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

1,3 1,4 2 JOSEPH W. VELDMAN, W. BRETT MATTINGLY, AND LARS A. BRUDVIG 1Department of Zoology, University of Wisconsin, Madison, Wisconsin 53706 USA 2Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824 USA

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 Rossatto et al. 2009). Evaluating the functional differ- In savannas, the relationship between trees and ences between savanna and forest trees has improved understory vegetation is often framed as an antagonistic our understanding of how tree demographics influence savanna–forest boundaries (Hoffmann et al. 2009), and interaction mediated by fire (e.g., van Langevelde et al. how savanna trees might contribute to ecosystem 2003). Indeed, tree canopy cover is often negatively flammability via positive feedbacks with fire (Beckage correlated with grass abundance (Scholes 2003), and et al. 2009). Yet, it remains unclear if savanna trees and surface fires fueled by the herbaceous understory can kill forest trees differ in their effects on savanna understory trees (Prior et al. 2010). Recently, the conceptual plant communities, which are often species-diverse framework of two contrasting life forms (i.e., grasses assemblages of C graminoids, herbs, and shrubs of and trees) has been replaced by models that recognize 4 high conservation value (Bond and Parr 2010). that tree species adapted to savanna environments are Functional differences between savanna trees and functionally distinct from species adapted to forests forest trees likely influence ecosystem processes in (Hoffmann et al. 2005b, Ratnam et al. 2011). In complex ways that, in turn, influence understory plant particular, savanna trees differ from forest trees in ways communities. For example, savanna trees might facili- related to fire tolerance (e.g., bark thickness; Hoffmann tate understory vegetation by producing highly flamma- et al. 2003), light capture (e.g., specific leaf area; Prior et ble leaf litter that helps to carry surface fires, whereas al. 2003), fire facilitation (e.g., leaf litter flammability; forest trees produce litter that impedes fire (Fonda 2001, Kane et al. 2008), and architecture (e.g., crown area; Kane et al. 2008, Beckage et al. 2009). The crown architecture of savanna trees allows relatively large Manuscript received 18 June 2012; accepted 29 August 2012. amounts of light to reach plants in the understory, Corresponding Editor: B. D. Inouye. whereas forest trees intercept more light and thereby 3 E-mail: [email protected] limit understory productivity (Hoffmann et al. 2005a, 4 Present address: Department of Biology, Eastern Con- necticut State University, Willimantic, Connecticut 06226 Rossatto et al. 2009). Because direct sunlight helps to USA. dry fuels (and shade can permit fuels to stay too damp to 424 February 2013 UNDERSTORY PLANTS AND SAVANNA TREES 425 burn, but see Hoffmann et al. 2012b), light capture and study provides an assessment of whether the distinction litter characteristics interact to influence the accumula- between tree functional groups is relevant to our tion of leaf litter, a factor that can limit understory understanding of the controls over savanna understory vegetation (Hiers et al. 2007). Whether these differences plant communities in ways that influence plant diversity are relevant for understory plant communities has yet to and grass–tree coexistence. be clarified, because most studies that investigate factors influencing savanna understory plants consider all trees METHODS collectively in canopy cover measurements (e.g., Peter- We conducted this study in savannas and woodlands son and Reich 2008). In this study, we distinguish (hereafter ‘‘savannas,’’ due to the prevalence of fire and between tree functional groups, and ask: Do savanna coexistence of trees and C4 graminoids; Ratnam et al. trees, forest trees, and pines differ in their effects on 2011) within the historical range of the longleaf pine savanna understory plant cover and diversity? ecosystem in the southeastern United States. Longleaf To answer this question, we used structural equation pine savannas are characterized by an overstory of modeling (SEM) to evaluate the influences of broadleaf scattered pine trees and highly diverse understory plant savanna trees, broadleaf forest trees, and pine trees on communities (Walker and Peet 1983, Peet 2006). Pine understory plant communities at 157 sites from three savannas once covered much of the coastal plain in the locations in the longleaf pine (Pinus palustris) ecosystem