Prescribed Fire

A Meta-Analysis of the Fire-Oak Hypothesis: Does Prescribed Burning Promote Oak Reproduction in Eastern North America?

Posted on November 15, 2012 by in Books/Articles

A Meta-Analysis of the Fire-Oak Hypothesis: Does Prescribed Burning Promote Oak Reproduction in Eastern North America?

Patrick H. Brose, Daniel C. Dey, Ross J. Phillips, and Thomas A. Waldrop

Abstract: The fire-oak hypothesis asserts that the current lack of fire is a reason behind the widespread oak (Quercus spp.) regeneration difficulties of eastern North America, and use of prescribed burning can help solve this problem. We performed a meta-analysis on the data from 32 prescribed fire studies conducted in mixed-oak forests to test whether they supported the latter assertion. Overall, the results suggested that prescribed fire can contribute to sustaining oak forests in some situations, and we identified several factors key to its successful use. Prescribed fire reduced midstory stem density, although this reduction was concentrated in the smaller-diameter stems. Prescribed fire preferentially selected for oak reproduction and against mesophytic hardwood reproduction, but this difference did not translate to an increase in the relative abundance of oak in the advance regeneration pool. Fire equalized the height growth rates of the two species groups. Establishment of new oak seedlings tended to be greater in burned areas than in unburned areas. Generally, prescribed burning provided the most benefit to oak reproduction when the fires occurred during the growing season and several years after a substantial reduction in overstory density. Single fires conducted in closed-canopy stands had little impact in
the short term, but multiple burns eventually did benefit oaks in the long term, especially when followed by a canopy disturbance. Finally, we identify several future research needs from our review and synthesis of the fire-oak literature. FOR. SCI. ❚❚(❚):000-000.

Keywords: fire effects, hardwoods, prescribed fire, Quercus spp., shelterwood

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THROUGHOUT EASTERN NORTH AMERICA, mixed-oak (Quercus spp.) forests on upland sites are highly valued for many ecological and economic reasons. Generally, these upland forests consist of one or more oak species (black [Quercus velutina Lam.], chestnut [Quercus montana Willd.], northern red [Quercus rubra L.], scarlet [Quercus coccinea Muenchh.], and white [Quercus alba L.]) dominating the canopy with a mix of other hardwood species in the midstory and understory strata. Despite wide- spread abundance and dominance of mixed-oak forests, regenerating them is a chronic challenge for land managers throughout eastern North America and they are slowly being replaced by mesophytic hardwoods such as black birch (Betula lenta L.), black cherry (Prunus serotina Ehrh.), red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), and yellow-poplar (Liriodendron tu- lipifera L.) (Abrams and Downs 1990, Healy et al. 1997, Schuler and Gillespie 2000, Aldrich et al. 2005, Woodall et al. 2008). Many factors contribute to this oak regeneration problem including loss of seed sources, destruction of acorns and seedlings by insects, disease, weather, and wild- life, dense understory shade, competing vegetation, and lack of periodic fire (Crow 1988, Loftis and McGee 1993, John- son et al. 2009). The implication of the lack of periodic fire  as a cause to the oak regeneration problem arises from the fact that many of these oak forests exist, in part, due to past fires, and this relationship has led to the creation of the fire-oak hypothesis (Abrams 1992, Lorimer 1993, Brose et al. 2001, Nowacki and Abrams 2008, McEwan et al.  2011). The fire-oak hypothesis consists of four parts: (1) peri odic fire has been an integral disturbance in the mixed-oak forests of eastern North America for millennia; (2) oaks have several physical and physiological characteristics that allow them to survive at higher rates than their competitors in a periodic fire regime; (3) the lack of fire in the latter 20th century is a major reason for the chronic, widespread oak regeneration problem; and (4) reintroducing fire via prescribed burning will promote oak reproduction. The first three parts are supported by the scientific literature to various degrees. For example, paleo-ecological studies and historical documents indicate that American Indian tribes used fire for numerous reasons (Day 1953, Wilkins et al. 1991, Patterson 2006, Ruffner 2006). Many studies reported the differences between oaks and mesophytic hardwood species (Gottschalk 1985, 1987, 1994, Kolb et al. 1990), and the concomitant decline of fire and increase in mesophytic hardwoods during the early 1900s is evident from fire history research (Shumway et al. 2001, Guyette et al. 2006, Hutchinson et al. 2008, Aldrich et al. 2010). It remains hard to verify the fourth part of the fire-oak hypothesis—that

