in savannas (Roitman et al. 2008, Geiger et al. 2011), necessitating an approach that disentangles the effects of trees vs. fire history on understory plant communities (Peterson et al. 2007). Additionally, prescribed fire and soil moisture both influence understory species diversity (Brockway and Lewis 1997, Kirkman et al. 2001) and tree species distributions (Cavender-Bares et al. 2004) in the study region; any approach to understanding the influence of trees on understory vegetation must account for these gradients. In this study, we combine a large-scale vegetation data set with SEM to determine how abundances of savanna trees, forest trees, and pines relate to understory plant communities (Fig. 1a). We also examine the relationships between trees and C4 graminoids (Fig. 1b), motivated by the importance of C4 species as a defining functional group in savannas and their role in vegetation–fire feedbacks (Bond and Parr 2010). In sum, this

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study provides an assessment of whether the distinction between tree functional groups is relevant to our understanding of the controls over savanna understory plant communities in ways that influence plant diversity and grass–tree coexistence. METHODS We conducted this study in savannas and woodlands (hereafter ‘‘savannas,’’ due to the prevalence of fire and coexistence of trees and C4 graminoids; Ratnam et al. 2011) within the historical range of the longleaf pine ecosystem in the southeastern United States. Longleaf pine savannas are characterized by an overstory of scattered pine trees and highly diverse understory plant communities (Walker and Peet 1983, Peet 2006). Pine savannas once covered much of the coastal plain in the southeastern United States (Frost 2006), but currently occupy ,3% of this original area due to human land use and fire suppression (Peet 2006). Coexistence of overstory trees and understory plants is maintained by frequent, low-intensity surface fires that kill or top-kill woody plants and consume leaf litter (Drewa et al. 2002, Thaxton and Platt 2006, Hiers et al. 2007). Although high abundances of broadleaf trees are associated with fire suppression (e.g., Gilliam and Platt 1999), certain fire-tolerant oaks (Quercus spp.) are a natural component of the otherwise pine-dominated tree community (Greenberg and Simons 1999; see Plate 1). Site selection We selected 157 study sites at three locations that support longleaf pine savannas: Fort Bragg, North Carolina (73 000 ha, elevation, 43–176 m; mean annual precipitation [MAP], 1270 mm; mean annual temperature [MAT], 168C; n ¼ 61 sites); Savannah River Site (a National Environmental Research Park), South Carolina (SRS, 80 000 ha, elevation, 20–130 m; MAP, 1225 mm; MAT, 188C; n ¼ 47 sites), and Fort Stewart, Georgia (114 000 ha, elevation, 2–56 m; MAP, 1220 mm; MAT, 198C; n ¼ 49 sites). These locations span ; 450 km and 38 latitude and are representative of the hydrological and topographic conditions typical of the eastern range of the longleaf pine ecosystem (Frost 2006). To inform the selection of study sites, we created a geographic information system that included historic maps and aerial photographs from the time of public acquisition (1919 for Fort Bragg, 1951 for SRS, 1947 for Fort Stewart), contemporary aerial photographs, and annual prescribed fire records from 1991 to 2009. We limited our study to sites that were savannas at the time of public acquisition (i.e., we excluded formerly cultivated land) because a history of agriculture results in reductions in plant diversity (Flinn and Vellend 2005, Brudvig and Damschen 2011) that could limit our ability to detect relationships between understory plants and trees. We ensured that all potential sites were at least 250 m apart and covered 1 ha of relatively uniform habitat (i.e., sites did not cross topographical, hydro-

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FIG. 1. Path diagrams of structural equation models relating tree functional groups to either (a) all understory plants or (b) C4 graminoids only. Black denotes hypotheses that are the focus of this study (i.e., relationships between trees of different functional groups and understory plants). Gray denotes underlying environmental gradients (i.e., fire and soil moisture) that can influence both trees and understory plants. Measured variables are represented by rectangles, composite variables by hexagons, and latent variables by circles. Arrows indicate direction of influence, with solid arrows significant at P , 0.05 and arrow thickness proportional to the strength of the correlations. Dashed lines indicate hypothesized paths that were modeled but were not significant (NS). Dotted lines are paths of fixed (equal) regression weights used to define composite and latent variables. Standardized regression weights are listed for significant (P , 0.05) paths. BA is basal area.