southeastern United States (Frost 2006), but currently of the southeastern United States. Like many of the occupy ,3% of this original area due to human land use world’s savannas (e.g., Bond and Parr 2010), fire and fire suppression (Peet 2006). Coexistence of suppression in longleaf pine savannas results in overstory trees and understory plants is maintained by increased abundances of forest trees, the accumulation frequent, low-intensity surface fires that kill or top-kill of litter, and a decline in understory plant species woody plants and consume leaf litter (Drewa et al. 2002, (Hartnett and Krofta 1989, Hiers et al. 2007). Most Thaxton and Platt 2006, Hiers et al. 2007). Although studies that assess the relationship between tree cover high abundances of broadleaf trees are associated with and understory plants in this ecosystem either do not fire suppression (e.g., Gilliam and Platt 1999), certain distinguish between savanna and forest trees (e.g., fire-tolerant oaks (Quercus spp.) are a natural compo- Brudvig and Damschen 2011), exclude pines from the nent of the otherwise pine-dominated tree community analyses (e.g., Hiers et al. 2007), or are focused on pines (Greenberg and Simons 1999; see Plate 1). alone (e.g., Platt et al. 2006). For this study, we distinguish broadleaf savanna trees from forest tree Site selection species and consider pines as a distinct functional We selected 157 study sites at three locations that group. support longleaf pine savannas: Fort Bragg, North SEM is an ideal approach for understanding complex Carolina (73 000 ha, elevation, 43–176 m; mean annual multivariate problems (Grace 2006), and allows us to precipitation [MAP], 1270 mm; mean annual tempera- explicitly test hypothesized relationships between tree ture [MAT], 168C; n ¼ 61 sites); Savannah River Site (a functional groups and understory plant communities, National Environmental Research Park), South Caro- while also incorporating the direct and indirect effects of lina (SRS, 80 000 ha, elevation, 20–130 m; MAP, 1225 underlying environmental gradients (i.e., fire frequency mm; MAT, 188C; n ¼ 47 sites), and Fort Stewart, and soil moisture; Fig. 1). Increased abundance of forest Georgia (114 000 ha, elevation, 2–56 m; MAP, 1220 trees is typically concurrent with fire suppression in mm; MAT, 198C; n ¼ 49 sites). These locations span ; savannas (Roitman et al. 2008, Geiger et al. 2011), 450 km and 38 latitude and are representative of the necessitating an approach that disentangles the effects of hydrological and topographic conditions typical of the trees vs. fire history on understory plant communities eastern range of the longleaf pine ecosystem (Frost (Peterson et al. 2007). Additionally, prescribed fire and 2006). To inform the selection of study sites, we created soil moisture both influence understory species diversity a geographic information system that included historic (Brockway and Lewis 1997, Kirkman et al. 2001) and maps and aerial photographs from the time of public tree species distributions (Cavender-Bares et al. 2004) in acquisition (1919 for Fort Bragg, 1951 for SRS, 1947 for the study region; any approach to understanding the Fort Stewart), contemporary aerial photographs, and influence of trees on understory vegetation must account annual prescribed fire records from 1991 to 2009. We for these gradients. limited our study to sites that were savannas at the time In this study, we combine a large-scale vegetation data of public acquisition (i.e., we excluded formerly set with SEM to determine how abundances of savanna cultivated land) because a history of agriculture results trees, forest trees, and pines relate to understory plant in reductions in plant diversity (Flinn and Vellend 2005, communities (Fig. 1a). We also examine the relation- Brudvig and Damschen 2011) that could limit our ability ships between trees and C4 graminoids (Fig. 1b), to detect relationships between understory plants and motivated by the importance of C4 species as a defining trees. We ensured that all potential sites were at least functional group in savannas and their role in vegeta- 250 m apart and covered 1 ha of relatively uniform tion–fire feedbacks (Bond and Parr 2010). In sum, this habitat (i.e., sites did not cross topographical, hydro- 426 JOSEPH W. VELDMAN ET AL. Ecology, Vol. 94, No. 2

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. February 2013 UNDERSTORY PLANTS AND SAVANNA TREES 427

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