Manuscript received April 3, 2012; accepted July 17, 2012; published online August 16, 2012;  http://dx.doi.org/10.5849/forsci.12-039. Patrick H. Brose, USDA Forest Service, Northern Research Station, PO Box 267, Irvine, PA 16329—Phone: (814) 563-1040; Fax: (814) 563-1048; pbrose@fs.fed.us. Daniel C. Dey, USDA Forest Service—ddey@fs.fed.us. Ross J. Phillips, USDA Forest Service—rjphillips@fs.fed.us. Thomas A. Waldrop, USDA Forest Service—twaldrop@fs.fed.us

Acknowledgments: We thank the many fellow scientists who stimulated our thinking on this subject via engaging conversations as well as by sharing insights on the details of their studies, especially data collection procedures, and pointing us toward publications that had escaped our searches. We thank Alejandro Royo, John Stanovick, and Matthew Trager for guidance with the meta-analysis. In addition, we thank them and three anonymous individuals for reviews of earlier drafts of this article that helped with clarity and conciseness. Funding for this study was provided by the Joint Fire Science Program (Project 10-2-01-1).

This article was written by U.S. Government employees and is therefore in the public domain.

prescribed burning promotes oaks—because the results reported in the literature vary widely. Results range from positive (Brown 1960, Swan 1970, Ward and Stephens 1989, Kruger and Reich 1997) to neutral (Teuke and Van Lear 1982, Merritt and Pope 1991, Hutchinson et al. 2005) to negative (Johnson 1974, Wendel and Smith 1986, Loftis 1990, Collins and Carson 2003). This inconsistency among findings suggests that multiple factors drive fire outcomes and the complex relationships among these factors complicate the development of reliable guidelines for prescribed burning of mixed-oak forests.

Despite the variability in study outcomes and lack of specific guidelines for using fire in oaks, land management agencies throughout eastern North America are increasingly using prescribed fire in mixed-oak forests. For example, the oak-dominated national forests of the Ohio River basin (Allegheny, Daniel Boone, Hoosier, Monongahela, Shaw-nee, and Wayne) all have prescribed fire as part of their respective forest plans and in 2011 conducted 59 burns totaling 7,776 ha (National Interagency Fire Center 2012). The rationale behind these prescribed fires is that they will benefit oaks by increasing the quantity and quality of understory light by reducing midstory stem density, will increase the overall density of oak reproduction, and will improve the relative abundance and height of oak reproduction in the regeneration pool.

This widespread use of prescribed fire in mixed-oak forests without specific guidelines potentially creates problems, i.e., fire may be applied to oak forests not suitable for burning or fire may be withheld from oak forests that would benefit from burning. A meta-analysis of the fire-oak literature would test the final part of the fire-oak hypothesis and provide guidance on how and when prescribed fire is appropriate or is not useful in the regeneration of mixed-oak forests.

Meta-analysis is a systematic review and statistical synthesis of the empirical data contained in the literature on a particular subject (Borenstein et al. 2009, Harrison 2011). In meta-analysis, a common basis or standard for comparing the results of related studies is chosen, the relevant literature is reviewed, and individual publications are selected or rejected based on meeting that predetermined standard. The means, standard deviations, and sample sizes of the selected publications are statistically analyzed; the result is concise findings that are more broadly applicable than the results of the individual publications.

In 2009, we identified a need for a meta-analysis of the fire-oak literature because no large-scale systematic review and synthesis had been done on the subject and there were a sufficient number of published articles, a lack of guidelines specifically for oak forests, and increasing use of prescribed fire in oak forests by land management agencies. For this meta-analysis we posed the following research hypothesis: Fire will disproportionately benefit oak relative to mesophytic tree species. Specifically, we predict the following:

  1. Fire will reduce the density of midstory trees of all species.
  2.  Oak reproduction will basal sprout after prescribed fires at a higher rate than the reproduction of mesophytic hardwood species.
  3.  The proportion of oak reproduction relative to that of mesophytic hardwood species will increase postfire.
  4. Oak reproduction will be at least as tall as the reproduction of mesophytic hardwood species postfire.
  5. Density of new oak seedlings (germinants) will increase postfire.