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TABLE 1. Summary of measured variables included in the structural equation models from 157 sites at three locations in the southeastern USA. Variable 2

Understory species density (species/m ) Understory plant cover (total %) C4 species density (species/m2) C4 cover (total %)

Mean

Range

Transformation

Description

6.6

1.5–17.9

log10

7–225

log10

0–3.1

log10

0–63

log10

0.7

0–9.4

log10

1.0

0–21.3

log10

15.1

1.3–35.7

none

109

0–2030

log10

97

0–1520

log10

342

30–2350

log10

5

0–17

none

41

30–57

none

mean number of plant species per 1 m2 subplot; 8 subplots/site mean total percent cover for all plant species in 8 1-m2 subplots/site mean number of C4 graminoid species per 1 m2 subplot; 8 subplots/site mean total percent cover for all C4 graminoid species in 8 1-m2 subplots/site basal area of savanna trees  2.5 cm dbh in one 20 3 50 m plot/site basal area of forest trees  2.5 cm dbh in one 20 3 50 m plot/site basal area of pine trees  2.5 cm dbh in one 20 3 50 m plot/site density of savanna trees  2.5 cm dbh in one 20 3 50 m plot/site density of forest trees  2.5 cm dbh in one 20 3 50 m plot/site density of pine trees  2.5 cm dbh in one 20 3 50 m plot/site number of prescribed fires between 1991 and 2009 moisture content at field capacity determined from a composite of six 20-cm soil cores per site

71 1.1 11

2

Savanna BA (m /ha) 2

Forest BA (m /ha) Pine BA (m2/ha) Savanna stems (stems/ha) Forest stems (stems/ha) Pine stems (stems/ha) Fire frequency (number of fires) Soil moisture holding capacity (% by mass)

Note: Locations were: Fort Bragg, North Carolina; Savanna River Site, South Carolina; and, Fort Stewart, Georgia.

logical, or land use boundaries). After identifying potential sites, we used fire records to select sites that were stratified across the range of fire frequencies at each location (Table 1). Although wildfires do occur, the overwhelming majority of fires are prescribed burns implemented by managers at each study location. Vegetation sampling Within each 1-ha site, we randomly positioned a 20 3 50 m sampling plot. Between August and November 2009, we counted, identified, and measured the diameter of all trees  2.5 cm diameter at breast height (1.37 m) within the plot. To assess species density (i.e., local-scale species richness), we recorded all plant species present within each of eight 1-m2 subplots nested within the larger 20 3 50 m plot (Brudvig and Damschen 2011) and calculated the mean number of species per 1-m2 subplot. To determine understory cover, we estimated the percent cover for each species in each 1-m2 subplot and used the sum of all species cover values to determine total understory cover. We chose to sample at the 1-m2 scale because species density is a common measurement in herbaceous communities (Grace 1999), including longleaf pine savannas (e.g., Myers and Harms 2009). In addition to the full understory community, we calculated the density and cover of C4 graminoids species for separate analyses. We measured soil moisture holding capacity (a major determinant of understory diversity and site productivity in the longleaf pine ecosystem; Kirkman et al. 2001) by extracting six soil cores (2.5 cm diameter and 15 cm in depth) at 10-m intervals along the

centerline of the plot; soil cores were homogenized and processed following Brudvig and Damschen (2011) to determine moisture content at field capacity by mass. Tree classification We used literature from the southeastern United States related to tree habitat preferences along fire gradients (Harnett and Krofta 1989, Cavender-Bares et al. 2004) to classify all trees as broadleaf savanna trees, broadleaf forest trees, or needleleaf trees (all pines), hereafter referred to as ‘‘savanna trees,’’ ‘‘forest trees,’’ and ‘‘pines,’’ respectively (Appendix A). Habitat associations are used to distinguish savanna trees and forest trees in Neotropical savannas (e.g., Hoffmann et al. 2003, Veldman and Putz 2011), and in combination with published data on bark thickness (Jackson et al. 1999), our savanna tree classification is consistent with definitions of savanna trees used internationally (Ratnam et al. 2011). Most of the pines in our study fit the general model of savanna trees (i.e., thick bark and highly flammable litter) but they differ from broadleaf savanna trees in a number of ways that warrant treatment as a functionally distinct group. Among these many functional differences (e.g., Cook et al. 2001) are thicker bark than broadleaf savanna trees (Jackson et al. 1999) and vastly different leaf morphologies. All tree species not defined as savanna trees or pines were considered forest trees, based on their relatively thin bark (Jackson et al. 1999) and typical habitat associations with infrequently burned mesic hardwood forests (Harnett and Krofta 1989, Jacqmain et al. 1999).