Variable Mean Range Transformation Description 2 2 Understory species density (species/m ) 6.6 1.5–17.9 log10 mean number of plant species per 1 m subplot; 8 subplots/site Understory plant cover (total %) 71 7–225 log10 mean total percent cover for all plant species in 8 1-m2 subplots/site 2 C4 species density (species/m ) 1.1 0–3.1 log10 mean number of C4 graminoid species per 1 m2 subplot; 8 subplots/site C4 cover (total %) 11 0–63 log10 mean total percent cover for all C4 graminoid species in 8 1-m2 subplots/site 2 Savanna BA (m /ha) 0.7 0–9.4 log10 basal area of savanna trees 2.5 cm dbh in one 20 3 50 m plot/site 2 Forest BA (m /ha) 1.0 0–21.3 log10 basal area of forest trees 2.5 cm dbh in one 20 3 50 m plot/site Pine BA (m2/ha) 15.1 1.3–35.7 none basal area of pine trees 2.5 cm dbh in one 20 3 50 m plot/site Savanna stems (stems/ha) 109 0–2030 log10 density of savanna trees 2.5 cm dbh in one 20 3 50 m plot/site Forest stems (stems/ha) 97 0–1520 log10 density of forest trees 2.5 cm dbh in one 20 3 50 m plot/site Pine stems (stems/ha) 342 30–2350 log10 density of pine trees 2.5 cm dbh in one 20 3 50 m plot/site Fire frequency (number of fires) 5 0–17 none number of prescribed fires between 1991 and 2009 Soil moisture holding capacity (% by mass) 41 30–57 none moisture content at field capacity determined from a composite of six 20-cm soil cores per site Note: Locations were: Fort Bragg, North Carolina; Savanna River Site, South Carolina; and, Fort Stewart, Georgia. logical, or land use boundaries). After identifying centerline of the plot; soil cores were homogenized and potential sites, we used fire records to select sites that processed following Brudvig and Damschen (2011) to were stratified across the range of fire frequencies at each determine moisture content at field capacity by mass. location (Table 1). Although wildfires do occur, the overwhelming majority of fires are prescribed burns Tree classification implemented by managers at each study location. We used literature from the southeastern United States related to tree habitat preferences along fire Vegetation sampling gradients (Harnett and Krofta 1989, Cavender-Bares et Within each 1-ha site, we randomly positioned a 20 3 al. 2004) to classify all trees as broadleaf savanna trees, 50 m sampling plot. Between August and November broadleaf forest trees, or needleleaf trees (all pines), 2009, we counted, identified, and measured the diameter hereafter referred to as ‘‘savanna trees,’’ ‘‘forest trees,’’ of all trees 2.5 cm diameter at breast height (1.37 m) and ‘‘pines,’’ respectively (Appendix A). Habitat associ- within the plot. To assess species density (i.e., local-scale ations are used to distinguish savanna trees and forest species richness), we recorded all plant species present trees in Neotropical savannas (e.g., Hoffmann et al. within each of eight 1-m2 subplots nested within the 2003, Veldman and Putz 2011), and in combination with larger 20 3 50 m plot (Brudvig and Damschen 2011) and published data on bark thickness (Jackson et al. 1999), calculated the mean number of species per 1-m2 subplot. our savanna tree classification is consistent with To determine understory cover, we estimated the percent definitions of savanna trees used internationally (Rat- cover for each species in each 1-m2 subplot and used the nam et al. 2011). Most of the pines in our study fit the sum of all species cover values to determine total general model of savanna trees (i.e., thick bark and understory cover. We chose to sample at the 1-m2 scale highly flammable litter) but they differ from broadleaf because species density is a common measurement in savanna trees in a number of ways that warrant herbaceous communities (Grace 1999), including long- treatment as a functionally distinct group. Among these leaf pine savannas (e.g., Myers and Harms 2009). In many functional differences (e.g., Cook et al. 2001) are addition to the full understory community, we calculat- thicker bark than broadleaf savanna trees (Jackson et al. ed the density and cover of C4 graminoids species for 1999) and vastly different leaf morphologies. All tree separate analyses. We measured soil moisture holding species not defined as savanna trees or pines were capacity (a major determinant of understory diversity considered forest trees, based on their relatively thin and site productivity in the longleaf pine ecosystem; bark (Jackson et al. 1999) and typical habitat associa- Kirkman et al. 2001) by extracting six soil cores (2.5 cm tions with infrequently burned mesic hardwood forests diameter and 15 cm in depth) at 10-m intervals along the (Harnett and Krofta 1989, Jacqmain et al. 1999). 428 JOSEPH W. VELDMAN ET AL. Ecology, Vol. 94, No. 2

