Vegetation Dynamics and the Efficacy of Prescribed Fires in Restoring Oak

VEGETATION DYNAMICS AND THE EFFICACY OF PRESCRIBED FIRES IN RESTORING OAK-

DOMINATED ECOSYSTEMS IN SOUTHERN OHIO

SHERYL M. PETERSEN

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Department of Biology

CASE WESTERN RESERVE UNIVERSITY

January, 2012

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Sheryl M. Petersen ______

Doctor of Philosophy candidate for the ______degree *.

Roy Ritzman (signed)______(chair of the committee)

Joseph F. Koonce ______

Robin Snyder ______

David Burke ______

Michael Benard ______

Matthew Dickinson ______

September 2, 2011 (date) ______

*We also certify that written approval has been obtained for any proprietary material contained therein.

Table of contents Table of contents ...... i List of Tables ...... iv List of Figures ...... viii Acknowledgments ...... xiii Abstract 1 Chapter 1: Introduction ...... 3 1.1. Disturbance ecology and restoration of disturbance-dependent plant communities ...... 4 1.2. The effects of fire suppression ...... 6 1.3. Prescribed fire as a restoration tool ...... 7 1.4. Fire ecology of oak-dominated ecosystems in southern Ohio’s Bluegrass Region ...... 11 1.4.1. Regional vegetation history ...... 11 1.4.2. Mixed-oak forests ...... 13 1.4.3. Barrens ...... 15 1.4.4. Fire regime ...... 16 1.4.5. Current burn prescriptions ...... 21 1.5. General experimental approach ...... 21 1.6. Study sites ...... 23 1.6.1. Location and region ...... 23 1.6.2. Barrens study sties ...... 25 1.6.3. Forest study sites ...... 26 1.7. Organization of dissertation...... 30 Chapter 2: Effects of biennial fire and clipping on woody and herbaceous ground layer vegetation in oak barrens of southern Ohio ...... 31 2.1. Abstract ...... 31 2.2. Introduction ...... 32 2.3. Methods ...... 35 2.3.1. Study sites ...... 35 2.3.2. Experimental design and data collection ...... 37 2.3.3. Response variables ...... 40 2.3.4. Data analysis ...... 42 2.4. Results ...... 45 2.4.1. Fire behavior...... 45 2.4.2. Treatment effects on plant functional groups ...... 45 2.4.3. Effects of treatment on competitive interactions between woody and herbaceous groundcover plants over time ...... 56 2.5. Discussion ...... 60 2.5.1. Potential for fire to reduce shrub encroachment ...... 60

2.5.2. Potential for fire to foster herb abundance and diversity ...... 63 2.5.3. Conclusions and management implications ...... 65 2.6. Appendices ...... 67 2.6.1. Appendix A: Full ANCOVA model results...... 67 2.6.2. Appendix B: Full ANOVA model results...... 71 2.6.3. Appendix C: Linear contrasts for effect of treatment over time...... 72 2.6.4. Appendix D: Changes in the relationship between herbaceous and woody groundcover plants between the first and last census by treatment and site...... 73 Chapter 3: Vegetation-environment relationships among the ground layers of four southern Ohio mixed oak forests ...... 75 3.1. Abstract ...... 75 3.2. Introduction ...... 76 3.3. Methods ...... 79 3.3.1. Study sites ...... 79 3.3.2. Study design and data collection ...... 82 3.3.3. Statistical analyses...... 85 3.4. Results ...... 90 3.4.1. Differences in community pattern, structure, and diversity between sites. 90 3.4.2. Differences in environmental characteristics between sites...... 95 3.4.3. Vegetation-environment relationships within individual sites and differences in ground layer assemblages between future burn management units. ... 99 3.5. Discussion ...... 109 3.5.1. Patterns in community pattern, structure, and diversity across sites...... 109 3.5.2. Differences in study site environmental characteristics...... 111 3.5.3. Within-site vegetation-environment relationships patterns ...... 113 3.5.4. Differences in ground layer assemblages between future burn management units within sites...... 116 3.5.5. Conclusions and management implications ...... 117 3.6. Appendix ...... 119 Chapter 4: Composition and structure of oak-dominated forests of the Bluegrass Region in southern Ohio...... 127 4.1. Abstract ...... 127 4.2. Introduction ...... 128 4.3. Methods ...... 130 4.3.1. Study sites ...... 130 4.3.2. Vegetation sampling and analysis ...... 130 4.4. Results ...... 131 4.5. Discussion ...... 142 4.5.1. Compositional shifts indicated by current forest structure ...... 142 4.5.2. Drivers of the oak-to-maple shift ...... 144 4.5.3. Conclusions and management implications ...... 146

Chapter 5: Effects of single spring and fall fires and a fire surrogate (clipping) on seedling layers in forests of the Bluegrass Region in southern Ohio...... 147 5.1. Abstract ...... 147 5.2. Introduction ...... 148 5.3. Methods ...... 152 5.3.1. Study sites ...... 152 5.3.2. Experimental design ...... 155 5.3.3. Prescribed fire and clipping treatments ...... 156 5.3.4. Vegetation data collection ...... 158 5.3.5. Response variables ...... 159 5.3.6. Data analysis ...... 160 5.4. Results ...... 165 5.4.1. Fire behavior...... 165 5.4.2. Season and treatment type effects on community trajectory ...... 166 5.4.3. Season effects on stem densities...... 169 5.4.4. Treatment type effects on stem densities by season ...... 174 5.5. Discussion ...... 181 5.5.1. Seasonal effects ...... 181 5.5.2. Effects of topkill with (fire) and without (clipping) heating ...... 182 5.5.3. Potential for improved oak regeneration ...... 183 5.5.4. Conclusions and management implications ...... 186 5.6. Appendices ...... 189 5.6.1. Appendix A: Full ACOVA model results ...... 189 5.6.2. Appendix B: Ordination analysis of seedling community trajectory...... 196 Chapter 6: Conclusion ...... 200 6.1. Summary ...... 200 6.2. Reflections ...... 200 6.3. Conclusions ...... 202 Bibliography ...... 204

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List of Tables Table 1.1 Locations and characteristics of oak-barrens sites adapted from Petersen and Drewa (2009)...... 25 Table 1.2 Locations and characteristics of oak-dominated forest sites and burn management units within each site...... 29 Table 2.1 Locations and characteristics of sampled oak-barrens sites adapted from Petersen and Drewa (2009)...... 36 Table 2.2 Schematic summary of linear mixed-effects analysis of covariance (ANCOVA) for effects of site, treatment, year and their interactions on (A) shrub and (B) forb and graminoid response variables. The pretreatment covariate was significant (P < 0.0001) in all models and is not included in this summary. Model details can be found in Appendix A Table 2.5 (shrubs), Appendix B Table 2.6(forbs), and Appendix C Table 2.7 (graminoids) ...... 49 Table 2.3 Schematic summary of three sets of linear contrasts used to examine effects of treatment over time for the three response variables that had significant treatment × time interactions. Contrasts were conducted for growth both across and within sites because there was a significant site × treatment × time interaction. Actual P values can be found in Appendix C Table 2.9...... 53 Table 2.4 Partial residual regression equations describing the relationship between shrub response variables and fire behavior variables (fuel consumption and maximum tablet temperature) following the first and second set of fires. Dependent variables are residuals following regressions relating data collected after fires to data collected before fires. For example, “shrub cover residuals 2004” are the residuals following regression of shrub cover after the second set of fires in 2005 against shrub cover prior to these fires in 2003...... 60 Table 2.5 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on woody plant response variables. For each response variable (columns), the details of the minimum model are listed in rows with additional model information listed at the bottom. Explanatory variables are ordered (i) by their denominator degrees of freedom and (ii) by the sequence in which they were entered into the model. Continued on next page...... 67 Table 2.6 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on forb response variables. Format and annotations follow Appendix A Table 2.5...... 69 Table 2.7 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on graminoid response variables. Format and annotations follow Appendix A Table 2.5...... 70 Table 2.8 Summary of linear mixed-effects analysis of variance for individual or interacting effects of site, treatment, and year on shrub cover and richness, the only response variables that had a significant treatment × time interaction in the ANCOVA model . In this analysis, pretreatment data is included as the first level of the factor year rather than as a covariate, such that year consists of five levels from 2003-2007.

For each response variable (columns), the details of the minimum model are listed in rows with additional model information listed at the bottom. Explanatory variables are ordered (i) by their denominator degrees of freedom and (ii) by the sequence in which they were entered into the model...... 71 Table 2.9 Summary of three sets of linear contrasts used to examine effects of treatment over time for the three response variables that had significant treatment × time interactions. Contrasts were conducted for growth both across and within sites because there was a significant site × treatment × time interaction...... 72 Table 2.10 ANOVA table for the linear models examining the effect of site, treatment, and time on the relationship between shrub cover (covariate) and forb response variable (columns). Year has two values: 2003, at the onset of the study, and 2007 at the completion of the study. Shrub cover was arcsine-square root transformed. Resulting regression equations are presented in Table 2.10...... 73 Table 2.11 Regression equations describing linear relationships between forb response (y) and arcsine-square root transformed shrub cover (x) for forb cover, richness, and diversity...... 74 Table 3.1 Locations and characteristics of oak-dominated forest sites and burn management units within each site. Repeated from Chapter 1.Table 1.2 ...... 81 Table 3.2 Average ± SE and cumulative (Total) species richness (S), Simpson’s diversity (1/D), and Simpson’s evenness (E1/D) for each site...... 92 Table 3.3 Environmental variable means ± SE...... 98 Table 3.4 Goodness of fit (R2) for environmental vectors with each set of site ordination scores. Labels follow Table 3.3. Those variables with P ≤ 0.05 (in bold) are depicted in Figure 3.5. Heat load index is not included for Cedar Falls, which is flat and therefore had no variation in heat load as calculated in this study...... 106 Table 3.5 Summary of all species in the ground layer at Bethany. Rank is the rank abundance of the species. Freq = Frequency. Importance value (IV) is the average of relative frequency and relative density. V/M = the variance to mean ratio, an indicator of clustering where V/M < 1 indicates more even distribution than expected by chance, V/M = 1 indicates an even distribution, and V/M > 1 indicates a clustered distribution. Bolded V/M ratio indicates a V/M ratio significantly greater than 1 at α < 0.05. V/M ratios not in bold are not significantly different than 1; no species had a V/M ratio significantly < 1. Green’s = Green’s index of clumping where 0 = even distribution and 1 = maximum clumping. Seedlings = the percent of stems in the regeneration layer < 30 cm in height. Tree tolerance classifications (Tol) are abbreviated as T = shade tolerant, M = intermediate in shade tolerance, and I = intolerant of shade. * indicates abundance measured in cover not counts...... 119 Table 3.6 Summary of all species in the ground layer at Cedar Falls. Columns and abbreviations follow Appendix Table 3.5 ...... 121 Table 3.7 Summary of all species in the ground layer at Hopkins. Columns and abbreviations follow Table 3.5...... 123 Table 3.8 Summary of all species in the ground layer at Sandstone. Columns and abbreviations follow Table 3.5...... 125 Table 4.1 Stand structural characteristics and species richness across sites...... 132

Table 4.2 Importance values for saplings, stems ≥ 2 cm and < 15 cm dbh, at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant...... 136 Table 4.3 Importance values for poles, stems ≥ 15 cm and < 45 cm dbh, tree species at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant...... 137 Table 4.4 Importance values for canopy adults, stems ≥ 45 cm dbh, at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant...... 138 Table 5.1 Locations and characteristics of oak-dominated forest sites and individual burn units within each site, adapted from Table 1.2 in Chapter 1...... 154 Table 5.2 Schematic summary of PERMANOVA for effects of (A) site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning), (B) site and treatment type (clipping, burning, and reference) within a given season (fall or spring) on community trajectory. In B, site includes Cedar Falls, Hopkins, and Sandstone in the fall, but only Cedar Falls and Hopkins in the spring. Model details can be found in Appendix A Table 5.4 ...... 167 Table 5.3 Schematic summary of ANCOVA for effects of (A) site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning), (B) site (Cedar Falls, Hopkins, and Sandstone) and treatment type (clipping, burning, and reference) in units treated in fall, and (C) site (Cedar Falls and Hopkins) and treatment type (clipping, burning, and reference) in units treated in the spring on seedling densities of different groups. Model details can be found in Appendix A Table 5.5-Table 5.7...... 171 Table 5.4 Summary of PERMANOVA analysis of multivariate community trajectory for individual and interacting effects of site, season, and treatment. For the Season and treatment model, sites included Cedar Falls and Hopkins and treatments included clipping and burning. In the Fall and Spring models, treatment type included clipping, burning, and reference. However, in the Fall model, site includes Cedar Falls, Hopkins, and Sandstone, but only Cedar Falls and Hopkins are in the Spring model. The design was a three-way or two-way ANOVA and all factors were treated as fixed...... 189 Table 5.5 Summary of ANCOVA models of seedling density by habit for individual and interacting effects of site, season, and treatment. Model A: site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning). Model B (Fall): site (Cedar Falls, Hopkins, and Sandstone) and treatment type (clipping, burning, and reference) in units treated in fall. Model C (Spring): site (Cedar Falls and Hopkins) and treatment type (clipping, burning, and reference) in units treated in the spring. Continued on next page...... 190

Table 5.6 Summary of ANCOVA models of seedling density by shade tolerance for individual and interacting effects of site, season, and treatment. Variable levels in each model follow Appendix A Table 5.5...... 192 Table 5.7 Summary of ANCOVA analysis of seedling density by species group for individual and interacting effects of site, season, and treatment. Variable levels in each model follow Appendix A Table 5.5...... 194

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List of Figures Figure 1.1 Locations of the Edge and Strait Creek preserves in southern Ohio...... 24 Figure 2.1 Timeline of treatment application and vegetation censuses...... 38 Figure 2.2 The effect of treatment on shrub cover, stem growth, and richness – responses having significant treatment × time interactions – over time. Approximate timing of treatments is indicated by the dashed orange line. Pretreatment values (collected in year 2003), when available, were used as a covariate in linear models and are indicated by isolated points in 2003. Letters indicate results of multiple comparisons between treatments within a given year; treatments with different letters are significantly different, treatments sharing similar letters are not different. Absence of letters also indicates lack of significant difference. Error bars are ± 1 SE. 50 Figure 2.3 The effect of site and treatment on shrub stem growth over time. Approximate timing of treatments is indicated by the dashed orange line. Pretreatment values were not measured. Different letters indicate significant differences between treatments within a given year. Letters follow Figure 2.2 Error bars are ± 1 SE...... 51 Figure 2.4 The effect of treatment on shrub evenness. The dashed orange line separates pre- and post- treatment values. Values presented in 2004-2007 are averages over these years, as there was no significant treatment × time interaction. Pretreatment values were collected in year 2003 and used as a covariate in the linear model. Letters follow Figure 2.2. Error bars are ± 1 SE...... 52 Figure 2.5 The change in shrub density and diversity over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE...... 52 Figure 2.6 The change in forb responses over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE...... 54 Figure 2.7 The change in graminoid responses over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE...... 55 Figure 2.8 The relationship between forb aerial cover and shrub aerial cover differed between sites and shifted over time, but not with treatment. Regression in 2003 (pretreatment) is indicated by the solid lines and regression in 2007 (at the final census) is indicated by a dashed line. Closed symbols indicate pretreatment data,

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open symbols represent data collected in final census. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11). Shrub aerial cover was arcsine square root transformed and the axis labels were back transformed to percent cover for ease of interpretation...... 57 Figure 2.9 The relationship between forb richness and shrub aerial cover differed between sites and shifted with treatment, but not time. Regression line color indicates site. Burned plots are orange, clipped plots are blue, and reference plots are green. Closed symbols indicate pretreatment data, open symbols represent data collected in final census. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11). Shrub aerial cover was arcsine square root transformed and the axis labels were back transformed to percent cover for ease of interpretation...... 58 Figure 2.10 The relationship between forb diversity and shrub aerial cover shifts between sites but does not change with year or treatment. The pretreatment census (2003) and final census (2007) data are presented on separate panels for clarity. Line type and symbol fill indicate site. Solid lines and symbols indicate the Edge. Dashed line and open symbol indicate SCA. Dotted line and hatched symbol indicate SCB. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11). ... 59 Figure 3.1 Non-metric multidimensional scaling (NMDS) ordination for all sites combined. The plot combines individual sample plots (open circles) with the average score, or centroid, for each site. Lines connect each plot to its corresponding site centroid. Ordination yielded a stress value of 19.06 %...... 91 Figure 3.2 Rank abundance curves for each site with the two most abundant species labeled. Each point on the graph represents a species. Symbol shape and shading indicate species’ habit (shrub/vine) or, for trees, shade tolerance. Species labels are a concatenation of the first two letters of both the genus and the specific epithet (e.g.: acru = Acer rubrum). See Appendix for a full list of species, ranks, and abbreviations for each site...... 93 Figure 3.3 Densities of woody ground layer vegetation (a) by habit and shade tolerance guild (b) in either the Quercus (oak) or Acer (maple) genus at each site. For site, B = Bethany, C = Cedar Falls, H = Hopkins, and S = Sandstone. Color reflects tolerance guild membership in both a and b...... 95 Figure 3.4 Non-metric multidimensional scaling (NMDS) ordinations for each site with fitted environmental vectors significant at the P < 0.05 level. The length and angle of the vector represent the strength and direction of the relationship with ordination scores. Vector lengths cannot be compared between ordinations of different sites. Filled and open circles represent sample plots from the two spatially distinct burn units at each site. Axes tick marks indicate a 0.20 change in dissimilarity. Dashed reference lines mark axes origins and cross at the center of the ordination. Environmental vector labels follow Table 3.3. Stress was 19.40 % for the ordination for Bethany; 22.54 % for Cedar Falls; 23.81 % for Hopkins; and 24.87 % for Sandstone...... 105 Figure 3.5 NMDS ordinations for each site with weighted average scores (centroids) of important species (those with importance values > 1). Density of each species is

indicated by symbol size. Species distribution pattern is indicated by symbol fill. No species had a uniform distribution. Species labels are a concatenation of the first two letters of the genus and the specific epithet (e.g., acru = Acer rubrum) except where specimens were only identified to genus (e.g., vaccinium = Vaccinium spp.). Ordinations, axes, and reference lines match Figure 3.4, but plots and environmental vectors have been removed from the display for clarity. See Appendix Table 3.5-Table 3.8 for a full list of species, abbreviations, densities, and V:M (V/M) values for each site...... 107 Figure 3.6 NMDS ordinations for each site with weighted average scores (centroids) of important species (those with importance values > 1). Ordinations and species are identical to Figure 3.5 but information displayed about each species is different. The proportion of stems classified as small seedlings (stem height < 30 cm) of each species is indicated by symbol size. Shade tolerance guild is indicated by symbol fill. See Appendix Table 3.5-Table 3.8 for a full list of species, abbreviations, seedling proportions, and tolerance classifications for each site...... 108 Figure 4.1 Densities of trees in each structural class at each site sorted by (a) shade tolerance guild and (b) in Quercus (oak) or Acer (maple) genus. Saplings include stems ≥ 2 cm dbh but < 15 cm dbh. Poles include stems ≥ 15 cm dbh but < 45 cm dbh. Canopy adults include stems ≥ 45 cm dbh. Sites: B = Bethany, C = Cedar Falls, H = Hopkins, and S = Sandstone. Color reflects tolerance guild membership in both a and b...... 135 Figure 4.2 Percentage of trees in select genera by diameter size class and study site. The far right size class (0-2 cm dbh) consists of seedling data collected in 2006 and presented in Chapter 3. Genera selected for display had importance values > 5 in at least two of the broad size class categories (saplings, poles, and canopy adults). Other tree genera that did not meet this criterion are included in the “Other” category. With the exception of “Other”, genera are displayed in the figure legend in order of increasing shade intolerance from top to bottom: Acer and Nyssa are shade tolerant; Carya, Quercus, and Fraxinus are intermediate, but Fraxinus is more intolerant than Carya and Quercus; and Liriodendron is considered highly intolerant of shade. The maximum x-axis value has been artificially truncated at 85 cm dbh to focus the display on smaller size classes. This removed a single tree from the figure, a 122.5 cm dbh white oak (Quercus alba) at Hopkins...... 139 Figure 4.3 Size class distributions for (a) maple and (b) major oak species at each study site. The maximum x-axis value had been truncated as in Figure 1.2. Note that y-axis scales differ for the two genera. Seedlings are not included in this figure; thus, the smallest size class (0-5 cm dbh) only includes stems 2-5 cm dbh...... 141 Figure 5.1 Schematic of study design showing nesting of treatment type within treatment season at a study site. Letters inside circular plots indicate treatment type: B = burning, C = clipping, R = reference. The arrangement of treatment types is for illustration only, and does not reflect their actual arrangement at the study site. .. 155 Figure 5.2 Paint tag temperatures among fire seasons (Fall and Spring) and pyrometer locations (25 cm above litter surface, at the litter surface, and at the interface between litter and duff layers). Significant seasonal differences within a pyrometer

location are indicated by different letters above bars. Significant differences (P < 0.05) between pyrometer locations across seasons are indicated by different letters below pyrometer location labels along top margin. Error bars are ± 1 SE...... 166 Figure 5.3 Post-treatment seedling community trajectory at Cedar Falls among (a) different seasons of clipping and fire and (b) different treatments applied in the fall. 95% confidence ellipses indicate groups with significantly different trajectories. See Appendix B for ordination and community trajectory of full dataset...... 168 Figure 5.4 Post-treatment seedling densities among seasons of application (Fall and Spring) within different sites (Cedar Falls and Hopkins) and treatment types (Burning and Clipping), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between seasons within a given site × treatment combination are indicated by different letters above bars. Letters with an asterisk indicate differences approached significance (P = 0.09- 0.05). Asterisks following seedling group labels, in the right margin, indicate that significant main or interaction effects of season were detected by ANCOVA, but not apparent when examined with post-hoc pairwise comparisons at some level. Specifically, a single asterisk (*) indicates a main effect of season was detected by ANCOVA, but these differences in season were not consistently found within site × treatment comparisons; a double asterisk (**) indicates that a season × site or season × treatment interaction was detected by ANCOVA, but seasonal differences were not detected in multiple comparisons at this same level, although, where indicated, were found within site × treatment combinations...... 173 Figure 5.5 Post-treatment seedling densities in fall treatment units among treatment types (fire, clipping, or reference) within different sites (Cedar Falls, Hopkins, Sandstone), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between treatment type within a given site are indicated by different letters above bars. Asterisks following seedling group labels (right margin) indicate a significant main effect of treatment type was detected by ANCOVA, but differences in treatment were not consistently found across when examined with post-hoc pairwise comparisons. Specifically, a single asterisk (*) indicates that reference plots were different from those treated with clipping and fire, but these differences were not consistent across sites; a double asterisk (**) indicates that burned plots were different from clipped plots and reference plots, but these differences were not consistent across sites. . 178 Figure 5.6 Post-treatment seedling densities in spring treatment units among treatment types (fire, clipping, or reference) within different sites (Cedar Falls and Hopkins), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between treatment types within a given site

are indicated by different letters above bars. Letters with an asterisk indicate differences approached significance (P = 0.06). Asterisks following seedling group labels (right margin) indicate a significant main effect of treatment type was detected by ANCOVA, but differences in treatment were not consistently found across sites when examined with post-hoc pairwise comparisons. Specifically, a single asterisk (*) indicates that reference plots were different from those treated with clipping and fire, but these differences were not consistent across sites; a double asterisk (**) indicates that global differences between treatments were not detected with pairwise comparisons, but treatments did differ within certain sites...... 180 Figure 5.7 Non-metric multidimensional scaling (NMDS) ordination of plots pre (small gray circles) and post (color symbols) treatment based on woody plant seedlings. Arrows connecting individual plots pre- and post-treatment and show community change over time. 95% confidence ellipses encircle plots at different sites. Dashed reference lines mark axes origins and cross at the center of the ordination. Stress was 19.14 %...... 197 Figure 5.8 Post-treatment seedling community trajectory of full dataset showing plots labeled by site (a) and treatment (b). -> next page ...... 198

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Acknowledgments This work would not have been possible without the guidance, support, and

generosity of numerous individuals and organizations.

The Ohio Biological Survey, Inc. provided financial support for the forest

descriptions. The Nature Conservancy and the Cincinnati Museum Center provided

access to field sites. The Nature Conservancy also conducted prescribed burns, with help

from their volunteers and individuals from the USDA Forest Service.

My research advisors, Paul Drewa and Joseph Koonce, provided much more than research mentoring.

Paul Drewa introduced me to disturbance ecology and experimental design. He

stressed the importance of knowing your question, cultivating statistical competence,

and treating the field like your office. Perhaps more importantly, he helped me find the

joy in public speaking.

Joseph Koonce opened my eyes to the issue of scale, and he taught me how to

write with sticky notes. He encouraged me to delve into the world of R, but reminded

me to reflect on my expectations when afflicted with analysis paralysis. He impressed

upon me the importance of using best practices in data management – I promise I’ll do

better with my next project! Most of all, he had the patience to let me develop

independence.

I could write in equal length about the contributions of each of my committee

members, but to express my sincere thanks for their support, I will spare them. Thank

you Mike Benard, David Burke, Matt Dickinson, and Robin Snyder!

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I also thank all the individuals at Case Western Reserve University, the Cincinnati

Museum Center, The Holden Arboretum, The Nature Conservancy, and the USDA Forest

Service who provided assistance both in and out of the field. Some of them walked a mile uphill both ways through the woods wearing a 45 lb water pack just to help with prescribed burns. Special thanks to Dave Minney – none of this research could have happened without him. When my nose was stuck a little too close to the clipboard, he reminded me to open my eyes and observe the natural history all around me. And thank you, all the folks at The Edge who tried to remind me that the field was not just my office. Pat and Mindy Abbott, you provide the poshest of research accommodations.

Special thanks to all the CWRU undergrads who contributed to this research:

Amanda Bachmann, Nick Brehl, Anna Droz, Megan Easley, and Mike Stentz. To my most excellent lab siblings Nellie Khalil and Sandra Albro, thank you for both helping me grow as a scientist and for being my psychologists – you really should have gotten a stipend just for that.

Thank you Jim Petersen and Joseph Donaldson for taking glamorous vacations

(see poshest accommodations, above) to Adams County every summer for years just to be the field crew I couldn’t fire.

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Vegetation Dynamics and the Efficacy of Prescribed Fires in Restoring Oak-Dominated

Ecosystems in Southern Ohio

Abstract By SHERYL M. PETERSEN

Most pryrogenic ecosystems are endangered due to encroachment of fire- sensitive species and loss of fire-tolerant species caused by altered fire regimes, especially fire suppression. Restoration of these degraded systems typically involves the reintroduction of fire via prescribed burning. I evaluated the efficacy of prescribed fire in reducing woody plant encroachment in fire-suppressed oak-dominated ecosystems in

the Bluegrass Region of southern Ohio. In the first study, I tested the effects of biennial

fire and a fire surrogate (clipping) on woody and herbaceous vegetation abundance in

oak barrens. I found that fire and clipping produce similar responses in vegetation, and

although these treatments reduce the aerial cover of shrubs, they do not lessen shrub resprouting or promote herbaceous plants. Next, I described the characteristics of oak- dominated forests prior to the reintroduction of fire. My snapshot of seedling layer vegetation in these forests highlights the variation in vegetation and environmental factors over small and large spatial scales. Despite their distinctions in composition, the structural patterns at all the forest stands provide evidence for a general shift in composition from oak (Quercus) to maple (Acer) dominance. Oaks are failing to regenerate and are being replaced by actively recruiting maples. Fires are predicted to reverse this shift by acting as a filter for maples resulting in the promotion oaks. In my

final study, I tested this prediction and evaluated the effects of fire season and topkill

with and without heating on forest seedling composition and abundance. I found no

clear effect of fire season, or heating, and only limited support for the prediction that fires act as a filter for maples. Overall, these results indicate that fire might maintain initial vegetation conditions, but is not effective in reversing encroachment in oak-

dominated ecosystems. Despite the limited spatial and temporal scale of my studies,

these results are consistent with the general findings in the literature. They underscore

the need to test multiple aspects of the fire regime (frequency, intensity, and season) in

concert with structural manipulations. They also suggest that we may need to modify

our expectation that fire will restore these highly altered systems.

Chapter 1: Introduction My research goal was to test the efficacy of fire in restoring fire-suppressed oak- dominated ecosystems in the Bluegrass Region of southern Ohio. In the following chapters, I describe the impact of altered disturbance regimes (i.e. fire suppression) on plant community composition and structure, and I test the effectiveness of current low- intensity dormant-season prescribed fires in reserving woody plant encroachment in oak barrens and oak-dominated forests. I ask three general questions:

1. How effective are low-intensity dormant-season fires in reducing encroaching

woody ground layer vegetation and fostering abundance and diversity of

herbaceous plant functional groups in oak barrens?

2. What are the characteristics of oak-dominated forests prior to the reintroduction of

fire?

3. How effective are low-intensity dormant-season fires in reducing encroaching shade-

tolerant woody plant species (especially maples) and fostering abundance of fire-

dependent woody vegetation (especially oaks) in the seedling layer of oak-

dominated forests?

In this first chapter, I provide a broad overview of disturbance ecology theory, review the application of prescribed fire to the restoration of degraded pyrogenic ecosystems, and review the expectations of using prescribed fire in the Bluegrass Region

of southern Ohio. I also describe my study sites and introduce the organization of my

dissertation.

1.1. Disturbance ecology and restoration of disturbance- dependent plant communities Natural disturbances (such as fire, floods, windstorms, and herbivory) are

important drivers of plant community structure (White 1979, Sousa 1984). Disturbances

can be defined as discrete events that remove some portion of biomass or even whole

individuals from a given area (White and Pickett 1985, Platt and Connell 2003). Most

natural disturbances tend to have non-catastrophic effects on vegetation, meaning that

plants present in the community survive or regenerate following disturbance (Platt and

Connell 2003). For example, plants can regenerate from protected meristems following herbivory (McNaughton 1976), germinate from stored seeds following fire and flooding

(Keddy and Reznicek 1986, Goto et al. 1996), and sprout from underground organs following a variety of disturbances (Bond and Midgley 2001).

Chronic disturbances occur with a somewhat predictable frequency, intensity,

seasonal timing, and spatial extent – collectively known as a disturbance regime (White

and Pickett 1985, Whelan 1995). Thus, they can act as selective agents, increasing the

fitness of species that tolerate or resist the disturbance event and the conditions it creates (Sousa 1984, Platt 1994, 1999). Many species even come to require disturbance or the post-disturbance environment to complete their life histories (Sousa 1984, Platt

1994, 1999). For example, many temperate forest trees require forest gaps for regeneration (Runkle 1981, Poulson and Platt 1996), Banksia shrubs and trees need heat

4 from fires to release seeds from serotinous cones (He et al. 2011), and riparian cottonwoods (Populus spp.) require flooding and ice scouring for seedbed formation

(Rood et al. 2007).

However, nearly all disturbance regimes have been altered by human activity, one of the most disruptive of which is the elimination or suppression of repeated disturbance. For example, water-level stabilization in coastal wetlands (Herrick and Wolf

2005), avalanche control (Kulakowski et al. 2011), and removal of grazers (Collins et al.

1998) all cause dramatic changes in plant community structure and composition.

Cessation of disturbance typically leads to a shift in community dominance towards species that are typically more sensitive to disturbance, and, along with the increasingly competitive environment, contributes to regeneration failure of disturbance-tolerant and disturbance-dependent species (Sousa 1984, Abrams 2003). This has lead to widespread losses in biodiversity and endangered disturbance-dependent ecosystems worldwide (Noss and Peters 1995, Neldner et al. 1997, Ward 1998).

Thus, reintroducing disturbance is often a priority in conservation efforts.

However, we are still struggling to apply disturbance theory to effectively restore ecosystems degraded by disturbance suppression, possibly because we often lack a thorough understanding of the context for restoration (MacDougall et al. 2004, Romme et al. 2009). Romme et al. (2009) outline this context for restoration as an understanding of 1) the variability in ecosystem structure and processes, 2) the prehistoric and historic disturbance regimes, and 3) the drivers of recent vegetation change. Misunderstanding the context for restoration may lead to the application of

disturbance regimes outside their evolutionary context (Platt 1994, Schwartz and

Hermann 1997) or the application of the historic regime to a community far removed

from its historic state (Hobbs and Huenneke 1992). Both alternatives could hinder

restoration by sending communities along a different trajectory than towards the

restoration target (Platt 1994, Anderson et al. 2000, Smith et al. 2002, Richardson et al.

2007).

In the next two sections, I focus on fire as a disturbance that we are struggling to effectively use as a restoration tool in degraded pyrogenic ecosystems. First, I

summarize the effects of fire suppression on plant community structure and

composition. I then describe the use of prescribed fire and discuss some of the

challenges that make evaluating the efficacy of current prescriptions critical.

1.2. The effects of fire suppression Most fire prone ecosystems have experienced long periods without fire as a result of modern human activities. These activities are both direct, including fire exclusion (i.e. not continuing aboriginal fire regimes) and fire suppression (i.e. extinguishing wildfires), as well as indirect: fragmenting ecosystems, converting the landscape into a less flammable agricultural matrix, and creating artificial fire breaks such as roads (Schwartz and Hermann 1997, Brose et al. 2001, Briggs et al. 2005, Keeley et al. 2009). These are often collectively referred to as fire suppression – as I will do throughout.

In general, fire suppression leads to an expansion (or encroachment) of woody plants into grasslands and savannas, as well as increasing tree density in savannas,

6 woodlands, and forests (Nuzzo 1986, Moriera 2000, Briggs et al. 2005, Keeley et al.

2009). In grasslands and savannas, encroaching shrubs and trees displace the primarily herbaceous groundcover through competition for light, moisture, and nutrients (Van

Auken 2000, Lett and Knapp 2005). More specifically, the reduction in light with increasing shrub and canopy tree density may be the primary driver of herbaceous plant loss in mesic grasslands and savannas of the Midwestern US (Anderson et al. 2000, Lett and Knapp 2005). This process of herbaceous plant loss had been described as a slow community disassembly as suites of herbaceous species drop out of the community based on their shade tolerance and dispersal ability (Leach and Givnish 1996, Taft 2009).

In forests, although compositional changes differ among forest types, dominance typically shifts towards fire-sensitive and shade-tolerant species and away from fire- tolerant species (Gilliam and Platt 1999, Abrams 2003, Keeley et al. 2009). The loss of fire-tolerant species is often driven by recruitment failure from the lack of post-fire seed bed formation and from seedling suppression under the low light levels from increasingly dense understories (Abrams 2003, Keeley et al. 2009). These changes in forest composition and structure following fire suppression can alter the behavior of fires when they do return to the landscape. In eastern deciduous forests, they reduce ecosystem flammability, such that fires are less likely to spread and are lower in intensity (Nowacki and Abrams 2008). By contrast, fire-suppressed mixed confer forests and pine savannas tend to increase flammability, at least temporarily, due to increases in density and continuity of fuels (Keeley et al. 2009).

1.3. Prescribed fire as a restoration tool

Restoration of the structure and composition of fire suppressed ecosystems

involves returning fire to the landscape, usually via prescribed burning (Mutch 1994,

Brose et al. 2001, Abrams 2005). Prescribed fire programs are often highly successful.