The first three predictions test direct fire effects, whereas the other two address indirect effects in that they are influenced by other factors (shading, seed production, and adequate seedbed). Prediction 2 is short-term (1 or 2 years postburn), whereas the others are longer, depending on the duration of the study. After testing each prediction, we dissect the result, examining the characteristics of the studies contributing to the outcome of that prediction to comprehend why fire produced that effect. Understanding how and why fire promotes oak reproduction will lay the groundwork for developing prescribed burning guidelines for oak forests.

Data and Methods

For this project, we initially formed a pool of fire-oak publications from our personal files and libraries that we could access directly. This collection was supplemented by Internet searches on Web sites such as Google Scholar and Web of Knowledge for fire-oak publications that we did not possess. Finally, we contacted colleagues involved in fireoak research for unpublished progress reports on active studies and recently accepted manuscripts. These searches resulted in a database of 187 manuscripts from throughout eastern North America.

We then began winnowing the database using three criteria. Our first criterion was whether the publication provided experimental data that addressed at least one of the five test predictions. This step eliminated the fire history and general discussion publications. Our next criterion was whether the publication contained a sufficient  replication of fire treatment(s) to permit statistical analysis. Case studies were thereby eliminated. Our last criterion was whether the publication contained a sufficient description of fire behavior (season of burn and fire intensity) and the site (stand density and management history) to help explain the results. Finally, we decided to focus on the prescribed fire projects instead of the individual publications because some of the projects, especially the large, long-term studies, produced multiple publications. Ultimately, we settled on 50 articles/ reports from 32 prescribed fire projects conducted in 15 states for this meta-analysis project (Table 1).

Meta-analysis requires the creation of standards or criteria to compare the results of the studies. These standards may be means, rates, or ratios. For this project, we created the following standards to test the predictions using preburn/postburn or burned/unburned data.

1. Midstory reduction: The mean decrease in the density of stems (2.5-28.0 cm dbh) of all species.

Table 1. Publications of the prescribed fire studies used in this meta-analysis project.

Study Location State Publications Data available
1 Daniel Boone NF KY Alexander et al. 2008 R
2 Clemson Forest SC Barnes and Van Lear 1998 M
3 Horsepen WMA VA Brose and Van Lear 1998, 2004, Brose et al. 1999, Brose 2010 R
4 State Game Land 29 PA Brose 2012 R
5 Allegheny NF PA Brose 2012* R
6 Clear Creek SF PA Brose et al. 2007 R
7 Westvaco Forest WV Collins and Carson 2003 M
8 Purdue Forest IN Dolan and Parker 2004 R
9 Chilton Creek Tract MO Sasseen and Muzika 2004, Dey and Hartman 2005, Fan et al. 2012 R
10 Land/Lakes NRA KY Franklin et al. 2003 R
11 Clemson Forest SC Geisinger et al. 1989 R
12 Moshannon SF PA Brose et al. 2007, Gottschalk et al. 2012 R
13 Red River Gorge KY Arthur et al. 1998, Gilbert et al. 2003, Blankenship and Arthur 2006, Green et al. 2010 R/M
14 University of MO Forest MO Paulsell 1957, Huddle and Pallardy 1996 M
15 Bankhead NF AL McGee 1979, 1980, Huntley and McGee 1981, 1983 R
16 Vinton Furnace EF OH Sutherland and Hutchinson 2003, Hutchinson et al. 2005, 2012 R/M
17 Powhatan WMA VH Keyser et al. 1996 R
18 Jordan Timberlands WI Kruger and Reich 1997 R
19 Dinsmore Woods KY Luken and Shea 2000 R
20 Duke Forest NC Maslen 1989 R/M
21 Broome County NY McGee et al. 1995 R
22 Morgan SF IN Merritt and Pope 1991 R/M
23 Schmeeckle Reserve WI Reich et al. 1990 R
24 Fernow EF WV Schuler et al. 2012 R
25 Ft. Indiantown Gap PA Signell et al. 2005 R/M
26 Clemson Forest SC Stottlemyer 2011 R
27 University of TN Forest TN Thor and Nichols 1973, DeSelm et al. 1991, Stratton 2007 R/M
28 Sumter NF SC Teuke and Van Lear 1982 R
29 Green River WMA NC Waldrop et al. 2008 R/M
30 Zaleski SF OH Albrecht and McCarthy 2006, Iverson et al. 2008, Waldrop et al. 2008 R/M
31 Zaleski SF CT Ward and Brose 2004 R
32 Zaleski SF WI Will-Wolf 1991 M