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Structural equation modeling We developed two structural equation models (SE models) to test the direct effects of savanna trees, forest trees, and pines on (1) all understory plants and (2) C4 graminoids only, while accounting for the direct and indirect effects of soil moisture availability and fire frequency. We began by specifying our hypotheses in multivariate conceptual models that formed the basis for the SEM (Fig. 1). Each model included 10 observed variables (Table 1), three composite overstory variables (savanna trees, forest trees, and pines), and one latent understory variable (Fig. 1). As such, we modeled the understory community as a latent variable that was represented by an equally weighted combination of local-scale species richness and total plant abundance. We modeled overstory trees in each functional group as composite variables defined by stem density and basal area. Latent variables are unmeasured variables (often theoretical constructs) for which we have no direct measure but that we infer from other (often measured) variables. Composite variables are unmeasured variables completely specified by causal (often measured) variables (see Grace et al. 2010 for an explanation of latent and composite variables). Prior to fitting the models, we generated bivariate plots and univariate density plots in R 2.9.2 (R Development Core Team 2009) to assess the linearity of relationships and skewness of variables (Appendix B). Because SEM models linear relationships between variables, and because extreme skew can affect covariance (Grace 2006), we applied a log10 transformation to all of the understory variables and most of the tree variables (Table 1; Appendix B); no transformations were required for fire frequency, soil moisture holding capacity, or pine basal area. We fit the SE models using the maximum likelihood function in IBM SPPS Amos 20.0 (Amos Development Corporation, Meadville, Pennsylvania, USA). We combined data from all three study locations to achieve analyses that both encompass a wide range of conditions that support longleaf pine savannas, and that span regional gradients in edaphic factors that influence tree–understory interactions. A statistical reason for analyzing all locations collectively, SEM requires large data sets, and models repeated at the location level would risk spurious results due to small sample sizes (Grace 2006). Although statistical analyses were performed on all sites combined (n ¼ 157), we coded bivariate graphics to facilitate visual interpretation of how sites from each location contribute to results. Additional statistical analyses A challenge to presenting SEM results is that, while potentially informative, multivariate relationships cannot be easily plotted in two dimensions. Additionally, relationships identified in SE models can be obscured or misrepresented by plotting simple bivariate relationships, if strong underlying gradients exist among sites, as in our study. In an attempt to provide visually

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interpretable bivariate plots between the tree and understory variables in our models, while controlling for underlying gradients, we plotted residuals of understory variables against tree abundances by functional group. To calculate the residuals, we used the lm function in R 2.9.2 (R Development Core Team 2009) to model the linear main effects of fire frequency and soil moisture holding capacity on understory species density, total cover, C4 species density, and C4 cover. Similar to SEM, this analysis accounts for underlying gradients in fire and soil moisture. Unlike SEM, this approach ignores covariance in abundances between tree functional groups and does not allow for composite or latent variables. As such, these residual bivariate plots should be interpreted as supplementary to the SEM, with the understanding that they do not incorporate much of the complexity of the SE models. RESULTS Our study sites encompassed a broad range of conditions spanning fire-maintained to fire-suppressed longleaf pine savannas (Table 1). Our sites varied in total tree stem density (80–2950 stems/ha) and basal area (4–43 m2/ha), with wide ranges in savanna tree, forest tree, and pine tree abundances (Table 1) that allow us to test hypotheses about the effects of tree functional groups on understory plant communities. We encountered a total of 35 tree species, including four savanna species, 27 forest species, and four species of pine (Appendix A). Not surprisingly, pines accounted for the majority of all stems (63% relative abundance), followed by savanna trees (20%), and forest trees (17%). Pines also accounted for the majority of total tree basal area (90% relative dominance) compared to savanna and forest trees (4.3% and 5.7%, respectively). See Appendix A for lists of tree species, frequencies, and abundances. Full understory community The SE model relating tree functional groups to understory plant species density and cover accounted for 46% of the variation in the understory plant community (R 2 ¼ 0.46, Fig. 1a). Savanna trees, forest trees, and pine trees each had direct negative effects on understory plants (Fig. 1a) driven by negative relationships between tree abundances and understory species density (Fig. 2a, b). Relationships between trees and understory cover were less consistent, with stem density unrelated to understory cover for each tree functional group (Fig. 2b). Fire frequency had a direct positive effect on understory plants, as well as an indirect positive effect by limiting forest trees (Fig. 1a). Soil moisture had a direct positive influence on understory richness and pine basal area, and a negative relationship with savanna stems (Fig. 1a). The model was relatively effective at explaining variation in forest tree stem density (R 2 ¼ 0.26), forest tree basal area (R 2 ¼ 0.17), and savanna tree stem densities (R 2 ¼ 0.11), but not pine basal area