For example, in midwestern oak savannas and southeastern pine savannas, frequent prescribed fires conducted over long time periods (> 15 years) have been particularly effective in restoring open park-like structure, reducing populations of encroaching woody vegetation, increasing populations of fire-tolerant species, and increasing overall biodiversity (White 1983a, Waldrop et al. 1992, Peterson and Reich 2001).

However, there are also many cases in which prescribed fires fail to effectively

restore degraded communities. At best, fires may have little impact. For example, fires

may alter shrub physiognomy, but not reduce shrub density (Ansley and Castellano

2006), or fires may not increase the abundance and diversity of plant groups targeted for recovery (i.e. grasses, forbs, or native species) (Lett and Knapp 2005, Kerns et al.

2006), or both (Pendergrass et al. 1998). At worst, prescribed fires may even facilitate woody plant encroachment (Olson and Platt 1995, Heisler et al. 2003) or cause increases

in invasive species (Hobbs and Huenneke 1992, Kerns et al. 2006).

A complex suite of factors can account for these restoration failures (Briggs et al.

2005). One major reason prescribed fires may not be effective is that the ecosystem may have changed such that natural fire no longer has the desired effects. For example, the landscape may now be filled with invasive disturbance-dependent species that are

likely to dominate following fire (Hobbs and Huenneke 1992, MacDougall et al. 2006).

Or, the ecosystem may have reached a threshold that cannot be reversed by return of

disturbance processes alone (Nielsen et al. 2003, Briggs et al. 2005, Peters et al. 2006).

This may be the case in some grassland and savanna ecosystems where encroaching shrubs are so well established that they are now resistant to disturbance; large root reserves allow established shrubs to persist even under an annual fire regime (Briggs et al. 2005). Further, the pyrogenicity of the ecosystem may have changed such that fires

are less intense and less likely to spread, such as the reduced flammability of now maple dominated former oak forests (Abrams 2005, Nowacki and Abrams 2008) and the reduction of grassy fuels in grasslands where grazing and fire interact (Briggs et al. 2005,

Havstad and James 2010).

Another reason fires may have different than expected effects is that prescribed fires may deviate from the mean natural or historical regime to which plants are adapted (Platt 1994, Bond and van Wilgen 1996). Regime characteristics such as frequency, intensity, and season tend to be particularly important in this respect. For example, in ecosystems where fire was historically frequent, a single fire may have little impact (Arthur et al. 1998, Havstad and James 2010), and low frequencies can lead to formation of a dense shrub or sapling layer (Peterson and Reich 2001, Heisler et al.

2003). In addition, fires lower in intensity than the historical mean may have minimal impact on forest structure, but fires higher in intensity may injure or kill the very species they intend to protect (Fule et al. 2004, Varner et al. 2005, Fule et al. 2006). Finally, fires in the spring and early summer, when root carbohydrates are at a seasonal low, tend to inflict greater harm to fire-sensitive woody plants compared to fires in the fall and winter, when plants are dormant (Brose and Van Lear 1998, Huddle and Pallardy 1999,

Drewa et al. 2002). Dormant season fires can actually stimulate sprouting, even of fire-

sensitive woody plants, especially in ecosystems characterized by a lightning-initiated

fire regime in which fires naturally occur in the growing-season (Glitzenstein et al. 1995,

Drewa et al. 2002, Slocum et al. 2007).

Even when the fire regime and the general context for restoration are well

understood, managers must balance restoration goals with the risks and costs

associated with applying prescribed fire (Schwartz and Hermann 1997). Public perception is often already skeptical of prescribed burns, so considerable effort is made to conduct burns under conditions that minimize the risk of fires escaping or generating large amounts of smoke (Brunson and Evans 2005, McCaffrey 2006). Also, concern over the effects of fire and smoke on endangered flora and fauna may limit the season, intensity, or frequency of burns (Robbins and Myers 1992, Dickinson et al. 2010). Safety of the prescribed fire crew is also a concern that may contribute to a preference for conducting burns when conditions are likely to be less physically taxing – such as the dormant season (fall – early spring) when temperatures are lower (Bowden 2009).

Finally, prescribed fires can only be conducted when weather conditions and logistical

constraints, such as burn crew availability, permit (Dettman and Mabry 2008, Bowden

2009). All of these issues can make mimicking a natural or historical fire regime

particularly challenging. Further, they make it difficult to apply fire over large enough

areas and frequently enough to be relevant.

Thus, regardless of our understanding of the context in which prescribed fire is

being used, evaluating its effectiveness in restoring structure, reducing encroachment,

and fostering the growth and abundance of fire-dependent plant groups is essential.

The goal of this dissertation is to evaluate the efficacy of current burn prescriptions in

oak-dominated plant communities of the Bluegrass Region in southern Ohio. The use of

fire management in this study system is fairly recent. The effectiveness of current burn

prescriptions on reducing the encroaching woody plants has not been quantitatively

evaluated.

In the next section I summarize the context for using prescribed fire in this study

system, and I explain my general experimental approach for evaluating the effectiveness of prescribed fire.

1.4. Fire ecology of oak-dominated ecosystems in southern Ohio’s Bluegrass Region 1.4.1. Regional vegetation history The Bluegrass Region of the Interior Low Plateau extends north into southern

Ohio primarily in Adams County. This Region is characterized by rolling to steep

topography overlain by Ordovician and Silurian limestone, dolomite, and calcareous

shale (Anderson 1983). Hilltops and ridges are topped with Devonian and Mississippian

shale and sandstone more typical of the Allegheny Plateau to the east (Braun 1928b).

The Bluegrass Region was not covered by the Illinoian and Wisconsinan glaciers, but

their boundaries were close enough to affect drainage patterns, climate, and vegetation

(Braun 1928b). Pollen data from sediments indicate broad shifts in vegetation have

occurred across the region since the maximum extent of Wisconsin glaciations, ca.

18,000 years before present (BP) (Hutchinson et al. 2003a). Forests in the Pleistocene and early Holocene were dominated by boreal forest mixed conifers including spruce

(Picea) (Delcourt et al. 1998, Williams et al. 2004). As the Laurentide ice sheet retreated, hardwoods and hemlock (Tsuga) became more abundant, and by ca. 7,000 BP the boreal forest was replaced by temperate deciduous forest (Webb 1981, Delcourt et al.

1998, Williams et al. 2004). According to these pollen records, oaks (Quercus) became abundant ca. 10,000 BP, dominant ca. 4,000 BP, and have remained so until modern times (Delcourt et al. 1998, Williams et al. 2004).

The temperate deciduous forests of this region are currently described as mixed mesophytic – meaning that they exhibit an array of canopy dominants and no clear dominant tree species over their entire range (Braun 1950, Dyer 2006). A variety of upland oak-dominated communities, including forests and savanna-like barrens, currently occur in the Bluegrass Region (Braun 1928b). The oak-dominated forests in southern Ohio were characterized by a variety of disturbances including fire, ice and wind storms, and effects of the now extinct passenger pigeon (Ellsworth and McComb

2003, Rentch et al. 2003b).

The region also has a long history of anthropogenic activity. Much of which is similar to that already described for the Allegheny Plateau just to the east (McCarthy et al. 2001, Hutchinson et al. 2003a). Southern Ohio has evidence of human occupation reaching back at least 10,000 years (Knepper 1989), including an earthworks currently called Serpent Mound located ca. 7 km southwest of one of my study areas. The influence of Native American cultures on the environment is generally not well

understood and an issue of disagreement, but there is evidence to suggest that these cultures both practiced agriculture and applied fire (Delcourt et al. 1998, McCarthy et al.

2001).

The impact made by Euro-Americans on the landscape is more clearly understood. Euro-American settlement of Adams County began in the in late 1700s and early 1800s (Evans and Stivers 1900). Many forests were cleared for agriculture; others were clearcut for charcoal production and mining (Evans and Stivers 1900, Braun

1928b). The latter occurred between 1811 and 1840 to supply several furnaces and at least one foundry for the iron ore smelting industry in Adams County (Evans and Stivers

1900, Braun 1928b). The land-use history between the phasing out of charcoal furnaces and the earliest aerial photographs (1937) is not clear. Analysis of historical census data from throughout the eastern deciduous forest indicate continuing widespread forest clearing from 1880 through 1930 (McEwan et al. 2011). Further, many current oak- dominated stands in southern Ohio initiated during this period of intense anthropogenic disturbance (McEwan et al. 2007). Thus, almost all current forests in the region are second-growth, established following several stand-replacing disturbances, and have experienced grazing and selective harvesting of trees, as indicated by frequent barbed wire and tree stumps (R. McCarty and D. Minney, The Nature Conservancy [TNC], 2005, personal communication).

1.4.2. Mixed-oak forests Oak-dominated forests in southern Ohio occur on hillsides and upland areas of the landscape and are underlain by a variety of substrates ranging from alkaline to acidic

(Braun 1928b). Various mixtures of oak (such as Quercus alba, prinus, velutina, and rubra) compose the overstory of these forests along with other species more and less tolerant of shade (Anderson and Vankat 1978, Dyer 2001). Multiple studies throughout the eastern deciduous forest biome comparing presettlement surveys to modern censuses indicate that mixed-oak forest composition has shifted from oak dominance to dominance by more shade-tolerant species, especially maples (Acer spp.) (Fralish et al.

1991, Dyer 2001, Rentch and Hicks 2005, Nowacki and Abrams 2008). This oak-to-maple transition is also an ongoing trend in current old-growth and second-growth oak forests

(Goebel and Hix 1996, 1997, Abrams 1998, McCarthy et al. 2001). Although oaks often dominate the canopy layer in these forests, they are scarce in understory layers; in contrast, maples are present in the canopy and extremely abundant in small size classes, broadly indicating recruitment failure of oaks and success in maples (Lorimer et al. 1994,

Goebel and Hix 1996). However, this shift in composition has not been quantitatively documented in oak-dominated forests in southern Ohio’s Bluegrass Region.

Changing fire regimes are invoked as the primary driver of oak regeneration failure and the shift from oak to maple dominance. Once-frequent surface fires are thought to have deterred recruitment of fire-sensitive shade-tolerant hardwoods, such as maples, and fostered the regeneration of oaks, hickories, and other species less tolerant of shade, in part, by maintaining an open forest structure (Abrams 1992,

Goebel and Hix 1996, Abrams 2003). Following fire suppression since the 1930s, maples have been able to recruit unimpeded. Their dense understories have reduced light to levels that suppress oaks and other species less tolerant of shade (Lorimer et al. 1994,

Rentch et al. 2003b). However, some research suggests that the relative importance of fire suppression in driving the oak-to-maple transition has been overemphasized

(McEwan et al. 2011). Rather, changing fire regimes have likely acted in synergy with other factors including a change to a moister climate, loss of American chestnut, and population booms in acorn browsers (McEwan et al. 2011).

1.4.3. Barrens Oak barrens and prairie communities in the Bluegrass Region are located within a matrix of oak-dominated forest and are thought to be relics from the eastward expansion of the prairie peninsula ca. 7000-5000 BP during the warm and dry hypsithermal period (Braun 1928a, Transeau 1935, Webb 1981, Rhoades et al. 2004).

The barrens in the Bluegrass Region have a savanna-like structure, with a sparse, often stunted, overstory of chinquapin oak (Quercus muehlenbergii). They have alkaline soil underlain by dolomitic bedrock. Soil depth varies across small spatial scales, ranging from 1 m deep to exposed bedrock in a distance of <10m (Petersen and Drewa 2009).

The groundcover contains a rich assemblage of forbs, grasses, and shrubs, many of which are also characteristic of tallgrass prairies and other oak savannas of the

Midwestern US (Braun 1928b, Leach and Givnish 1999, Petersen and Drewa 2009).

The structure and composition of savannas and barrens throughout the

Midwestern US are highly influenced by disturbance (Nuzzo 1986). They are also among the most endangered ecosystems because of conversion to agriculture, especially on deep-soiled mesic sites, as well as decades of fire suppression (Heikens and Robertson

1994, Bowles and McBride 1998, Anderson et al. 1999). Following fire suppression,

15 shrubs and trees increase in abundance, understory light levels decrease, and herbaceous vegetation becomes dominated by shade-tolerant species (Nuzzo 1986,

Bowles and McBride 1998). In the Bluegrass region of southern Ohio, severely degraded barrens often have a canopy dominated by Juniperus virginiana (eastern red cedar), a conifer that is fire sensitive when small, but tolerates surface fires as an overstory tree

(Annala et al. 1983).

1.4.4. Fire regime Although fire is recognized as an important disturbance process in oak forests, savannas, and prairies in southern Ohio and across the eastern deciduous forest biome, there is still much that is unclear about its history and characteristics. Inferences about the past fire regime in the eastern US are drawn from a variety of sources including paleoecology, dendrochronology, historical accounts, and contemporary fire records

(Yaussy and Sutherland 1994, Ruffner 2006). Sediment charcoal profiles indicate that fires have occurred in the eastern deciduous forest biome for at least the last 10,000 years (Delcourt et al. 1998). Although these charcoal profiles demonstrate the importance of fire over broad temporal and spatial scales, they do not have fine enough resolution to provide details on fire frequency, season, or intensity – this information must be obtained from fire scars on trees or inferred from the behavior of contemporary fires (McEwan et al. 2011).

It is widely acknowledged that the prehistoric and historic fire regime in the eastern deciduous forest biome was driven by anthropogenic ignitions (Sutherland

1997, Hicks 2000, Brose et al. 2001, Guyette et al. 2002, Nowacki and Abrams 2008). In

16 general, it is thought that fires caused by Native Americans resulted in periodic low- intensity surface fires prior to Euro-American settlement (Brose et al. 2001). Attribution of presettlement fires to Native Americans is based on the association of charcoal and domesticated plant pollen with occupied archeological sites for over 3,000 BP (Delcourt and Delcourt 1998, Delcourt et al. 1998), the accounts by early European travelers close to the time of settlement (Day 1953), and the fact that < 1% of contemporary fires are ignited by lightning (Guyette et al. 2002). However, there is little fire-scar evidence available to reconstruct the presettlement fire regime because most of the eastern deciduous forests were cleared after settlement (Ruffner 2006, McEwan et al. 2011).

This is especially the case in southern Ohio, where the presettlement fire record comes from a single windblown old-growth tree (McCarthy et al. 2001). This record spans

1624-1997, but indicates a period of fire activity just around the time of settlement from

1731- 1881, and only six presettlement fires (pre-1780) (McCarthy et al. 2001).

Most of the evidence for the presttlement fire history comes from fires scar studies at the western margin of the eastern deciduous forest biome. Collectively, the fire return interval found in these studies ranges from 2-24 years (Shumway et al. 2001,

Ruffner 2006, McEwan et al. 2011). It has been suggested that the temporal variation in fire history in landscapes dominated by anthropogenic fire regimes is related to temporal patterns in human population densities and cultural development (Guyette et al. 2002). For example, in an analysis of fire scar and human population data from the

1650s to the 1990s from the Missouri Ozarks, Guyette et al. (2002) demonstrate that reduced fire frequencies in the century and a half prior to Euro-American settlement

were related to the decimation of indigenous populations by plagues, while the increase

in frequency in the decades immediately prior to Euro-American settlement were

associated with the migrations of Native American populations into the region. Thus,

different sites and regions likely experienced different presettlement fire regimes, and

we do not fully understand the degree to which the records we do have represent the

average presettlement fire regime or its variation across the landscape.

The fire history after Euro-American settlement is clearer. Throughout much of

the eastern deciduous forest biome, fires increased in frequency, and sometimes

intensity, following settlement, then decreased following programs to prevent and

suppress fire that began in the early 1900s (Abrams 1992, Brose et al. 2001, Ruffner

2006). However, some regions and locations experienced decreases in fire frequency from the onset of settlement (Abrams 1992, Ruffner 2006). In southeastern Ohio and

eastern Kentucky, fires scars from trees in stands that initiated after 1850 suggest a

period of frequent fires followed by a period in which fires rarely occurred, beginning in

the 1930s – the start of active fire suppression in this region – and continuing to the

present (Sutherland 1997, McEwan et al. 2007). These post-settlement fires were

widespread across the landscape and had an average return interval (time since last fire)

of about 7.5 years, ranging from 2-16 years (McEwan et al. 2007).

Information regarding the seasonal timing of fires also comes from these post- settlement and contemporary fires. In southern Ohio, and in much of the eastern deciduous forest, fires primarily occur and cover the greatest area at two times of year: during the fall (October-November) and in the early spring (March-April) (Haines et al.

1975, Yaussy and Sutherland 1994). In post-settlement stands from southeastern Ohio

and eastern Kentucky, most fire scars formed in the dormant season – after trees had

stopped forming vascular cambium in the fall, but before they started growing again in

the spring (Sutherland 1997, McEwan et al. 2007). Fewer scars formed during spring

growth and even less formed in the summer (Sutherland 1997, McEwan et al. 2007).

This also corresponds with the presettlement fire scars from 20 trees at a site in western

Maryland (Shumway et al. 2001). Fires are thought to be less likely in the summer because humidity is high, vegetation is green, and closed forest canopies keep fuels shaded and retain moisture, making the likelihood of fire propagation low (Haines et al.

1975). Interestingly, fire occurrence and extent is not related to climate patterns; fires were just as likely to occur in years with and without drought (Yaussy and Sutherland

1994, Sutherland 1997, McEwan et al. 2007). This has been taken as further evidence

that the fire regime is driven by humans rather than weather.

Lightning is also an ignition source for fires in eastern deciduous forests, but

characteristics of lightning-initiated fires have received relatively little attention (Ruffner

and Abrams 1998, Cohen and Dellinger 2006). In contrast to the season of

contemporary anthropogenic fires in southern Ohio, Petersen and Drewa (2006) hypothesized that lighting-initiated fires would have occurred in the growing season, primarily in July and August, when a high frequency of lightning strikes coincide with extended dry periods. This corresponds with the seasonal timing of lightning strikes and tree ignitions in archival and recent records in Pennsylvania (1914-1917 and 1960-1996;

Ruffner and Abrams 1998), lightning-ignitions in the Great Smokey Mountains National

Park in the southern Appalachians (1940-2006; Cohen et al. 2007), and the confirmed

lightning fires that do currently occur in Ohio (1993-2005; M. Bowden, Ohio Department

of Natural Resources [ODNR], unpublished data). Collectively, these data suggest that

the variability in seasonal timing of lightning-initiated fires deserves more attention; the

lightning strikes in Pennsylvania peaked in July (Ruffner and Abrams 1998), but the

lightning-ignitions in the GSMNP occurred most frequently in the early growing season,

especially April and May (Cohen et al. 2007).

The role of lightning-initiated fires within the landscape may have been greatly

underestimated (Cohen and Dellinger 2006). They are generally considered relatively

unimportant (McEwan et al. 2011), in part, due to their limited role in the contemporary

landscape, in which they occur infrequently and are typically small in spatial extent. For

example, confirmed lightning ignitions account for only 0.5% of the fires reported and

0.13% of the total area burned in lands under fire protection in southern Ohio (1993-

2005; M. Bowden, ODNR, unpublished data). These fires burned 0.6 ha on average and

ranged in size from 0.004-10 ha before being extinguished. These are smaller than

suppressed lightning fires for the Great Smokey Mountains National Park (Cohen et al.

2007). Further, lighting-ignited fires are thought to be unlikely to propagate because

they occur in the summer and are typically accompanied by rain (Haines et al. 1975).

This assumption contrasts with the information collected on ten unsuppressed lightning

fires that occurred from 1998 – 2006 in the Great Smokey Mountains National Park under their wildland fire use policy, which allows lightning-caused fires to run their

course from ignition to natural extinction when it is safe to do so (Cohen et al. 2007).

These fires lasted for long durations and persisted through precipitation events. They

remained dormant in damp conditions, smoldering in trees and damp fuel, but could

became active when conditions changed.

Thus, despite their current scarcity in an anthropogenically altered landscape, the role of and variability in lightning caused fires deserves more attention. This is

especially the case, given the suggestions that the differences in seasonal timing of

lightning fires compared to most current anthropogenic fires (growing versus dormant)

could result in different effects on vegetation (Petersen and Drewa 2006).

1.4.5. Current burn prescriptions Prescribed fires in the eastern deciduous forest biome are typically conducted in

the dormant season, either in the fall or spring, in part, to mimic the historic fire regime

(Hicks 2000, Brose et al. 2001). Frequency of fire varies greatly from system to system.

Burns are typically planned for moderate conditions that allow fires to propagate, but

minimize the likelihood of escape or severe damage (e.g. when litter is dry enough to

burn, but wind speed is < 10 mph and relative humidity is > 30 %) (Schwartz and

Hermann 1997, Bowden 2009). Thus, prescribed fires tend to be low in intensity, and

may have minimal effects on reducing woody plant encroachment (Briggs et al. 2005,

Haney et al. 2008).

1.5. General experimental approach The most straightforward way to test the effectiveness of a particular fire regime

in restoration is to apply it and use plant responses as your indicator (Beckage et al.

2005, Slocum et al. 2007). If fire-dependent populations recover and encroaching

populations decrease, it is likely that you have approximated the natural or historic

regime. However, one must realize that there is usually no proper control in such an experiment (Platt et al. 1988). Ideally, one would want to compare sites managed with

prescribed fire to sites with an intact disturbance regime, but these seldom exist.

Further, a condition of no fire, while useful as a comparison state, is not a true control

because the absence of fire is itself a manipulation to which plants are responding.

A more informative manipulation for comparison is the application of a fire

surrogate – such as thinning, weeding, or ash application – which mimic the

hypothesized mechanisms driving vegetation response to fire. For example, MacDougall

and Turkington (2007) tested the effectiveness of current burn prescriptions in reducing

the dominance of invasive grasses in a degraded oak savanna by comparing fire to both

a mowing and a weeding treatment. Comparing fire to a fire surrogate can also serve a

practical role. The windows for conducting prescribed burns are often temporally

narrow. If a fire surrogate and fire are similarly effective, the surrogate may be

considered for substitution when prescribed fire is not possible. This is not to imply that

a fire surrogate is a complete substitute for the effects of repeated fires. Fires have

many effects, such as litter reduction, nutrient release, and char deposition that may

not be mimicked by a fire surrogate (Rooney and Leach 2010). However, it would

provide land managers with an option to apply a perturbation to a disturbance-

dependent system when they otherwise might not be able.

In this dissertation, I will compare low-intensity dormant-season fire treatments

to a clipping treatment. Clipping has been employed as a fire surrogate in numerous

studies testing the effectiveness of fires in reducing encroaching woody vegetation in

seedling and shrub layers (Matlack et al. 1993, Calvo et al. 2005, Tix and Charvat 2005).

In these studies, clipping treatments act like fires with neither a heating component nor intensity; they mimic the topkill of woody stems, but leave dormant buds and underground organs intact (Drewa 2003). If the plant response to clipping and fire is similar, this indicates that the effect of fire is primarily caused by topkill and temporary removal of aboveground biomass, not injury to dormant buds or release of nutrients.

1.6. Study sites 1.6.1. Location and region The studies in this dissertation were conducted in southern Ohio at the Edge of

Appalachia Preserve (38° 45' N, 83° 24' W) and Strait Creek Prairie Bluffs Preserve (39°

03' N, 83° 21' W) (Figure 1.1). At the time of this writing, Strait Creek Prairie Bluffs

Preserve (Strait Creek, SC), in Pike, Adams, and Highland counties, was approximately

250 ha and owned and managed by The Nature Conservancy (TNC). The Richard and

Lucile Durrell Edge of Appalachia Preserve System (the Edge) is an approximately 5660 ha system of eleven contiguous preserves in Adams County, and is jointly owned and managed by TNC and The Cincinnati Museum Center.

The climate in the region is temperate continental, with annual precipitation averaging 112 cm (1971-2000; Ripley Experimental Farm and Hillsboro stations) and evenly distributed throughout the year (NCDC, 2004). The average annual temperature is 11°C, and the mean monthly maximum and minimum temperatures occur in July

(23°C) and January (-2°C), respectively (1971-2000) (NCDC, 2004).

These preserves are located within the Bluegrass Region of the Interior Low

Plateau, but at its eastern edge along the Appalachian Escarpment, or what Braun

(1950) called the Knobs Border subdivision of the Bluegrass Region (Swinford 1985). The

Appalachian Escarpment defines the edge of the Appalachian Plateau in Ohio and occurs at the border of two physiographic provinces – the Allegheny Plateau to the east and the Interior Low Plateau to the west (Swinford 1985). Both physiographic provinces are unglaciated, but the Allegheny Plateau has a more rugged and dissected topography, while the Interior Low Plateau has a mixture of rolling and steep terrain. The specific study sites used for this dissertation reflect this combination of topography as well as the range in substrates, soils, and aspects found in the region. However, all sites were restricted to upland locations – slopes, ridges, and plateaus – rather than valley bottoms, and their elevations fall within a narrow range of 220 to 300 m.

Figure 1.1 Locations of the Edge and Strait Creek preserves in southern Ohio.

1.6.2. Barrens study sties I used three oak barrens sites located at 232-244 m in elevation on south-facing

slopes for my study: one in the Santoro unit of the lynx prairie complex at Edge of

Appalachia (Edge) and two located at Strait Creek, (Strait Creek A; SCA) and (Strait Creek

B; SCB) (Table 1.1). Soils are well-drained, eroded, alkaline silt clay loams of a Bratton-

Opequon complex and underlain by Silurian dolomitic limestone (Braun 1928b, Soil

Survey Staff, 2008; http:websoilsurvey.nrcs.usda.gov/). This dolomitic bedrock is often

very close to the spoil surface and even exposed. Thus, soils are shallow, averaging 25.5

cm (±2.7 S.E.) deep, but also vary considerably in depth ranging from 2 to 136 cm in the

sampling units used in this study (Petersen and Drewa 2009). The study areas are

characterized by a sparse overstory, dominated by Quercus muehlenbergii (chinquapin

oak) and Juniperus virginiana (eastern red cedar) . Liriodendron tulipifera L. (tulip

poplar), Acer saccharum (sugar maple), and Fraxinus spp. (ash species) are also common

(Petersen and Drewa 2009). Overstory density and basal area average 309 stems∙ha-1

and 11.2 m2∙ha-1, respectively, but differ between sites (Table 1.1). The groundcover

consists of a rich assemblage of forbs, graminoids, shrubs, and woody vines that can be

found in both woodlands and prairies (Petersen and Drewa 2009).

Table 1.1 Locations and characteristics of oak-barrens sites adapted from Petersen and Drewa (2009). Site Preserve Location Size Elevation Slope* Canopy layer name (longitude, latitude) (ha) (m a.s.l) (%) BA (m2∙ha-1) Density (Stems∙ha-1) Edge EOA 38° 45' N, 83° 24' W 1 232 5 222 10.3 SCA SC 39° 03' N, 83° 21' W 1 244 26 408 13.0 SCB SC 39° 03' N, 83° 22' W 0.5 238 57 397 11.6 Annotations: *Slope represents the general landscape level values; EOA = Edge of Appalachia Preserve System; SC = Strait Creek Preserve; m a.s.l. = meters above sea level; BA = Basal area.

These sites also share a similar land use and management history (Petersen and

Drewa 2009). Briefly, they were likely grazed in the past, given the presence of barbed- wire. More recently, the site at the Edge was used as a campsite before acquisition by the TNC. However, aerial photographs from the US Department of Agriculture dating back to 1937 indicate that no clearcutting has occurred in the last seven decades because canopy trees were present in these barrens since the earliest photographs.

These photographs also provide evidence for an increase in the abundance of woody plants over time, especially eastern red cedar (Annala et al. 1983). Since the mid-1990s these sites have been actively managed to restore the structure to a more open condition using prescribed fires, canopy cedar removal, or a combination the two (D.

Minney, Southern Ohio Land Steward, The Nature Conservancy, 2003, personal communication). Sites have experienced from two-six prescribed fires conducted during the dormant season (October through mid-April), the most recent occurring two years prior to the start of my study (D. Minney, TNC, 2003, personal communication).

1.6.3. Forest study sites I used four forested study sites. Sandstone and Hopkins are located 1.5 km apart at Strait Creek Preserve, and Cedar Falls and Bethany are located in the Edge of

Appalachia Preserve, and are 25 and 30 km south, respectively, of the Strait Creek sites

(Table 1.2). These study sites range from about 2-4 ha and each consists of two burn management units established as part of a larger study investigating fire effects on groundcover vegetation. Sites were not burned by TNC prior to or during my studies.

Sandstone and Bethany are divisible into the two burn units by minor drainages, and

Cedar falls and Hopkins are divided along larger anthropogenic or geographical features.

Cedar falls is divided into western and eastern units, 0.90 km apart, by two ravines and a wooded plateau, and Hopkins is divided into northern and southern units, 0.15 km apart, by a power line cut.

These study sites differ in physical characteristics such as topography, aspect, and bedrock (Table 1.2). Except for Cedar Falls, which is relatively flat, the sites are located on steep to rolling slopes which encompass a variety of aspects. They are underlain by non-calcareous shale (Hopkins and Bethany) of Devonian origin,

Mississippian sandstone (Sandstone), and Silurian dolomitic limestone (Cedar Falls)

(Braun 1928b, Anderson 1983). Overall, soils are formed of residuum, and to a lesser extent colluvium, and the upper layers are silt loams with moderate to very low water holding capacity (Soil Survey Staff, 2010; http:websoilsurvey.nrcs.usda.gov/). The major soil associations and complexes at the sites include the Shelocta-Muse-Colyer association at Bethany, Trappis-Shelocta association at Sandstone and Hopkins, and the alkaline Bratton silt loam and Bratton-Opequon complex with high calcium carbonate content at Cedar Falls and possibly some small patches at Hopkins (Soil Survey Staff,

2010; http:websoilsurvey.nrcs.usda.gov/).

The present forest vegetation of the Bluegrass Region and Appalachian

Escarpment is classified in the mesophytic forest association (Braun 1950, Dyer 2006).

Mesophytic forest is characterized by the lack of a consistent canopy dominant across the region, but Quercus alba (white oak) and maples, especially Acer rubrum (red maple), are currently prominent in many stands in the region (Dyer 2006). The structure

and composition of my forests sites will be the subjects of Chapters 3 and 4. Briefly, all

sites are mature (over 70 years old, given their presence in aerial photographs from

1937) second-growth stands where the largest trees are oaks (Quercus). Oaks are also the most important species in the overstory at Bethany, Hopkins, and Sandstone, although different species of oaks dominate each site. At Cedar Falls, Acer saccharum

(sugar maple) is the canopy dominant and oaks are relatively rare. The understory layers are dominated by maples (Acer saccharum and Acer rubrum) at all sites, but the

dominant maple species differ between sites.

These forest sites have not been actively managed by the TNC, but they do bear evidence of past land use. No evidence of clearcut logging has occurred at my sites since at least the earliest aerial photographs (1937) from the USDA, Soil Conservation Service.

But past selective logging is apparent from decaying stumps present at Cedar Falls and

Sandstone. Also, anecdotal evidence suggests that some of the sites were used for

grazing and may have been burned prior to acquisition by The Nature Conservancy (R.

McCarty, Edge of Appalachia Preserve Manager, personal communication). Sandstone

and Cedar Falls also have evidence of refuse dumping within the last three decades as

well as much earlier in the western burn management unit at Cedar Falls. The western

burn management of Cedar Falls is also within 1 km of Brush Creek Furnace, which had a

forge in operation from ca. 1811- 1830 (Evans and Stivers 1900). Iron ore was extracted

from this burn management unit via surface mining as evidenced by 1-3-m-deep pits

outside my sampling area (personal observation; R. McCarty and D. Minney, 2004,

personal communication).

Table 1.2 Locations and characteristics of oak-dominated forest sites and burn management units within each site. Site Preserve County Burn unit Location Size Elevation (m a.s.l) Aspect* Slope* Bedrock label, location (longitude, latitude) (ha) min-max (range) (%) Bethany EOA Adams 2, west 38° 47' 13" N., 83° 23' 31" W. 1 250-275 (25) 160° (SE) 25 shale 1, east 38° 47' 9" N., 83° 23' 27" W. 1 255-262 (7) 160° (SE) 25 shale Cedar Falls EOA Adams 2, west 38° 49' 47" N., 83° 23' 40" W. 2 230-233 (3) horizontal 2 dolomite 1, east 38° 49' 51" N., 83° 22' 54" W. 2 223-226 (3) horizontal 2 dolomite Hopkins SC Pike 1, south 39° 3' 52" N., 83° 22' 14" W. 1 270-275 (5) 270° (W) 20 shale 2, north 39° 3' 59" N., 83° 22' 13" W. 1 273-287 (14) 330° (NW) 45 shale Sandstone SC Pike 1, east 39° 3' 28" N., 83° 23' 4" W. 1.5 249-282 (33) 30° (NE) 30 sandstone Highland 2, west 39° 3' 29" N., 83° 23' 11" W. 1.5 226-267 (41) 30° (NE) 30 sandstone Annotations: * Aspect and slope represent the general landscape level values and do not reflect the microtopography at the level of the sampling plot. Slope was measured from the lowest to highest plot within each unit, and aspect was measured from the middle of each unit. EOA = Edge of Appalachia Preserve System, SC = Strait Creek Preserve, m a.s.l. = meters above sea level. 29

1.7. Organization of dissertation This dissertation contains four separate but interrelated studies that describe the vegetation of oak-dominated ecosystems in the Bluegrass Region of southern Ohio and evaluate the efficacy of prescribed fire in reducing woody plant encroachment in these systems. In Chapter 2, I test the effects of biennial fire and a fire surrogate

(clipping) on woody and herbaceous vegetation abundance in oak barrens. In Chapters 3 and 4, I document the characteristics of oak-dominated forests prior to reintroducing fire: Chapter 3 is a quantitative description of the vegetation-environment relationships in the seedling layer, and Chapter 4 is a description of overstory composition and structure. In Chapter 5, I test the effects of treatment season (fall versus spring) and treatment type (prescribed fire versus a fire surrogate) on forest seedling community composition and abundance. Finally, I conclude with a summary of my findings and a reflection on their significance in Chapter 6.

Chapter 2: Effects of biennial fire and clipping on woody and herbaceous ground layer vegetation in oak barrens of southern Ohio 2.1. Abstract Oak barrens are among the most endangered ecosystems in North America due,

in part, to alterations of disturbance regimes that maintained their open structure and

fostered a diverse herbaceous ground flora. In the absence of frequent fires, woody

plants expand and decrease the abundance and diversity of herbaceous species.

Frequent low-intensity fires are used to restore the open canopy structure of savannas and barrens, but less is understood about how such fires affect the dynamics of woody

ground layer populations or their interactions with herbaceous plants. From 2003-2007,

I compared biennial burn prescriptions to a fire surrogate – clipping – and tested the

effectiveness of both in impeding woody ground layer vegetation and in promoting herbaceous plant functional groups (forbs and graminoids) using three oak barrens sites in the Bluegrass Region of southern Ohio. Biennial fire and clipping treatments did little to suppress the resprouting ability of shrubs, which regrew rapidly and in equivalent densities following fire and clipping. However, burning and clipping both reduced shrub aerial cover, resulting in a 35% decrease in shrub cover over the course of the study. In contrast, non-manipulated plots experienced a 44% increase in shrub cover over the same time period. Despite this reduction in shrub cover, fire and clipping treatments had no effect on herbaceous plant aerial cover, richness, diversity, or evenness. These results suggest that the current use of prescribed dormant season fire, as employed in this study, is equivalent to clipping, and although effective at temporarily reducing shrub

cover, is not effective in deterring the proliferation of encroaching woody ground layer

vegetation. Land managers seeking to reduce shrub cover may want to consider clipping in lieu of fire when weather and logistics preclude the use of fire. Refinement of future burn prescriptions should also consider high intensity fires or growing season fires which

both may be more effective than low-intensity dormant-season fires in reducing the

resprouting potential of woody vegetation.