Structural equation modeling interpretable bivariate plots between the tree and We developed two structural equation models (SE understory variables in our models, while controlling models) to test the direct effects of savanna trees, forest for underlying gradients, we plotted residuals of understory variables against tree abundances by func- trees, and pines on (1) all understory plants and (2) C4 graminoids only, while accounting for the direct and tional group. To calculate the residuals, we used the lm indirect effects of soil moisture availability and fire function in R 2.9.2 (R Development Core Team 2009) to frequency. We began by specifying our hypotheses in model the linear main effects of fire frequency and soil multivariate conceptual models that formed the basis for moisture holding capacity on understory species density, the SEM (Fig. 1). Each model included 10 observed total cover, C4 species density, and C4 cover. Similar to variables (Table 1), three composite overstory variables SEM, this analysis accounts for underlying gradients in (savanna trees, forest trees, and pines), and one latent fire and soil moisture. Unlike SEM, this approach understory variable (Fig. 1). As such, we modeled the ignores covariance in abundances between tree func- understory community as a latent variable that was tional groups and does not allow for composite or latent represented by an equally weighted combination of variables. As such, these residual bivariate plots should local-scale species richness and total plant abundance. be interpreted as supplementary to the SEM, with the We modeled overstory trees in each functional group as understanding that they do not incorporate much of the composite variables defined by stem density and basal complexity of the SE models. area. Latent variables are unmeasured variables (often theoretical constructs) for which we have no direct RESULTS measure but that we infer from other (often measured) Our study sites encompassed a broad range of variables. Composite variables are unmeasured variables conditions spanning fire-maintained to fire-suppressed completely specified by causal (often measured) vari- longleaf pine savannas (Table 1). Our sites varied in ables (see Grace et al. 2010 for an explanation of latent total tree stem density (80–2950 stems/ha) and basal and composite variables). Prior to fitting the models, we area (4–43 m2/ha), with wide ranges in savanna tree, generated bivariate plots and univariate density plots in forest tree, and pine tree abundances (Table 1) that R 2.9.2 (R Development Core Team 2009) to assess the allow us to test hypotheses about the effects of tree linearity of relationships and skewness of variables functional groups on understory plant communities. We (Appendix B). Because SEM models linear relationships encountered a total of 35 tree species, including four between variables, and because extreme skew can affect savanna species, 27 forest species, and four species of covariance (Grace 2006), we applied a log10 transfor- pine (Appendix A). Not surprisingly, pines accounted mation to all of the understory variables and most of the for the majority of all stems (63% relative abundance), tree variables (Table 1; Appendix B); no transforma- followed by savanna trees (20%), and forest trees (17%). tions were required for fire frequency, soil moisture Pines also accounted for the majority of total tree basal holding capacity, or pine basal area. We fit the SE area (90% relative dominance) compared to savanna and models using the maximum likelihood function in IBM forest trees (4.3% and 5.7%, respectively). See Appendix SPPS Amos 20.0 (Amos Development Corporation, A for lists of tree species, frequencies, and abundances. Meadville, Pennsylvania, USA). We combined data from all three study locations to Full understory community achieve analyses that both encompass a wide range of The SE model relating tree functional groups to conditions that support longleaf pine savannas, and that understory plant species density and cover accounted for span regional gradients in edaphic factors that influence 46% of the variation in the understory plant community tree–understory interactions. A statistical reason for 2 analyzing all locations collectively, SEM requires large (R ¼ 0.46, Fig. 1a). Savanna trees, forest trees, and pine data sets, and models repeated at the location level trees each had direct negative effects