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FIG. 2. Full understory plant community species density (species/m2) and cover (total %) in relation to (a) tree basal area (m2/ ha) and (b) tree stem density (stems/ha) of savanna trees, forest trees, and pines. To account for effects of fire and soil moisture, understory variables are displayed as residuals from generalized linear models of the main effects of fire frequency and soil moisture. Symbols correspond to the three study locations. Linear regression lines are shown for significant relationships (P , 0.05) between tree variables and understory residuals. Note the different x-axis scale for pine basal area compared to savanna and forest trees.

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(R 2 ¼ 0.07), pine density (R 2 ¼ 0.01), or savanna tree basal area (R 2 ¼ 0.02). C4 graminoids The SE model relating tree functional groups to C4 species density and cover accounted for 56% of the variation in C4 graminoids (R 2 ¼ 0.56; Fig. 1b). Whereas forest trees and pine trees were negatively correlated with the C4 understory community (Fig. 1b), savanna tree abundance was unrelated to C4 graminoids. Overall negative relationships of forest trees and pines with C4 graminoids appear to be primarily due to negative effects of forest tree and pine basal area on C4 species density and cover (Fig. 3a). In supporting analyses (Fig. 3a, b), there was no relationship between savanna stem density and C4 species density and cover, and no relationship between savanna tree basal area and C4 cover, but there was a weak negative relationship between savanna basal area and C4 species density. Fire frequency had a direct positive effect on C4 graminoids, as well as an indirect positive effect by limiting forest trees (Fig. 1b). Unlike the full community analysis, soil moisture was not correlated with C4 graminoids (Fig. 1b). Because of the identical structure of the full community and C4 graminoid models, relationships between fire frequency, soil moisture, and tree abundances are identical in both models (Fig. 1a, b). DISCUSSION Our results show that savanna trees, in contrast to forest trees and pines, do not limit a defining group of savanna understory plants: C4 graminoids. Tree functional identity was also important in mediating the indirect effects of fire on the full understory, although all functional groups were negatively correlated with understory diversity and plant cover. By linking tree functional identity to understory plant community diversity and C4 graminoid abundance, our results support a growing body of research on the distinct roles of tree functional groups in savanna vegetation dynamics (Hoffmann et al. 2012a). The full understory results are consistent with numerous studies showing that increasing tree abundances are concurrent with declines in savanna understory plant communities (e.g., Ratajczak et al. 2012). By incorporating fire frequency in our models, our results extend this literature to show that trees exert direct negative control over understory communities and are not simply correlated with declines in understory communities driven by fire suppression. Further, our results suggest that aboveground differences in tree functional groups (i.e., those associated with light interception and litter characteristics) do not result in qualitatively different effects on full understory plant community diversity and cover (although the negative overstory effect on understory plants was weakest for savanna trees; Fig. 1a). It is plausible that the negative effects of trees could be due to similar function in