2.2. Introduction Oak barrens have a vegetation structure similar to savannas, intermediate

between prairies and forests, with a continuous ground cover of herbs and grasses and

typically a scattered canopy of trees (Heikens and Robertson 1994, Homoya 1994,

Anderson et al. 1999). This open structure is maintained by a suite of factors including edaphic restrictions, grazing, periodic drought, frequent disturbances (often fire), or a combination of these (Homoya 1994, House et al. 2003, Anderson 2006b). Oak barrens

are now among the most endangered ecosystems because of conversion to agriculture, urbanization, and decades of fire suppression (Nuzzo 1986, Bowles and McBride 1998,

Anderson et al. 1999). Following fire suppression in the few remnant barrens, tree cover increases and woody plant populations expand, leading to deceases in light availability and loss of light-dependent herbaceous vegetation (Bowles and McBride 1998).

Thus, restoration efforts (often involving the reintroduction of fire) have become a priority in barrens. Because the formation of a dense overstory is a chief factor limiting ground layer species diversity (Leach and Givnish 1996), the effects of frequent prescribed fire on restoring overstory structure and, in turn, promoting herbaceous

groundcover have been a focus of much research (e.g., Peterson et al. 2007, Haney et al.

2008, Taft 2009). However, less information is available on how fire in barrens affects woody plants within the ground layer itself and how such changes affect herb cover and diversity. These ground layer dynamics have received much greater attention in prairies, where reduction of light by ground layer shrubs and litter suppresses graminoid productivity and cover (Lett and Knapp 2003).

Fires used in prescribed burns are typically conducted under moderate conditions in the dormant season and thus tend to be low in intensity and may not be effective in restricting encroachment of woody understory vegetation (Briggs et al.

2005, Haney et al. 2008). These low intensity fires tend to topkill understory woody plants, which resprout into smaller size classes and recover rapidly (Taft 2003). Shrub and sapling layers have been show to achieve pre-burn densities within 2-4 years

(Peterson and Reich 2001, Haney et al. 2008), sometimes even increasing in density after repeated infrequent fires (Taft 2009). Thus, it is likely that the ground layer shrubs and trees are recovering even more quickly, possibly within 1-2 years. If low-intensity dormant season fires are not effective in reducing resprouting potential of ground layer shrubs, are they effective in releasing herbaceous plants?

We might better understand the mechanisms underlying the effects of these low intensity fires on ground layer woody and herbaceous plants by comparing fire effects to a fire surrogate. Clipping has been employed as a fire surrogate in numerous studies testing the effectiveness of fires in reducing encroaching woody vegetation in ground layers (Matlack et al. 1993, Calvo et al. 2005, Tix and Charvat 2005). In these studies,

clipping treatments act like fires without a heating component, mimicking the topkilling

of woody stems, but leaving dormant buds and underground organs intact (Drewa

2003). If the plant response to clipping and fire is similar, this indicates that the effect of fire is primarily caused by the removal of aboveground biomass, not damage to dormant buds or release of nutrients. Further, comparison of fire to a fire surrogate can serve a

practical role. The windows for conducting prescribed burns are often temporally

narrow. If a fire surrogate and fire are similarly effective, the surrogate may be

considered for substitution when prescribed fire is not possible.

In this study, I tested the effects of biennial dormant season treatments of fire

on groundcover woody and herbaceous plant populations in oak barrens by comparing

fires to clipping and an un-manipulated condition. The main question I addressed was:

(1) How effective are these fires in reducing woody groundcover encroachment and promoting herbaceous functional groups (forbs and graminoids)? If fires are effective, I expected to see reductions in the cover, density, growth, and richness of woody ground layer plants and increases in herb cover and diversity. I expected these responses to be transient and to rapidly return to initial conditions after burn (possibly by the second season after treatment). However, if fires are truly effective in reducing encroachment and resprouting potential of shrubs, they should have a cumulative effect over time.

This would be indicated by greater reductions in densities, cover, or diversity of shrubs following two fires, or less recovery following the second fire. Likewise, this should manifest as greater abundances or diversity of forbs and graminoids following the second fire and less return to pretreatment values after the second treatment.

I addressed two additional questions to further elucidate the effects and mechanisms of fire in barrens: (2) Is there evidence that ground layer woody plants are suppressing herbaceous functional groups in these barrens, and does this relationship change over time with burning and clipping treatments? I expected there to be a negative relationship between shrub cover, density, and richness with herbaceous variables (as indicated by negative slopes) at the onset of the study. If fire and clipping treatments are effective, then the strength of these relationships should weaken over the course of the study as herbs become less influenced by groundcover woody plants.

(3) What are the characteristics of the fires (temperature and fuel consumption) and does variation in fire behavior influence vegetation responses? If fires are low in intensity and their effects are driven by topkill, then temperature and fuel consumption should be low and have little effect on plant responses. This would be a further indication that fire and clipping have similar effects on groundcover vegetation.

2.3. Methods 2.3.1. Study sites This study was conducted at the Edge of Appalachia Preserve and Strait Creek

Preserve along the Appalachian Escarpment in southern Ohio. The study region and study sites are fully described in Chapter 1, so study site information will only be mentioned briefly here. I used three oak barrens sites located at 232-244 m in elevation on south-facing slopes for my study including the Edge, SCA, and SCB (Table 2.1). Soils are well-drained, alkaline silt clay loams and underlain by dolomitic limestone, which is

often very close to the surface. The groundcover consists of a rich assemblage of forbs, graminoids, shrubs, and woody vines that can be found in both woodlands and prairies.

Table 2.1 Locations and characteristics of sampled oak-barrens sites adapted from Petersen and Drewa (2009). Site Preserve Location Size Elevation Slope* Canopy layer (longitude, latitude) (ha) (m a.s.l) (%) BA (m2∙ha-1) Density (Stems∙ha-1)†

Edge EOA 38° 45' N, 83° 24' W 1 232 5 222 10.3 SCA SC 39° 03' N, 83° 21' W 1 244 26 408 13.0 SCB SC 39° 03' N, 83° 22' W 0.5 238 57 397 11.6 Annotations: *Slope represents the general landscape level values; † Density was measured for all trees ≥ 2 cm diameter at breast height (dbh; 1.5 m above ground level); EOA = Edge of Appalachia Preserve System; SC = Strait Creek Preserve; m a.s.l. = meters above sea level; BA = Basal area.

The study areas are characterized by an overstory dominated by Quercus

muehlenbergii (chinquapin oak) and Juniperus virginiana (eastern red cedar). These

canopy trees are spaced widely apart, giving most sites a savanna-like appearance, but

the density and basal area of the canopy varies across sites (Table 2.1). An increase in

the abundance of canopy trees, especially Juniperus virginiana, has been documented

over time (Annala et al. 1983). Since the mid-1990’s these sites have been actively

managed to restore the structure to a more open condition using prescribed fires or a

combination of prescribed fires and canopy cedar removal (per. comm., 2003, D.

Minney, Southern Ohio Land Steward, The Nature Conservancy). Sites have experienced

from two-six prescribed fires conducted during the dormant season (October -March),

the most recent occurring two years prior to the start of our study (per. comm., 2003, D.

Minney).

2.3.2. Experimental design and data collection I conducted a randomized field experiment where 1×1-m plots served as my experimental unit. I randomly positioned a total of seventy-five plots across the three oak barrens sites. Thirty plots were used in the single 1 ha site at the Edge, another 30 were used at the 1 ha site at SCA, and 15 plots were employed in the smaller 0.5 ha site at SCB. Sampling units were not located within 10 m of the interface between barrens and forest or trails. At each site, one third of the plots were randomly assigned to a clipping treatment, another third were assigned to a burning treatment, and remaining plots served as a reference condition.

Treatments were applied to 3×3-m areas centered on each plot during the 2003-

2004 and 2005-2006 dormant seasons (October-mid-April) (Figure 2.1). For the clipping treatment, all plant stems were cut at ground level and removed from the plot, surface litter was raked out of the plot, and then woody stems were placed back in the plot. The clipping treatment was intended to simulate fires with no heating component as in

Drewa (2003), thus it mimicked the reduction of groundcover fuels but the retention of topkilled woody plant stems following fire. For the burning treatment, each plot was ringed with a blackline and then headfires, or a combination of headfires and backing fires were set. A drip torch was used to burn remaining patches of vegetation. This was to ensure complete burn and topkill of all shrub-layer woody stems < 2 cm dbh; I was testing the impact of fire not the effects of patchiness of burns. Prescribed fires were conducted during marginal, but safe, burn weather when wind speeds were ≤ 8 mph and relative humidity was ≥ 30 %. In the 2003-2004 burn season, one site was burned in

December 2003, one in February 2004, and one in April 2004. However, in the 2005-

2006 burn season, burns were all conducted in April 2006 before leaf out. Clipping

treatments were applied concurrently with burns.

Figure 2.1 Timeline of treatment application and vegetation censuses.

Temperature data was collected at ground level during each experimental fire using heat sensitive tablets. Prior to each fire, I placed a set of Tempil heat sensitive tablets (Big Three Industries, Inc. Tempil° Division, South Plainfield, NJ, USA discontinued) in the center of the plot at ground level. Each set consisted of a series of tablets designed to melt at 48°C, 132°C, 212°C, 302°C, 371°C, 454°C, 538°C, 621°C, and

704°C. These tablets were each individually wrapped in aluminum foil and strung on a steel wire. Tablet sets were collected immediately after fires and individual tablets were scored as melted if any portion was melted or heavily charred. Melting temperature of the tablets was adjusted for wrapping in aluminum foil using an existing regression equation, Y = 1.24X + 25.63, where Y = actual melting temperature (°C) of wrapped tablet and X = Tempil temperature rating (°C) of the tablet (P < 0.001, r2=0.999; Drewa et al. 2002).

Fuel consumed by each experimental fire was estimated from an adjacent pair of

25 × 25-cm subplots randomly located outside, but within 1 m, of the focal plot

(Glitzenstein et al. 1995). Fine fuels (standing grass and forbs and leaf litter) were

collected from one subplot in each pair prior to burning. Following the burn, fuels not

consumed by the fire were collected from the adjacent subplot. Litter was sorted and

the biomass was measured after drying at 105°C for 24 hours. Fuel consumption (kg∙m-2) was estimated as the difference between pre- and post-burn fine fuel biomass. While coarse woody debris was collected, it was not used in fuel consumption calculations because its biomass was highly variable between adjacent subplots and it was not consumed in the fires. Further, the litter layer at these sites consisted almost exclusively of intact leaves; there was very little fermentation layer, and no duff at most fuel sampling locations.

Vegetation data were collected in the summer (July-August) five times from

2003-2007, beginning with an initial census in the growing season, before the first set of treatments, and continuing with censuses during the next four growing seasons (Figure

2.1). Aerial cover of all vascular plant species, including woody vegetation < 2 cm dbh

(diameter at breast height, ~1.5 m), was measured using a point-intercept method

(Bonham 1989, Petersen and Drewa 2009). Cover was based on the percentage of total pin drops (100) that hit a particular species. Some taxa were identified only at the genus level because of unidentifiable immature non-flowering or non-fruiting plants and included Aster spp. Carex spp., Prunella spp., and Geum spp. Nomenclature follows

Gleason and Cronquist (1991).

I collected additional measurements on shrubs and trees in the ground layer (all woody plant individuals < 2 cm dbh) of each plot. Stem densities were measured each census; five times from 2003-2007. Individual genets were not distinguished in this study, rather, each stem or ramet originating from the soil was considered an individual.

Lengths of individual woody plant stems were also evaluated five times in reference plots, but only four times in plots receiving clipping or burning treatments – they were not measured in the initial census. To ensure that the same individual was measured for length on each census, all shrub and trees stems < 2 cm dbh were individually tagged and indentified to species in the initial census (pretreatment census) during July-August of the 2003 growing season. All ground layer stems were topkilled in plots that received burning or clipping treatments during the 2003-2004 dormant season. Thus, all stems in treated plots were resprouts in 2004 (post-treatment census 1). Topkilled stems were recorded as dead and the resprouts and new recruits were individually tagged and identified to species. Plots were recensused similarly in 2005, 2006, and 2007 (post- treatment census 2, 3, and 4, respectively).

2.3.3. Response variables To assess responses by plant functional group, I classified plant species by major growth form: shrubs, forbs, and graminoids. Shrubs include any woody plant in the ground layer (with a dbh < 2 cm) and possessing a shrub or tree habit, including juvenile trees. Forbs encompassed all forbs, including legumes. Graminoids included C3 and C4 grasses as well as sedges. Woody vines, legumes, C4 grasses, and sedges were either too few in number or patchily distributed among sites and treatments for individual

analysis. Rather than incorporating woody vines with shrub layer woody plants, woody

vines were entirely excluded from analysis because they were not sampled for density

and stem length like other woody plants.

I calculated abundance (aerial cover), diversity, richness, and evenness for each

functional group in each plot. Richness and evenness were included in addition to a

standard diversity index to clarify which components of diversity were driving any

diversity patterns (McCune and Grace, 2002). Aerial cover was calculated as the

percentage of total points (out of 100) in a plot intercepted by a particular plant

functional group. Diversity was quantified using the Shannon index,

= ( ( )), ′ 푆 푖=1 푖 푖 where p퐻i is the− prop∑ ortional푝 푙푛 푝 abundance of species i, and S is the total number of species.

Richness was quantified as the number of species encountered in a plot using the point

intercept method. Evenness was quantified using Evar,

2 = 1 푆 푙푛�푥푖� �푙푛 푥푖−∑푖=1 푆 � 2 푆 퐸푣푎푟 − 휋 푎푟푐푡푎푛 �∑푖=1 푆 �

where xi is the abundance of species i, and S is the total number of species (Smith and

Wilson 1996). This index has several features that make it more tractable than more widely used evenness indices: it takes into account the variance in abundance over species, is based on proportional differences, is not dependent on units, ranges from 0-1 for minimum and maximum evenness, and has been used in other savanna studies

(Smith and Wilson 1996, MacDougall and Turkington 2007).

I calculated two additional variables to characterize woody ground layer plant

resprouting responses: density (stems ∙ m2) and growth (cm). Stem growth was

calculated for each plot by subtracting the previous year’s average length from the

current year (e.g., growth in 2004 for untreated plots was the average stem length in

2004 – average stem length in 2003). Resprout growth was calculated as the average

stem length, since all growth following clipping or burning was new (e.g., growth in 2004 for a burned plot was the average stem length in 2004).

2.3.4. Data analysis All analyses were conducted using R version 2.12.2 (R Development Core Team

2009). For all analyses α=0.05. Data were log10 (n + 1) or arcsine square root

transformed where necessary to meet model assumptions of normality and

homogeneity of variance. Model assumptions were assessed by examining residuals

versus fitted plots and normal qq-plots for the analysis as a whole and for each factor

(Zuur et al. 2009).

Treatment effects. Plant functional group responses to biennially applied

treatments were tested using linear mixed effects models in the context of a

randomized block design where a plot served as the experimental unit and time was a split plot factor (nlme package in R; Pinheiro and Bates 2000, Pinheiro et al. 2011).

Analysis was conducted separately for each functional group response variable. In all

models, plot was a random effect. Variance functions were used to model

heteroscedasticity in the within group errors for site and or treatment. Repeated

measures were accounted for using autoregressive variance-covariance structure of the

first order (AR1). Models with the best fit of random effects, variance components, and autocorrelations were found using Akaike’s Information Criterion (AIC). Blocks (sites) were entered in the fixed rather than random effects because (1) block positions were not randomized and (2) there were only three levels of the blocking factor, resulting in unstable variance estimation as a random effect (Zuur et al. 2007). After finding the

optimal random structure, the optimal fixed structure was determined from the

maximal models by backwards selection using AIC of models fit with maximum

likelihood estimation (Zuur et al. 2009).

Responses were analyzed using analysis of covariance (ANCOVA), in which pretreatment data served as a covariate and site, treatment, year and their interactions

as fixed effects. Thus, the maximal linear model for a given functional group response, Y,

in plot i at site j with treatment k in year l was:

Yijkl = µ + pi + (initial pretreatment value)1 + (site)j + (treatment)k + (site

× treatment)jk + (year)l + (site × year)jl + (treatment × year)kl + (site × (Equation 2.1)

treatment × year)jkl + εijkl,

where µ is the overall population mean, p is the plot random effect (pi ~ N(0, )), initial 2 푝 pretreatment value is the covariate, site and treatment are between plot factors,σ year is

2 a within plot factor, and ε is the residual error (εijkl ~ N(0,σ )). The assumption of parallel slopes was assessed graphically and by including interaction terms between the covariate and main fixed effects (Engqvist 2005). When response variables exhibited a significant treatment × time interaction, a second analysis was conducted in which initial

plant responses were compared to those following treatment using ANOVA, where

pretreatment data was not used as a covariate. This was done so pretreatment data

could be directly compared with responses following treatment. Stem growth was only

analyzed using ANOVA because it had no pre-treatment equivalent.

Where overall F-tests for treatment were significant (P < 0.05), post-hoc pairwise

comparisons were conducted to determine if there were significant differences between

treatment means using the multcomp package in R (Hothorn et al. 2008). Further,

following ANCOVAs with a significant treatment × time interaction, I conducted three

additional sets of planned contrasts to test for (1) biennial treatment effects, (2)

recovery after a year without treatment, and (3) differences between initial and final

census values. P-values for each set of comparisons were adjusted using a single-step

procedure which incorporates the correlations between the test statistics and controls

the family-wise error rate at alpha < 0.05 (Bretz et al. 2011).

Influence of treatments on the interaction between woody plants and herbs over time. In a third set of linear models, I tested whether the relationship between woody and herbaceous plants changed over time and with treatment. In this model, I only used data from the initial and final censuses of the study. I used a simple linear regression model, where the herbaceous plant response was the dependent variable and woody plant cover, site, treatment, and year and all interaction terms were the independent variables. Thus, this analysis was in an ANCOVA context, but one where the

continuous variable (woody plant cover) and its interactions with factors included in the

model were of primary interest. The minimal model was selected using stepwise backwards elimination using AIC as before.

Fire behavior effects. I used simple linear regression to test the hypothesis that fire behavior variables (maximum temperature and fine fuel consumption) would influence post-fire plant responses. After adjusting for pre-fire levels, each response variable in the census following fire was regressed against maximum fire temperature.

Another regression was performed using fine fuel consumption as the independent variable. Analyses were conducted separately for each set of fires. This analysis was only done for variables that had significant effect of fire treatments.

2.4. Results 2.4.1. Fire behavior Overall, maximum tablet temperatures, reflecting the fire heat budget, did not

vary much. During the first set of fires, the maximum temperature registered by the

heat-sensitive tables averaged 321 °C (± 27 SE) and ranged from 85 to 589°C. During the

second set of fires, maximum temperature averaged 298 °C (± 25 SE) and ranged from

85 to 486°C. Fine fuel consumption in the first set of fires averaged 337 g∙m-2 (± 23.41

SE), or 82%, and 261 g∙m-2 (±16.26 SE), or 76%, in the second set of fires.

2.4.2. Treatment effects on plant functional groups Shrub responses. Among shrub response variables, aerial cover, stem growth, richness, and evenness exhibited a significant effect of treatment or treatment interaction (Table 2.2). Aerial cover of shrubs exhibited a significant treatment × time

effect (Table 2.2, Figure 2.2). Aerial cover of shrubs in plots treated with fire and clipping

was equivalent and consistently less (37-70% less, depending on the year) than in reference plots (Figure 2.2). These differences between treated and reference plots were due both to decreases in cover in treated plots over time as well as increases in aerial cover of shrubs in plots not receiving manipulation; over the course of the study, aerial cover of woody vegetation decreased 35% in treated plots, but increased by 44% compared to initial levels in non-manipulated plots (Table 2.3). However, treated plots exhibited rapid recovery following each burn, increasing 24-34% in cover in the second year after each treatment, but not to levels in reference plots at the same time (Figure

2.2; Table 2.3). But there did appear to be a cumulative effect of fire; the second fire treatment reduced cover more than the first (42% reduction in cover after the second treatment) and this effect persisted even after a year of recovery (37% less cover)

(Figure 2.2; Table 2.3).

Shrub stem growth exhibited a significant site × treatment × time effect (Table

2.2; Figure 2.2). Inter-site differences aside, fire and clipping treatments had nearly equivalent effects, exhibiting spurts of growth in the year immediately after treatment

(Figure 2.2). Although stems in non-manipulated reference plots grew an average of 2 cm each year, resprouts in clipped and burned plots grew an average of 24 cm in the growing season immediately after treatment. This increased growth in treated plots was transient; the second growing season following treatment, stems in treated plots grew average lengths equivalent to reference plots (Table 2.3). However, the growth response did not significantly diminish with biennial treatment; shrubs grew equivalent average lengths after both the first and the second burning or clipping treatment (Table

2.3). Each site exhibited subtle variation in growth dynamics (Figure 2.3, Table 2.3). For example, in 2004, burned and clipped plots grew equivalent stem lengths at the Edge, but at SCB, burned plots grew an average of 50% greater than clipped plots. Despite this variation in growth by site, the overall trend in growth over time with treatment appears consistent.

Treatment × time also had a significant effect on the species richness of shrubs

(Table 2.2, Figure 2.2). At the end of the study, burned plots had, on average, one less species than reference plots (Figure 2.2). This effect was due primarily to an increase in species (on average, one species) in reference plots over the course of the study (Table

2.3). However, this interpretation is somewhat blurred by the fact that the ANOVA model on which this inference is based indicated a significant site × treatment × time effect (Appendix B Table 2.8). Multiple comparisons of the difference in shrub richness from the beginning to the end of the study by site indicated that the only significant effect was the change in reference plots over time at SCA (2003 vs. 2007: z = -4.693, P <

0.01). Thus, the difference in richness with treatment over time was largely driven by an increase of about two species at SCA.

Shrub evenness exhibited a significant main effect of treatment (Table 2.2;

Figure 2.4). Burned plots had an evenness index value that was 43% greater than reference plots and 19% greater than clipped plots (Figure 2.4).

ANCOVA models did not indicate a significant effect of treatment on either collective shrub stem density or shrub diversity (Table 2.2; Figure 2.5). Stem densities, in particular, were highly variable among species and plots. The appearance of increased

densities following burning and clipping treatments was largely driven by a few study plots dominated by multi-stemmed Rhus aromatica and or Corylus americana shrubs.

Forb and graminoid responses. Forb and graminoid responses did not exhibit significant treatment effects for any variable except evenness (Table 2.2; Figure 2.6;

Figure 2.7). Instead, most variables were affected by site, time, or their interaction

(Table 2.2). In general, cover, richness and diversity tended to peak in 2004 and decrease over time, especially for forbs (Figure 2.6; Figure 2.7). However, the more complex interactions for evenness involving site, year, and treatment also did not indicate any clear effects of treatment. The ANCOVA model indicated a weakly significant site × treatment effect for forb evenness, but pairwise comparisons of treatments within sites failed to detect any significant difference in treatments.

Likewise, graminoid evenness exhibited a weakly significant site × treatment × time effect, but pairwise comparisons of treatments within years at each site failed to identify any significant treatment effects.

Table 2.2 Schematic summary of linear mixed-effects analysis of covariance (ANCOVA) for effects of site, treatment, year and their interactions on (A) shrub and (B) forb and graminoid response variables. The pretreatment covariate was significant (P < 0.0001) in all models and is not included in this summary. Model details can be found in Appendix A Table 2.5 (shrubs), Appendix B Table 2.6(forbs), and Appendix C Table 2.7 (graminoids) A. Shrub Source Cover Density Growth* Richness Diversity Evenness Site X

Treatment XXX XXX XXX XXX

Site × Treatment XX

Year XXX XXX XXX X Site × Year X X

Treatment × Year XXX XXX XX

Site × Treatment × Year XXX

B. Forb Graminoid Source Cover Richness Diversity Evenness Cover Richness Diversity Evenness Site XXX X XX X

Treatment

Site × Treatment X

Year XXX XXX XXX XXX XXX XXX X

Site × Year XXX XXX X XXX XX X

Treatment × Year

Site × Treatment × Year X

Annotations: *shrub growth was analyzed only as an ANOVA; X (P < 0.05), XX (P < 0.01), XXX (P < 0.001)

Figure 2.2 The effect of treatment on shrub cover, stem growth, and richness – responses having significant treatment × time interactions – over time. Approximate timing of treatments is indicated by the dashed orange line. Pretreatment values (collected in year 2003), when available, were used as a covariate in linear models and are indicated by isolated points in 2003. Letters indicate results of multiple comparisons between treatments within a given year; treatments with different letters are significantly different, treatments sharing similar letters are not different. Absence of letters also indicates lack of significant difference. Error bars are ± 1 SE.

Figure 2.3 The effect of site and treatment on shrub stem growth over time. Approximate timing of treatments is indicated by the dashed orange line. Pretreatment values were not measured. Different letters indicate significant differences between treatments within a given year. Letters follow Figure 2.2 Error bars are ± 1 SE.

Figure 2.4 The effect of treatment on shrub evenness. The dashed orange line separates pre- and post- treatment values. Values presented in 2004-2007 are averages over these years, as there was no significant treatment × time interaction. Pretreatment values were collected in year 2003 and used as a covariate in the linear model. Letters follow Figure 2.2. Error bars are ± 1 SE.

Figure 2.5 The change in shrub density and diversity over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE.

Table 2.3 Schematic summary of three sets of linear contrasts used to examine effects of treatment over time for the three response variables that had significant treatment × time interactions. Contrasts were conducted for growth both across and within sites because there was a significant site × treatment × time interaction. Actual P values can be found in Appendix C Table 2.9. Shrub Contrast Cover Growth Richness All sites All sites Edge SCA SCB All sites Biennial treatment effects: 1st summer post treatment Fire 2004 vs. 2006 X - - - - - Clipping 2004 vs. 2006 ------Reference 2004 vs. 2006 ------

2nd summer post treatment Fire 2005 vs. 2007 X - - - - - Clipping 2005 vs. 2007 X - - - - - Reference 2005 vs. 2007 ------

Recovery: After 1st treatment Fire 2004 vs. 2005 X X X - X - Clipping 2004 vs. 2005 X X X X X - Reference 2004 vs. 2005 X X - - - -

After 2nd treatment Fire 2006 vs. 2007 X X X - X - Clipping 2006 vs. 2007 - X X - X - Reference 2006 vs. 2007 ------

Pretreatment vs. end of study: Fire 2003 vs. 2007 X NA NA NA NA - Clipping 2003 vs. 2007 X NA NA NA NA - Reference 2003 vs. 2007 X NA NA NA NA X Annotations: ‘X’ indicates significant contrast, ‘-‘ indicates not significant contrast, ‘NA’ indicates contrast not run because pretreatment data for growth was not available

Figure 2.6 The change in forb responses over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE.

Figure 2.7 The change in graminoid responses over time. Treatment, though shown, did not have a significant effect. Approximate timing of treatments is indicated by the dashed orange lines. Pretreatment values (collected in year 2003) were used as a covariate in linear models and are indicated by isolated points at 2003. There were no significant differences between treatments within a given year. Error bars are ± 1 SE.

2.4.3. Effects of treatment on competitive interactions between woody and herbaceous groundcover plants over time Only three forb response variables (cover, richness, and evenness) had a significant linear relationship with shrub cover. Of these, forb cover and richness exhibited some evidence of a competitive relationship with shrubs; they were both negatively associated with shrub cover, but only at two sites, SCA and SCB (Figure 2.8;

Figure 2.9). At the Edge, the relationship between forbs and shrub cover tended to be neutral for both variables. There was also a negative relationship between forb diversity and shrub cover across all sites, but this was weak (Figure 2.10). Despite the evidence for a competitive relationship, in no case did this relationship change over time with treatment. Instead, slopes differed only by site as with forb cover and richness, and intercepts differed with time (cover; Figure 2.8), treatment × site (richness; Figure 2.9), or site alone (diversity; Figure 2.10).

Figure 2.8 The relationship between forb aerial cover and shrub aerial cover differed between sites and shifted over time, but not with treatment. Regression in 2003 (pretreatment) is indicated by the solid lines and regression in 2007 (at the final census) is indicated by a dashed line. Closed symbols indicate pretreatment data, open symbols represent data collected in final census. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11). Shrub aerial cover was arcsine square root transformed and the axis labels were back transformed to percent cover for ease of interpretation.

Figure 2.9 The relationship between forb richness and shrub aerial cover differed between sites and shifted with treatment, but not time. Regression line color indicates site. Burned plots are orange, clipped plots are blue, and reference plots are green. Closed symbols indicate pretreatment data, open symbols represent data collected in final census. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11). Shrub aerial cover was arcsine square root transformed and the axis labels were back transformed to percent cover for ease of interpretation.

Figure 2.10 The relationship between forb diversity and shrub aerial cover shifts between sites but does not change with year or treatment. The pretreatment census (2003) and final census (2007) data are presented on separate panels for clarity. Line type and symbol fill indicate site. Solid lines and symbols indicate the Edge. Dashed line and open symbol indicate SCA. Dotted line and hatched symbol indicate SCB. See Appendix D for model details (Table 2.10) and regression equations (Table 2.11).

Effect of fire behavior on vegetation responses. Neither variation in maximum tablet temperature nor fine fuel consumption was related to variation in plant responses following the first or second set of fires (Table 2.4).

Table 2.4 Partial residual regression equations describing the relationship between shrub response variables and fire behavior variables (fuel consumption and maximum tablet temperature) following the first and second set of fires. Dependent variables are residuals following regressions relating data collected after fires to data collected before fires. For example, “shrub cover residuals 2004” are the residuals following regression of shrub cover after the second set of fires in 2005 against shrub cover prior to these fires in 2003. Dependent variable Regression equation N r2 P-value Shrub covera residuals 2004 y = 0.000 fc1 - 0.000 temp1 + 0.087 25 0.074 0.427 residuals 2006 y = 0.000 fc2 - 0.000 temp2 - 0.035 24 0.007 0.931

Shrub growth b response in 2004 y = 0.063 fc1 - 0.057 temp1 + 27.907 25 0.175 0.121 residuals 2006 y = 0.030 fc2 + 0.003 temp2 - 8.926 24 0.076 0.434

Shrub richness residuals 2004 y = 0.002 fc1 - 0.003 temp1 + 0.434 25 0.107 0.288 residuals 2006 y = 0.002 fc2 + 0.001 temp2 - 0.632 24 0.026 0.762

Shrub evenness residuals 2004 y = 0.000 fc1 + 0.000 temp1 - 0.123 25 0.054 0.544 residuals 2006 y = 0.000 fc2 - 0.000 temp2 - 0.011 24 0.030 0.723 Annotations: a arcsine-square root transformed; b 2004 data was used as the dependent variable because there was no data in 2003 for shrub growth; fc1 = fuel consumption in the first set of fires; fc2 = fuel consumption in the second set of fires; temp1 = maximum fire temperature in the first set of fires; temp2 = maximum fire temperature in the second set of fires.

2.5. Discussion 2.5.1. Potential for fire to reduce shrub encroachment The dormant season fires conducted in oak barrens in this study were low in

intensity, and their effect on vegetation was likely driven by removal of above ground

biomass, rather than damage to underground buds or nutrient removal or addition. This

conclusion is based on the evidence that burning and clipping treatments had

equivalent effects, and that there was no relationship between tablet temperature or

fuel consumption and plant responses. Further, the average tablet temperature in this study was considerably less than the fire temperature reported in similar limestone 60

glades in Kentucky which exceeded 500°C, yet did not result in nutrient loss or gain

(Trammell et al. 2004). However, comparisons between device temperatures may not be possible unless the measurement devices are identical (Kennard et al. 2005, Bova and Dickinson 2008).

The one possible exception to the similarity between fire and clipping was the distinct effect of fire on shrub evenness. The increase in evenness following two fires

may have been driven by the loss of rare seedlings and increase in average abundance

per species in burned plots. For example, some plots contained a few 2-5 cm tall eastern

red cedar (Juniperus virginiana) seedlings, which are killed by fire and do not resprout.

This corresponds with the decrease in richness by an average of one species in burned

plots. It is unclear why this pattern was not apparent in clipped plots. An opposite trend

occurred in reference plots. The increase in richness in reference plots may be due to an

increase in encroaching seedlings like buckthorn (Rhamnus caroliniana). The decrease in

evenness in reference plots was likely driven by increasing cover of dominant woody

plant species and corresponding suppression of less shade-tolerant shrubs (Anderson et

al. 2000). Confirmation of these trends and evaluation of these explanations requires

following shrub patterns over a longer term.

Clipping and burning treatments were not effective in reducing resprouting

potential of woody ground layer plants. Although both treatments were effective in

reducing woody plant cover, they did not reduce stem densities, and stems grew rapidly

and with equal vigor following two fires. This corresponds with the rapid recovery of

shrub and sapling strata in savannas and barrens burned periodically in Indiana (Haney

et al. 2008), Minnesota (Peterson and Reich 2001), and Illinois (Taft 2003, Taft 2009). It

also corresponds with ground layer shrub responses in southeastern pine savannas

(Drewa et al. 2002), Virginian pine barrens (Polcher 1999), southwestern desert

grasslands (Kupfer and Miller 2005), and Midwestern tallgrass prairie (Heisler et al.

2003). In several of these studies, ground layer shrubs even increased in density

following fires (Heisler et al. 2003, Taft 2009).

Biennial dormant season fires in this study seemed effective in maintaining the

pretreatment densities of shrubs in these oak barrens, but did not reduce encroachment. Longer term studies of fire frequency and woody plant structure in savannas and barrens suggest that a fire frequency of 3-4 burns per decade is needed just to maintain initial shrub and sapling densities (Haney et al. 2008). Even more frequent (continuous annual or biennial burns) may be needed to effectively reduce ground layer cover and density (White 1983a, Bowles et al. 2007). However, others caution of the existence of threshold states, in which there is little possibility of fires ever reducing shrubs, such as in some tallgrass prairies where large root reserves allow established shrubs to persist even under an annual fire regime (Briggs et al. 2005). This may be the case in mesic grassland and savanna ecosystems, but there is some evidence to suggest that thresholds (at least for shrubs) do not apply to the oak barrens in this study. These oak barrens have relatively shallow, rocky soil and occur on dry south- facing slopes – conditions which tend to delay the speed of woody plant encroachment

(Nuzzo 1986). Further, the second set of treatments reduced shrub cover more that the first set, suggesting repeated topkill in this system might be effective in reducing shrubs

over the long term. However, this could also be the result of different conditions during

the two sets of treatments, such as different amounts of growing season precipitation.

2.5.2. Potential for fire to foster herb abundance and diversity Neither burning nor clipping treatments were effective in increasing the cover or diversity of herbaceous species. Nor was there any effect of treatment on altering potential competitive relationships between shrubs and herbs. There are three general suites of explanations for these findings: (1) herbaceous species do not respond to low intensity fire in these barrens, (2) herbaceous species do respond to fire, but they already did so in response to site-wide fires and structural manipulations conducted prior to this study, (3) herbaceous species did respond to the treatments used in this study, but the forb and graminoid classification categories were too broad to detect the response.

If herbaceous groundcover vegetation does not respond to low intensity fires in these barrens, it could be for one of several reasons. Herbaceous layers in severely degraded savannas and barrens sometimes do not have the capacity to respond to restoration because they are impoverished by years of species loss and lack of seed sources (Nielsen et al. 2003, Taft 2009). This is unlikely in these barrens, given the diversity of typical savanna and prairie species (Petersen and Drewa 2009).