NF, National Forest; WMA, Wildlife Management Area; SF, State Forest; EF, Experimental Forest; NRA, National Recreation Area; R, reproduction; M, midstory.
* Unpublished data on file at the Forestry Sciences Laboratory, Irvine, PA.

  1. Differential sprouting: The difference in postfire basal sprouting rates between oak reproduction (  2.5 cm dbh) and those of mesophytic hardwood species.
  1. Oak relative abundance: The change in the proportion of oak reproduction in the regeneration pool ( 2.5 cm dbh) between the beginning and end of the study.
  2.  Oak relative height: The height of the oak reproduc-tion compared with that of mesophytic hardwood species at the end of the study.
  3. Oak seedling establishment: The increase in the meannumber of new oak seedlings during the course of the study.

Generally, each project provided data for three or four of the standards. Nine projects provided data for just one of the standards and only three of the projects provided data for all five standards. Sometimes the publications  provided the data for the standard in the format we needed for the meta-analysis. For example, the publications containing mean preburn/postburn oak seedling or midstory stem densities generally had these data in a ready-to-use format for andards 1 and 5, but for standards 2, 3, and 4, we had to do some simple grouping and calculations before conducting the meta-analysis. For these three standards, we made two species groups: oak and mesophytic species. Hickory (Carya spp.) was included with oak because these two genera share many silvical characteristics, whereas the mesophytic group included all other hardwoods generally considered to be competitors to oak and potential oak replacements. For the oak sprouting standard (no. 2), we used the preburn and the immediate  postburn stem densities to calculate the mean oak basal sprouting rate by dividing the postburn oak stem density by the corresponding preburn density. We did likewise for the mesophytic group and the two basal sprouting rates (oak and mesophytic) were then used in the meta-analysis. For the oak relative abundance standard (no. 3), we divided the preburn oak stem density by the total preburn stem density and did likewise for the oak and total stem densities reported at the end of the study. For the oak height standard (no. 4), we divided the mean oak seedling height at the end of the study by the corresponding height of the mesophytic species.

Once the standards are extracted from the publications or derived from the results, meta-analysis uses them and the corresponding variances and sample sizes to calculate the “effect size,” a measure of the magnitude of the effect of that experiment (Borenstein et al. 2009, Harrison 2011). There are several effect size indices and software programs for calculating them. We chose to use the log response ratio (ln R) as this index because it quantifies the proportionate change that results from experimental manipulation and is commonly used for conducting meta analysis of ecological studies (Osenberg et al. 1997, Hedges et al. 1999) and MetaWin 2.0 software (Rosenberg et al. 1997) for our project. When the effect size (ln R) is positive, then the fire increases the standard, whereas a negative ln R value indicates that fire decreases the standard. An effect size not significantly different from zero indicates that the fire had no discernible effect on the standard. For each standard, once an effect size is calculated, a cumulative effect size (grand mean) is calculated for all studies providing data for that standard.The effects of a fire on hardwood reproduction or midstory trees are a function of several factors (Brose and Van Lear 2004) and we tested the influence of some of these factors with summary analysis. This procedure is similar to analysis of variance in that the effect sizes and variances of the studies applicable to each factor are sorted into categories and tested by comparing resulting P values to a critical threshold indicating a significant difference between or among categories (Borenstein et al. 2009, Harrison 2011).