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belowground processes among tree groups (e.g., root competition for limiting resources; Harrington et al. 2003); little is known about the belowground functional differences (or equivalencies) among tree functional groups, and more work is needed to assess this hypothesis. In contrast to effects on understory communities as a whole, savanna trees appear better able to coexist with C4 graminoids compared to forest trees and pines. Our results do not support the hypothesis that savanna trees facilitate (i.e., are positively correlated with) C4 species, but rather that C4 graminoid diversity and abundance are controlled by factors other than savanna tree abundance (Fig. 1b). Among these factors are fire, which strongly promotes C4 species density and cover, and pine and forest tree abundances, which are negatively correlated with C4 graminoids. Although distinct functional characteristics might explain coexistence of savanna trees and C4 species, high rates of selfthinning (Sea and Hanan 2012) and inferior competitive abilities relative to shade-tolerant forest trees (e.g., Geiger et al. 2011) could also contribute to coexistence. Indeed, maximum stem densities for savanna trees, forest trees, and pines were comparable (Table 1), yet maximum savanna tree basal area (9.4 m2/ha) was just half that of forest trees (21.3 m2/ha) and only onequarter of maximum pine basal area (35.7 m2/ha); savanna trees may not reach sufficient basal area to limit C4 cover. Although the mechanisms need further exploration, our results do support the differentiation between savanna and forest trees (Ratnam et al. 2011) by providing evidence that savanna trees and forest trees differ in their relationships with C4 graminoids, a distinction with important implications for savanna– forest dynamics (Hoffmann et al. 2009) and grass–fire feedbacks (Hoffmann et al. 2012b). Pine abundance in this study was negatively correlated with both C4 graminoids and full understory community species density and cover. Pines are considered a keystone functional group in the longleaf pine ecosystem because they produce fire-promoting leaf litter (Fonda 2001) and are associated with highdiversity plant communities (Walker and Peet 1983). Pines likely limit succession of savannas to forests via vegetation–fire feedbacks (e.g., Beckage et al. 2009); in this regard, pines may be viewed as facilitators of understory plant communities, at least relative to infrequently burned mesic forests (e.g., Nowacki and Abrams 2008). Nonetheless, across the range of sites we sampled there was a negative influence of pines on understory plants. This result is consistent with studies that document increased species richness following removal of longleaf pine overstory trees (Platt et al. 2006). Surprisingly, pine tree abundances were unaffected by fire frequency in our study. This may be because prescribed fires are implemented within prescription conditions (i.e., high fuel moisture, low ambient temperatures) that are unlikely to kill fire-tolerant pines

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FIG. 3. C4 graminoid species density (species/m2) and cover (total %) in relation to (a) tree basal area (m2/ha) and (b) tree stem density (stems/ha) of savanna trees, forest trees, and pines. To account for effects of fire and soil moisture, understory variables are displayed as residuals from generalized linear models of the main effects of fire frequency and soil moisture. Symbols correspond to the three study locations. Linear regression lines are shown for significant relationships (P , 0.05) between tree variables and C4 residuals. Note the different x-axis scale for pine basal area compared to savanna and forest trees.

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PLATE 1. Example of a longleaf pine savanna at Ft. Stewart, Georgia, USA, with fire-tolerant broadleaf savanna trees and an understory dominated by C4 grasses. Photo credit: J. W. Veldman.

(e.g., Beckage et al. 2005), or because pine abundance reflects historical timber management more so than contemporary fire frequency. Among trees in this study that fit the general model of savanna species (Ratnam et al. 2011), broadleaf savanna trees and pines appear to represent two functional groups that differ in their effects on C4 graminoids and relationship with fire (Fig. 1b), including different effects on fire behavior (Wenk et al. 2011). Pines are dominant, fire-promoting, and highly fire tolerant compared to broadleaf savanna trees, which are relatively less abundant, less flammable, and less fire tolerant (but far more fire tolerant than forest trees; Fig. 1a, b; Appendix B). With two-functional groups of fireadapted trees (one dominant and fire promoting), the longleaf pine ecosystem may have more in common with highly flammable eucalypt-dominated savannas of Australia (Lawes et al. 2011, Bond et al. 2012) than with savannas of Africa and South America that lack highly flammable tree species (Lehmann et al. 2011). Studies seeking to understand global distributions of savannas or tree coexistence with savanna understory plants should consider not only the distinction between savanna trees and forest trees (Ratnam et al. 2011, Hoffmann et al. 2012a), but also functional variation within fire-adapted trees as a group (Lawes et al. 2011, Wenk et al. 2011). We should expect these different functional groups to have different effects on savanna– forest dynamics (Beckage et al. 2009, Lehmann et al. 2011), and thus different consequences for understory plant communities.