Alternatively, low intensity burn treatments may not have altered competitive dynamics between shrubs and herbs (as seems to be the case in: Pendergrass et al. 1998, Ansley and Castellano 2006). This explanation is likely because regressions of forb variables against shrub cover did not change with treatment. However, it is also possible that

herbaceous species are influenced more by light gradients generated by overstory

conditions (which were not altered by the fires in this study), than by groundcover

woody plants (Leach and Givnish 1996, Leach and Givnish 1999). I cannot rule out this

possibility.

The influence of overstory structure on herbaceous groundcover dynamics also

corresponds with the hypothesis that herb layers have already responded to previous

fires and structural manipulations conducted prior to this study. Increases in herb layer

density, diversity, and cover have all been documented following frequent fires that

reduced canopy density (Taft 2003, Peterson and Reich 2008, Taft 2009) as well as

following low intensity fires paired with overstory thinning (Nielsen et al. 2003). Given

the strong influence of site and time in most of my linear models for herbaceous

response variables, this is a possibility. If this is the case, further temporal sampling at

these sites should continue to see a decrease in herbaceous abundance and richness

over time across all sites as the overstory increases.

This explanation does not rule out the possibility that my herb functional groups

(forbs and graminoids) are too broad to detect shifts in composition and abundance

(Peterson et al. 2007). The response of herbaceous species to fire and varying light levels often depends on photosynthetic pathway, symbiotic relationships, shade tolerance, and phenology (Howe 1994, Leach and Givnish 1996, Anderson et al. 2000).

Studies that do detect herbaceous plant responses to fire typically use narrower

functional categories such as C3 and C4 graminoids, legumes, and vines (e.g., Nielsen et

al. 2003, Taft 2009), as well as habitat preference such as barrens, transitional

woodland, and woodland (Anderson et al. 2000) to group species. The former was not possible in this study because most of these functional categories had too few individuals to warrant separate analysis, but the latter might be possible in future studies.

2.5.3. Conclusions and management implications Biennial dormant season fires in the oak barrens used in this study are effective in maintaining the pretreatment densities of shrubs, but do not reduce shrub encroachment or foster herbaceous plant abundance and diversity. The ground layer vegetation dynamics in this study may reflect the conditions after previous structural manipulations and fires. Other research suggests that 3-4 fires per decade are needed just to maintain an open overstory structure once it is achieved (Haney et al. 2008), but the research in this study suggests that even more frequent (annual or biennial) burns may be needed to reduce woody ground layer encroachment.

The comparison of clipping to fires in this study suggests that mowing or clipping could be used as an alternative to burning when fires cannot be carried out. However, this should not be used as a permanent surrogate for fire, as there are likely additional effects of frequent fire not explored in this study that will not be replicated by mowing or clipping alone (Rooney and Leach 2010). Other alternatives to frequent low-intensity dormant-season burns that were not tested in this study, but may prove fruitful include higher intensity fires and growing season fires. Less frequent higher intensity burns have the potential to alter overstory stand structure in ways comparable to several low- intensity fires (Haney et al. 2008). But it is not yet clear how ground layer vegetation

compares in these two alternatives. Further, burns in the growing season may be more

effective in reducing sprouting potential of shrubs than dormant season fires (Drewa et

al. 2002, Briggs et al. 2005, Pelc et al. 2011). These possibilities have not received as

much attention as frequent low-intensity dormant-season burns, but merit further

study.

One additional consideration that arises when discussing the possibility of annual

or biennial burns to reduce woody plant encroachment in savannas and barrens is that

such a regime, continued for an extended time, would eliminate woody plants in all but

the ground layer and old canopy. Even though the logistical and weather constraints

ensure that “too much fire” is seldom a problem for land managers, plans for

maintenance of trees and shrubs endemic to barrens should be included in restoration

plans. Shrubs and trees are a natural and historical component in these systems and

should be maintained by management (Anderson et al. 2000). One could ensure that a

variety of fire frequencies are employed across the landscape to generate a mosaic that reflects the dynamic and transitional nature of barrens vegetation. Further, shrubs and trees, just like herbs can be classified according to their habitat requirements in censuses to more clearly distinguish between population dynamics of savanna shrubs such as Corylus americana, versus forest invaders (Anderson et al. 2000).

2.6. Appendices 2.6.1. Appendix A: Full ANCOVA model results. Table 2.5 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on woody plant response variables. For each response variable (columns), the details of the minimum model are listed in rows with additional model information listed at the bottom. Explanatory variables are ordered (i) by their denominator degrees of freedom and (ii) by the sequence in which they were entered into the model. Continued on next page. Shrub covera Shrub densityb Shrub growth* Source Num df Den df F-value P-value F-value P-value F-value P-value Main effects Covariate (pretreatment) 1 plot 131.70 <.0001 250.04 <.0001 NA NA Site 2 plot 3.33 0.0416 X X 0.63 0.5346 Treatment 2 plot 58.67 <.0001 2.29 0.1088 38.69 <.0001 Site × Treatment 4 plot X X X X 4.27 0.0039 Split effects Year 3 split -plot 45.26 <.0001 11.75 <.0001 104.12 <.0001 Site × Year 6 split-plot 2.31 0.0349 X X 2.30 0.0361 Treatment × Year 6 split-plot 8.92 <.0001 1.88 0.0864 12.76 <.0001 Site × Treatment × Year 12 split-plot X X X X 3.17 0.0004

Den df (plot) 68 70 65 Den df (split-plot) 207 213 195 AIC maximal model -103.77 -131.95 2075.55 AIC minimal model -212.21 -312.00 2075.55 correlation structure AR1 AR1 none variance weights Site Site*Treatment Site*Treatment Continued on next page.

Table 2.5 Continued from previous page. Shrub richness Shrub diversity Shrub evenness Source Num df Den df F-value P-value F-value P-value F-value P-value Main effects Covariate (pretreatment) 1 plot 132.89 <.0001 143.50 <.0001 16.63 0.0001 Site 2 plot X X X X X X Treatment 2 plot 8.80 0.0004 X X 10.29 0.0001 Site × Treatment 4 plot X X X X X X Split effects Year 3 split -plot 1.49 0.2175 3.31 0.0209 X X Site × Year 6 split-plot X X X X X X Treatment × Year 6 split-plot 3.13 0.0058 X X X X Site × Treatment × Year 12 split-plot X X X X X X

Den df (plot) 70 72 70 Den df (split-plot) 213 219 222 AIC maximal model 977.77 258.60 128.98 AIC minimal model 913.64 53.71 -103.00 correlation structure none none AR1 variance weights none none none The analysis was conducted as a split-plot design, with year as the split effect. Significant P- values are presented in bold. a b * Annotations: arcsine-square root; log10 (value + 1) transformed; shrub growth was analyzed only as an ANOVA, where year ranged from 2004-2007; Den df, Denominator degrees freedom; Num df, Numerator degrees freedom; AIC, Akaike information criterion; 'X' indicates terms excluded during model simplification, ‘NA’ indicates covariate not included because pretreatment data for growth was not available.

Table 2.6 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on forb response variables. Format and annotations follow Appendix A Forb cover Forb richness Forb diversity Forb evenness Source Num df Den df F-value P-value F-value P-value F-value P-value F-value P-value Main effects Covariate (pretreatment) 1 plot 134.82 <.0001 168.31 <.0001 231.96 <.0001 58.44 <.0001 Site 2 plot 8.46 0.0005 4.85 0.0107 5.16 0.0081 1.91 0.1569 Treatment 2 plot X X X X X X 0.16 0.8548 Site × Treatment 4 plot X X X X X X 2.68 0.0395 Split effects Year 3 split -plot 93.04 <.0001 32.10 <.0001 9.36 <.0001 25.48 <.0001 Site × Year 6 split-plot 8.28 <.0001 4.09 0.0007 2.06 0.0594 2.34 0.0328 Treatment × Year 6 split-plot X X X X X X X X Site × Treatment × Year 12 split-plot X X X X X X X X

Den. df (plot) 70 70 70 64 Den. df (split-plot) 213 213 213 213 AIC maximal model 2097.13 1440.60 91.89 -386.22 AIC minimal model 2129.73 1413.19 -68.62 -532.07 correlation structure AR1 AR1 AR1 AR1 variance weights none none Site none

Table 2.7 Summary of linear mixed-effects analysis of covariance for individual or interacting effects of pretreatment value, site, treatment, and year on graminoid response variables. Format and annotations follow Appendix A Graminoid covera Graminoid richness Graminoid diversity Graminoid evenness Source Num df Den df F-value P-value F-value P-value F-value P-value F-value P-value Main effects Covariate (pretreatment) 1 plot 158.29 <.0001 128.78 <.0001 181.91 <.0001 33.19 <.0001 Site 2 plot 1.15 0.3239 1.09 0.3407 4.26 0.0179 3.11 0.0514 Treatment 2 plot X X X X X X 0.12 0.8852 Site × Treatment 4 plot X X X X X X 1.31 0.2758 Split effects Year 3 split -plot 20.98 <.0001 8.50 <.0001 1.74 0.1589 3.42 0.0184 Site × Year 6 split-plot 6.83 <.0001 3.46 0.0028 2.85 0.0107 1.55 0.1639 Treatment × Year 6 split-plot X X X X X X 0.78 0.5857 Site × Treatment × Year 12 split-plot X X X X X X 2.25 0.0108

Den. df (plot) 70 70 70 64 Den. df (split-plot) 213 213 213 195 AIC maximal model -215.35 1015.12 213.34 91.17 AIC minimal model -397.91 938.64 68.12 91.17 correlation structure AR1 none AR1 none variance weights Site Site Site Site

2.6.2. Appendix B: Full ANOVA model results. Full ANOVA model results are presented for shrub cover and shrub richness, the

only variables that had a significant treatment × time interaction in ANCOVA. These

were conducted solely for use in making multiple comparisons between pre-treatment

and end of study measurements. Note that shrub growth also had a significant

treatment × time interaction, but was only run as an ANOVA.

Table 2.8 Summary of linear mixed-effects analysis of variance for individual or interacting effects of site, treatment, and year on shrub cover and richness, the only response variables that had a significant treatment × time interaction in the ANCOVA model . In this analysis, pretreatment data is included as the first level of the factor year rather than as a covariate, such that year consists of five levels from 2003-2007. For each response variable (columns), the details of the minimum model are listed in rows with additional model information listed at the bottom. Explanatory variables are ordered (i) by their denominator degrees of freedom and (ii) by the sequence in which they were entered into the model. Shrub covera Shrub richness Source Num df Den df F-value P-value F-value P-value Main effects Site 2 plot 0.75 0.4742 0.83 0.4412 Treatment 2 plot 2.69 0.0751 1.31 0.2765 Site × Treatment 4 plot X X 1.68 0.1648 Split effects Year 4 split -plot 35.85 <.0001 1.15 0.3325 Site × Year 8 split-plot 2.46 0.0138 1.34 0.2229 Treatment × Year 8 split-plot 21.09 <.0001 4.94 <.0001 Site × Treatment × Year 16 split-plot X X 1.94 0.0174

Den. df (plot) 69 65 Den. df (split-plot) 276 260 AIC maximal model -35.58 1280.50 AIC minimal model -165.36 1280.50 correlation structure AR1 none variance weights Site none The analysis was conducted as a split-plot design, with year as the split effect. Significant P- values are presented in bold. Annotations: aarcsine-square root; Den df, Denominator degrees freedom; Num df, Numerator degrees freedom; AIC, Akaike information criterion; 'X' indicates terms excluded during model simplification.

2.6.3. Appendix C: Linear contrasts for effect of treatment over time. Table 2.9 Summary of three sets of linear contrasts used to examine effects of treatment over time for the three response variables that had significant treatment × time interactions. Contrasts were conducted for growth both across and within sites because there was a significant site × treatment × time interaction. Shrub Shrub growth Shrub Contrast Cover Richness All All sites sites Edge SCA SCB All sites Biennial treatment effects: 1st summer post treatment Fire 2004 vs. 2006 <0.01 0.0716 1 0.465 0.2808 0.9990 Clipping 2004 vs. 2006 0.6779 0.4115 1 0.1001 0.9981 0.9480 Reference 2004 vs. 2006 0.1206 0.1512 0.977 0.2035 0.9865 0.1400

2nd summer post treatment Fire 2005 vs. 2007 <0.01 0.4718 0.966 0.9494 0.9129 1 Clipping 2005 vs. 2007 0.0131 1 1 1 1 1 Reference 2005 vs. 2007 0.9978 0.8766 0.566 1 0.804 0.0900

Recovery: After 1st treatment Fire 2004 vs. 2005 0.0146 <0.01 <0.01 0.0785 <0.01 1 Clipping 2004 vs. 2005 <0.01 <0.01 <0.01 <0.01 <0.01 0.9800 Reference 2004 vs. 2005 <0.01 0.0493 0.874 0.1147 0.9474 1

After 2nd treatment Fire 2006 vs. 2007 0.0385 <0.01 <0.01 0.4677 <0.01 0.9990 Clipping 2006 vs. 2007 0.4953 <0.01 <0.01 0.1493 0.0245 0.4440 Reference 2006 vs. 2007 0.9983 0.9851 0.809 0.9997 0.9141 1

Pretreatment vs. end of study: Fire 2003 vs. 2007 < 0.001 NA NA NA NA 0.9808 Clipping 2003 vs. 2007 < 0.001 NA NA NA NA 0.2287 Reference 2003 vs. 2007 < 0.001 NA NA NA NA 0.0033 Annotations: Bold indicates significant contrast, ‘NA’ indicates contrast not run because pretreatment data for growth was not available.

2.6.4. Appendix D: Changes in the relationship between herbaceous and woody groundcover plants between the first and last census by treatment and site. Table 2.10 ANOVA table for the linear models examining the effect of site, treatment, and time on the relationship between shrub cover (covariate) and forb response variable (columns). Year has two values: 2003, at the onset of the study, and 2007 at the completion of the study. Shrub cover was arcsine-square root transformed. Resulting regression equations are presented in Table 2.10. Source of variation Forb Cover Forb richness Forb diversity df F-value P-value F-value P-value F-value P-value Factors Site 2 14.98 <.0001 22.03 <.0001 14.21 <.0001 Year 1 20.35 <.0001 X X X X Treatment 2 X X 0.26 0.7686 X X Site × Year X X X X X X Site × Treatment 4 X X 3.45 0.0102 X X Year × Treatment X X X X X X Site × Year × Treatment X X X X X X Covariate and interactions Shrub cover 1 9.22 0.0029 10.99 0.0012 4.56 0.0344 Shrub cover × Site 2 6.70 0.0017 3.91 0.0224 X X Shrub cover × Year X X X X X X Shrub cover × Treatment X X X X X X Shrub cover × Site × Year X X X X X X Shrub cover × Site × Treatment X X X X X X Shrub cover × Year × Treatment X X X X X X Shrub cover × Site × Year × Treatment X X X X X X Note: Sources of variation are listed out of sequence. Variables were actually entered into the model in the following order: Site (block), Shrub cover (covariate), Year, Treatment, interactions. Annotations: df, degrees freedom; 'X' indicates terms excluded during model simplification.

Table 2.11 Regression equations describing linear relationships between forb response (y) and arcsine-square root transformed shrub cover (x) for forb cover, richness, and diversity. Dependent variable Grouping variable Regression equation r2 P-value Forb cover 0.341 <.0001 Edge 2003 y = 8.03x + 46.778 Edge 2007 y = 8.03x + 35.421 Sca 2003 y = -22.207x + 85.066 SCA 2007 y = -22.207x + 73.709 SCB 2003 y = -37.498x + 86.255 SCB 2007 y = -37.498x + 74.898

Forb richness 0.362 <.0001 Edge Fire y = -0.043 x + 12.683 Edge Clipping y = -0.043 x + 11.829 Edge Reference y = -0.043 x + 13.367 SCA Fire y = -5.842 x + 19.953 SCA Clipping y = -5.842 x + 24.259 SCA Reference y = -5.842 x + 22.372 SCB Fire y = -9.411 x + 25.051 SCB Clipping y = -9.411 x + 21.602 SCB Reference y = -9.411 x + 22.318

Forb diversity 0.186 <.0001 Edge y = -0.242 x + 2.175 SCA y = -0.242 x + 0.376 SCB y = -0.242 x + 0.328

Chapter 3: Vegetation-environment relationships among the ground layers of four southern Ohio mixed oak forests 3.1. Abstract In the unglaciated portion of southern Ohio, a complex topographic and geologic

mosaic generates tremendous variation in forest composition between stands.

However, the ground layers of these forests may become increasingly homogenized as

disturbance regimes and nutrient dynamics are altered by human activities in eastern

oak forests. I examined the ground layers of four, second-growth oak forest fragments

located along the Appalachian Escarpment in southern Ohio. These forest stands differ

in bedrock, topography, and overstory composition. At each site, I evaluated densities of

woody ground layer vegetation (stems < 2 cm dbh) and collected environmental data in

30, circular (19.6-m2) plots. Ordination distinguished four ground layer assemblages that

corresponded to the four study sites, but all sites shared similar relative abundance

distributions. Three of the sites were dominated by shade tolerant species – mainly Acer

rubrum (red maple) and Acer saccharum (sugar maple). Quercus spp. (oaks) were present in the ground layer of all sites, indicating that oak regeneration was not limited by lack of seedlings in a mast year. However, few oak seedlings were > 30 cm in height, and they were dominant at only one site, suggesting they may be experiencing a bottleneck in recruitment. Sites also differed in environmental characteristics, but shared similar relationships between vegetation community patterns and primary environmental drivers, especially nutrients, water stress, and overstory structure. In particular, N and C:N differed between sites and were consistently related to vegetation

pattern within sites. By contrast, light availability and canopy openness were associated

with vegetation patterns at only two sites. However, few individual species exhibited

relationships with the environmental variables one would expect based on sapling-layer

studies. This suggests that dispersal patterns, rather than environmental variation, are

shaping species distributions at the seedling stage. On a cautionary note, there were

also differences in species assemblages between the future burn management units

established at each site. Therefore, when comparing vegetation response to burn

treatments within and across such heterogeneous forest stands, it may be necessary to

explore responses among tolerance or regeneration guilds rather than individual

species.

3.2. Introduction The ground layer of forests serves as an advanced regeneration pool or seedling

bank from which understory and overstory trees will be recruited (Grime 1979, Marks

and Gardescu 1998). Thus, future forest composition and abundance is influenced, in

part, by the heterogeneous mosaic of environmental factors that act as a filter upon this

regeneration layer (Harper 1977). In the topographically complex mesophytic and oak forests of southern Ohio, landscape patterns in forest regeneration have long been associated with variation in soil characteristics driven by differences in geomorphology, particularly bedrock (Anderson and Vankat 1978, Hutchinson et al. 1999). Within a particular forest type underlain by the same bedrock, ground layer vegetation composition and diversity varies along gradients in soil moisture and fertility

(Hutchinson et al. 1999, McEwan et al. 2005). However, it is not well understood if

similar relationships underlie the variation in ground layer vegetation at smaller scales

(2-4 ha) within a single forest stand.

The primary abiotic and biotic gradients that affect temperate forest seedling patterns – soil moisture status, overstory characteristics, light availability, and soil fertility – are comprised of suites of variables that interact and are dynamic (Ribbens et al. 1994, Iverson et al. 1997, Boerner 2006). For example, the long term soil moisture status experienced by seedlings is driven by topography, soil texture, soil depth, solar radiation, and transpiration of overstory trees (Iverson et al. 1997, Tromp-van Meerveld and McDonnell 2006). In addition, overstory trees not only influence understory abundance and composition through changes in soil water, but through competition for other resources, such as light and soil nutrients (Barbier et al. 2008). Finally, overstory trees can influence the spatial pattern of seedlings because they are the source for seeds (Hille Ris Lambers and Clark 2003).

Light has been considered as the primary driver behind seedling regeneration and sapling growth in temperate forests (Pacala et al. 1996, Finzi and Canham 2000), and community patterns of shrubs and tree seedlings are often related to variation in light (North et al. 2005, Tinya et al. 2009). However, the strength of this association is often weak (Tinya et al. 2009) or nonexistent (Hutchinson et al. 1999) in temperate forests with closed understories. Without major canopy disturbances, there may be less variation in light and shade-tolerant plants will tend to dominate the regeneration layer

(Hutchinson et al. 2003b, Rentch et al. 2003b).

In fact, eastern oak forests are shifting in dominance from oaks (Quercus spp.) and hickories (Carya spp.)to more mesophytic and shade-tolerant species, especially maples (Acer spp.), following decades of fire suppression (Goebel and Hix 1997, Abrams

2000, Hicks 2000). As shade tolerant species proliferate and reduce understory light levels, species that are less tolerant of shade, such as oaks, are failing to regenerate

(Lorimer et al. 1994, Abrams 1998, 2003). Thus, as disturbance decreases, we might expect to see a reduction in understory light variation and homogenization of forest ground layers.

Further changes in the composition and structure of eastern oak forests may result as variation in soil fertility is altered by human activities. Temperate forests have historically been considered N limited, but nitrogen deposition from fossil fuel burning and agricultural activities has increased the fertility of temperate forest ecosystems

(Vitousek and Howarth 1991, Aber et al. 1998, Zaccherio and Finzi 2007). This chronic N deposition may also decrease the spatial heterogeneity in N availability in forest ecosystems, resulting in losses of biodiversity (Gilliam 2006). In southern Ohio, a strong correlation between groundcover vegetation patterns and N availability suggests that N deposition could have a major impact on understory vegetation diversity and dynamics

(Hutchinson et al. 1999, McEwan et al. 2005, Small and McCarthy 2005). The high nitrate levels (13-23 mg ∙ kg-1 of soil) and high inorganic N to P ratios (60:1 to 240:1) found recently in southern Ohio forest soils suggest that these forests have already become strongly N enriched and may no longer be N limited (Boerner et al. 2003, Boerner 2006).

Thus, an understanding of the vegetation-environment patterns in the

regeneration layer of diverse eastern deciduous oak forests is particularly important due

to the rapid pace of continued anthropogenic alterations in disturbance regimes and

nutrient dynamics. In this study I examined patterns in the ground layer of four mixed

oak forest stands in southern Ohio. Three main questions were addressed: (1) What are

the patterns in community composition, diversity, and structure of the ground layers across the study sites? (2) What is the variation in environmental characteristics across the study sites? (3) What are the vegetation-environment patterns within each site?

Additionally, as this study serves to assess pretreatment conditions prior to the reintroduction of fire, I asked an additional question: (4) How similar are vegetation assemblages between the two future burn management units at each site?

3.3. Methods 3.3.1. Study sites In 2006, four mature second-growth forest study sites were established along

the Appalachian Escarpment in the Bluegrass Region in southern Ohio, USA. The study

region and study sites are described in detail in Chapter 1. Briefly, Sandstone and

Hopkins are located at Strait Creek Preserve. Cedar Falls and Bethany are located at the

Edge of Appalachia Preserve. The study sites range from about 2-4 ha each, and each

consists of two burn management units established as part of a larger study

investigating the effects of prescribed fire on ground layer vegetation; however, sites

were not burned by TNC prior to or during this study.

As described in Chapter 1, the study sites reflect a range of substrate, soils, and

aspects (Table 3.1). However, all sites were restricted to upland locations – slopes, ridges, and plateaus – rather than valley bottoms, and their elevations fall within a narrow range of 220 to 300-m. Hopkins and Bethany are both underlain primarily by shale, Sandstone by sandstone, and Cedar Falls by dolomitic limestone. Except for Cedar

Falls, which is relatively flat, the sites are located on steep to rolling slopes and differ in dominant aspect. Canopy composition differs from site to site, but across most of the study sites, maples dominate the understory and oaks dominate the overstory. Cedar

Falls is an exception; maples dominate all canopy layers. However, oaks are the largest trees at all sites.

Table 3.1 Locations and characteristics of oak-dominated forest sites and burn management units within each site. Repeated from Chapter 1.Table 1.2 Site Preserve County Burn unit Location Size Elevation (m a.s.l) Aspect* Slope* Bedrock label, location (longitude, latitude) (ha) min-max (range) (%)

Bethany EOA Adams 2, west 38° 47' 13" N., 83° 23' 31" W. 1 250-275 (25) 160° (SE) 25 shale 1, east 38° 47' 9" N., 83° 23' 27" W. 1 255-262 (7) 160° (SE) 25 shale

Cedar Falls EOA Adams 2, west 38° 49' 47" N., 83° 23' 40" W. 2 230-233 (3) horizontal 2 dolomite 1, east 38° 49' 51" N., 83° 22' 54" W. 2 223-226 (3) horizontal 2 dolomite

Hopkins SC Pike 1, south 39° 3' 52" N., 83° 22' 14" W. 1 270-275 (5) 270° (W) 20 shale 2, north 39° 3' 59" N., 83° 22' 13" W. 1 273-287 (14) 330° (NW) 45 shale

Sandstone SC Pike 1, east 39° 3' 28" N., 83° 23' 4" W. 1.5 249-282 (33) 30° (NE) 30 sandstone Highland 2, west 39° 3' 29" N., 83° 23' 11" W. 1.5 226-267 (41) 30° (NE) 30 sandstone

Annotations: * Aspect and slope represent the general landscape level values and do not reflect the microtopography at the level of the sampling plot. Slope was measured from the lowest to highest plot within each unit, and aspect was measured from the middle of each unit. EOA = Edge of Appalachia Preserve System, SC = Strait Creek Preserve, m a.s.l. = meters above sea level.

3.3.2. Study design and data collection Vegetation sampling. During June through August 2006, woody ground layer vegetation was sampled at each site using 30 circular plots (15 each per burn unit; radius = 2.5 m; 19.6-m2) systematically located every 15-20 m along transects. Parallel

transects were randomly positioned at the upslope axis (or short axis of the site, where

no slope was present) and then run downhill. Total transect number and length

depended on the shape of the site, but transects were spaced a minimum of 15 m apart.

Within each of the 120 plots, all woody stems < 2 cm dbh (diameter at breast height,

about 150 cm) were identified to species, classified by height class, and counted. Height

class categories included small seedlings (stems < 30 cm height) and juveniles, which are

also called large seedlings (stems ≥ 30 cm height, <2 cm dbh) (Poulson and Platt 1996,

McCarthy et al. 2001). Two woody vines – Smilax spp. and Toxodendron radicans – were

evaluated using percent cover rather than density. Nomenclature follows Gleason and

Cronquist (1991). Some closely related taxa were treated collectively.

Environmental variables. Slope and aspect were measured at each plot using a

clinometer and compass, respectively, and were used to estimate an above-canopy heat

index. Aspect was transformed to reflect the gradient in temperature symmetrical about

a northeast to southwest aspect (McCune and Keon 2002). This transformed aspect was

combined in an equation with slope and latitude that uses Buffo et al.’s (1972)

calculations of potential direct solar radiation to obtain an index of heat load (McCune

and Keon 2002). As this index does not take into account shading by overstory trees, it

should be considered an above-canopy heat index that would influence transpiration

and water stress. Where slopes were < 2 %, both slope and aspect were considered

zero.

Three soil cores were collected from the perimeter of each plot at three

randomly selected cardinal directions. Cores were taken from the uppermost 5-cm using

a 2.5-cm-diameter soil auger and pooled in the field. Soil samples were air-dried at room

temperature and passed through a 2-mm sieve to remove large debris. Smaller debris

was removed by hand tweezing. Samples were oven dried immediately before analysis.

Soils were analyzed for texture (% sand, clay, and silt content by dry weight) using the

hydrometer method (Sheldrick and Wang 1993). Soil pH was measured using a 1:1

distilled water:soil ratio (Eckert and Sims 2009). Organic matter was estimated via loss

on ignition at 360°C for 2 hrs (Schulte 2009).

For C, N, and P analysis, ~10 g of soil was dried at 60°C for 48 hrs and finely

ground using a Precellys homogenizer (Berlin Technologies, Montigny-le-Bretonneux,

France). Soil organic phosphorous (Po) and inorganic phosphorous (Pi) were extracted

using a modified Olsen Method by adding 0.5 M NaHCO3 (pH 8.5) and shaking at 100

rpm on an orbital shaker (Lab-Line, Melrose Park, IL) for 30 min (Olsen et al. 1954). Pi

was determined colorimetrically using a modified ascorbic acid method on the extracts

(Kuo 1996), and Po was determined by subtracting Pi from the total P measured after extracts were digested with 1.8 N H2SO4 and (NH4)2S2O2 (Environmental Protection

Agency 1971). Total N and C content (%) were determined with 10 mg subsamples using an elemental combustion system (Cosetech Analytical, Valencia, CA). Plant available

water was estimated from soil texture and organic matter using equations developed for predicting soil water characteristics (Saxon and Rawls 2006).

Understory light levels were quantified using hemispherical photographs taken at the center of each plot approximately 1-m off the ground using a Nikon Coolpix 8700 digital camera fitted with a 7-mm Nikon FC-E9 fisheye converter lens. Photographs were taken on uniformly cloudy days or at dusk or dawn to avoid distortion of the image by direct sunlight. The digital photographs were analyzed using Gap Light Analyzer 2.0 software for Windows to determine canopy openness and the total amount of photosynthetically active growing season radiation (PAR) transmitted through the canopy, after accounting for local terrain via topographic masking (Frazer et al. 1999). A topographic mask was applied to all photographs where plots had a visible slope

(usually > 5 %). The blue color plane was applied to all photographs to improve contrast.

Configuration settings included site latitude and longitude, average site aspect, slope, and elevation (Table 3.1). Additionally, for calculation of PAR, the growing season was considered to be from April 29-October 8, based on the average dates of last and first frost (Robison and McCarthy 1999).

The influence of the immediate overstory neighborhood was quantified by calculating the density (stems ∙ ha-1) and BA (basal area ∙ ha-1) of adult trees (≥ 2 cm dbh) in square 10 × 10-m (0.01-ha) plots centered on each ground layer plot. The density and

BA for oaks, maples, and all trees combined were used as separate environmental variables representing overstory tree influence in the subsequent analyses.

3.3.3. Statistical analyses Comparisons in community pattern, structure, and diversity between sites. To summarize vegetation composition, abundance, and distribution patterns within each site, species at each site were ranked by abundance and values were calculated for density (stems ∙ ha-1), frequency (% plots), importance value, V:M, and Green’s index

(Appendix Table 3.5-3.8). Here, importance value is the average of relative abundance and relative frequency of a species. The variance to mean ratio (V:M), or index of dispersion, is an indicator of clumping (Ludwig and Reynolds 1988); a V:M value < 1 indicates a uniform or regular distribution pattern, a V:M value = 1 indicates a random or Poisson distribution, and V:M value > 1 indicates a clumped or clustered distribution pattern. I tested for significant departures from the null, V:M = 1, using a chi-squared test (Ludwig and Reynolds 1988). Green’s index was then used to describe the degree of clumping (Green 1966). A Green’s index of 0 indicates a random distribution, and an index of 1 indicates maximum clumping. This index is useful for comparing the degree of clumping between samples that vary in total number of individuals and mean value

(Ludwig and Reynolds 1988).

Additionally, the size class representation of each species was calculated, and species were categorized according to functional group and shade tolerance. To quantify size class representation, the proportion of stems that were small seedlings (<

30 cm tall) was calculated for each species. Species were classified into functional groups based on habit (shrub/vine or tree seedling) using the USDA Plants Database

(http://plants.usda.gov) and shade tolerance (shade-tolerant, intermediate, or light

demanding, i.e. intolerant) based on compilations by White (1983b), Burns and Honkala

(1990), and Sutherland et al. (2000) (Appendix Table 3.5-3.8). When classifications differed among sources, the category used by the most sources was adopted. Shrubs and vines, as groundcover residents, are assumed to be tolerant of shade.

Community composition and structure were compared across sites using a combination of classification, ordination, and nonparametric multivariate analysis of variance. Raw woody plant ground layer abundance data was organized into a matrix of

120 plots x 51 species. A Bray-Curtis distance metric was then applied to the abundance data to construct a dissimilarity matrix (Bray and Curtis 1957, Faith et al. 1987). In order to detect general trends in vegetation patterns, Nonmetric multidimensional scaling

(NMDS), an unconstrained ordination technique, was used because it makes no assumptions about species distributions along gradients in ecological space and is the most robust form of ordination for detection of ecological pattern (Minchin 1987,

Ludwig and Reynolds 1988).

NMDS was performed on the dissimilarity matrix after square root transformation and Wisconsin double standardization, in which species are first standardized by maxima and then plots by plot totals, to reduce the emphasis of extremely abundant species and adjust for the extreme variability in abundance by plot

(Legendre and Gallagher 2001, McCune and Grace 2002). NMDS was carried out following the recommendations of Minchin (1987). Briefly, a maximum of 100 different random starting configurations was used. Stress, or “badness-of-fit,” was compared using ordinations with 1, 2, 3, and 4 dimensions, and a two-dimensional ordination was

selected because stress was not appreciably reduced with more dimensions. Ordination output was centered about the average of the axes, rotated to maximize the variation along the first dimension (x axis), and scaled by half-change units.

Differences in vegetation dissimilarity accounted for by site were explored using

β-flexible cluster analysis (β = -0.25) (Lance and Williams 1967) and assessed using permutational multivariate analysis of variance (PERMANOVA), a non-parametric test

(Anderson 2001). PERMANOVA was performed on the transformed dissimilarity matrix with site as the grouping variable and 999 permutations. Following PERMANOVA, pairwise contrasts between site means were corrected for multiple comparisons by a

Bonferroni adjustment (α = .05/6 or 0.008) (Sokal and Rohlf 1995). While PERMANOVA is less sensitive to heterogeneity than multi-response permutation procedures (MRPP), significant differences can still arise because of spatial and heterogeneity differences between groups. Thus, a permutational multivariate test for the homogeneity of variances among sites was done using a multivariate analogue of Levene's test for equality of variance (Anderson 2006a). This test was run under the same conditions as

PERMANOVA with the exception that pair-wise comparisons were assessed using

Tukey’s Honest Significant Difference (HSD).

The average and cumulative observed richness (Sobs; the number of species per

2 sample), Simpson’s diversity (I/D = 1/ Σ pi , where pi is the proportion of individuals found in the ith species), and Simpson’s evenness (E1/D = (1/D)/Sobs) were calculated for each study site following the recommendations by (Magurran 2004). In addition, a non-

2 parametric, incidence based richness estimator, Chao1 (SChao1 = Sobs(F1 /2F2), where F1

is the number of species observed once and F2 is the number of species observed

twice), was used to obtain a less biased estimate of species richness (Chao 1987).

Simpson’s index was chosen because it provides a good estimate of diversity at

relatively small sample sizes and it ranks assemblages consistently (Magurran 2004).

Simpson’s evenness index was used as its complement to quantify rarity. ANOVA was

used to compare means across sites and pair-wise comparisons were made using

Tukey’s HSD. Further, rank abundance diagrams were generated for each site to

highlight the differences in proportional representation of species that might drive any

structural differences (Magurran 2004). All measures, except observed species richness,

did not include Smilax spp. and Toxodendron radicans because the measure of

abundance for these groups was aerial cover, rather than density.

Differences in environment between sites. Differences in environmental variables across sites were examined using ANOVA, followed by and pair-wise

comparisons using Tukey’s HSD. Residuals were examined to check if they met the

assumptions of normality and equality of variance, and where necessary, appropriate

transformations were applied to meet model assumptions.