For our summary analyses, we chose five factors that we considered to be likely influences on the individual and cumulative effect sizes and that were readily available from the literature (Table 2). These factors were status of oak reproduction, season of burn, number of fires, stem size class, and study duration. Each of these factors contained two or three categories, and the studies were assigned to these categories for the summary analyses. Status of oak reproduction was either released or suppressed. Released oak reproduction consisted of oak seedlings or sprouts that were not limited by lack of sunlight. They had been growing in stands treated with a shelterwood release cut or final harvest several years before the prescribed fire. Suppressed oak reproduction was growing in uncut stands. Season of burn was either dormant or growing season. Dormant-season burns occurred between leaf abscission in the autumn and the beginning of leaf expansion of the mesophytic hardwoods the following spring; growing-season fires occurred during the other months. Number of fires referred to how many prescribed burns were conducted during the study (one, two, or more than two). Stem size class was either saplings (2.5-14.0 cm dbh) or poles (15.0-28.0 cm dbh). Study duration was short-term ( 5 years) or long-term ( 5 years). Not

Table 2. Characteristics of the prescribed fire studies used in this meta-analysis project.

Study Location State Seedling status Season of burn No.of fire Study duration No.of Replicates
1 Daniel Boone NF KY Sup Dor 2 5 3
2 Clemson Forest SC Sup Dor 3 6 3
3a Horsepen WMA VA Rel Dor 1 10 3
3b Horsepen WMA VA Rel Gro 1 10 6
4 State Game Land 29 PA Rel Gro 1 3 2
5 Allegheny NF PA Rel Gro 2 7 4
6 Clear Creek SF PA Sup Gro 1 3 3
7 Westvaco Forest WV Sup Dor 1 3 4
8 Purdue Forest IN Sup Dor 1 2 3
9 Chilton Creek Tract MO Sup Dor 1,3,4 5 5
10 Land/Lakes NRA KY Sup Dor 1,2 2 6
11 Clemson Forest SC Sup Gro 1 2 3
12 Moshannon SF PA Sup Gro 1 5 3
13 Red River Gorge KY Sup Dor 2,3 10 3
14 University of MO Forest MO Sup Dor 10 10 2
15a Bankhead NF AL Rel Dor 1 5 3
15b Bankhead NF AL Sup Dor 1 5 3
16 Vinton Furnace EF OH Sup Dor 2,4 7 4
17 Powhatan WMA VA Rel Gro 1 2 2
18 Jordan Timberlands WI Rel Gro 2 2 4
19 Dinsmore Woods KY Sup Dor 2,3 3 2
20 Duke Forest NC Rel Dor 1 8 3
21 Broome County NY Sup Dor 1,2 10 2
22 Morgane SF IN Sup Dor 1,2 5 4
23 Schmeeckle Reserve WI Sup Dor 1 2 4
24 Fernow EF WV Sup Dor 2 9 2
25 Ft. Indiantown Gap PA Sup Dor 3,4 1 4
26 Clemson Forest SC Rel Gro 1 3 4
27 University of TN Forest TN Rel Dor 10 10 6
28 Sumter NF SC Sup Dor 1 2 3
29a Green River WMA NC Rel Dor 2 5 3
29b Green River WMA NC Sup Dor 2 5 3
30a Zaleski SF OH Rel Dor 2 5 3
30b Zaleski SF OH Sup Dor 2 5 3
31 Goodwin/ SF CT Rel Gro 1 4 2
32 Baxter Hollow WI Sup Dor 1,2 4 6

NF, National Forest; WMA, Wildlife Management Area; SF, State Forest; EF, Experimental Forest; NRA, National Recreation Area; Rel, Released; Sup, suppressed; Dor, dormant; Gro, growing.

all factors were pertinent to summary analysis of each standard. For our summary analyses, we used random effects models with an value of 0.05 for determining statistical significance.