on understory would risk spurious results due to small sample sizes plants (Fig. 1a) driven by negative relationships between (Grace 2006). Although statistical analyses were per- tree abundances and understory species density (Fig. formed on all sites combined (n ¼ 157), we coded 2a, b). Relationships between trees and understory cover bivariate graphics to facilitate visual interpretation of were less consistent, with stem density unrelated to how sites from each location contribute to results. understory cover for each tree functional group (Fig. 2b). Fire frequency had a direct positive effect on Additional statistical analyses understory plants, as well as an indirect positive effect A challenge to presenting SEM results is that, while by limiting forest trees (Fig. 1a). Soil moisture had a potentially informative, multivariate relationships can- direct positive influence on understory richness and pine not be easily plotted in two dimensions. Additionally, basal area, and a negative relationship with savanna relationships identified in SE models can be obscured or stems (Fig. 1a). The model was relatively effective at misrepresented by plotting simple bivariate relation- explaining variation in forest tree stem density (R2 ¼ ships, if strong underlying gradients exist among sites, as 0.26), forest tree basal area (R2 ¼ 0.17), and savanna in our study. In an attempt to provide visually tree stem densities (R2 ¼ 0.11), but not pine basal area February 2013 UNDERSTORY PLANTS AND SAVANNA TREES 429

2 2 FIG. 2. Full understory plant community species density (species/m ) and cover (total %) in relation to (a) tree basal area (m / 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. 430 JOSEPH W. VELDMAN ET AL. Ecology, Vol. 94, No. 2

(R2 ¼ 0.07), pine density (R2 ¼ 0.01), or savanna tree belowground processes among tree groups (e.g., root basal area (R2 ¼ 0.02). competition for limiting resources; Harrington et al. 2003); little is known about the belowground functional C4 graminoids differences (or equivalencies) among tree functional The SE model relating tree functional groups to C4 groups, and more work is needed to assess this species density and cover accounted for 56% of the hypothesis. 2 variation in C4 graminoids (R ¼ 0.56; Fig. 1b). Whereas In contrast to effects on understory communities as a forest trees and pine trees were negatively correlated whole, savanna trees appear better able to coexist with with the C4 understory community (Fig. 1b), savanna C4 graminoids compared to forest trees and pines. Our tree abundance was unrelated to C4 graminoids. Overall results do not support the hypothesis that savanna trees negative relationships of forest trees and pines with C4 facilitate (i.e., are positively correlated with) C4 species, graminoids appear to be primarily due to negative but rather that C4 graminoid diversity and abundance effects of forest tree and pine basal area on C4 species are controlled by factors other than savanna tree density and cover (Fig. 3a). In supporting analyses (Fig. abundance (Fig. 1b). Among these factors are fire, 3a, b), there was no relationship between savanna stem which strongly promotes C4 species density and cover, density and C4 species density and cover, and no and pine and forest tree abundances, which are relationship between savanna tree basal area and C4 negatively correlated with C4 graminoids. Although cover, but there was a weak negative relationship distinct functional characteristics might explain coexis- between savanna basal area and C4 species density. Fire tence of savanna trees and C4 species, high rates of self- frequency had a direct positive effect on C4 graminoids, thinning (Sea and Hanan 2012) and inferior competitive as well as an indirect positive effect by limiting forest abilities relative to shade-tolerant forest trees (e.g., trees (Fig. 1b). Unlike the full community analysis, soil Geiger et al. 2011) could also contribute to coexistence. moisture was not correlated with C4 graminoids (Fig. Indeed, maximum stem densities for savanna trees, 1b). Because of the identical structure of the full forest trees, and pines were comparable (Table 1), yet 2 community and C4 graminoid models, relationships maximum savanna tree basal area (9.4 m /ha) was just between fire frequency, soil moisture, and tree abun- half that of forest trees (21.3 m2/ha) and only one- dances are identical in both models (Fig. 1a, b). quarter of maximum pine basal area (35.7 m2/ha); savanna trees may not reach sufficient basal area to limit DISCUSSION C4 