Within site vegetation-environment relationships. Vegetation assemblage

patterns within each site were explored using NMDS as described above except that the

starting point for each analysis was a species × plot community matrix for each site,

resulting in four additional site-specific ordinations. Vegetation-environment

relationships within each site were studied by vector fitting (sensu Kantvilas and

Minchin 1989) on site-level ordinations according to the method of Dargie (1984), in

which all dimensions are considered simultaneously using a least squares fit in the form:

V = bo + b1X1 + b2X2 + … bkXk, where V is the environmental variable, and Xk is the

ordination score for the kth dimension. The tip of the vector is located proportionally to

the sum of the regression coefficients and the length of the vector is proportional to the

R2 of the regression when there are only two dimensions (Dargie 1984, McCune and

Grace 2002). The coefficient of determination (R2) was also used as a measure of

goodness of fit and the significance of the regression was assessed by permutation

(1000 permutations) (Faith and Norris 1989). Only variables with P ≤ 0.05 were plotted

on ordinations. Additionally, relationships of specific species to ordination patterns were

investigated by plotting centroids of species abundance using weighted averaging, in

which the plot scores are weighted by species abundance (McCune and Grace 2002).

Comparison of ground layer assemblages between future burn management

units. Differences in community pattern associated with future burn units were examined using PERMANOVA, as above.

All analyses were performed in the R statistical environment (version 2.10.1) (R

Development Core Team 2009) with the exception of PEMANOVA, which was done

using PERMANOVA v1.6 (Anderson 2005). Specifically, cluster analysis was performed

cluster library and all additional multivariate and diversity analyses were performed

using the Vegan library (Oksanen et al. 2010). For all analyses, unless otherwise noted, α

= 0.05.

3.4. Results Across my study sites, I encountered a total of 54,315 woody ground layer plants, 93% of which were small seedlings (stems < 30 cm tall). Stem densities varied greatly between sites, ranging from 136,983 stems ha-1 at Sandstone to 294,203 stems ha-1 at Bethany (Appendix). At Hopkins and Cedar Falls, densities were 281,962 and

208,930 stems ha-1, respectively.

3.4.1. Differences in community pattern, structure, and diversity between sites. My four forest study sites differed in woody ground layer vegetation assemblages. Ordination (Figure 3.1) and β-flexible cluster analysis of woody ground layer plant abundance data resulted in four distinct groupings of plots corresponding to the four study sites. Additionally, the cluster dendrogram cut at four groupings indicated limited overlap among sites; two plots from Hopkins were more similar to those found at Bethany, and four plots from Sandstone were more similar to either plots found at

Bethany (2) or Cedar Falls (2). This corresponds with the PERMANOVA results which confirmed that assemblages differed by site (P = 0.001) and that each site was dissimilar to all other sites (post-hoc pair-wise comparison P < 0.010). However, sites are both spatially distinct and, based on the test for homogeneity in variance, heterogeneous (P <

0.001). Only Hopkins and Bethany were similar in heterogeneity (Tukey’s HSD P = 0.272).

Figure 3.1 Non-metric multidimensional scaling (NMDS) ordination for all sites combined. The plot combines individual sample plots (open circles) with the average score, or centroid, for each site. Lines connect each plot to its corresponding site centroid. Ordination yielded a stress value of 19.06 %.

Study sites differed in average richness, diversity, and evenness (Table 3.2).

However, there was considerable overlap in values between sites, and cumulative values for richness and evenness yielded somewhat different rankings of sites than

those derived from plot averages. Overall, richness (S) was greater for Cedar Falls and

Hopkins than it was for Bethany and Sandstone; Hopkins had the greatest average

observed number of species (Sobs), but Cedar Falls had the greatest cumulative observed

number of species (Sobs) as well as cumulative estimated richness (SChao1). Both average

and cumulative diversity (1/D) and evenness E1/D were greater at Cedar Falls and

Sandstone than they were at and Bethany and Hopkins.

Table 3.2 Average ± SE and cumulative (Total) species richness (S), Simpson’s diversity (1/D), and

Simpson’s evenness (E1/D) for each site. Richness (S) Diversity (1/D) Evenness (E ) 1/D (S ) (S ) obs Chao1 Mean*** Total Total Mean** Total Mean*** Total

Bethany 15.70 ± 0.64 ab 37 39 2.68 ± 0.11 a 2.93 0.20 ± 0.01 ac 0.08

Cedar Falls 16.70 ± 0.63 a 40 48 3.73 ± 0.23 b 4.99 0.24 ± 0.02 ab 0.13

Hopkins 19.83 ± 0.66 c 38 42 3.08 ± 0.21 ab 3.15 0.17 ± 0.01 c 0.09

Sandstone 13.40 ± 0.80 b 37 41 3.65 ± 0.34 b 3.70 0.30 ± 0.03 b 0.11

Annotations: ** = P ≤ 0.01, *** = P ≤ 0.001 for differences between site means for a variable. Means with different letters within columns are significantly different at P ≤ 0.05 based on Tukey’s HSD following ANOVA. N = 120 for all analyses.

Rank abundance diagrams showed that all four sites exhibited a common pattern of dominance, having a few abundant species – mostly maples – and numerous rare ones, as indicated by their overall low evenness values (Figure 3.2). At three sites, ca.

50% of the stems observed belonged to one species. Highly shade tolerant maples (Acer rubrum and Acer saccharum) were the most abundant species at three sites. However, a species intermediate in shade tolerance, Quercus alba (white oak), was most abundant at Sandstone, where the two maple species were ranked three and four. Despite the overall dominance of shade-tolerant species at all sites, the top five ranked species were a mix of functional groups and tree tolerance types.

Figure 3.2 Rank abundance curves for each site with the two most abundant species labeled. Each point on the graph represents a species. Symbol shape and shading indicate species’ habit (shrub/vine) or, for trees, shade tolerance. Species labels are a concatenation of the first two letters of both the genus and the specific epithet (e.g.: acru = Acer rubrum). See Appendix for a full list of species, ranks, and abbreviations for each site.

With the exception of Sandstone, sites were dominated by shade tolerant

individuals (Figure 3.3a). Across all sites, shrubs and vines ranged from 14-28% of the

total stem density and were represented by 11-14 species. At three sites, Bethany,

Hopkins, and Cedar Falls, the majority of stems (56-61%) were highly shade tolerant 93

trees (8-10 species), while tree species intermediate in shade tolerance were lower in

abundance (10-23% of stems) and represented by 11-12 species. The opposite was true

at Sandstone where only 20% of stems, 12 species, were highly shade tolerant and the

majority of stems (57%) were classified as intermediate in shade tolerance (10 species).

Highly shade intolerant trees were relatively rare at all sites (< 1-7% of stems) and represented by 4-5 species.

Maples were much more abundant than oaks in the ground layers of all the sites except at Sandstone, where the opposite was true (Figure 3.3b). At Bethany, Hopkins, and Cedar Falls, maples outnumbered oaks about 4:1, 22:1, and 7:1, respectively. At

Sandstone, oaks outnumbered maples about 3:1. Thus, the majority of shade tolerant stems in Figure 3.3a were maples at all the sites. However, at Cedar Falls, Ulmus rubra contributed almost as much to the relative abundance of shade tolerant species as did maples (Figure 3.2). Further, most of the stems classified as intermediate in shade tolerance in Figure 3.3a were oaks at Bethany and Sandstone, but at Cedar Falls, the most abundant species in this tolerance class was Fraxinus quadrangulata. At Hopkins several species contributed to the small relative abundance of stems intermediate in

shade tolerance, but no single species dominated.

Figure 3.3 Densities of woody ground layer vegetation (a) by habit and shade tolerance guild (b) in either the Quercus (oak) or Acer (maple) genus at each site. For site, B = Bethany, C = Cedar Falls, H = Hopkins, and S = Sandstone. Color reflects tolerance guild membership in both a and b.

3.4.2. Differences in environmental characteristics between sites. Study sites differed in environmental characteristics, but also exhibited considerable overlap in variables (Table 3.3). Sites were most distinct in soil fertility

differences. Bethany and Cedar Falls were on opposite ends of a pH and total N nutrient

continuum. While Bethany had the lowest pH, low N, and lowest C:N ratio, Cedar Falls

had the highest pH and N levels. The opposite was true for organic and inorganic P;

Bethany had the highest levels, but Cedar Falls fell towards the low end of the range in

mean P values. Hopkins also had lower N and inorganic P levels, while Sandstone tended to be intermediate in all nutrient levels compared to other sites.

By contrast, sites were most similar in light and structural characteristics. All sites had equivalent mean values of canopy openness. However, the ground layers at Hopkins and Sandstone were exposed to different levels of photosnythetically active radiation

(PAR), with Hopkins at the high end of the light continuum and Sandstone at the low end. Likewise, Sandstone had higher adult density and basal area than all sites except, possibly, Bethany. The high basal area at Sandstone was driven primarily by oaks.

However, it was difficult to rank sites by the potential water stress experienced by understory plants because of the contrasting effects individual variables exerted on long term soil moisture status within a given site. For example, Sandstone had the lowest above-canopy heat load (heat load index), but also the greatest potential depletion of soil moisture by overstory transpiration since it had the greatest adult tree basal area. On the other hand, Cedar Falls represented the higher end of the heat load continuum, but its soils had the greatest potential water holding capacity and the lowest transpiration pull from its less dense overstory.

If sites are ranked 1-4 in each of these three variables (heat load index, plant available water, and tree BA), and their rankings summed with equal weighting, this

should give an general indicator of long term moisture status that ranges from 3 (dry) to

12 (wet). Bethany, Hopkins, and Sandstone all received a value of 7, indicating moderate

long term soil moisture status, but Cedar Falls received a 9, indicating that this site is more mesic than the rest.

Table 3.3 Environmental variable means ± SE. Variable Abbreviation Bethany Cedar Falls Hopkins Sandstone Heat load index *** HLI 0.94 ± 0.01 ab 0.97 ± 0 a 0.91 ± 0.02 b 0.79 ± 0.01 c Sand (%) *** 24.93 ± 2.96 a 15.87 ± 2.04 a 48.13 ± 3.3 b 45.7 ± 3.7 b Clay (%) *** 21.58 ± 1.36 ab 16.72 ± 0.73 c 24.43 ± 0.39 a 19.8 ± 0.89 bc Silt (%) *** 53.72 ± 2.43 a 67.43 ± 1.95 b 27.7 ± 3.14 c 34.51 ± 3.55 c Plant avail. water (%) *** PAW 18.18 ± 0.5 a 22.94 ± 0.52 b 13.09 ± 0.56 c 15.04 ± 0.69 c Organic matter (%) *** OM 5.57 ± 0.25 a 7.36 ± 0.5 b 4.39 ± 0.13 c 6.12 ± 0.2 a pH *** 3.73 ± 0.06 a 5.86 ± 0.12 b 4.75 ± 0.05 c 4.86 ± 0.06 c Total N (%) *** N 0.22 ± 0.01 a 0.33 ± 0.02 b 0.18 ± 0.01 a 0.27 ± 0.01 c Total C (%) *** C 3.36 ± 0.13 a 4.05 ± 0.28 b 2.39 ± 0.07 c 3.45 ± 0.13 ab C:N ratio *** C:N 16.47 ± 0.76 a 12.46 ± 0.25 b 13.5 ± 0.54 b 12.97 ± 0.41 b Inorganic P (µg ∙ g-1) *** Pi 10.29 ± 0.7 a 6.17 ± 0.36 b 5.22 ± 0.45 b 9.38 ± 0.49 a Organic P (µg ∙ g-1) *** Po 37.40 ± 2.52 a 19.99 ± 0.89 b 28.16 ± 1.61 c 30.96 ± 1.34 c PAR (mols ∙ m-2 ∙ d-1) * 6.47 ± 0.26 ab 6.01 ± 0.2 ab 6.67 ± 0.3 b 5.63 ± 0.18 a Canopy openness (%) 9.89 ± 0.31 10.05 ± 0.32 10.6 ± 0.3 10.57 ± 0.29 Tree BA (m2 ∙ ha-1) ** 33.74 ± 4.15 ab 25.08 ± 2.57 a 30.39 ± 2.85 a 49.17 ± 6.69 b Oak BA (m2 ∙ ha-1) *** 23.69 ± 4.09 ab 3.24 ± 1.42 c 16.73 ± 3.18 ac 39.04 ± 7.00 b Maple BA (m2 ∙ ha-1) *** 5.73 ± 1.24 a 15.80 ± 2.4 b 3.46 ± 1.04 a 5.29 ± 1.13 a Tree density (stems ∙ ha-1) *** Den 1,070 ± 96 ab 846 ± 99 a 770 ± 49 a 1,216 ± 75 b Oak density (stems ∙ ha-1) *** 160 ± 29 a 30 ± 13 b 160 ± 25 a 230 ± 34 a Maple density (stems ∙ ha-1) *** 597 ± 89 a 520 ± 80 a 240 ± 32 b 763 ± 68 a Annotations: * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001 for differences between site variable means. Means with different letters within rows are significantly different at the P ≤ 0.05 based on Tukey’s post hoc paired comparisons following ANOVA. N = 120 for all analyses. PAR = photosynthetically active radiation.

3.4.3. Vegetation-environment relationships within individual sites and differences in ground layer assemblages between future burn management units. NMDS ordinations of site specific woody ground layer plant abundance data do

not indicate distinct groupings of vegetation at any site, except in relation to future burn

units (Figure 3.4). While assemblages at Cedar Falls and Sandstone were significantly

dissimilar between future burn units (PERMANOVA, P < 0.001), at Bethany and Hopkins

they only approached significance (PERMANOVA, P = 0.085 and 0.065, respectively).

Components of the key environmental drivers investigated – soil fertility, soil

moisture, light availability, and overstory structure – were all related to vegetation

assemblage patterns at different sites (Figure 3.4; Table 3.4). However, not all drivers

were strongly related to vegetation patterns at all sites and no two sites had an identical

suite of variables associated with a particular driver. Further, environmental vectors tended to form gradients between plots associated with the two burn units at each site, suggesting spatial variation underlying both vegetation pattern and environmental variation.

Ordination overlays of common species’ weighted averages and summary data show that most species had patchy or clumped distributions (Figure 3.5). Despite this, the degree of clumping, as measured by Green’s index, which ranges from 0-1 in degree of clumping, was relatively small (< 0.10) for most species that were not rare (Appendix).

Generally, the most abundant species were broadly distributed at a given site and their

weighted averages were located towards the center of the ordination, in the middle of

the dominant gradients in composition and environment. Further, most species

consisted of individuals classified as seedlings (< 30 cm in height) (Figure 3.6). Finally,

species did not group on the ordination according to shade tolerance, even at Bethany

where light availability (PAR) was related to vegetation patterns. Instead, three

abundant shade intolerant species were located toward the middle of the light

availability gradient along with species both intermediate and extreme in shade

tolerance.

Bethany. NMDS ordination of ground layer woody vegetation at Bethany had strong linear relationships (R2) with a broad suite of environmental variables related to

soil fertility (N, organic P, and C:N ratio), water stress (% sand, % clay, and heat load

index), overstory structure (oak density), and light (PAR) (Figure 3.4; Table 3.4). These

constitute two main gradients in environmental variation. The first is a gradient from

plots on the right side of the ordination, mostly in burn unit 1, with fertile clay soils but

low light and heat loads to plots on the left side of the ordination, mostly in burn unit 2,

with low fertility sandy soils that receive greater amounts of light and heat. A second

orthogonal gradient in oak density seems to capture variation within both burn units.

Despite the strong relationship between vegetation community pattern and environmental variation, few individual species at Bethany were distributed away from the center of these gradients. The most abundant tree species in the ground layer at

Bethany were Acer rubrum, Sassifras albidum, and Quercus prinus (Figure 3.5). The weighted averages of these species all fell towards the center of the ordination, and individuals of these species were mostly seedlings (stems < 30 cm tall) (Figure 3.6).

Specifically, Q. prinus and A. rubrum were 99-100% seedlings, while S. albidum also had some taller individuals, being only 75% seedlings. Other common tree species at

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Bethany included Amelanchier arborea, Quercus alba, Prunus serotina, and Nyssa sylvatica.

Vaccinium spp., Viburnum acerfolium, and Parthenocissus quinquefolia were common among the shrub and vine species at Bethany (Figure 3.6). Of these, Vaccinium was found in the upper left quadrant of the ordination towards the low end of the nutrient gradient. P. quinquefolia was located in the upper right quadrant of the ordination along with F. americana and Vitis spp., and these all were primarily found in the western section of the study site, designated as burn unit 2, where soil clay and nutrient content were greater.

Cedar Falls. The ordination of ground layer woody vegetation at Cedar Falls had strong linear relationships with soil C, which may represent variation in soil fertility, and with the structural variable maple density (Figure 3.4; Table 3.4); these were both greater in burn unit 2. Other variables (not shown) that approached significance for

Cedar Falls were PAR and those related to soil nutrient levels (soil organic matter, pH, and N) (Table 3.4).

At Cedar Falls, there was a greater spread in species distributions away from the center of the ordination, likely because some species were found in greater abundance, or exclusively, in one burn unit compared to the other (Figure 3.5). Acer saccharum,

Ulmus rubra, and Fraxinus quadrangulata were the trees in greatest abundance at

Cedar Falls. Prunus serotina, Fraxinus americana, and Quercus alba where also common trees. All of these tree species were comprised mostly of seedlings (> 80%), especially A. saccharum and U. rubra, which were 98 and 99 % seedlings (Figure 3.6). Although most

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of these trees were found in the center of the ordination or slightly to the left, indicating

about equal presence in both burn units or slightly greater representation in unit 2, U.

rubra and Q. alba were located a farther to the right, indicating strong prevalence in

burn unit 1. Similarly, the dominant shrub and vine species, P. quinquefolia, Dicra

palustris, and Ribes cynosbati were also more abundant in unit 1, where vines and

shrubs were more abundant in general. Unlike most of these species, a few species at

Cedar Falls, which were also shade tolerant – Carpinus caroliniana, Ostrya virginiana, in unit 2 and Asimina triloba in unit 1 – were comprised of stems mostly greater than 30 cm (Figure 3.6).

Hopkins. The ordination at Hopkins was strongly related to variation in soil nutrients (C:N ratio, pH, and N) and water stress (heat load index) (Figure 3.4; Table 3.4).

Burn units at Hopkins primarily differed along opposing gradients in N and C:N ratio, with plots on the right side of the ordination, and mostly in burn unit 1, having higher N levels and lower C:N ratios. However, heat load and soil pH both increased in plots towards the bottom of the ordination and seemed to be a source of variation within both units.

A. rubrum was extremely abundant in plots located at Hopkins, with > 100,000 stems ha-1 (Figure 3.5). S. albidum, F. americana, Carya spp. and P. serotina, were also

common along with several other tree species. All of these tree species were located

towards the center of the ordination diagram, and, for most, over 90% of their stems

were seedlings (Figure 3.6). In contrast, F. americana and P. serotina had a greater

proportion of stems that were juveniles, as 61% and 74%, respectively, were classified

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as seedlings. Among the shrubs and vines, P. quinquefolia was a very abundant vine and

Rubus spp. were common shrubs at Hopkins. These were also centrally located on the

ordination. Two species clearly not centrally located were Cornus florida and U. rubra, which were located in the ordination quadrant with higher soil N and pH and heat load index. These are both shade tolerant species that were more abundant in unit 1 and exhibited a trailing groundcover habit at this study site. Of the three shade intolerant species at this site, two – L. tulipifera and Juniperus virginiana – were located closer to the higher end of the N gradient.

Sandstone. The ordination for Sandstone was strongly associated with the greatest number of environmental variables, which were related to soil fertility (soil pH,

N, C, C:N ratio, and organic matter), water stress (estimated plant available water, silt, sand, and organic matter), and overstory structure (canopy openness and oak density)

(Figure 3.4; Table 3.4). Unit 1 at Sandstone generally had higher soil nutrient levels, but lower potential soil water retention, while unit 2 had the opposite. Also, unit 2 had both greater densities of oaks in the overstory as well as greater canopy openness. However, this structural variability did not seem related to actual understory light availability patterns, which was not strongly linearly related to the ordination.

At Sandstone, Q. alba was the most abundant tree species, but A. rubrum, A. saccharum, F. americana, and U. rubra were also common trees at this site. P. quinquefolia was the most abundant vine, and like most of the abundant tree species, was found in the center of the ordination (Figure 3.5). Again, all of the common tree species were > 90% seedlings in composition, except for F. americana, which was 65%

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seedlings (Figure 3.6). F. americana was also unique because it was in the upper left quadrant of the ordination along with Aesculus glabra, Rubus spp., and Vitis spp. which were associated with unit 1 and higher soil nutrient levels, but lower soil water holding capacity.

The species quantified using aerial cover rather than stem counts, Smilax spp. and Toxicodendron radicans (poison ivy), were not overlaid onto ordination diagrams.

Three species of Smilax, S. hispda, S. rotundifolia, and S. glauca, were found primarily as small stems in low densities throughout all sites. However, at Bethany, S. glauca formed dense thickets in nearly half the plots. Similarly, T. radicans was represented by a few small stems at all the sites, but dominated and entire plot at Bethany and two at

Sandstone.

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Figure 3.4 Non-metric multidimensional scaling (NMDS) ordinations for each site with fitted environmental vectors significant at the P < 0.05 level. The length and angle of the vector represent the strength and direction of the relationship with ordination scores. Vector lengths cannot be compared between ordinations of different sites. Filled and open circles represent sample plots from the two spatially distinct burn units at each site. Axes tick marks indicate a 0.20 change in dissimilarity. Dashed reference lines mark axes origins and cross at the center of the ordination. Environmental vector labels follow Table 3.3. Stress was 19.40 % for the ordination for Bethany; 22.54 % for Cedar Falls; 23.81 % for Hopkins; and 24.87 % for Sandstone. 105

Table 3.4 Goodness of fit (R2) for environmental vectors with each set of site ordination scores. Labels follow Table 3.3. Those variables with P ≤ 0.05 (in bold) are depicted in Figure 3.5. Heat load index is not included for Cedar Falls, which is flat and therefore had no variation in heat load as calculated in this study. Variable Bethany Cedar falls Hopkins Sandstone R2 P R2 P R2 P R2 P Heat load index 0.26 0.014 0.22 0.042 0.00 0.969

Sand 0.42 0.002 0.06 0.406 0.04 0.555 0.30 0.011 Clay 0.47 0.002 0.02 0.695 0.04 0.572 0.17 0.067 Silt 0.18 0.069 0.04 0.573 0.05 0.500 0.23 0.031 Plant Available Water 0.11 0.197 0.04 0.564 0.05 0.512 0.21 0.047 Organic matter 0.15 0.103 0.19 0.065 0.05 0.522 0.30 0.010 pH 0.17 0.083 0.18 0.068 0.20 0.045 0.50 0.001 Total N 0.40 0.001 0.18 0.069 0.27 0.020 0.38 0.004 Total C 0.13 0.144 0.21 0.039 0.01 0.851 0.22 0.030 C-to-N ratio 0.43 0.001 0.02 0.805 0.28 0.015 0.24 0.032 Inorganic P 0.20 0.055 0.14 0.140 0.04 0.557 0.02 0.768 Organic P 0.24 0.020 0.07 0.405 0.08 0.354 0.08 0.368 PAR 0.23 0.034 0.19 0.063 0.08 0.359 0.05 0.471 Canopy openness 0.15 0.115 0.16 0.084 0.03 0.670 0.19 0.048 Tree BA 0.05 0.477 0.05 0.478 0.12 0.180 0.06 0.429 Oak BA 0.15 0.111 0.04 0.541 0.11 0.224 0.03 0.617 Maple BA 0.00 0.941 0.01 0.820 0.09 0.281 0.00 0.972 Tree density 0.11 0.207 0.19 0.056 0.01 0.863 0.02 0.782 Oak density 0.36 0.007 0.05 0.471 0.03 0.691 0.23 0.022 Maple density 0.05 0.524 0.43 0.002 0.10 0.255 0.13 0.156

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Figure 3.5 NMDS ordinations for each site with weighted average scores (centroids) of important species (those with importance values > 1). Density of each species is indicated by symbol size. Species distribution pattern is indicated by symbol fill. No species had a uniform distribution. Species labels are a concatenation of the first two letters of the genus and the specific epithet (e.g., acru = Acer rubrum) except where specimens were only identified to genus (e.g., vaccinium = Vaccinium spp.). Ordinations, axes, and reference lines match Figure 3.4, but plots and environmental vectors have been removed from the display for clarity. See Appendix Table 3.5-Table 3.8 for a full list of species, 107 abbreviations, densities, and V:M (V/M) values for each site.

Figure 3.6 NMDS ordinations for each site with weighted average scores (centroids) of important species (those with importance values > 1). Ordinations and species are identical to Figure 3.5 but information displayed about each species is different. The proportion of stems classified as small seedlings (stem height < 30 cm) of each species is indicated by symbol size. Shade tolerance guild is indicated by symbol fill. See Appendix Table 3.5-Table 3.8 for a full list of species, abbreviations, seedling proportions, and tolerance classifications for each site. 108

3.5. Discussion 3.5.1. Patterns in community pattern, structure, and diversity across sites. The average densities I encountered at my study sites (136,983-294,203 total

woody seedling stems ∙ ha-1 and about 107,000-250,000 tree seedling stems ∙ ha-1) were at the high end of the range found for trees in the ground layer of other eastern oak forests (27,300-130,000 tree stems ha-1) (Goebel and Hix 1996, Arthur et al. 1998,

McCarthy et al. 2001, Hutchinson et al. 2003b, Hart and Grissino-Mayer 2008). This may

be because 2006 was a mast year for oaks and maples at my sites. However, other

studies also found considerable variation in densities among stands at a single sampling

time point, some of which was related to differences in stand age (Goebel and Hix 1996)

or environment (McCarthy et al. 2001, Hutchinson et al. 2003b). Further, as in the

ground layers in other southern Ohio forests, most of these stems were seedlings (small

stems) (McCarthy et al. 2001, Hutchinson et al. 2003b).

Although sites differed in vegetation composition, diversity, evenness, and

density, the ground layers of the majority of my sites exhibited strong patterns of dominance by shade-tolerant maples, Acer rubrum (red maple) and Acer saccharum

(sugar maple). This is common in eastern oak forests, especially where oak species

dominate the overstory, but maples dominate the understory (Hutchinson et al. 2003b,

McEwan et al. 2005, Hart and Grissino-Mayer 2008). Red maple and sugar maple are the

primary replacement species of oak and hickory in the absence of fire and major canopy

disturbance, with red maple being ubiquitous, but sugar maple dominating on mesic

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nutrient rich sites (Abrams 1998). The success of red maple and its abundance in

seedling layers is generally attributed to its flexible germination strategy; it produces

abundant seed at an early age, germinates in the spring when conditions are favorable,

yet tolerates a variety of conditions, and even generates a short-term seed bank (Burns

and Honkala 1990, Abrams 1998, Hille Ris Lambers and Clark 2005).

Sandstone was uniquely dominated by Quercus alba (white oak) rather than a

maple species. Such differences in composition between stands also correspond with

those in southern Ohio oak forests, where it is not unusual to find stands where the

groundcover is dominated by oaks, especially Q. alba, particularly on older, drier sites

with a high basal area of oak in the canopy (Goebel and Hix 1996, Hutchinson et al.

2003b).

Although maples were generally a minimum of four fold greater than oaks, oaks were still very abundant in the seedling layers, ranging on average from 3,888 – 66,972

stems ∙ ha-1. Thus, if oaks are failing to regenerate it is not because of a lack of seed

sources or predation at the seed or seedling stage, at least during this single mast year.

However, oak regeneration failure more often occurs because long residence times in

shaded understories prevent recruitment of oaks beyond the seedling and sapling strata

(Lorimer et al. 1994, Rentch et al. 2003b). This can lead to stands in which oaks are

restricted to the seedling layer and a handful of aging canopy trees (Goebel and Hix

1997). This may also be the case at my study sites, where I found very few oaks larger >

30 cm in the understory. It remains to be seen whether oak seedling establishment is

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similarly high in other mast years and if this current cohort of oak seedlings will

contribute to advanced regeneration at my study sites.

3.5.2. Differences in study site environmental characteristics. Studies of the vegetation and environment of Appalachian and eastern oak forests have long recognized that differences in parent materials, topography, soil properties, and vegetation are all related and form complex feedbacks (Braun 1928b,

Anderson and Vankat 1978, Boerner 2006). All of these physical characteristics differed between my study sites, and some differences relate to well established relationships between species’ distributions and environmental tolerances. For example, A. saccharum was greatest in abundance at the mesic nutrient rich site, Cedar Falls (Burns and Honkala 1990, Abrams 1998), and Quercus prinus (chestnut oak) was most abundant on the dry south facing slopes underlain by shale at Bethany (Anderson and

Vankat 1978). However, without replication of this variation in environment over a broader landscape, I cannot attribute differences in assemblages to variation in specific environmental factors in my study. Thus, it is more informative to explore relationships between assemblages and environmental variables within sites.

The variation in values for soil physical and chemical properties was generally consistent with that of the larger area of the unglaciated Allegheny and Appalachian

Plateaus of southern Ohio and Kentucky (Muller 1982, Boerner et al. 2003, Small and

McCarthy 2005, Dyer 2010). In particular, the range in values for total N (0.18-0.33 %), total C (2.39-4.05 %) and C:N ratio (12.46-16.47) across my study sites was similar to that found by McCarthy and Small (2005) (total C, 1.75-2.5 %; total N, 0.1-0.25 %; and

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C:N ratio, 12-20) and Dyer(2010) (total N, 0.27-0.38 %) for forests in southeastern Ohio

where the region as a whole receives high N deposition loads (total organic and

inorganic N deposition of 20.5-24 kg ha-1 yr-1, National Atmospheric Deposition

Program/National Trends Network, http://nadp.sws.uiuc.edu, 2006).

The values obtained for understory light conditions through hemispherical

photography at my sites (total solar radiation, 14.7 % [a measure equivalent to global

light index below]; and average canopy openness, 9.9-10.6 %) were about twice as high

as those found in other oak-dominated forests in southeastern Ohio. In old growth

mesophytic forests at Dysart woods, McCarthy et al. (2001) observed average global

light index values of 3.5-5.8 %. McCarthy and Robison (2003) found global light index values ranging from 5.5-8.3 % (6.6 % on average) and canopy openness of 3.2-5.7 %

across mature second growth oak forest stands in southeastern Ohio, which they

considered high compared to other temperate and oak forests. These differences in understory light levels may be due, in part, to differences in stand density and composition (Canham et al. 1994).

Despite these high light levels, oak regeneration into the canopy may still be

light-limited at my study sites. Although oaks in ground layers survive and grow in height

under even lower light levels (Hodges and Gardiner 1993), especially when dense

understory layers have been removed (Lorimer et al. 1994). Even when light is optimal,

oaks are often outcompeted by more mesophytic species at moderate to intermediate

moisture levels (Hodges and Gardiner 1993). Oaks are relatively tolerant of moisture

and nutrient stress and capable of vigorous resprouting, in part, because of carbon

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allocation patterns that lead to high root:shoot ratios, but this allocation pattern also

causes oak to be rapidly over-topped by maples and other tree species under ideal resource conditions (Hodges and Gardiner 1993, Abrams 2006). Thus, at these study sites, where long term moisture is likely high to moderate, oak regeneration may be unsuccessful without understory disturbance to reduce competing vegetation. Iverson et al. (2008) suggest that opening the canopy to 8.5-19% via mechanical thinning and reducing competitors with repeated burning could result in oak regeneration on dry sites, but probably not on mesic sites.

3.5.3. Within-site vegetation-environment relationships patterns Vegetation patterns at the small scale (2-4 ha) of my study sites were strongly related to suites of environmental factors, especially soil fertility and moisture, that have been found in other studies at much larger scales (e.g., Hutchinson et al. 1999,

McEwan et al. 2005). In particular, variation in herb layer richness and composition with

N availability and C:N ratio have been observed across oak forest landscapes

(Hutchinson et al. 1999) and between sites within a single forest type in southeastern

Ohio (Small and McCarthy 2005). Likewise, at each of my different forest stands some combination of C:N, N, and C was strongly associated with vegetation pattern. The fact that these vectors were also perpendicular to the distribution of plots among burn units suggests that these sites are not yet experiencing the homogenization in nitrogen levels that has been predicted by Gilliam (2006) for landscapes in eastern temperate forests experiencing high levels of N deposition. Similarly, the association I found between vegetation patterns and indicators of long-term soil moisture (heat load index, soil

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texture, and estimated plant available water) along my small forest slopes correspond

with the variation in sapling and ground layer tree composition found with complex

topographically derived gradients in soil moisture at landscape scales (Iverson et al.

1997, Hutchinson et al. 2003b, McEwan et al. 2005).

Although light is important to understory tree growth and survival, light levels

were not strongly related to vegetation patterns on most site ordinations. This

corresponds with patterns in other southeastern Ohio oak forests where light levels did

not correlate with sapling composition (Hutchinson et al. 2003b). This is likely because in

both studies, light levels varied little and understory was dominated by shade tolerant

individuals. When tree seedling community patterns are related to variation in light

availability, this association is typically driven by light flexible, rather than shade

tolerant, species (Tinya et al. 2009). Further, although overstory trees can also influence

understory dynamics through resource imitation, especially light (Chen et al. 2004,

Barbier et al. 2008), I found strong relationships between ordinations and the density of the dominant canopy tree genus, rather than all species combined, suggesting that canopy influences were more related to seed availability and dispersal than resource limitation at my study sites.

My site specific ordinations did not find evidence for distribution of specific species along environmental gradients as found in other studies conducted at broader scales and or using larger size classes of trees. For example, Bigelow and Canham (2002) found Acer saccharum and Fraxinus americana saplings segregated at the upper end of a pH gradient, but Quercus rubra and Acer rubrum at the lower end, in a northern oak-

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hardwood forest. McEwan et al. (2005) found that across all forest strata, oaks were found in well lit upper slopes, while A. saccharum was found in mesic areas, and A. rubrum across all environmental conditions. Similarly, in southeastern Ohio, red and sugar maple saplings separated out along a moisture gradient, with red maple in more xeric and sugar maple in more mesic plots (Hutchinson et al. 2003b).

This discrepancy may be because the patchy distribution of seedlings at the scale of a single study site can be driven more by spatial variation in seed rain than environmental variables, especially in a mast year (Garcia and Houle 2005, Szewczyk and

Szwagrzyk 2010). This could also be the case at my study sites where most of the individuals I encountered were < 30 cm tall, and many of these were likely new germinants, particularly species of maples and oak. Further, almost all species at my study sites exhibited pronounced clumping. This corresponds with studies of spatial pattern of oaks and maples (McDonald et al. 2003) and seedlings and germinants in general (Szewczyk and Szwagrzyk 2010). In addition, large seed size is associated with relative resource limitation independence at the seedling stage, so the distribution of oak seedlings and other large seeded species may only be weakly related to environmental variation (Garcia and Houle 2005).

The prevalence of masting species – species that produce seed crops only intermittently and exhibit large variation in annual recruitment – in the ground layers of my study sites highlights the limitations inherent in interpreting data acquired at a single sampling time. Many studies in temperate forests have found larger temporal variability in seedling recruitment and survival compared to spatial variability (Beckage and Clark

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2005), due in part, to mast seeding species (Szewczyk and Szwagrzyk 2010). Further,

temporal variation in masting can also interact with variation in conditions for

establishment to influence seedling dynamics (Frey et al. 2007). Thus, the ground layer

of oak forests is incredibly dynamic and the strength of vegetation-environment

relationships may also vary temporally.