Our C4 model (Fig. 1b), in combination with previous work on grass and leaf litter flammability, suggests that savanna trees, forest trees, and pines may influence fire– vegetation relationships via different mechanisms. Broadleaf savanna trees are able to coexist with C4 graminoids and thereby permit the accumulation of graminoid fuels that are the primary fine fuel source in most savannas (Hoffmann et al. 2012b). Unlike broadleaf savanna trees, pines limit C4 graminoids but nonetheless contribute to ecosystem flammability by producing highly flammable leaf litter (Fonda 2001). In contrast, forest trees are incompatible with savanna environments because they are susceptible to fire and suppress understory plant communities, including lightdemanding, fire-promoting C4 graminoids (Hoffmann et al. 2012a). By studying understory plant communities in relation to overstory trees, we can achieve a more comprehensive understanding of the factors that maintain savanna environments and the plant diversity they support. CONCLUSIONS This study links overstory tree functional identity to savanna understory plant communities, and shows that the distinction between savanna trees, forest trees, and pines is important for understanding tree coexistence with C4 graminoids. The functional distinction among trees appears less important for the full understory community, given that abundances of all tree functional groups were negatively correlated with understory species density and cover. By illustrating relationships between tree functional groups and savanna understory

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plant communities, we provide the basis for future inquiry into likely mechanisms (e.g., light interception, litter flammability, belowground competition) by which trees interact with understory plant communities. As a practical implication of this study, we suggest that ecosystem managers wishing to promote C4 grasses and sedges via management of overstory trees should explicitly consider tree species functional groups and, in our study system, manage abundances of forest trees (through fire) and pines (through cutting), but not remove broadleaf savanna tree species. ACKNOWLEDGMENTS We thank E. Damschen, J. Orrock, and J. Walker for helping to develop the network of research sites associated with this study. Thanks to L. Bizarri, C. Christopher, C. Collins, A. Powell, and R. Ranalli for help with vegetation sampling. For logistical support we thank: the Fish and Wildlife Branch and Forestry Branch of Fort Stewart; the USDA Forest Service– Savannah River; and the Endangered Species Branch, Forestry Branch, and the Cultural Resources Program of Fort Bragg. This project was funded by the Strategic Environmental Research and Development Program (Project RC-1695) and by the Department of Agriculture, Forest Service, Savannah River, Interagency Agreement (DE-AI09-00SR22188) with the Department of Energy, Aiken, South Carolina. E. Grman, N. Swenson, and R. Globus Veldman provided helpful suggestions on this manuscript. LITERATURE CITED Beckage, B., W. J. Platt, and L. J. Gross. 2009. Vegetation, fire, and feedbacks: a disturbance-mediated model of savannas. American Naturalist 174:805–818. Beckage, B., W. J. Platt, and B. Panko. 2005. A climate-based approach to the restoration of fire-dependent ecosystems. Restoration Ecology 13:429–431. Bond, W. J., G. D. Cook, and R. J. Williams. 2012. Which trees dominate in savannas? The escape hypothesis and eucalypts in northern Australia. Austral Ecology 37:678–685. Bond, W. J., and C. L. Parr. 2010. Beyond the forest edge: ecology, diversity and conservation of the grassy biomes. Biological Conservation 143:2395–2404. Brockway, D. G., and C. E. Lewis. 1997. Long-term effects of dormant-season prescribed fire on plant community diversity, structure and productivity in a longleaf pine wiregrass ecosystem. Forest Ecology and Management 96:167–183. Brudvig, L. A., and E. I. Damschen. 2011. Land-use history, historical connectivity, and land management interact to determine longleaf pine woodland understory richness and composition. Ecography 34:257–266. Cavender-Bares, J., K. Kitajima, and F. A. Bazzaz. 2004. Multiple trait associations in relation to habitat differentiation among 17 Floridian oak species. Ecological Monographs 74:635–662. Cook, E. R., J. S. Glitzenstein, P. J. Krusic, and P. A. Harcombe. 2001. Identifying functional groups of trees in west Gulf Coast forests (USA): a tree-ring approach. Ecological Applications 11:883–903. Drewa, P. B., W. J. Platt, and F. B. Moser. 2002. Fire effects on resprouting of shrubs in headwaters of southeastern longleaf pine savannas. Ecology 83:755–767. Flinn, K. M., and M. Vellend. 2005. Recovery of forest plant communities in post-agricultural landscapes. Frontiers in Ecology and the Environment 3:243–250. Fonda, R. W. 2001. Burning characteristics of needles from eight pine species. Forest Science 47:390–396. Frost, C. C. 2006. History and future of the longleaf pine ecosystem. Pages 9–42 in S. Jose, E. J. Jokela, and D. L.

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SUPPLEMENTAL MATERIAL Appendix A Classification, frequencies, and abundances of tree species (Ecological Archives E094-036-A1). Appendix B Bivariate plots of all (untransformed) measured variables included in the SEM (Ecological Archives E094-036-A2).