cover. Although the mechanisms need further Our results show that savanna trees, in contrast to exploration, our results do support the differentiation forest trees and pines, do not limit a defining group of between savanna and forest trees (Ratnam et al. 2011) savanna understory plants: C4 graminoids. Tree func- by providing evidence that savanna trees and forest trees tional identity was also important in mediating the differ in their relationships with C4 graminoids, a indirect effects of fire on the full understory, although all distinction with important implications for savanna– functional groups were negatively correlated with forest dynamics (Hoffmann et al. 2009) and grass–fire understory diversity and plant cover. By linking tree feedbacks (Hoffmann et al. 2012b). functional identity to understory plant community Pine abundance in this study was negatively correlat- diversity and C4 graminoid abundance, our results ed with both C4 graminoids and full understory support a growing body of research on the distinct roles community species density and cover. Pines are consid- of tree functional groups in savanna vegetation dynam- ered a keystone functional group in the longleaf pine ics (Hoffmann et al. 2012a). ecosystem because they produce fire-promoting leaf The full understory results are consistent with litter (Fonda 2001) and are associated with high- numerous studies showing that increasing tree abun- diversity plant communities (Walker and Peet 1983). dances are concurrent with declines in savanna under- Pines likely limit succession of savannas to forests via story plant communities (e.g., Ratajczak et al. 2012). By vegetation–fire feedbacks (e.g., Beckage et al. 2009); in incorporating fire frequency in our models, our results this regard, pines may be viewed as facilitators of extend this literature to show that trees exert direct understory plant communities, at least relative to negative control over understory communities and are infrequently burned mesic forests (e.g., Nowacki and not simply correlated with declines in understory Abrams 2008). Nonetheless, across the range of sites we communities driven by fire suppression. Further, our sampled there was a negative influence of pines on results suggest that aboveground differences in tree understory plants. This result is consistent with studies functional groups (i.e., those associated with light that document increased species richness following interception and litter characteristics) do not result in removal of longleaf pine overstory trees (Platt et al. qualitatively different effects on full understory plant 2006). Surprisingly, pine tree abundances were unaffect- community diversity and cover (although the negative ed by fire frequency in our study. This may be because overstory effect on understory plants was weakest for prescribed fires are implemented within prescription savanna trees; Fig. 1a). It is plausible that the negative conditions (i.e., high fuel moisture, low ambient effects of trees could be due to similar function in temperatures) that are unlikely to kill fire-tolerant pines February 2013 UNDERSTORY PLANTS AND SAVANNA TREES 431

2 2 FIG.3. C4 graminoid species density (species/m ) and cover (total %) in relation to (a) tree basal area (m /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. 432 JOSEPH W. VELDMAN ET AL. Ecology, Vol. 94, No. 2

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 Our C4 model (Fig. 1b), in combination with previous reflects historical timber management more so than work on grass and leaf litter flammability, suggests that contemporary fire frequency. savanna trees, forest trees, and pines may influence fire– Among trees in this study that fit the general model of vegetation relationships via different mechanisms. savanna species (Ratnam et al. 2011), broadleaf savanna Broadleaf savanna trees are able to coexist with C4 trees and pines appear to represent two functional graminoids and thereby permit the accumulation of graminoid fuels that are the primary fine fuel source in groups that differ in their effects on C4 graminoids and relationship with fire (Fig. 1b), including different effects most savannas (Hoffmann et al. 2012b). Unlike broad- on fire behavior (Wenk et al. 2011). Pines are dominant, leaf savanna trees, pines limit C4 graminoids but fire-promoting, and highly fire tolerant compared to nonetheless contribute to ecosystem flammability by broadleaf savanna trees, which are relatively less producing highly flammable leaf litter (Fonda 2001). In abundant, less