3.5.4. Differences in ground layer assemblages between future burn management units within sites. Vegetation assemblages, and sometimes environmental variables, differed between the two burn units at each site, especially Cedar Falls and Sandstone. At Cedar

Falls, the lack of differences in environmental factors, combined with the distance between burn units, suggests that differences in vegetation patterns may be related to variation in site history. In southeastern Ohio forests, historic land-use practices such as cultivation, pasturing, and clearcutting can account for as much variation in species composition in the herb and tree strata as environmental gradients themselves (Small and McCarthy 2005, Dyer 2010). Both Cedar Falls burn units were likely clearcut at least when the furnace at Cedar Mills was in operation and also show evidence of more recent selective logging. However, the possible difference in timing, frequency, and intensity of these events between the two sites is not known.

The pronounced differences in environment and vegetation between both study sites and even closely located future burn management units may pose may pose difficulties in interpreting treatment effects of proposed prescribed burns. Given the greater consistency in the abundance of shade tolerance guilds and oaks versus maples across study sites, it may be more informative to compare effects of disturbance among

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guilds or functional groups rather than individual species. Such an approach has been used to describe vegetation response to fire in oak forests (e.g., Signell et al. 2005) and to study general disturbance effects (e.g., Mou et al. 2005). In fact, Sutherland et al.

(2000) have already categorized species into regeneration guilds for eastern oak forests.

3.5.5. Conclusions and management implications There were distinct assemblages of woody ground layer vegetation and suites of environmental characteristics at each of my study sites in the topographically complex

Appalachian escarpment in southern Ohio. Despite these differences, the ground layers of three sites were dominated by shade tolerant vegetation, primarily maple species.

This corresponds with a common phenomenon in oak-dominated forests in which oaks and hickories are being replaced by more mesophytic and often shade tolerant species in the absence of disturbance. However, oaks were still present in abundance at all sites, even dominating the groundcover at one the site. Thus, if oaks are indeed failing to regenerate at these sites, it is not from lack of seeds and seedlings.

Within sites, patterns in composition and abundance of woody ground layer vegetation were less distinct, but were related to fertility, soil moisture status, and canopy structure, similar to studies conducted at broader scales. This was true even for nitrogen content and C:N ratio, which one might expect to be more homogeneous given the input from atmospheric deposition. Conversely, individual species did not tend to be arrayed on gradients related to their shade tolerance status or moisture or nutrient requirements. Rather, the abundance of seedlings and mast seeding species suggests that the availability of seed sources may be a greater driver of individual species

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patterns than abiotic variation within sites in this forest stratum. The degree to which

the community patterns found in this snapshot vary over time will be the subject of future studies.

On site-level ordinations, ground layers were patchy and exhibited strong differences in composition and abundance between future burn management units. This stresses that one should use caution when interpreting effects of disturbance treatments, such as fire, in heterogeneous forest understories. Pretreatment data such as this is essential, and analysis of vegetation responses at the functional group level should be strongly considered.

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3.6. Appendix Table 3.5 Summary of all species in the ground layer at Bethany. Rank is the rank abundance of the species. Freq = Frequency. Importance value (IV) is the average of relative frequency and relative density. V/M = the variance to mean ratio, an indicator of clustering where V/M < 1 indicates more even distribution than expected by chance, V/M = 1 indicates an even distribution, and V/M > 1 indicates a clustered distribution. Bolded V/M ratio indicates a V/M ratio significantly greater than 1 at α < 0.05. V/M ratios not in bold are not significantly different than 1; no species had a V/M ratio significantly < 1. Green’s = Green’s index of clumping where 0 = even distribution and 1 = maximum clumping. Seedlings = the percent of stems in the regeneration layer < 30 cm in height. Tree tolerance classifications (Tol) are abbreviated as T = shade tolerant, M = intermediate in shade tolerance, and I = intolerant of shade. * indicates abundance measured in cover not counts. Species Abbreviation Rank Density Freq IV V/M Green’s Seedlings Tol stems ∙ ha-1 % % %

Trees Acer rubrum acru 1 16,5504 100 31.55 159.89 0.02 99 T Acer saccharum acsa 26 68 10 0.35 1.41 0.14 100 T Amelanchier arborea amar 5 15,822 83 5.54 105.62 0.11 71 M Asimina triloba astr 28 51 7 0.24 1.62 0.31 33 T Carpinus caroliniana caca 27 68 13 0.47 0.90 -0.03 75 T Carya spp. carya 11 1,732 87 3.26 2.47 0.01 98 M Celtis occidentalis ceoc 29 51 7 0.24 1.62 0.31 100 M Cornus florida cofl 23 187 23 0.83 1.78 0.08 73 T Fagus grandifolia fagr 33 17 3 0.12 1.00 - 0 T Fraxinus americana fram 21 407 30 1.10 3.57 0.11 62 M Fraxinus quadrangulata frqu 34 17 3 0.12 1.00 - 100 M Juglans nigra juni 31 34 7 0.23 0.97 -0.03 50 I Juniperus virginiana juvi 25 85 17 0.59 0.86 -0.04 100 I Liriodendron tulipifera litu 15 985 63 2.34 3.78 0.05 88 I Magnolia acuminata maac 17 696 23 0.92 13.90 0.32 73 M Nyssa sylvatica nysy 9 2,529 87 3.40 14.43 0.09 62 T 119 Oxydendrum arboreum oxar 24 170 13 0.49 2.76 0.20 30 T

Pinus spp. pinus 14 1,613 77 2.90 2.70 0.02 100 I Prunus serotina prse 8 4,770 90 3.89 22.05 0.08 82 M Quercus alba qual 7 9,133 83 4.41 29.35 0.05 99 M Quercus imbricaria quim 35 17 3 0.12 1.00 - 100 M Quercus prinus qupr 2 27,841 93 7.93 82.57 0.05 100 M Quercus rubra quru 16 934 73 2.67 2.11 0.02 87 M Quercus velutina quve 13 1,664 90 3.36 2.15 0.01 91 M Sassafras albidum saal 4 18,997 100 6.65 25.26 0.02 75 I Shrubs and vines Celastrus scandens cesc 32 17 3 0.12 1.00 - 100 Corylus americana coam 12 1,715 10 0.63 54.54 0.54 30 Lindera benzoin libe 30 51 7 0.24 1.62 0.31 100 Parthenocissus quinquefolia paqu 10 2,496 50 2.14 24.45 0.16 100 Rubus spp. rubus 22 357 23 0.86 4.35 0.17 100 Smilax spp.* smilax 16.79 ± 3.24 100 - - - - Toxicodendron radicans* tora 1.72 ± 1.67 10 - - - - Vaccinium spp. vaccinium 3 23,309 60 6.02 152.57 0.11 91 Viburnum acerifolium viac 6 11,188 47 3.50 150.25 0.23 62 Viburnum dentatum vide 20 475 30 1.11 5.91 0.18 79 Viburnum prunifolium vipr 18 679 7 0.34 36.07 0.90 75 Vitis spp. vitis 19 526 37 1.35 4.30 0.11 94 Total density 294,203 Tolerance guilds shrubs and vines 40,811 18 156.10 81 tolerant 168,594 18 448.45 99 intermediate 63,085 48 93.70 90 intolerant 21,713 44 47.85 77

120 Oaks and maples

maples 165,572 55 322.15 99 oaks 39,589 57 93.14 99

Table 3.6 Summary of all species in the ground layer at Cedar Falls. Columns and abbreviations follow Appendix Table 3.5 Species Abbreviation Rank Density Freq IV V/M Green’s Seedlings Tol stems ∙ ha-1 % % %

Trees Acer rubrum acru 26 289 20 0.69 7.26 0.39 100 T Acer saccharum acsa 1 65,597 100 18.80 148.69 0.04 99 T Aesculus glabra aegl 30 204 23 0.77 2.52 0.14 0 T Amelanchier arborea amar 33 85 10 0.33 2.10 0.28 40 M Asimina triloba astr 10 1,969 27 1.30 34.17 0.29 29 T Carpinus caroliniana caca 13 747 40 1.42 4.92 0.09 18 T Carya cordiformis caco 20 509 47 1.57 2.34 0.05 73 M Carya spp. carya 24 407 57 1.86 0.90 0.00 83 M Celtis occidentalis ceoc 17 662 50 1.71 3.83 0.07 62 M Cercis canadensis ceca 27 272 33 1.10 1.52 0.03 100 T Cornus florida cofl 29 255 33 1.10 1.62 0.04 67 T Fraxinus americana fram 7 6,417 100 4.64 11.36 0.03 85 M Fraxinus quadrangulata frqu 3 27,774 100 9.75 63.19 0.04 98 M Gleditsia triacanthos gltr 38 17 3 0.11 1.00 - 100 I Juniperus virginiana juvi 32 119 23 0.75 0.79 -0.04 86 I Liriodendron tulipifera litu 28 272 17 0.58 5.01 0.27 100 I Morus rubra moru 36 51 10 0.32 0.93 -0.04 67 T Ostrya virginiana osvi 11 1,816 67 2.51 6.59 0.05 9 T Pinus spp. pinus 37 34 3 0.11 2.00 1.00 100 I

121 Prunus serotina prse 6 9,015 93 5.06 22.80 0.04 83 M

Quercus alba qual 9 2,037 67 2.56 11.07 0.08 88 M Quercus muehlenbergii qumu 15 679 43 1.51 4.26 0.08 75 M Quercus rubra quru 14 696 67 2.24 1.99 0.02 78 M Quercus velutina quve 22 475 60 1.98 1.33 0.01 89 M Sassafras albidum saal 34 68 10 0.33 1.41 0.14 100 I Ulmus rubra ulru 2 56,735 100 16.68 97.78 0.03 82 T Shrubs and vines Celastrus scandens cesc 21 509 23 0.85 4.14 0.11 90 Dirca palustris dipa 5 10,016 50 3.95 58.32 0.10 89 Lindera benzoin libe 12 866 57 1.97 2.97 0.04 59 Parthenocissus quinquefolia paqu 4 14,634 100 6.61 53.96 0.06 100 Rhus aromatica rhar 31 170 7 0.25 6.69 0.63 40 Ribes cynosbati ricy 8 2,869 20 1.31 57.72 0.34 92 Rosa spp. rosa 19 526 10 0.44 15.92 0.50 61 Rubus spp. rubus 18 577 27 0.97 4.61 0.11 88 Smilax spp.* smilax 0.73 ± 0.18 57 - - - - Toxicodendron radicans* tora 0.03 ± 0.03 3 - - - - Vaccinium spp. vaccinium 35 68 3 0.12 4.00 1.00 75 Viburnum dentatum vide 25 373 30 1.02 3.76 0.13 64 Viburnum prunifolium vipr 23 441 27 0.93 3.40 0.10 73 Vitis spp. vitis 16 679 53 1.82 2.19 0.03 98 Total density 208,930 Tolerance guilds shrubs and vines 31,729 27 63.66 92 tolerant 127,935 32 214.40 88 intermediate 48,756 50 70.52 91 intolerant 509 9 3.32 97

122 Oaks and maples

maples 65,886 60 210.19 99 oaks 3888 39 8.29 84

Table 3.7 Summary of all species in the ground layer at Hopkins. Columns and abbreviations follow Table 3.5. Species Abbreviation Rank Density Freq IV V/M Green’s Seedlings Tol stems ∙ ha-1 % % %

Trees Acer rubrum acru 1 141,516 100 27.75 114.32 0.01 99 T Acer saccharum acsa 16 2,156 83 2.60 10.34 0.07 87 T Amelanchier arborea amar 11 3,226 63 2.26 17.82 0.09 64 M Carpinus caroliniana caca 7 3,803 67 2.45 28.34 0.12 40 T Carya cordiformis caco 29 85 17 0.46 0.86 -0.04 60 M Carya spp. cary 5 5,636 100 3.66 5.42 0.01 94 M Celtis occidentalis ceoc 28 102 17 0.46 1.17 0.03 100 M Cercis canadensis ceca 10 3,327 80 2.72 10.42 0.05 67 T Cornus florida cofl 15 2,411 53 1.85 36.42 0.25 85 T Crataegus spp. crataegus 32 34 3 0.09 2.00 1.00 100 T Fagus grandifolia fagr 27 255 33 0.93 1.62 0.04 67 T Fraxinus americana fram 4 8,302 97 4.04 21.08 0.04 61 M Fraxinus quadrangulata frqu 30 85 17 0.46 0.86 -0.04 60 M Gleditsia triacanthos gltr 31 51 7 0.19 1.62 0.31 100 I Juniperus virginiana juvi 23 373 40 1.13 2.72 0.08 95 I Liriodendron tulipifera litu 8 3,786 87 2.98 8.37 0.03 72 I Nyssa sylvatica nysy 12 3,039 83 2.76 9.62 0.05 75 T Ostrya virginiana osvi 21 985 30 0.97 16.09 0.26 86 T Pinus spp. pinus 33 34 7 0.18 0.97 -0.03 100 I

123 Prunus serotina prse 6 5,585 100 3.65 9.00 0.02 74 M

Quercus alba qual 14 2,546 70 2.31 21.37 0.14 87 M Quercus imbricaria quim 34 17 3 0.09 1.00 - 100 M Quercus prinus qupr 35 17 3 0.09 1.00 - 100 M Quercus rubra quru 22 764 60 1.73 2.98 0.05 58 M Quercus velutina quve 13 3,022 100 3.20 2.44 0.01 89 M Sassafras albidum saal 3 10,220 97 4.38 11.63 0.02 79 I Ulmus rubra ulru 19 1,528 50 1.60 10.46 0.11 89 T Shrubs and vines Celastrus scandens cesc 17 1,935 60 1.94 6.83 0.05 98 Corylus americana coam 26 272 13 0.40 9.15 0.54 31 Lindera benzoin libe 24 340 27 0.77 2.83 0.10 70 Parthenocissus quinquefolia paqu 2 69,570 100 15.00 44.9 0.01 100 Rosa spp. rosa 18 1,545 30 1.07 14.38 0.15 87 Rubus spp. rubus 9 3,735 73 2.61 19.87 0.09 70 Smilax spp.* smilax 1.07 ± 0.23 90 - - - - Toxicodendron radicans* tora 3.38 ± 2.61 13 - - - - Vaccinium spp. vaccinium 36 17 3 0.09 1.00 - 100 Viburnum prunifolium vipr 25 289 27 0.76 1.91 0.06 71 Vitis spp. vitis 20 1,358 80 2.37 2.59 0.02 88 Total density 281,962 Tolerance guilds shrubs and vines 79,060 28 150.37 97 tolerant 159,053 42 326.53 96 intermediate 29,386 46 18.94 75 intolerant 14464 39 21.66 78 Oaks and maples maples 143,672 92 245.81 99

124 oaks 6,366 39 12.72 85

Table 3.8 Summary of all species in the ground layer at Sandstone. Columns and abbreviations follow Table 3.5. Species Abbreviation Rank Density Freq IV V/M Green’s Seedlings Tol stems ∙ ha-1 % % %

Trees Acer rubrum acru 3 11,442 97 7.95 19.87 0.03 97 T Acer saccharum acsa 4 9,099 93 6.97 28.62 0.05 96 T Aesculus glabra aegl 23 340 43 1.82 1.48 0.03 30 T Amelanchier arborea amar 14 662 33 1.54 17.30 0.43 72 M Asimina triloba astr 19 441 20 0.94 8.65 0.31 35 T Carpinus caroliniana caca 15 594 30 1.39 4.56 0.10 43 T Carya cordiformis caco 24 289 47 1.93 0.94 0.00 100 M Carya spp. carya 9 1,205 70 3.17 3.92 0.04 97 M Celtis occidentalis ceoc 29 102 10 0.43 2.21 0.24 67 M Cornus florida cofl 18 543 23 1.11 8.92 0.26 91 T Crataegus spp. crataegus 27 153 10 0.45 3.71 0.34 100 T Fagus grandifolia fagr 34 17 3 0.14 1.00 - 0 T Fraxinus americana fram 5 6,994 53 4.64 36.97 0.09 65 M Gleditsia triacanthos gltr 33 17 3 0.14 1.00 - 100 I Juniperus virginiana juvi 32 51 10 0.41 0.93 -0.04 100 I Liriodendron tulipifera litu 28 119 7 0.30 3.45 0.41 100 I Morus rubra moru 31 85 17 0.68 0.86 -0.04 80 T Nyssa sylvatica nysy 30 102 13 0.56 1.52 0.10 100 T Ostrya virginiana osvi 21 424 47 1.98 1.25 0.01 52 T Prunus serotina prse 8 2,173 90 4.31 3.60 0.02 98 M Quercus alba qual 1 65,207 100 27.71 287.89 0.07 100 M

125 Quercus prinus qupr 12 917 3 0.46 54.00 1.00 100 M

Quercus rubra quru 17 560 40 1.77 2.34 0.04 91 M Quercus velutina quve 25 289 23 1.02 2.52 0.10 94 M Sassafras albidum saal 13 883 33 1.62 20.49 0.38 96 I Ulmus rubra ulru 6 4,414 80 4.74 14.92 0.05 95 T Shrubs and vines Celastrus scandens cesc 20 424 13 0.68 11.18 0.42 100 Lindera benzoin libe 11 968 43 2.05 8.76 0.14 51 Parthenocissus quinquefolia paqu 2 22,681 97 12.05 115.77 0.09 100 Rhamnus frangula rhfr 35 17 3 0.14 1.00 - 100 Rosa spp. rosa 26 289 10 0.50 7.75 0.42 71 Rubus spp. rubus 10 1,205 27 1.48 16.97 0.23 79 Smilax spp.* smilax 0.25 ± 0.05 50 - - - - Toxicodendron radicans* tora 0.05 ± 0.03 10 - - - - Viburnum acerifolium viac 22 390 7 0.40 16.16 0.69 83 Viburnum prunifolium vipr 7 3,293 47 3.03 49.22 0.25 78 Vitis spp. vitis 16 594 33 1.52 4.62 0.11 94 Total density 136,983 Tolerance guilds shrubs and vines 29,862 19 122.81 94 tolerant 27,655 34 32.65 93 intermediate 78,397 34 333.31 96 intolerant 1,070 9 17.99 97 Oaks and maples maples 20,542 95 23.61 97 oaks 66,972 28 376.57 100

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Chapter 4: Composition and structure of oak-dominated forests of the Bluegrass Region in southern Ohio. 4.1. Abstract A shift from oak (Quercus spp.) to shade-tolerant species, primarily maples, has been documented in oak-dominated forests throughout the eastern US. The extent to which this is also the case in oak-dominated forests in the Bluegrass Region of southern

Ohio is uncertain because they have not been characterized extensively. I describe the structure and composition of the four, second-growth forest stands, with different soil characteristics and underlying bedrock. At each site, I sampled trees ≥2 cm dbh in 16, square (0.0625-ha) plots. Consistent with other studies documenting a shift from oaks to maples, oaks were typically canopy dominants, but nearly absent from sapling layers, suggesting negligible recruitment. By contrast, the sapling layers of all sites were dominated by shade-tolerant hardwoods, especially maples (Acer rubrum, Acer saccharum), indicating the potential for continuous recruitment. Subtle differences in size structures between sites also suggest that the general transition from oaks to maples will take on unique trajectories at each site. The prevalence of shade-tolerant vegetation and lack of oak recruitment may be driven by changes in disturbance regimes. If management goals are to maintain oak-dominated stands, reintroduction of disturbances that regenerate oak – overstory thinning to >20% canopy openness and surface fires – will be necessary.

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4.2. Introduction Oak species (Quercus spp.) were once dominant across much of the eastern

deciduous forest prior to Euro-American settlement (Abrams 1996, Goebel and Hix

1996). But current oak-dominated forests, both old- and second-growth, are undergoing

a similar shift in species composition from oak to shade-tolerant species, primarily

maples (Acer spp.) (McEwan et al. 2011). Oaks still dominate the canopy layer of these

forests and are abundant in the seedling layer, but they are scarce in understory layers,

indicating recruitment failure (Goebel and Hix 1996, 1997, McCarthy et al. 2001, Abrams

2003). In contrast, shade-tolerant species, especially maples have become increasingly abundant (Goebel and Hix 1996, 1997, Abrams 1998, McEwan et al. 2011). Given an increase in dominance over time, shade-tolerant species will likely replace oaks entirely,

especially in mesic stands (Lorimer 1984, Goebel and Hix 1996, Pierce et al. 2006).

Altered disturbance regimes, especially fire suppression, are commonly

hypothesized as the driver of this broad oak-to-maple shift (Abrams 2003, Nowacki and

Abrams 2008). Once-frequent surface fires are thought to have deterred recruitment of

fire-sensitive shade-tolerant hardwoods, such as maples, and fostered the regeneration

of disturbance-dependent oaks, hickories, and other species less tolerant of shade

(Abrams 1992, Goebel and Hix 1996, Abrams 2003). Upland oaks, in particular, have a

suite of physiological characteristics that make them resistant to both fire and drought,

but sensitive to competition (Abrams 1996, 2003). But following decades of fire

suppression beginning in the early 20th century, maples have increased in abundance and oaks are failing to regenerate under their dense understories (Lorimer et al. 1994).

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Although the oak-to-maple shift is widespread, it has not been quantitatively described in oak-dominated forests in the Bluegrass Region of southern Ohio. Prior to

Euro-American settlement, oak-dominated forests covered much of the Appalachian

escarpment in the Bluegrass Region of southern Ohio (Braun 1950). The varied

topography and geology of the region contributes to diverse forest composition, with

canopy dominants typically differing with substrate (Braun 1928b, Anderson and Vankat

1978). Current forests are second-growth because of a history of extensive clearcutting and timber harvesting that began in the late 1700s for agriculture and the charcoal iron industry (Evans and Stivers 1900). As a result, oak-dominated forests in the Bluegrass

Region of southern Ohio are highly fragmented and anthropogenically altered. Thus, it is

likely that they are also generally experiencing compositional shifts, but the pattern may

differ depending on their geology, topography, and stand history.

The goal of this study was to describe the current composition and structure of

oak-dominated forests across a diversity of substrate types in the Bluegrass Region of

southern Ohio and to determine if they too are characterized by a compositional shift. I

focused on the following questions: (1) What are the structural patterns of shade

tolerant species (especially maples) and species less tolerant of shade (especially oaks)?

(2) Are these size structures indicative of an oak-to-maple shift and increasing

dominance by shade-tolerant species? (3) Do different sites share a common pattern in

size structures?

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4.3. Methods 4.3.1. Study sites This study was conducted at the Edge of Appalachia Preserve and Strait Creek

Preserve along the Appalachian Escarpment in southern Ohio. The study region and study sites are fully described in Chapter 1. Briefly, I selected four mature (at least 70 years old) second-growth forest study sites where the largest trees were oaks (Quercus spp.). These sites differ in physical characteristics. Hopkins and Bethany are both underlain primarily by shale, Sandstone by sandstone, and Cedar Falls by dolomitic limestone. Except for Cedar Falls, which is relatively flat, the sites are located on steep to rolling slopes and differ in dominant aspect. Bethany faces southeast, Sandstone faces north, and Hopkins faces west-northwest.

4.3.2. Vegetation sampling and analysis At each site, I measured the diameter at breast height (dbh; measured at 1.5 m above ground) of trees ≥2 cm dbh in 16, square 25 × 25 m (0.0625-ha) plots. Plots were sampled once over the summers of 2007 and 2008. Dbh was used to calculate basal area, the cross-sectional area occupied by each tree trunk, for each tree. For trees with multiple trunks 1.5 m above ground, basal area was summed across live trunks.

To address the hypotheses that these forests are transitioning from species less tolerant of shade (e.g., oaks) to shade-tolerant species (e.g., maples), I examined the abundance of trees in each shade tolerance guild and in the Quercus and Acer genera across broad structural categories. I classified trees by shade tolerance (shade tolerant, intermediate, or light demanding, i.e. intolerant) based on compilations by White

(1983b), Burns and Honkala (1990), and Sutherland et al. (2000). When shade tolerance

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classifications differed among sources, the category used by the most sources was adopted. I classified trees into three broad structural classes (saplings, poles, and canopy adults) based on (Poulson and Platt 1996). Saplings include stems ≥ 2 cm dbh but < 15 cm dbh. Poles include stems ≥ 15 cm dbh but < 45 cm dbh. Canopy adults include stems ≥ 45 cm dbh. If a tree had multiple trunks, the largest trunk was used to determine structural class.

I explored the specific differences in species composition and structure by examining the importance value (IV) for each species in each structural category. IV is the average of relative frequency, relative density, and relative dominance (i.e. relative basal area).

I examined more subtle patterns in composition and structure among sites by plotting the relative abundance of important genera (those having an IV > 10 for at least two broad structural categories). Seedling data collected in the summer of 2006

(Chapter 3) from these study sites were used to supplement the adult tree data. I also examined the distributions of abundant species in the Quercus and Acer genera across narrower size class categories.

4.4. Results Tree density and basal area averaged 920 (± 56 SE) stems ∙ ha-1 and 32 (± 1.2 SE) m2 ∙ ha-1, respectively across all sites (Table 4.1). Hopkins was both the least dense (794 stems ∙ ha-1) and had the smallest basal area (29.2 m2 ∙ ha-1). Sandstone was the opposite; having both the greatest stem density (1048 stems ∙ ha-1) and basal area (34.5

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m2 ∙ ha-1). I recorded a total of 34 tree species, with an average of 22 species occurring

at a site (Table 4.1).

The relative importance of shade tolerance guilds differed among structural classes. Shade tolerant species were the most abundant and important saplings, but species intermediate in shade tolerance outnumbered shade tolerant species in the pole structural class, and dominated the canopy adult class (Figure 4.1a; Table 4.2-Table 4.4).

Cedar Falls was an exception; shade tolerant species were the most abundant poles and

canopy adults (Figure 4.1a).

The relative importance of oaks and maples also differed among structural

classes. Maples (Acer rubrum and Acer saccharum), were the most important and

abundant saplings, but oaks outnumbered maples in the pole structural class, and

dominated the canopy adult class at most sites (Figure 4.1b; Table 4.2-Table 4.4). Again,

Cedar Falls was an exception because maples, not oaks, were the most abundant poles

and canopy adults, and oaks were relatively unimportant (Figure 4.1b).

Table 4.1 Stand structural characteristics and species richness across sites. Bethany Cedar Falls Hopkins Sandstone mean SE Density (stems ∙ ha-1) 973 865 794 1048 920 56 BA (m2 ∙ ha-1) 33.4 31.0 29.2 34.5 32.0 1.2 Species richness 19 24 22 24 22 1

The general shift in relative abundance from genera less tolerant of shade

(mainly oaks), in large size classes, to shade tolerant genera (mainly maples), in smaller

size classes, took on a slightly different pattern at each site (Figure 4.2). At Bethany, Acer

rubrum (red maple) was the most important species in the sapling structural class, but a

different shade tolerant species, Nyssa sylvatica (black tupelo), was also relatively 132

abundant from 2-10 cm dbh (Table 4.2; Figure 4.2). In the pole structural class, importance shifted to the oak species Quercus prinus (chestnut oak) and Quercus alba

(white oak), but red maple was still the second most important species and remained relatively abundant until about the 30 cm dbh size class (Table 4.3; Figure 4.2). Oaks dominated after about 30 cm dbh, and white oak and chestnut oak were the most important canopy adult species (Table 4.4; Figure 4.2). However, there were still several large red maple trees from 40-55 cm dbh, making red maple the third most important canopy adult species. It is also noteworthy that one very shade-sensitive species,

Lirodendron tulipifera (tulip poplar), was intermittently abundant throughout the sapling

and pole structural classes.

At Cedar Falls, Acer saccharum (sugar maple) was the most important species in

every structural class, but nearly the only species in the sapling class (Table 4.2; Figure

4.2). Although missing in several smaller size classes, Fraxinus quadrangulata (blue ash)

was second in importance in the pole structural class, instead of oak, and peaked in

relative abundance around 20 cm dbh (Table 4.3; Figure 4.2). Sugar maple also

dominated the larger size classes from about 30-55 cm dbh, but white oaks and Quercus

rubra (red oaks) comprised the largest trees at the site and were second and third in

importance, respectively, among canopy adults (Table 4.4; Figure 4.2).

At Hopkins, red maple and sugar maple co-dominated the sapling structural

class, but not to the degree as at other sites (Table 4.2; Figure 4.2). Although red maple

was third in importance in the pole structural class, maples decreased in relative

abundance as size class increased, and Carya glabra (pignut hickory) – a species

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intermediate in shade tolerance – and Quercus velutina (black oak) were the most important poles (Table 4.3; Figure 4.2). Finally, the canopy adults at Hopkins were nearly exclusively black oak and white oak (Table 4.4; Figure 4.2). Similar to Bethany, tulip poplar remained abundant throughout the sapling and pole structural classes.

The transition in size class from oak to maple was much sharper at Sandstone compared to other sites (Figure 4.2). Sugar maple and some red maple dominated the sapling structural class (Table 4.2; Figure 4.2). After about 20 cm dbh, dominance shifted to white oak and pignut hickory, and all but two trees greater than 40 cm were oak

(white, black, or red) (Table 4.3, Table 4.4; Figure 4.2).

Seedling data from 2006 sheds further light on size class patterns. At each site, oak seedlings sampled in 2006 had greater relative abundance than oak trees in the small size classes (Figure 4.2). At Sandstone, oaks made up over 60 % of the seedling layer. However, maples were also high in relative abundance in the seedling layer.

Although different species of oaks and maples were abundant at different sites, each genus had similar size class distributions across all sites. Size class structures of maple species were unevenly distributed and exhibited abundant representation in small size classes (<15 cm dbh) (Figure 4.3a); stem densities in the lowest size-class (2-5 cm dbh) ranged from 60-330 stems ∙ ha-1. By contrast, size structures of dominant oaks at all sites displayed bell-shaped or even-aged diameter distributions and had almost no stems < 15 cm dbh, particularly at Bethany, Cedar Falls, and Sandstone (Figure 4.3b).

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Figure 4.1 Densities of trees in each structural class at each site sorted by (a) shade tolerance guild and (b) in Quercus (oak) or Acer (maple) genus. Saplings include stems ≥ 2 cm dbh but < 15 cm dbh. Poles include stems ≥ 15 cm dbh but < 45 cm dbh. Canopy adults include stems ≥ 45 cm dbh. Sites: B = Bethany, C = Cedar Falls, H = Hopkins, and S = Sandstone. Color reflects tolerance guild membership in both a and b.

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Table 4.2 Importance values for saplings, stems ≥ 2 cm and < 15 cm dbh, at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant. Species name Tolerance Bethany Cedar Falls Hopkins Sandstone Acer rubrum T 56.6 -- 20.3 19.0 Acer saccharum T 2.1 55.7 18.1 51.3 Aesculus glabra T -- 4.1 -- 6.1 Amelanchier arborea M 3.7 0.6 1.3 0.5 Asimina triloba T -- 4.3 -- 2.9 Carpinus caroliniana T -- 2.6 1.9 1.6 Carya glabra M 2.5 1.8 2.0 3.5 Carya ovata M -- 0.6 0.5 -- Carya tomentosa M 1.3 -- -- 0.7 Cercis canadensis T -- 2.2 2.7 -- Celtis occidentalis M -- 0.6 -- -- Cornus florida T 1.7 -- 9.3 1.2 Fagus grandifolia T -- -- 6.6 3.4 Fraxinus americana M 1.1 1.2 0.9 2.1 Fraxinus quadrangulata M -- 5.4 0 0.5 Juniperus virginiana I 1.2 2.6 8.3 -- Liriodendron tulipifera I 1.3 0.8 3.4 0.5 Magnolia acuminata M 0.6 ------Morus rubra T -- 1.7 -- -- Nyssa sylvatica T 10.5 -- 9.2 1.2 Ostrya virginiana T -- 5.8 4.8 3.0 Oxydendrum arboreum T 6.3 ------Prunus serotina M -- 1.0 -- -- Quercus alba M 0.7 -- 1.7 -- Quercus prinus M 1.5 ------Quercus rubra M 0.5 0.6 4.1 -- Quercus velutina M -- -- 1.6 -- Sassafras albidum I 8.4 -- 2.8 -- Ulmus rubra T -- 8.4 0.4 2.5

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Table 4.3 Importance values for poles, stems ≥ 15 cm and < 45 cm dbh, tree species at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant. Species name Tolerance Bethany Cedar Falls Hopkins Sandstone Acer rubrum T 22.5 -- 11.6 1.5 Acer saccharum T 2.0 45.8 4.0 18.6 Aesculus glabra T -- 0.8 -- 1.4 Carpinus caroliniana T -- -- 0.5 -- Carya cordiformis M -- 2.9 -- 2.2 Carya glabra M 7.5 2.8 25.8 15.1 Carya ovata M -- -- 3.5 1.4 Carya tomentosa M 2.5 0.9 0.5 0.7 Celtis occidentalis M -- 0.9 -- -- Fagus grandifolia T -- -- 1.6 0.7 Fraxinus americana M -- 2.7 -- 0.8 Fraxinus quadrangulata M -- 21.5 -- -- Juniperus virginiana I 1.5 -- 4.2 -- Liriodendron tulipifera I 4.7 -- 7.3 -- Nyssa sylvatica T 1.6 -- 4.1 -- Oxydendrum arboreum T 2.7 ------Pinus spp. I -- -- 1.1 -- Platanus occidentalis M -- -- 0.5 -- Populus grandidentata I 4.2 ------Prunus serotina M 1.1 3.4 -- 0.7 Quercus alba M 13.6 6.6 9.0 39.6 Quercus muehlenbergii M -- 2.5 -- -- Quercus prinus M 23.1 -- -- 2.0 Quercus rubra M 6.6 3.2 7.9 5.7 Quercus velutina M 5.9 1.8 16.0 8.3 Sassafras albidum I 0.6 1.0 2.3 0.7 Ulmus rubra T -- 3.3 -- 0.7

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Table 4.4 Importance values for canopy adults, stems ≥ 45 cm dbh, at the four study sites. Importance value = (relative density + relative frequency + relative dominance)/3. For Tolerance types, T = tolerant, M = intermediate, and I = intolerant. Species name Tolerance Bethany Cedar Falls Hopkins Sandstone Acer rubrum T 12.5 -- 6.7 -- Acer saccharum T -- 49.4 -- -- Carya cordiformis M ------3.0 Carya glabra M -- -- 6.4 -- Fraxinus quadrangulata M -- 4.0 -- -- Liriodendron tulipifera I 1.9 2.1 6.6 -- Nyssa sylvatica T -- -- 7.0 -- Prunus serotina M -- 2.3 -- -- Quercus alba M 37.3 18.7 27.1 56.5 Quercus muehlenbergii M -- 4.3 -- -- Quercus prinus M 32.7 ------Quercus rubra M 6.3 12.7 6.1 13.9 Quercus velutina M 9.3 6.6 40.1 26.6

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Figure 4.2 Percentage of trees in select genera by diameter size class and study site. The far right size class (0-2 cm dbh) consists of seedling data collected in 2006 and presented in Chapter 3. Genera selected for display had importance values > 5 in at least two of the broad size class categories (saplings, poles, and canopy adults). Other tree genera that did not meet this criterion are included in the “Other” category. With the exception of “Other”, genera are 139

displayed in the figure legend in order of increasing shade intolerance from top to bottom: Acer and Nyssa are shade tolerant; Carya, Quercus, and Fraxinus are intermediate, but Fraxinus is more intolerant than Carya and Quercus; and Liriodendron is considered highly intolerant of shade. The maximum x-axis value has been artificially truncated at 85 cm dbh to focus the display on smaller size classes. This removed a single tree from the figure, a 122.5 cm dbh white oak (Quercus alba) at Hopkins.

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Figure 4.3 Size class distributions for (a) maple and (b) major oak species at each study site. The maximum x-axis value had been truncated as in Error! Reference source not found.. Note that y-axis scales differ for the two genera. Seedlings are not included in this figure; thus, the smallest size class (0-5 cm dbh) only includes stems 2-5 cm dbh.

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4.5. Discussion 4.5.1. Compositional shifts indicated by current forest structure The structural and compositional patterns observed in this study are indicative of

an ongoing oak-to-maple shift. In four second-growth oak-dominated forests along the

Appalachian Escarpment in southern Ohio, oaks were the largest canopy trees, but Acer

saccharum (sugar maple) and Acer rubrum (red maple) were the most abundant species.

Although oaks dominated the canopy layer and oak seedlings were common, few oaks

saplings were found. Instead, sapling and pole layers were dominated by shade tolerant

species, especially red maple and sugar maple. These patterns are consistent with

observations in second-growth (Goebel and Hix 1996, Hutchinson et al. 2003b, Yaussy et

al. 2003) and old-growth (McCarthy et al. 1987, Cho and Boerner 1991, Goebel and Hix

1996, McCarthy et al. 2001, McEwan et al. 2005) eastern oak-dominated forests in Ohio,

Kentucky, and West Virginia. Further, several long term studies have documented that

this pattern corresponds with a decrease in oak and increase in maple abundance and

importance over time (McCarthy et al. 2001, Aldrich et al. 2005, Pierce et al. 2006).

The differences in size class patterns among the four stands sampled in this

study suggest that they will follow slightly different trajectories during the shift from oak

to maple dominance. Oaks are likely to decrease in importance most rapidly at Cedar

Falls as there are few intermediate-sized stems to replace the handful of remaining

canopy oaks. The loss of oaks at this site may be more advanced because conversion to

maple happens more rapidly on mesic sites (Fralish et al. 1991, Abrams 2003) or because of differences in historic land use, such as selective logging of oaks (Abrams

2005). However, the abundance of pole-sized stems of oaks and hickories at Bethany, 142

Hopkins, and Sandstone, indicate that these species likely continue to be an important

component in the near future, but the lack of saplings indicates a decline in their

abundance in the long-term (Pierce et al. 2006).

If not for emerald ash borer, Fraxinus spp. (ash species) at Cedar Falls might be expected to follow a pattern similar to oaks over time. Ash peaks in pole-sizes at Cedar

Falls, likely following the decline of oaks, but is also failing to regenerate. As a fast-

growing opportunist, ash can respond to gaps more rapidly than oaks, but cannot

regenerate under heavy shade (Burns and Honkala 1990, Sutherland et al. 2000, Pierce

et al. 2006). Ash species have also increased in importance in modern stands compared

to presettlement oak-dominated forests (Rentch and Hicks 2005), but are unlikely to remain important because of increased shade in maple-dominated understories and emerald ash-borer induced mortality of adult ash trees (Herms et al. 2004, Prasad et al.

2009). The inevitable invasion of emerald ash borer and subsequent mortality of remaining ash at the sites used for this study will likely speed the conversion to maple at

Cedar Falls.

Maples and other shade tolerant species are likely to increase in abundance and dominance in the long term. Based on their relative abundances in the sapling layer,

Cedar Falls will be dominated exclusively by sugar maple and Bethany will be dominated by red maple. Both maple species are likely to co-dominate Hopkins and Sandstone. Red maple tends to be ubiquitous in modern oak-dominated forests, but sugar maple is the dominant species replacing oaks on mesic nutrient rich sites (Fralish et al. 1991, Goebel and Hix 1997, Abrams 2003, Yaussy et al. 2003). Other shade-tolerant species are also

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likely to increase in the future, especially Nyssa sylvatica (black tupelo) at Bethany and

Hopkins and Ulmus rubra (red elm) at Cedar Falls. These species are common in the

sapling layers, but currently not prominent in the pole or canopy classes. These trends in

shade-tolerant species have been noted in long term studies as well (Pierce et al. 2006).

One common exception to the trend in increasing abundance of shade-tolerant species is an increase in shade-intolerant Liriodendron tulipifera (tulip poplar) (Rentch and Hicks 2005, Pierce et al. 2006). It is possible that this might occur at Bethany and

Hopkins where tulip polar exhibits continuous presence from the sapling and pole size classes. Similar to ash, tulip poplar is a fast-growing opportunist, but it is also long lived and exhibits prolific seed production and seed banking (Burns and Honkala 1990,

Sutherland et al. 2000), making it likely to be one of the few shade-intolerant species to maintain a common presence in these stands.

4.5.2. Drivers of the oak-to-maple shift Oak establishment failure is thought to be directly caused by prolonged

suppression and eventual mortality under heavy shade from a dense understory

(Lorimer 1984, Abrams 1992, Lorimer et al. 1994, McDonald et al. 2003). The current

canopy oaks in old- and second-growth stands achieved canopy status either after a

stand initiating disturbance or release from the sapling layer following one or more

small canopy gaps (Cho and Boerner 1991, Rentch et al. 2003a, Rentch et al. 2003b).

Oak recruitment via canopy gaps depends on a bank of suppressed oak saplings that

historically spent long residence time in the understory (Rentch et al. 2003a). However,

current understory light levels in stands with maple understories exceed the ability of

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most oak species to survive over the length of time needed to achieve canopy release

(Rentch et al. 2003a). Instead, the same regime of frequent small canopy disturbances that once fostered oak establishment now leads to canopy recruitment of shade- tolerant species (Cho and Boerner 1991, Goebel and Hix 1997).

Changes in disturbance regimes, particularly reduction in fire, are thought to have driven the increase in shade-tolerant species (Abrams 2003, Yaussy et al. 2003,

Nowacki and Abrams 2008). Initiation of maple invasion corresponds with decreases in fire frequency in many stands (Abrams 1992, Yaussy et al. 2003). Once-frequent surface fires are thought to have deterred recruitment of fire-sensitive shade-tolerant

hardwoods, such as maples, and fostered the regeneration of disturbance-dependent

oaks, hickories, and other species less tolerant of shade (Abrams 1992, Goebel and Hix

1996, Abrams 2003). Oaks, in particular, have a suite of physiologic features that make

them especially tolerant of fires including deep rooting, vigorous sprouting, and thick

bark; whereas, maple seedlings and saplings tend experience greater damage by fire

(Abrams 1992, 1996).

The extent to which the combination of fire suppression and increased

competition has driven oak regeneration failure and increased maple dominance is not

entirely clear. In addition to periodic fire, drought and low nutrient availability would

also have tipped the competitive balance in favor of oak seedlings (Abrams 1992). For

example, climate change may have been the primary cause of oak regeneration failure

in Dysart woods, an old growth forest remnant, because regeneration failure preceded

fire suppression but coincided with reduced frequency of droughts (McCarthy et al.

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2001). In fact, changing fire regimes may have acted in synergy with a suite of other factors including a change to a moister climate, loss of American chestnut, and population booms in acorn browsers to drive widespread maple dominance and oak recruitment failure (McEwan et al. 2011).

4.5.3. Conclusions and management implications Oak-dominated forests along the Appalachian Escarpment in southern Ohio

exhibit structural patterns that indicate a shift in composition from oak to maple

dominance that has also been documented throughout the eastern deciduous forest.

Oaks are characterized by a bottleneck in regeneration (Abrams 2003). They are present

in seedling layers but failing to recruit to sapling layers, possibly because of competition

for light from the dense maple sapling layers. Given current conditions, oaks will rarely

recruit to canopy positions, and maple will increase in abundance, with sugar maple

dominating on more mesic and nutrient rich sites, and red maple dominating more zeric

stands.

If hypotheses about the past role of canopy and understory disturbances in

maintaining oak dominance are correct, then active management to increase understory

light levels and to control oak competitors might reverse this trend (Goebel and Hix

1996, Rentch et al. 2003a, Abrams 2005). This might be achieved by a combination of

overstory thinning to > 20% canopy openness and low intensity prescribed fires (Iverson

et al. 2008). Future work at these study sites will test whether prescribed fires are

effective in filtering seedling layer species – restricting shade-tolerant species, especially

maples, but retaining oaks.

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Chapter 5: Effects of single spring and fall fires and a fire surrogate (clipping) on seedling layers in forests of the Bluegrass Region in southern Ohio. 5.1. Abstract Oak-dominated forests throughout the eastern deciduous forest biome are

characterized by a shift from oaks to more shade-tolerant species, especially maples

(Acer spp.), that is due, in part, to decades of fire suppression. Thus, prescribed fires have been studied as a means of impeding this oak-to-maple transition and restoring

oak-dominated ecosystems. However, few have examined the effects of burning season

and heating on seedling responses. In this study, I examine the effectiveness of fire in

filtering shade-tolerant seedlings, especially maples, and shifting seedling communities towards greater relative abundance of oak in oak-dominated forests. I tested the relative effects of fire season (fall versus spring) and a fire surrogate (fire versus a clipping) on seedling layer community trajectory and functional group abundance across three forest sites along the Appalachian escarpment in southern Ohio. I found that the effects of season were confounded by variation in burn management units. Further, fires were low in intensity, and there was little evidence for a difference between topkill by fire and the fire surrogate on seedlings. However, fire played a larger role in generating seedbeds for shade-intolerant competitor species (especially Liriodendron tulipifera and Sassafras albidum). Oaks seedlings were equivalent across season and treatments, but fires reduced maple densities, primarily at one site. Overall, I did not find evidence that fire would alter the oak to maple shift across these sites. Future studies seeking to regenerate oaks at these sites should involve restoration of both

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structure (canopy thinning) and processes (prescribed fire) to increase understory light

levels and reduce oak competitors. This may also be a better context in which to

evaluate the relative roles of fire season and intensity.

5.2. Introduction Oak-dominated forests throughout the eastern deciduous forest biome are

shifting in composition from oaks to more shade-tolerant species, especially maples

(Acer spp.) (Fralish et al. 1991, Dyer 2001, Rentch and Hicks 2005, Nowacki and Abrams

2008). Although oaks dominate the canopy layer of these forests, their understories are

dominated by shade tolerant species such as maples, and few oak trees are recruiting

from seedling to sapling layers (Goebel and Hix 1997, Abrams 2005). The structure and

dynamics of these forests were maintained, in part, by periodic, non-catastrophic surface fires that deterred recruitment of shade-tolerant hardwoods and fostered the regeneration of oaks, hickories, and other species less tolerant of shade (Abrams 1992,

Goebel and Hix 1996, Abrams 2003).

However, recent decades of fire suppression have altered the character of these forests; oaks and hickories are failing to regenerate, and shade tolerant species are proliferating (Fralish et al. 1991, Dyer 2001, Hutchinson et al. 2003b). The dense understories have reduced light to levels that suppress oaks and other species less tolerant of shade such that they are relegated to the seedling layer (Lorimer et al. 1994,

Rentch et al. 2003b). This transition from seedling to sapling represents a bottleneck in oak regeneration that many hypothesize should be alleviated by reintroducing historic fire regimes (Abrams 2003).

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Thus, numerous studies have evaluated the effects of prescribed fire on eastern

oak-dominated forest seedling and understory layers with the aim of shifting the

competitive balance in favor of oaks (e.g., Vandermast et al. 2004, Hutchinson et al.

2005, Alexander et al. 2008). However, relatively few have tested the effects of the

seasonal timing of fire as a filter for maples and shade tolerant species in the seedling

layer (Brose and Van Lear 1998). In southern Ohio, and much of the eastern deciduous forest, modern fires primarily occur and cover the greatest area at two times of year: during the fall (October-November) and in the early spring (March-April) (Haines et al.

1975, Yaussy and Sutherland 1994).

Fire in these two seasons may have different effects on the mortality and sprouting of oak and maple seedlings. Seasonal timing of fires can greatly impact the sprouting of woody plants (Brose and Van Lear 1998, Drewa et al. 2002). Root-stored

carbohydrate reserves of woody plants vary seasonally, peaking as dormancy sets in the

fall and reaching their lowest levels as plants come out of dormancy in the spring, but

fire-adapted woody plants, such as oaks, tend to have more seasonally stable root

reserves (Pate et al. 1990, Huddle and Pallardy 1999). Thus, fires during the spring and

summer can be relatively more damaging to fire-sensitive species (Olson and Platt 1995,

Brose and Van Lear 1998, Drewa et al. 2002).

However, the effects of season may be contingent on fire intensity. Intensity can

influence resprouting dynamics, primarily by damaging buds on underground organs

and at the base of stems (Moreno and Oechel 1993, Drewa 2003). The location of these

buds on underground organs in relation to the ground surface, and thus level of

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exposure to fire intensity, can influence the susceptibility of a plant to fire and its ability

to resprout (Matlack et al. 1993). Woody plant functional groups in oak-dominated

forests differ in the location of their root crowns, which contain underground buds.

Most shade tolerant species have root crowns at the soil surface, while oaks and other

species less tolerant of shade have root crowns a few centimeters below the soil surface

(Burns and Honkala 1990).

The relative importance of fire season versus intensity in driving post-fire

seedling layer is not well understood. Drewa (2003) proposed that fire season and

intensity interact, such that season determines the potential for resprouting, but

realization of sprouting depends on the amount of injury to dormant buds through heat

damage. Brose (2010) suggests that fire intensity generally outweighs the effects of fire

season. The effects of season and heat damage are further complicated by the fact that

it is not well understood if fires will generally be more intense in the spring or fall. In

eastern oak forests, spring fires might cause less injury to woody stems than fall fires

because the litter layer compacts and begins to decompose over the winter, thus only the top layer of litter may burn in the spring unless there has been an extended dry

period (Graham and McCarthy 2006, Bowden 2009).

One can attempt to isolate the potentially confounding effects of season and

heat damage by comparing fires in both seasons to a fire surrogate that does not

damage underground buds. Clipping has been employed as a fire surrogate in numerous

studies testing the effectiveness of fires in reducing encroaching woody vegetation in

seedling and shrub layers (Matlack et al. 1993, Calvo et al. 2005, Tix and Charvat 2005).

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In these studies, clipping treatments act like fires by topkilling woody stems, but the associated heating is absent, leaving dormant buds and underground organs intact

(Drewa 2003). This framework allows one to generate a series of predictions that can be used to test the relative effects of fire season and heating.

If fire season has an effect, one would expect greater densities in fall regardless of treatment type (clipping or burning), because carbohydrates will be at their peak in roots, allowing for greater sprouting. If heating has an effect, one would expect to see greater densities in clipped plots relative to burned plots because the heat from fires will have reduced sprouting by damaging underground buds. Alternatively, fires may reduce litter, generating seed beds and stimulating germination, resulting in greater densities in burned plots than clipped plots. Further interactions between season and treatment type would indicate a more complex relationship between fire season and intensity.

In this study, I examined the effectiveness of fire in filtering shade-tolerant seedlings, especially maples, and shifting seedling communities towards greater relative abundance of oak in oak-dominated forests along the Appalachian escarpment in southern Ohio. The shift in composition from oaks to maples has recently been documented in these forests (Chapter 4), but the efficacy of prescribed fire in their restoration has not been evaluated. In this unique region of Appalachia, two physiographic sections converge, generating a combination of varied bedrock, soil types, and topography that foster a diverse forest flora. This offers an opportunity to search

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for general trends in the effects of fire on the trajectory of tree regeneration across

forest types.

I tested the relative effects of fire season (fall vs. spring) and topkill with or without heating (fire vs. clipping) on seedling layer community trajectory and functional group abundance across three forest sites. I hypothesized that spring burns will be more effective in reducing densities of shade tolerant species, especially maples, shifting the community trajectory towards greater relative abundance of oaks. I also hypothesized

that clipping and burning treatments will be equivalent because heating likely plays a relatively minor role in the effects of prescribed fires in this system; because burns are conducted under conditions that limit the spread of fire.

5.3. Methods 5.3.1. Study sites This study was conducted at the Edge of Appalachia Preserve and Strait Creek

Preserve along the Appalachian Escarpment in southern Ohio. The study region and study sites are fully described in Chapter 1, so study site information will only be mentioned briefly here. Three of the four forest sites discussed in Chapters 1, 3, and 4 of this dissertation were utilized in this study including Sandstone, Hopkins, and Cedar

Falls. Bethany was not used because logistical challenges and weather conditions prevented prescribed fires there. All sites are mature (at least 70 years old) second- growth stands where the largest trees are oaks (Quercus). Oaks are also the most important species in the overstory at Hopkins and Sandstone, although different species of oaks dominate each site. At Cedar Falls, Acer saccharum (sugar maple) is the

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dominant canopy tree species, and oaks are rare. The understory layers are dominated by maples at all sites, but the most prominent species differ between sites.

These sites also differ in physical characteristics (Table 5.1). Hopkins is underlain

primarily by shale, Sandstone by sandstone, and Cedar Falls by dolomitic limestone.

Except for Cedar Falls, which is relatively flat, the sites are located on steep to rolling

slopes and differ in dominant aspect.

I do not have a record of the disturbance regimes at these study sites, but I did

observe a canopy disturbance during this study. In July of 2007, after vegetation

sampling was completed, high winds removed the crowns of several oak trees at

Sandstone. Although no branches fell directly on the vegetation sampling plots used in

this study, openings in the canopy likely altered understory light levels.

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Table 5.1 Locations and characteristics of oak-dominated forest sites and individual burn units within each site, adapted from Table 1.2 in Chapter 1. Preserve Site Burn unit Treatment Location Size Elevation (m a.s.l) Aspect* Slope* Bedrock location season (longitude, latitude) (ha) min-max (range) (%) (dominant)

EOA Cedar Falls west fall 38° 49' 47" N., 83° 23' 40" W. 2 230-233 (3) horizontal 2 dolomite EOA Cedar Falls east spring 38° 49' 51" N., 83° 22' 54" W. 2 223-226 (3) horizontal 2 dolomite SC Hopkins south fall 39° 3' 52" N., 83° 22' 14" W. 1 270-275 (5) 270° (W) 20 shale SC Hopkins north spring 39° 3' 59" N., 83° 22' 13" W. 1 273-287 (14) 330° (NW) 45 shale SC Sandstone west fall 39° 3' 29" N., 83° 23' 11" W. 1.5 226-267 (41) 30° (NE) 30 sandstone Annotations: * Aspect and slope represent the general landscape level values and do not reflect the microtopography at the level of the sampling plot. Slope was measured from the lowest to highest plot within each unit and aspect from the middle. Burn management unit locations refer to the local, within site, position of the units relative to each other. EOA = Edge of Appalachia Preserve System, SC = Strait Creek Preserve, m a.s.l. = meters above sea level. 154

5.3.2. Experimental design To test the effects of study site, treatment season, and treatment type on woody

plant seedling abundance, I conducted a randomized split-plot field experiment, where study site (three levels) and treatment season (two levels) both served as whole-plot factors, with treatment type (three levels) as the split-plot effect (Figure 5.1). The combination of season and site was not replicated, but circular sampling units were designed as pseudoreplicates within each treatment type.

Figure 5.1 Schematic of study design showing nesting of treatment type within treatment season at a study site. Letters inside circular plots indicate treatment type: B = burning, C = clipping, R = reference. The arrangement of treatment types is for illustration only, and does not reflect their actual arrangement at the study site.

Each study site was divided into two burn management units approximately 1-2

ha each prior to the initiation of this study, as described previously in Chapter 1. Cedar

falls was divided into western and eastern units located 0.90 km apart by two ravines

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and a wooded plateau. Hopkins was divided into northern and southern units 0.15 km

apart by a power line cut. Sandstone was divided into adjacent burn units by a minor

drainage. Sites were not burned by The Nature Conservancy prior to this study.

Burn management units at each site were randomly assigned to either the fall or spring treatment season (Table 5.1, Figure 5.1). Three disturbance type treatments, burning, clipping, and reference (untreated), were applied to circular plots (radius = 2.5 m; 19.6-m2) nested within each management unit. Reference plots served to ensure that post-treatment seedling responses were not attributable to extraneous environmental conditions (Drewa 2003). Fifteen circular plots were used in each unit and randomly assigned to a treatment type such that one third of the plots in a unit

were assigned to each treatment. The circular plots were systematically located every

15-20 m along parallel transects in each management unit. Transects were randomly

positioned at the upslope axis (or short axis of the site, where no slope was present) and

then run downhill. Total transect number and length depended on the shape of the site,

but they were spaced a minimum of 15 m apart.

Prescribed burns could not be carried out at Sandstone’s spring burn

management unit. Thus, a total of 75 circular plots were used in subsequent analysis, 30

each from Cedar Falls and Hopkins, and 15 from the fall unit at Sandstone.

5.3.3. Prescribed fire and clipping treatments Treatments were applied to an area that extended 1 m beyond the edge of each

circular plot. For the burning treatment, prescribed fires were conducted such that each

plot was ringed with a blackline and then headfires or a combination of headfires and

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backing fires were set. A drip torch was used to burn remaining patches of vegetation.

Because I was testing the impact of fire, not the effects of burn patchiness, I ensured

complete burn and topkill of all woody seedling stems < 2 cm dbh.

Except for fall burns at Hopkins, burns at a given site and season combination were done on a single day. Fall prescribed fires were conducted from September 6 to

October 4, 2007. Air temperatures at the onset of burns averaged 26.7 °C (80 °F) and ranged from 23.9 to 33.9 °C (75-93 °F). Relative humidity ranged from 48 to 62% during fires. At Hopkins, two plots were burned on September 6, when it was 93 °F, and the remaining plots were burned six days later on September 12. Sandstone was burned on

September 13, and Cedar Falls on October 4. Spring prescribed fires were conducted on

April 7 and 8, 2008: Hopkins on April 7, and Cedar Falls on April 8. Air temperature at the onset of burns on both days was 23.3 °C (74 °F), and relative humidity ranged from

39 to 55% during spring fires. Flame lengths in both sets of fires were usually < 1m and fuel consumption was generally limited to unconsolidated leaf litter.

Maximum device temperatures, often used as a surrogate the fire heat budget in

other studies in this system (Hutchinson et al. 2005, Glasgow and Matlack 2007b), were

measured using thermocolor pyrometers. Pyrometers were constructed from thin

aluminum tags (JIM-GEM® aluminum tags; Forestry Suppliers, Jackson, MS) painted with

a series of temperature sensitive liquids (Big Three Industries Inc., Tempil° Division,

South Plainfield, NJ) ranging in melting point from 79-621 °C (Kennard et al. 2005,

Graham and McCarthy 2006). Painted tags were positioned at the duff-litter interface

(where damage to higher root crowns might occur), at the litter surface (where

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temperatures are likely to be greatest: e.g., Franklin et al. 1997, Graham and McCarthy

2006), and suspended 25 cm above the litter surface from a steel wire (for comparison with other studies in this region: e.g., Iverson et al. 2004, Glasgow and Matlack 2007b,

Knoepp et al. 2009). Three replicate pyrometer set ups were deployed in each circular plot: one at the southwest edge, one in the middle, and one at the northeast edge. After the fires, I inspected the tags and used the mean of the three replicate tags per plot in data analysis. Any paint dot that was melted or bubbled and charred was scored as melted. When no paint melted, ambient temperature was recorded.

For the clipping treatment, all woody seedling stems < 2 cm dbh were cut at ground level and removed from the plot. Unconsolidated leaf litter was removed from the plot either by hand or with the aid of a rake. Then, woody stems were placed back in the plot. The clipping treatment was intended to simulate fires with no heating component as in Drewa (2003). Thus, it mimicked the reduction of groundcover fuels but the retention of topkilled woody plant stems following fire.

5.3.4. Vegetation data collection Woody plant seedlings (< 2 cm diameter at breast height) were tallied by species in each circular plot during the summer of 2007 (pre-treatment) and 2008 (post- treatment). Stems ≥ 30 cm height, but < 2 cm dbh, called juveniles (Poulson and Platt

1996) or large seedlings (McCarthy et al. 2001), were counted in the entire circular plot

(area = 19.6-m2) . Stems < 30 cm height, which I call small seedlings after McCarthy et al.

(2001), were counted in half the circle (the northeast and southwest quadrants;

combined area = 9.6-m2) due to the abundance of small seedlings. Nomenclature

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follows Gleason and Cronquist (1991). Some closely related taxa were treated

collectively.

5.3.5. Response variables Total seedling (stems < 2 cm dbh) density of each species was calculated from

tally data. Densities were standardized to stems per 1 ha. Seedling densities of

individual species are highly heterogeneous in these forest understories (Chapter 4)

resulting in many plots with zero abundance. Thus, I combined data from several species

to form groups based on habit, shade tolerance, and fire ecology. I classified species into

functional groups based on habit (shrub and vine or tree seedling) using the USDA Plants

Database (http://plants.usda.gov). I also calculated the percentage of small tree

seedlings to better understand how season of treatment and treatment type might shift

tree seedling size class. Tree seedlings were further classified by shade tolerance (shade tolerant, intermediate, or light demanding, i.e. intolerant) based on compilations by

White (1983b), Burns and Honkala (1990), and Sutherland et al. (2000). When shade tolerance classifications differed among sources, the category used by the most sources was adopted. Finally, I isolated groups of trees of particular interest to fire ecology in oak-dominated forests including maples (species in the genus Acer), oaks (species in the genus Quercus), and tulip poplar (Liriodendron tulipifera). Maples, oaks, and tulip poplar also correspond to the three shade tolerance classifications, belonging, respectively, to the shade tolerant, intermediate, and light demanding groups. They were also typically the dominant species in each shade tolerance category at each site.

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5.3.6. Data analysis All analyses were conducted using R version 2.12.2 (R Development Core Team

2009). For all analyses α = 0.05. Model assumptions were assessed by examining residuals versus fitted plots and normal qq-plots for the analysis as a whole and for each factor (Zuur et al. 2010).

Device temperatures. I used analysis of variance (ANOVA) to test for relative differences in fire heat budgets between sites, seasons, and pyrometer locations. All three factors and their interactions were included in an unbalanced three-way ANOVA.

Backwards selection using Akaike’s Information Criterion (AIC) indicated that site could be removed from the model, thus the model with only season and pyrometer location is presented. Where overall F-tests were significant (P < 0.05), pairwise comparisons were conducted to determine if there were significant differences between season within pyrometer location and pyrometer location within season using the multcomp package in R (Hothorn et al. 2008). P-values were adjusted using a single-step procedure which incorporates the correlations between the test statistics and controls the family-wise error rate at alpha < 0.05 (Bretz et al. 2011).

Community trajectory. I tested the effect of site, treatment season, and treatment type on overall rate and direction of community change using a combination of ordination, compositional vectors, and nonparametric multivariate analysis of variance. In preparation for ordination, the woody plant seedling density data from both censuses was organized into a matrix of 150 plots (75 plots each sampled twice) x 52 species, and rare species (< 5% in frequency) were removed from the matrix. A Bray-

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Curtis distance metric was then applied to the density data to construct a dissimilarity

matrix (Bray and Curtis 1957, Faith et al. 1987). Nonmetric multidimensional scaling

(NMDS), an unconstrained ordination technique, was performed on the dissimilarity

matrix after square root transformation and Wisconsin double standardization, in which

species are first standardized by maxima and then plots by plot totals, to reduce the emphasis of extremely abundant species and adjust for the extreme variability in abundance by plot (Legendre and Gallagher 2001, McCune and Grace 2002). NMDS was carried out following the recommendations of Minchin (1987) using the metaMDS function in the vegan package (Oksanen et al. 2010).

Compositional vectors were analyzed by the method outlined in McCune and

Grace (2002). Briefly, a vector represents the trajectory of a plot in species space by extending from the pre-treatment plot position to the post-treatment plot position on the ordination. In order to test overall differences in site, season, and treatment types, vectors were translated to a common origin by subtracting the pre-treatment ordination dimension scores from the post-treatment dimension scores. These new dimension scores of the vector heads were then used as variables in permutational multivariate analysis of variance (PERMANOVA), (Anderson 2001). Thus, by comparing differences between groups of vector heads, one can simultaneously compare rate and direction of change of the compositional vectors.

Five separate PERMANOVAs were conducted on the vector head data. Each employed a Euclidian distance with no transformation and no ranking to generate a dissimilarity matrix. Also, 999 permutations were used for tests of significance. Because

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PERMANOVA does not accommodate split-plot designs, analyses were run as factorial

ANOVAs, where a circular plot served as the experimental unit, study site served as a block, but was treated as a fixed, rather than a random, effect, and treatment season and treatment type were fixed effects. Following PERMANOVAs, P-values for post-hoc pairwise comparisons between means were adjusted using the Bonferroni procedure to corrected for multiple comparisons and control the family-wise Type I error rate at 0.05

(Sokal and Rohlf 1995).

The first set of analyses focused on the effects of treatment season on plot trajectory; thus, only data from Cedar Falls and Hopkins, sites which received both seasons of treatment, were included. The primary analysis tested the effects of site

(Cedar Falls and Hopkins), season (fall and spring), treatment type (burning and clipping) and their interactions. Where significant interactions occurred with season, pairwise comparisons were conducted to determine if season differed within a given treatment and within a given site. To ensure that differences in season did not arise because of differences in burn management units, I also ran two additional PERMANOVAs, one for each site, testing the effect of management unit on reference plot trajectory.

A second set of analyses focused on the effects of treatment type (burning, clipping, and reference) within each season. One analysis used management units treated in the fall and tested the effects of site (Cedar Falls, Hopkins, and Sandstone), treatment type, and their interaction. Another analysis used only management units treated in the spring and tested the effects of site (Cedar Falls and Hopkins), treatment type, and their interaction. In both analysis, where treatment type had a significant

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main effect, pairwise comparisons were conducted to determine if treatment types

differed, and where there was a significant site × treatment type interaction, pairwise

comparisons were conducted to determine if treatment types differed within each site.

Univariate responses. I used mixed linear models to test for significant

treatment effects on seedling density using the gls function in the nmle package

(Pinheiro and Bates 2000, Pinheiro et al. 2011). Variance functions were used to model

heteroscedasticity in the with-in group errors for site, treatment season, treatment

type, or their interactions. Models with the best fit of variance components were found using Akaike’s Information Criterion (AIC).

All univariate linear models were run as analyses of covariance (ANCOVA), in which a circular plot served as the experimental unit, and study site, treatment season, and treatment type were fixed effects. Pre-treatment (2007) data were used as the covariate to test for post-treatment effects of season and treatment type. Management unit was not directly included in the analysis; thus, the overall model was that of a factorial ANCOVA. Thus, the maximal linear model for a given response variable, Y, at site i in season j in unit ij with treatment type k was:

Yijkl = µ + (initial pretreatment value)1 + (site)i + (season)j + (site ×

season)ij + (treatment type)k + (site × treatment type)ik + (season × (Equation 5.1) treatment type)jk + (site × season × treatment)ijk + εijkl,

where µ is the overall population mean; initial pretreatment value is the covariate; site,

2 season, and treatment are the fixed effects; and ε is the residual error (εijkl ~ N(0,σ )).

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Analysis was conducted separately for each response variable, and full fixed

structures were employed in all models. Density data were log10 (n + 1) and % small seedling data were arcsine square root transformed when necessary to meet model assumptions of normality and homogeneity of variance. Following ANCOVAs, P values for post-hoc pairwise comparisons between means were adjusted using the single-step

procedure of Bretz et al. (2011) to correct for multiple comparisons and control the family-wise Type I error rate at 0.05 using the multcomp package in R (Hothorn et al.

2008).

Five separate ANCOVAs were conducted on each response variable, employing

an identical structure to the PERMANOVAS used to analyze compositional vectors.

Briefly, the first analysis focused on the effects of treatment season and tested the

effects of site (Cedar Falls and Hopkins), season (fall and spring), treatment type

(burning and clipping) and their interactions. Two related ANCOVAs, one for each site

(Cedar Falls and Hopkins), tested the effect of management unit on reference plot

density. A second set of analyses focused on the effects of treatment type (burning, clipping, and reference) within each season. One analysis used management units treated in the fall, and tested the effects of site (Cedar Falls, Hopkins, and Sandstone), treatment type, and their interaction. Another analysis used only management units treated in the spring, and tested the effects of site (Cedar Falls and Hopkins), treatment type, and their interaction.

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5.4. Results

5.4.1. Fire behavior Temperatures recorded by paint tags averaged 201°C (± 17.95 SE) across all

burned plots, but were highly variable, ranging from 24°C - 547°C (for which the minimum was the ambient temperature recorded when there was no detectable fire temperature). Temperatures did not differ by site in a one way ANOVA (F2,72 = 0.949, P =

0.3919); thus, sites were combined for analysis. ANOVA indicated that there was a

significant interaction effect between season of burn and pyrometer height on device

temperature (F2,69 = 4.778, P = 0.0114). Seasonal differences in temperature were only detected between paint tags placed at the litter surface; spring fires were about 1.5 times hotter than fall fires, averaging 483°C compared to 302°C in the fall (Figure 5.2).

Regardless of season, paint tags placed at the litter surface were higher in temperature

(374°C) than at the duff surface (98°C) or 25 cm above the litter surface (131°C) (t = 6.9 -

9.9, and P = <0.001 for all comparisons).

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Figure 5.2 Paint tag temperatures among fire seasons (Fall and Spring) and pyrometer locations (25 cm above litter surface, at the litter surface, and at the interface between litter and duff layers). Significant seasonal differences within a pyrometer location are indicated by different letters above bars. Significant differences (P < 0.05) between pyrometer locations across seasons are indicated by different letters below pyrometer location labels along top margin. Error bars are ± 1 SE.

5.4.2. Season and treatment type effects on community trajectory Season. Community trajectories exhibited a significant season × treatment effect

in the PERMANOVA model (Table 5.2). However, the only significant difference in community trajectory between fall- and spring-treated plots was found at Cedar Falls among clipped, but not burned plots (Figure 5.3a; t = 1.91, P = 0.0292). This does not appear to be due to underlying differences in units, because reference plots at both sites (Cedar Falls and Hopkins) did not differ significantly in trajectory between units allocated to treatment in a particular season (PERMANOVA, F1,7 = 0.1247, P = 0.9439;

F1,7 = 1.6069, P = 0.2026, respectively).

Treatment type effects by season. Separate PERMANOVA’s within units treated

in each season indicated no significant differences in community trajectory among 166

treatment types in spring units, but a site × treatment interaction was indicated in the fall-treatment units (Table 5.2). This was driven primarily by differences among treatments at Cedar Falls where burned plots approached significant differences in trajectory when compared to clipped and reference plots (Figure 5.3b; t = 2.268, P =

0.0738; t = 2.587, P = 0.0738, respectively).

Table 5.2 Schematic summary of PERMANOVA for effects of (A) site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning), (B) site and treatment type (clipping, burning, and reference) within a given season (fall or spring) on community trajectory. In B, site includes Cedar Falls, Hopkins, and Sandstone in the fall, but only Cedar Falls and Hopkins in the spring. Model details can be found in Appendix A Table 5.4 A. B. Source Source Fall Spring Site Site Season Treatment Treatment Site × Treatment X Site × Season Site × Treatment Season × Treatment X Site × Season × Treatment X Annotations: X = (P < 0.05); otherwise, (P > 0.05); P values obtained through permutation.