flammable, and less fire tolerant (but contrast, forest trees are incompatible with savanna environments because they are susceptible to fire and far more fire tolerant than forest trees; Fig. 1a, b; suppress understory plant communities, including light- Appendix B). With two-functional groups of fire- demanding, fire-promoting C graminoids (Hoffmann et adapted trees (one dominant and fire promoting), the 4 al. 2012a). By studying understory plant communities in longleaf pine ecosystem may have more in common with relation to overstory trees, we can achieve a more highly flammable eucalypt-dominated savannas of comprehensive understanding of the factors that main- Australia (Lawes et al. 2011, Bond et al. 2012) than tain savanna environments and the plant diversity they with savannas of Africa and South America that lack support. highly flammable tree species (Lehmann et al. 2011). Studies seeking to understand global distributions of CONCLUSIONS savannas or tree coexistence with savanna understory This study links overstory tree functional identity to plants should consider not only the distinction between savanna understory plant communities, and shows that savanna trees and forest trees (Ratnam et al. 2011, the distinction between savanna trees, forest trees, and Hoffmann et al. 2012a), but also functional variation pines is important for understanding tree coexistence within fire-adapted trees as a group (Lawes et al. 2011, with C4 graminoids. The functional distinction among Wenk et al. 2011). We should expect these different trees appears less important for the full understory functional groups to have different effects on savanna– community, given that abundances of all tree functional forest dynamics (Beckage et al. 2009, Lehmann et al. groups were negatively correlated with understory 2011), and thus different consequences for understory species density and cover. By illustrating relationships plant communities. between tree functional groups and savanna understory February 2013 UNDERSTORY PLANTS AND SAVANNA TREES 433 plant communities, we provide the basis for future Miller, editors. The longleaf pine ecosystem: ecology, inquiry into likely mechanisms (e.g., light interception, silviculture, and restoration. Springer, New York, New litter flammability, belowground competition) by which York, USA. Geiger, E. L., S. G. Gotsch, G. Damasco, M. Haridasan, A. C. trees interact with understory plant communities. As a Franco, and W. A. Hoffmann. 2011. Distinct roles of practical implication of this study, we suggest that savanna and forest tree species in regeneration under fire ecosystem managers wishing to promote C4 grasses and suppression in a Brazilian savanna. Journal of Vegetation sedges via management of overstory trees should Science 22:312–321. Gilliam, F. S., and W. J. Platt. 1999. Effects of long-term fire explicitly consider tree species functional groups and, exclusion on tree species composition and stand structure in in our study system, manage abundances of forest trees an old-growth Pinus palustris (longleaf pine) forest. Plant (through fire) and pines (through cutting), but not Ecology 140:15–26. remove broadleaf savanna tree species. Grace, J. B. 1999. The factors controlling species density in herbaceous plant communities: an assessment. Perspectives in ACKNOWLEDGMENTS Plant Ecology, Evolution and Systematics 2:1–28. Grace, J. B. 2006. Structural equation modeling and natural We thank E. Damschen, J. Orrock, and J. Walker for helping systems. Cambridge University Press, Cambridge, UK. to develop the network of research sites associated with this Grace, J. B., T. M. Anderson, H. Olff, and S. M. Scheiner. study. Thanks to L. Bizarri, C. Christopher, C. Collins, A. 2010. On the specification of structural equation models for Powell, and R. Ranalli for help with vegetation sampling. For ecological systems. Ecological Monographs 80:67–87. logistical support we thank: the Fish and Wildlife Branch and Greenberg, C. H., and R. W. Simons. 1999. Age, composition, Forestry Branch of Fort Stewart; the USDA Forest Service– and stand structure of old-growth oak sites in the Florida Savannah River; and the Endangered Species Branch, Forestry high pine landscape: implications for ecosystem management Branch, and the Cultural Resources Program of Fort Bragg. and restoration. Natural Areas Journal 19:30–40. This project was funded by the Strategic Environmental Harrington, T. B., C. M. 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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).