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Figure 5.3 Post- treatment seedling community trajectory at Cedar Falls among (a) different seasons of clipping and fire and (b) different treatments applied in the fall. 95% confidence ellipses indicate groups with significantly different trajectories. See Appendix B for ordination and community trajectory

168 of full dataset.

5.4.3. Season effects on stem densities. Among groups of seedlings classified by habit, post-treatment densities of vines

and shrubs exhibited a main effect of season, and trees exhibited a site × season ×

treatment effect (Table 5.3). Vines and shrubs were about 1.5 times greater in fall

(41,491 ha-1) than in spring treatments (28,625 ha-1), with the greatest seasonal

difference between burned plots at Cedar Falls (Figure 5.4a). Post-treatment densities of

tree seedlings were also greater in fall treatments than spring treatments, but only in

burned plots at Cedar Falls (1.9 times greater) and clipped plots at Hopkins (2.9 times

greater) (Figure 5.4a). However, some caution is warranted in attributing the differences

in stem densities in both habit groups at Cedar Falls to the effects of treatment season.

Separate analyses of stem densities in reference plots at Cedar Falls indicated a

significant effect of treatment unit (i.e. season allocation) on both habits (vines and

shrubs: F1,7 = 10.21, P = 0.015; trees: F1,7 = 6.31, P = 0.040). However, there was no significant effect of unit allocation to different treatment seasons at Hopkins (vines and shrubs: F1,7 = 2.02, P = 0.198; trees: F1,7 = 1.58, P = 0.249).

The percentage of small (<30 cm) tree seedlings exhibited a significant effect of

treatment season (Table 5.3), with a greater percentage of small tree seedlings

following the spring (99.3%) compared to fall treatments (97.8%). However, these

seasonal differences were small in magnitude, and were too subtle to be detected

within treatment types in a given site (Figure 5.4a).

Among tree species grouped by shade tolerance, treatment season had a

significant effect on post-treatment densities of shade tolerant species, but not on

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shade intolerant species or species intermediate in shade tolerance (Table 5.3).

Densities of shade tolerant species were 1.6 times greater in the fall (58,912 ha-1) than

in the spring (36,437 ha-1). This was primarily driven by plots at Cedar Falls (Figure 5.4b),

where sugar maple (Acer saccharum), red elm (Ulmus rubra), and pawpaw (Asimina

triloba) were the dominant shade tolerant species. This difference should be interpreted

with caution because reference plots at Cedar Falls were 42,379 ha-1 in the fall burn

management unit, and were 27,640 ha-1 in the spring management unit, but this difference was not significant (F1,7 = 2.56, P = 0.154).

The effect of treatment season on groups of species of particular relevance to the fire ecology of oak-dominated systems was weak or insignificant. Oak seedling densities did not respond to season of treatment, but post-treatment seedling densities of maple exhibited a season × treatment interaction and tulip poplar (Liriodendron tulipifera) exhibited a significant site × season interaction (Figure 5.4). However, a lack of significant seasonal differences in post-hoc pairwise comparisons within treatments in maples (burning: z = 0.99, P = 0.542; clipping: z = 1.36, P = 0.32) and within sites in tulip poplars (Cedar Falls: z = 0.61, P = 0.793; Hopkins; z = 1.27, P = 0.37) suggests that variation in seedling densities of these species groups was driven primarily by treatment and site differences, rather than season (Figure 5.4c).

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Table 5.3 Schematic summary of ANCOVA for effects of (A) site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning), (B) site (Cedar Falls, Hopkins, and Sandstone) and treatment type (clipping, burning, and reference) in units treated in fall, and (C) site (Cedar Falls and Hopkins) and treatment type (clipping, burning, and reference) in units treated in the spring on seedling densities of different groups. Model details can be found in Appendix A Table 5.5-Table 5.7. Source Habit Shade tolerance Species groups shrubs tree % small tree inter- A. Fall and spring and vines seedlings seedlings tolerant mediate intolerant maples oaks litu Covariate XXX XXX XXX XXX XXX XXX XXX XXX Site XXX XXX XXX XX XXX XX Season XXX XXX X XXX XX Treatment X XXX XX XXX Site × Season X Site × Treatment XXX XX Season × Treatment X Site × Season × Treatment X B. Fall Covariate XXX XXX XXX XXX XXX XXX XXX XXX XXX Site XXX XXX XXX XXX XXX XXX XXX Treatment XXX XXX XX XXX Site × Treatment X XX XXX C. Spring Covariate XXX XXX XXX XXX XXX XXX XXX XX Site XX XXX X XXX XXX XXX X Treatment XXX XXX XX XXX Site × Treatment X XX XX

171 Annotations: X (P < 0.05), XX (P < 0.01), and XXX (P < 0.001); otherwise (P > 0.05

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Figure 5.4 Post-treatment seedling densities among seasons of application (Fall and Spring) within different sites (Cedar Falls and Hopkins) and treatment types (Burning and Clipping), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between seasons within a given site × treatment combination are indicated by different letters above bars. Letters with an asterisk indicate differences approached significance (P = 0.09-0.05). Asterisks following seedling group labels, in the right margin, indicate that significant main or interaction effects of season were detected by ANCOVA, but not apparent when examined with post-hoc pairwise comparisons at some level. Specifically, a single asterisk (*) indicates a main effect of season was detected by ANCOVA, but these differences in season were not consistently found within site × treatment comparisons; a double asterisk (**) indicates that a season × site or season × treatment interaction was detected by ANCOVA, but seasonal differences were not detected in multiple comparisons at this same level, although, where indicated, were found within site × treatment combinations.

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5.4.4. Treatment type effects on stem densities by season Fall. Among habit groups, post-treatment stem densities of vines and shrubs did not significantly differ among fall treatment types (Table 5.3). However, tree seedling densities exhibited a significant site × treatment type interaction (Table 5.3). At Hopkins, fall burned plots were twice as dense as reference plots; however, this trend was not apparent at other study sites (Figure 5.5a). The percentage of small (<30 cm) tree seedlings was also affected by treatment type (Table 5.3), as plots treated with fall clipping or fall fire had a significantly greater percentage of small tree seedlings compared to reference plots (clipping vs. reference: z = 5.24, P = <0.001; burning vs. reference: z = 3.46, P = 0.001). This shift towards smaller size classes with clipping and burning was most apparent at Cedar Falls and Hopkins (Figure 5.5a).

Among tree seedling shade tolerance groups, post-treatment densities of shade tolerant species and species intermediate in shade tolerance were not significantly affected by treatment type in the fall, but shade intolerant species exhibited a significant site × treatment type interaction effect (Table 5.3). A significant difference in shade intolerant seedling densities between treatment types only occurred at Hopkins, where burned plots were 15 times greater in density than reference plots and 3.7 times denser than clipped plots; clipped plots were themselves about 4 times greater in density than reference (Figure 5.5b). These differences were driven primarily by tulip poplar and sassafras (Sassafras albidum).

Among particular groups of species, oak seedling densities did not respond to different fall treatment types, but maple and tulip poplar seedlings both differed with

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treatment type: maple seedling densities had a significant main treatment type effect

and tulip poplar seedling densities exhibited a significant site × treatment type effect

(Table 5.3). However, the response of maples and tulip poplars to fall treatment type,

particularly burning, was nearly opposite – maples were reduced by fire, and tulip poplars stimulated by fire (Figure 5.5c). Maples were about half as dense in burned plots

(8,100 ha-1) compared to clipped (14,479 ha-1) and reference plots (17,297 ha-1) (burning vs. clipping: z = -2.65, P = 0.0224; burning vs. reference: z = -3.60, P = <0.001). In contrast, tulip poplar seedling densities were 4-18 times greater, depending on the site, in burned plots than in reference plots, and were about five times greater in burned than clipped plots at Hopkins (Figure 5.5c). Sassafras, the other dominant shade intolerant species, exhibited a similar response to tulip poplar, having a significant main effect of treatment (F2,35 = 5.79, P = 0.0067). Sassafras was also greater in burned

(10,041 ha-1) than clipped (2,752 ha-1) or reference plots (3,900 ha-1) (burning vs.

clipping: z = 3.40, P = 0.002; burning vs. reference: z = 3.07, P = 0.005).

Spring. Overall, responses of woody seedlings to different spring treatment

types were similar to those in the fall. Among broad habit classifications, post-treatment

stem densities of vines and shrubs did not differ among spring treatment (Table 5.3).

However, trees seedling densities had a significant site × treatment interaction (Table

5.3); plots burned at Hopkins were three times as dense as reference plots, but there

was no difference in densities among treatments at Cedar Falls (Figure 5.6a). As in fall

treatments, the percentage of small (<30 cm) tree seedlings exhibited a significant

treatment type effect in the spring (Table 5.3). Clipping and fire treatments were 10%

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greater than reference plots, averaging 99% percent small tree seedlings in clipped and

burn plots, respectively, compared to 90% in reference (Figure 5.6a; clipping vs.

reference: z = 5.588, P = < 0.0001; burning vs. reference: z = 4.346, P = < 0.001).

Similar to fall treatments, among tree seedling shade tolerance groups, only shade intolerant species exhibited a significant site × treatment type interaction effect in the spring (Table 5.3). Burning stimulated a flush of germination and sprouting from shade intolerant species. At Cedar Falls, burned plots were about seven times greater in density of shade intolerant species than reference plots; at Hopkins, burned plots were ten times greater than clipped and 13 times greater than reference plots (Figure 5.6b).

Again, these differences were driven primarily by tulip poplar and sassafras (see below).

As in fall treatments, maple and tulip poplar seedling densities responded to different spring treatments; maples exhibiting a main treatment type effect and tulip poplar a site × treatment type interaction effect (Table 5.3). However, post-hoc pairwise comparisons of maple densities failed to distinguish among treatments, possibly because maple densities differed by an order of magnitude between sites (Figure 5.6c).

At Hopkins, burning stimulated germination of tulip poplars, as seedling densities in burned plots were 236 times greater than in reference plots and 19 times greater than in clipped plots (Figure 5.6c). The other dominant shade intolerant species, sassafras, exhibited identical significant effects, but seedling densities in burned plots (78,059 ha-1) were 9.2 times greater than in reference plots (8,448 ha-1) and 7.5 times greater than in

clipped plots (10,369 ha-1).

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Figure 5.5 Post-treatment seedling densities in fall treatment units among treatment types (fire, clipping, or reference) within different sites (Cedar Falls, Hopkins, Sandstone), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between treatment type within a given site are indicated by different letters above bars. Asterisks following seedling group labels (right margin) indicate a significant main effect of treatment type was detected by ANCOVA, but differences in treatment were not consistently found across when examined with post-hoc pairwise comparisons. Specifically, a single asterisk (*) indicates that reference plots were different from those treated with clipping and fire, but these differences were not consistent across sites; a double asterisk (**) indicates that burned plots were different from clipped plots and reference plots, but these differences were not consistent across sites.

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Figure 5.6 Post-treatment seedling densities in spring treatment units among treatment types (fire, clipping, or reference) within different sites (Cedar Falls and Hopkins), grouped by seedling (a) habit, (b) shade tolerance, and (c) species group, where Litu = Liriodendron tulipifera; tulip poplar. Bars indicate marginal (least squares) means adjusted by pre-treatment values as covariates from the linear model. Error bars are ± 1 SE. Significant differences (P < 0.05) between treatment types within a given site are indicated by different letters above bars. Letters with an asterisk indicate differences approached significance (P = 0.06). Asterisks following seedling group labels (right margin) indicate a significant main effect of treatment type was detected by ANCOVA, but differences in treatment were not consistently found across sites when examined with post-hoc pairwise comparisons. Specifically, a single asterisk (*) indicates that reference plots were different from those treated with clipping and fire, but these differences were not consistent across sites; a double asterisk (**) indicates that global differences between treatments were not detected with pairwise comparisons, but treatments did differ within certain sites.

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5.5. Discussion 5.5.1. Seasonal effects Contrary to expectations, season of treatment did not have any clear effect on

either community trajectory or stem densities of seedling groups. This was largely due

to limitations in my study design. Because there was no replication of site or season

within site, underlying differences in burn management units confounded the effects of

season. Previous descriptions of the seedling layer highlight the compositional variability

between burn management units at these sites (Chapter 3), but my attempt to screen

for confounding effects of unit by comparing reference plots between management

units was hampered by low power.

Despite this, some seemingly significant differences in response variables were identified as false positives by my screening process. This included the differences in vine densities and tree seedling densities between seasons at Cedar Falls. These findings also bring into question the difference detected in community trajectory between seasons at Cedar Falls, which did not itself fail the screening test. One other suspect result, that also did not fail the screening test, was the difference in density of shade- tolerant tree seedlings between seasons; treated plots did not differ from reference plots in the subsequent fall and spring analyses, indicating that this was also likely an artifact of management unit.

Thus, the only clear effect of season was the difference in the percentage of small tree seedlings between spring and fall treatments. A greater proportion of small seedlings were found following spring treatments than fall treatments. Despite the significant difference in temperatures recorded at the litter surface between seasons, 181

which indicated that spring fires were hotter (483°C) than fall fires (302°C), the seedling

response was equivalent in clipped and burned plots in each season. This suggests that

this effect was not related to fire intensity, but rather, may have been related to either a reduction in sprouting response or greater proportion of new germinants following removal of biomass in the spring.

5.5.2. Effects of topkill with (fire) and without (clipping) heating Mean temperatures recorded by paint tags located 25 cm above the litter surface in this study (131°C) were low to moderate in intensity in comparison to those described by Glasglow and Matlack (2007b, a) in their low (56-87°C) and high (252-

317°C) intensity treatments in southeastern Ohio oak forests.

There was also only limited evidence that fire intensity had an effect on seedling dynamics. Maple (Acer) densities were lower in fall-burned plots than fall-clipped and

fall-reference plots. There were also differences between treatments in the spring, but

these were too weak to be statistically resolved. Thus, it is unclear if this is an effect of

topkill in general, or a specific response to burning. The reduction in maples was most

distinct at Cedar Falls where the predominant species was sugar maple (Acer

saccharum). At least one other study documented suppression of maple seedlings

following single prescribed fires of both high and low intensity (Glasgow and Matlack

2007b). Another study also showed reductions in density after multiple fires (Green et

al. 2010). But many more have documented increases in maple seedling densities

following both single (Elliott et al. 1999, Kuddes-Fischer and Arthur 2002, Albrecht and

McCarthy 2006) and repeated fires (Arthur et al. 1998, Blankenship and Arthur 2006).

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Fires had the opposite effect on shade-intolerant species. The greatest densities of shade-intolerant species, especially Liriodendron tulipifera (tulip poplar) and

Sassafras albidum (sassafras), occurred in burned plots. Densities were also greater on burned compared to clipped plots, especially at Hopkins where tulip poplar was common in the overstory. Burns that are low in intensity or on moist sites stimulate increases in tulip poplar seedlings both through sprouting as well as germination from a persistent seedbank (Brose et al. 1999, Vandermast et al. 2004, Albrecht and McCarthy

2006). Glasgow and Matlack (2007b) document that tulip poplar seedlings increased following either burning or litter removal (without topkill), suggesting that this response was primarily driven by germination following consumption and removal of litter in burned plots. Sassafras also exhibits strong resprouting and germination following low intensity fires (Albrecht and McCarthy 2006), but high light serves as a greater germination cue than reduced litter (Sutherland et al. 2000).

5.5.3. Potential for improved oak regeneration Oaks seedlings were not affected by burning or clipping treatments in this study.

Although several studies document increased oak seedling following single or multiple fires (Arthur et al. 1998, Elliott et al. 1999, Blankenship and Arthur 2006, Holzmueller et al. 2009), lack of response is also not uncommon (Kuddes-Fischer and Arthur 2002,

Hutchinson et al. 2005, Albrecht and McCarthy 2006). Still other studies have documented decreases in oak seedlings following fires (Vandermast et al. 2004). These discrepancies are due, in part, to the timing of masting relative to burning and sampling.

Oaks produce seed crops every 3-5 years, depending on the species (Burns and Honkala

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1990). Fires during a mast year can kill new oak germinants (e.g., Vandermast et al.

2004) and fires a year or so after a mast crop may stimulate resprouting of oaks but do little to change their densities (e.g., Albrecht and McCarthy 2006). This was likely the

case in this study, as most oak seedlings originated from a crop produced two years

before treatments (Chapter 3). However, fires that precede a mast crop by a few years

and reduce litter can enhance oak germination and survival, at least in the short term

(Wang et al. 2005, Iverson et al. 2008, Royse et al. 2010).

Although oak seedlings densities did not increase, reductions in maple density

shifted the relative abundance of oaks and maples in burned plots at Cedar Falls. At

Cedar falls, oaks outnumbered maples 13:1 and 2.4:1 in plots burned in the fall and

spring, respectively, but were nearly equal to maples in clipped plots. Yet in all the other site and treatment combinations, maples outnumbered oaks 2-12-fold. This shift in relative abundance may also be reflected in the community trajectory patterns, in which only Cedar Falls plots in the fall management unit differed by treatment (fire approached significant difference from clipping and reference in post-hoc pairwise comparisons). Whether this shift in relative abundance of oaks and maples at Cedar Falls will lead to a change in the competitive balance between oaks and maples is unclear, but unlikely.

Changes in the relative abundance of oaks and maples after burning do not necessarily translate into a competitive advantage for oaks over maples. In an oak-pine forest in Kentucky, Gilbert et al. (2003) found that burning enhanced growth not only of oaks, but of competitor species as well, such that three years following fire red maples

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were taller than oaks. Likewise, although repeated low-intensity fires in the Daniel

Boone National Forest in Kentucky reduced red maple seedling density and improved

oak seedling growth, the red maples that survived were more competitive; they were

taller and had greater basal diameter than oaks (Green et al. 2010).

Low-intensity prescribed fires likely fail to shift the competitive balance between oaks and maples, even when they do enhance growth and abundance of oaks, because they do not alter the understory light environment (Green et al. 2010). Low light levels

under dense maple-dominated understories suppress shade-intolerant oaks, allowing them to be outcompeted by more shade-tolerant species (Lorimer 1984, Abrams 1992,

Lorimer et al. 1994, McDonald et al. 2003). If light is the factor most limiting oak establishment, then low-intensity dormant-season fires, which do not cause high canopy

tree mortality or increase understory light levels, are unlikely to foster oak regeneration

(Hutchinson et al. 2005, Green et al. 2010).

Instead, a combination of functional restoration – using prescribed fires – and structural restoration – by mechanical thinning – have a greater potential to regenerate oak forests (Kruger and Reich 1997, Boerner et al. 2008, Iverson et al. 2008, Brose

2010). This combination of treatments aims to increase canopy openness, relieving oaks of light limitation, while simultaneously reducing the dominance of shade-tolerant and shade-intolerant understory competitors. However, the success of these efforts varies with both the intensity and frequency of fire as well as stand conditions. In general, burning and thinning combinations have been more successful using high-intensity fire

(Brose 2010) or repeated low-intensity fires (Kruger and Reich 1997, Iverson et al. 2008)

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than a single low-intensity fire (Albrecht and McCarthy 2006). Further, these methods

are more likely to regenerate oaks on drier upland sites than mesic sites (Iverson et al.

2008, Povak et al. 2008). Finally, as with other techniques, application of treatments

relative to oak masting determines if there are oak seedlings available for recruitment

(Iverson et al. 2008, Brose 2010).

5.5.4. Conclusions and management implications I found limited support for my original hypotheses concerning the relative effects

of fire season and fire intensity on oak forest seedling community trajectory. Spring burns were not more effective in reducing densities of maples. Nor did fire season have a clear or consistent role in shaping seedling responses. As expected, fire intensity was not a driver of vegetation responses, because fire temperatures were low. Thus, neither the seasonal timing nor the intensity of fires influenced seedling responses. Instead, the effects of fire were mainly driven by litter removal that stimulated a germination of shade-intolerant species.

Overall, the effectiveness of prescribed fires in filtering maples and shifting forest seedling layers towards a greater relative abundance of oaks was inconclusive.

Although fires reduced the abundance of sugar maple seedlings at one site, such that oaks outnumbered maples, this was not consistent across sites. Further, this shift in the relative abundance of oaks and maples at Cedar Falls, a mesic maple-dominated stand, is unlikely to lead to an increase in oak recruitment unless the plots are in canopy gaps and subjected to repeated burning to control competitors.

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Despite the short temporal scale and confounding effects of initial variation

between sites and burn units, the inconclusive results found in this study are consistent

with the literature. Single and repeated low-intensity prescribed fires have generally

been effective in slowing or reversing the shift from oak to maple dominance (Martin et

al. 2011, McEwan et al. 2011). Instead, techniques that restore both forest structure and

processes, such as canopy manipulation coupled with prescribed fire, are necessary to

shift communities back to oak dominance (Kruger and Reich 1997, Iverson et al. 2008,

Martin et al. 2011). In this context, future research efforts should focus on fire regime

characteristics (intensity, frequency, and season) that shift competitive balance to fire

tolerant species (Brose 2010, Green et al. 2010). Fire season, in particular, has received

little attention. Consideration should also be given to growing season fires, which may

differentially favor oaks (Petersen and Drewa 2006, Green et al. 2010).

Further attention is also needed to understand the drivers and dynamics of the oak-to-maple shift. For example, Nowacki and Abrams (2008) hypothesize that fire suppression initiates a positive feedback of increasing abundance of shade-tolerant fire- sensitive species, which in turn alter conditions in a way that improves their persistence and recruitment. They suggest that the conversion of a community to maple dominance represents an alternative stable state that is resistant to change. But McEwan et al.

(2011) take a slightly different approach. They propose that changing fire regimes may have been only one of a suite of other factors driving this compositional shift. These other factors including a change to a moister climate, loss of American chestnut, and population booms in acorn browsers. A better understanding of the relative importance

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of these various drivers and the resilience of communities to change would aid in directing restoration efforts across the eastern deciduous forest (Martin et al. 2011).

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5.6. Appendices 5.6.1. Appendix A: Full ACOVA model results Table 5.4 Summary of PERMANOVA analysis of multivariate community trajectory for individual and interacting effects of site, season, and treatment. For the Season and treatment model, sites included Cedar Falls and Hopkins and treatments included clipping and burning. In the Fall and Spring models, treatment type included clipping, burning, and reference. However, in the Fall model, site includes Cedar Falls, Hopkins, and Sandstone, but only Cedar Falls and Hopkins are in the Spring model. The design was a three-way or two-way ANOVA and all factors were treated as fixed. Season and treatment Fall Spring Source df F-value P-value df F-value P-value df F-value P-value Site 1 5.1094 0.0017 2 2.1175 0.0676 1 2.2736 0.0857 Season 1 0.5707 0.6343 Treatment 1 1.1811 0.3173 2 2.2372 0.0570 2 1.9357 0.0869 Site × Season 1 1.0361 0.3861 Site × Treatment 1 0.6945 0.5481 2 1.9297 0.0465 2 1.7288 0.1312 Season × Treatment 1 4.0425 0.0083 Site × Season × Treatment 1 3.3996 0.0209 Residual 32 36 24 Total 39 44 29

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Table 5.5 Summary of ANCOVA models of seedling density by habit for individual and interacting effects of site, season, and treatment. Model A: site (Cedar Falls and Hopkins), season (fall and spring), and treatment type (clipping and burning). Model B (Fall): site (Cedar Falls, Hopkins, and Sandstone) and treatment type (clipping, burning, and reference) in units treated in fall. Model C (Spring): site (Cedar Falls and Hopkins) and treatment type (clipping, burning, and reference) in units treated in the spring. Continued on next page. df Habit A. shrubs and vines a tree seedlings % small tree seedlings b Source F-value P-value F-value P-value F-value P-value Covariate 1 204.71 <.0001 50.74 <.0001 30.44 <.0001 Site 1 3.81 0.0600 97.96 <.0001 0.01 0.9133 Season 1 6.94 0.0131 28.41 <.0001 4.29 0.0468 Treatment 1 0.28 0.5978 10.16 0.0033 1.30 0.2629 Site × Season 1 1.52 0.2262 0.03 0.8600 0.37 0.5470 Site × Treatment 1 0.26 0.6134 2.73 0.1086 1.80 0.1897 Season × Treatment 1 0.12 0.7346 0.07 0.7939 0.12 0.7334 Site × Season × Treatment 1 1.67 0.2059 5.82 0.0220 0.01 0.9429 Residuals 31 Variance weights none Season*Treatment Site

B. Fall shrubs and vines tree seedlings % small tree seedlings b Source F-value P-value F-value P-value F-value P-value Covariate 312.47 <.0001 282.73 <.0001 192.70 <.0001 Site 1.98 0.1537 20.97 <.0001 27.40 <.0001 Treatment 1.07 0.3555 3.02 0.0615 26.48 <.0001 Site × Treatment 1.09 0.3775 2.68 0.0473 2.40 0.0686 Residuals Variance weights none none Site*Treatment 190 Continued on next page.

Table 5.5 Continued from previous page.

% small tree C. Spring shrubs and vines tree seedlings seedlings Source F-value P-value F-value P-value F-value P-value Covariate 216.03 <.0001 599.63 <.0001 2.10 0.1620 Site 11.69 0.0023 64.26 <.0001 0.30 0.6155 Treatment 1.10 0.3511 0.47 0.6283 23.20 <.0001 Site × Treatment 1.24 0.3094 5.57 0.0107 0.80 0.4491 Residuals Variance weights none Site*Treatment Site*Treatment Significant P-values are presented in bold. a b Annotations: log10 (value + 1) transformed; arcsine-square root; df, degrees freedom

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Table 5.6 Summary of ANCOVA models of seedling density by shade tolerance for individual and interacting effects of site, season, and treatment. Variable levels in each model follow Appendix A Table 5.5 df Shade tolerance A. tolerant intermediate intolerant a Source F-value P-value F-value P-value F-value P-value Covariate 1 116.91 <.0001 35.24 <.0001 284.59 <.0001 Site 1 1.80 0.1897 85.10 <.0001 8.25 0.007 Season 1 24.04 <.0001 1.72 0.1993 3.20 0.083 Treatment 1 0.07 0.7865 0.12 0.7296 57.16 <.0001 Site × Season 1 2.00 0.1673 0.46 0.5005 5.03 0.032 Site × Treatment 1 2.42 0.1301 0.47 0.4999 17.59 <.0001 Season × Treatment 1 0.11 0.7455 0.15 0.6993 0.09 0.764 Site × Season × Treatment 1 3.28 0.0799 1.71 0.2007 0.84 0.366 Residuals 31 Variance weights Site Site Site

B. Fall tolerant a intermediate a intolerant a Source F-value P-value F-value P-value F-value P-value Covariate 389.02 <.0001 116.17 <.0001 294.55 <.0001 Site 2.28 0.1177 19.46 <.0001 211.12 <.0001 Treatment 2.82 0.0734 2.00 0.151 28.76 <.0001 Site × Treatment 1.96 0.1219 2.44 0.065 4.66 0.004 Residuals Variance weights none none Site*Treatment Continued on next page.

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Table 5.6 Continued from previous page.

C. Spring tolerant intermediate intolerant a Source F-value P-value F-value P-value F-value P-value Covariate 33.18 <.0001 28.06 <.0001 61.56 <.0001 Site 9.46 0.0054 19.71 <.0001 22.53 <.0001 Treatment 0.22 0.8026 1.12 0.344 13.98 <.0001 Site × Treatment 0.29 0.7538 1.02 0.375 8.99 0.001 Residuals Variance weights none none Site*Treatment

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Table 5.7 Summary of ANCOVA analysis of seedling density by species group for individual and interacting effects of site, season, and treatment. Variable levels in each model follow Appendix A Table 5.5. df Species group A. maples oaks litu a Source F-value P-value F-value P-value F-value P-value Covariate 1 2.45 0.128 37.94 <.0001 95.65 <.0001 Site 1 26.59 <.0001 4.14 0.051 10.21 0.003 Season 1 11.20 0.002 0.79 0.381 0.48 0.495 Treatment 1 14.69 0.001 0.97 0.331 28.89 <.0001 Site × Season 1 1.42 0.243 0.81 0.376 4.32 0.046 Site × Treatment 1 0.01 0.935 0.02 0.888 14.68 0.001 Season × Treatment 1 5.91 0.021 0.13 0.722 0.06 0.803 Site × Season × Treatment 1 0.01 0.916 0.06 0.814 0.42 0.520 Residuals 31 Variance weights Site Site*Treatment Site*Treatment

B. Fall maples a oaks a litu a Source F-value P-value F-value P-value F-value P-value Covariate 146.69 <.0001 85.95 <.0001 110.87 <.0001 Site 17.70 <.0001 8.75 0.001 12.11 <.0001 Treatment 5.09 0.012 0.44 0.644 9.73 <.0001 Site × Treatment 1.89 0.134 0.94 0.452 10.43 <.0001 Residuals Variance weights Site none none Continued on next page.

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Table 5.7 Continued from previous page.

C. Spring maples oaks litu Source F-value P-value F-value P-value F-value P-value Covariate 26.01 <.0001 36.54 <.0001 11.64 0.002 Site 29.89 <.0001 5.49 0.028 0.34 0.567 Treatment 7.61 0.003 0.71 0.503 17.07 <.0001 Site × Treatment 0.31 0.735 0.26 0.772 7.51 0.003 Residuals Variance weights Site*Treatment Site*Treatment Site*Treatment Significant P-values are presented in bold. a b Annotations: litu, Liriodendron tulipifera; log10 (value + 1) transformed; arcsine-square root; df, degrees freedom

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5.6.2. Appendix B: Ordination analysis of seedling community trajectory Non-meteric multidimensional scaling (NMDS) ordination of the entire woody seedling dataset indicated a three dimensional ordination best represented the dissimilarity in species composition among plots (Appendix B Figure 5.7). The arrows connecting individual plots pre- and post-treatment represent a community trajectory or compositional vector that has both a direction and magnitude of change (length).

These compositional vectors were re-centered at the origin (Appendix B Figure 5.8) and the heads of the vectors were used for additional analysis using PERMANOVA.

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Figure 5.7 Non-metric multidimensional scaling (NMDS) ordination of plots pre (small gray circles) and post (color symbols) treatment based on woody plant seedlings. Arrows connecting individual plots pre- and post-treatment and show community change over time. 95% confidence ellipses encircle plots at different sites. Dashed reference lines mark axes origins and cross at the center of the ordination. Stress was 19.14 %.

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198 Figure 5.8 Post-treatment seedling community trajectory of full dataset showing plots labeled by site (a) and treatment (b). -> next page

Figure 5.8 continued from previous page Arrows on the first panel of a and b illustrate the compositional vectors shown on Appendix B Figure 5.7 centered on the origin by subtracting the pre-treatment coordinates from post- treatment coordinates. Arrows have not been drawn on remaining panels.

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Chapter 6: Conclusion 6.1. Summary My goal in this research was to test the efficacy of current burn prescriptions in reversing the effects of fire suppression in oak-dominated communities (barrens and forests) of the Bluegrass Region of southern Ohio. In oak barrens, biennial dormant season fires reduced shrub aerial cover and were effective in maintaining pretreatment densities of shrubs but did not reduce shrub encroachment or foster herbaceous plant abundance and diversity. Oak forests with different topographic and environmental characteristics were compositionally distinct but shared a common structural pattern indicative of increasing abundances of shade-tolerant trees, especially maples, and a lack of oak regeneration. This trend is characteristic of the shift from oak-to-maple dominance occurring throughout the eastern deciduous forest. A single set of fires in these oak forests resulted in site specific responses in the seedling layer. Prescribed fires stimulated germination of tulip poplar and reduced sugar maple seedlings, primarily at two different sites, but had little consistent effect on changing landscape-level compositional trends.

6.2. Reflections Overall, these results indicate that fire might maintain current vegetation composition and structure, but is not effective in reversing woody plant encroachment in oak-dominated ecosystems. However, I hesitate to extend these results beyond these study sites and burning conditions employed here because of the limited spatial and temporal scale of my studies. A single fire in forests and two fires in barrens are not

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sufficient to evaluate the effectiveness of a burning regime. Further, one year since the fire is a short time frame on which to base success or failure of management objectives when testing the effects of fire on tree regeneration.

The large variation in study site composition and condition coupled with the lack of replication may be an even larger limitation in generalizing my conclusions. Such pseudo-replication is common in ecological experiments, and generally limits the extension of findings beyond the specific sites used. But I suspect this is an even greater issue in my study region because of the incredible variation in topography, geology, land use history, and composition exhibited in oak forests along the Appalachian Escarpment.

The small subset of forest stands I sampled likely does not represent the average stand nor capture the variation in composition and condition across the landscape. The scarcity of available study sites and the logistic challenge involved in sampling them, if they existed, limit the execution of large scale, replicated, and extendable fire studies in this region to larger research teams.

The small spatial scale of the burns conducted in these studies may also diminish their utility in evaluating the efficacy of current burn prescriptions. Based on personal observations, I suspect that these small burns may not accurately mimic the behavior and effects of prescribed burns used in restoration at these sites. Small research burns tend to be lower in intensity, in part, because they do not generate the active flame front that occurs in larger burns. This flame front dries fuel in advance of burning as the fire moves across the landscape. Comparison of fire behavior and effects in small versus larger burns would help address these concerns.

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6.3. Conclusions Despite all of these misgivings, my findings are consistent with the generally inconsistent effects of fire in reversing woody plant encroachment across studies. Fires, especially single fires, do not reduce woody plant encroachment in barrens and can actually stimulate sprouting as well as foster maple establishment in oak forests.

Further, low-intensity dormant-season prescribed fire is rarely effective in reversing encroachment unless extremely frequent and or coupled with other large scale disturbances such as canopy removal.

However, the annual or biennial fire regime suggested for reducing encroachment (at least in barrens and savannas) is likely unsustainable from both a logistical and a natural resource management perspective. Few organizations have the resources to burn extensive areas of landscape every 1-2 years. Further, if one expects to retain the shrubs and trees that are naturally a component in barrens and savannas, such a regime seems counterproductive. Alternatively, higher fire intensities, fire in the growing season, and structural manipulation might be more effective management techniques that require further consideration and research, particularly in forests. The

Nature Conservancy is actively employing these strategies in barrens; replication and quantification of their effects would lead to broader refinement in the use of fire to restore fire-suppressed ecosystems.

However, given the frequent mismatch between the expectations and outcomes of prescribed fire, instead of modifying the prescribed fire regime, we may need to modify our expectation that fire can restore systems that have shifted considerably

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from their historic counterparts. Consider, for example, mesic grasslands now dominated by shrubs that have become fire-resistant via extensive root systems. And oak forests now dominated by maples that have reduced forest flammability and shifted understory light regimes. Manipulations other than prescribed fires may help push these systems towards the restoration target, but we need to consider the possibility that they may have reached a threshold state extremely resistant to change. Further, we may be trying to restore these ecosystems using a single driver (disturbance) when multiple factors actually precipitated the changes in vegetation. This may be the case in oak forests, where the decline in oak abundance and increase in maple correlates with changes in a suite of factors in addition to changing fire regimes. If this is the case, restoring fire regimes may never achieve a return to a restoration target.

In summary, the effective use prescribed fire to restore vegetation structure and composition in fire-suppressed ecosystems will continue to remain a challenge both because prescribed fire is itself an altered process and because it is being applied to altered systems. An awareness of the context for restoration – the variability in ecosystem structure and processes, the prehistoric and historic disturbance regimes, and the drivers of recent vegetation change – can help us evaluate whether fires are likely to be effective or if, instead, our expectations about the effects of prescribed fire need to be modified.

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