Results

Of the 32 prescribed fire projects, 14 provided data on the changes in midstory density (Figure 1). Mean preburn midstory densities were 513 115 stems/ha and meanpostfire midstory densities were 234 45 stems/ha, a 54% reduction. Overall, this reduction in stem density was sig-nificant; the grand mean was 0.88 0.61 ln R with the log response ratios of the individual studies ranging from0.06 to 1.94 ln R. Subsequent summary analysis indicated differences in midstory density reduction by size class(P 0.008) and the number of fires (P 0.036). The decrease in stem density was concentrated in the saplings, especially those less than 10 cm dbh, as postburn sapling densities declined by 88% whereas pole densities dropped by only 15%. Of the three fire categories, single fires did not reduce midstory stem density (13% decline), but two fires and more than two fires did, leading to 36 and 71% declines, respectively. It was not possible to test fire season because all 14 projects used dormant-season fires.Twenty-three prescribed fire projects provided appropriate data to examine the postfire basal sprouting rates of oak and mesophytic reproduction (Figure 2). Postfire basal sprouting rates reported in the studies or calculated from their data ranged from 13 to 96% for oak and from 5 to 85% for mesophytic species. Overall, oak reproduction sprouted postfire at a 32% higher rate than the mesophytic species, resulting in a significant grand mean of 0.421 ln R. Sum- mary analysis found significant differences between the two species groups by fire season (P 0.009) and status of thereproduction (P 0.002). For growing-season fires, oak reproduction sprouted at a 58% higher rate than the meso- phytic species, but after dormant-season fires the difference in sprouting rates between the two groups was nearly zero. Similarly, released oak reproduction sprouted at a 56% higher rate than the mesophytic species, whereas suppressed oak reproduction had a 14% greater sprouting rate than the mesophytic species. When these two factors were combined, sprouting rates were 56% higher for released oaks than for the mesophytic species after growing-season fires, 20% higher for released oaks than for the mesophytic species after dormant-season fires, 14% higher for suppressed  oaks than for the mesophytic species after dormant season fires, and 65% lower for suppressed oaks than for the mesophytic species after growing-season fires. No significant differences were found for number of fires.

Twenty-three studies provided suitable data for examining the change in the relative abundance of oak reproduction (Figure 3). Overall, prescribed burning did not significantly change the proportion of oak reproduction in the advance regeneration pool. The grand mean was 0.3420.393 ln R. Before burning, mean oak abundance was 25.6% of the seedling pool and after burning it was 26.0%. Summary analysis found only one significant difference: oak relative abundance in studies involving growing-season fire and released reproduction was greater than that with

Figure 1. The reduction of pole and sapling stem density (log response ratio 95% confidence interval) after prescribed fires conducted throughout the eastern United States. Log response ratios significantly less than zero indicate that the number of midstory stems decreased, whereas log response ratios not different from zero indicate that the postburn densities were unchanged. The numbers refer to the prescribed fire projects in Table 2.

Figure 2. The relative sprouting (log response ratio 95% confidence interval) of released (Rel) and suppressed (Sup) oak reproduction in comparison to mesophytic hardwood reproduction following dormant-season (Dor) and growing-season (Gro) prescribed fires conducted throughout the eastern United States. Log response ratios significantly greater than zero indicate that the oak reproduction sprouted postfire at a higher rate than the mesophytic reproduction. Log response ratios significantly less than zero indicate the opposite, and log response ratios not different from zero indicate that the survival rates of the two species groups were equivalent. The numbers refer to the prescribed fire projects in Table 2.

Figure 3. The relative abundance (log response ratio 95% confidence interval) of released (Rel) and suppressed (Sup) oak reproduction after dormant-season (Dor) and growing-season (Gro) prescribed fires conducted throughout the eastern United States. Log response ratios significantly greater than zero indicate that the proportion of oak reproduction increased in the regeneration pool. Log response ratios significantly less than zero indicate the opposite, and log response ratios not different from zero indicate that the proportion of oak did not change. The numbers refer to the prescribed fire projects in Table 2.

dormant-season fire and suppressed reproduction (P0.006). Otherwise, no differences were found among number of fires (P 0.873) or between seasons of burn (P0.62) or by study duration (P 0.982). Only 11 studies provided postburn height data of the oak and mesophytic reproduction (Figure 4). Overall, heights of the oaks were 95% of the heights of the mesophytic species. The grand mean was 0.16 0.18 ln R, indicating no

Figure 4. The relative height (log response ratio 95% confidence interval) of oak reproduction in comparison to mesophytic hardwood reproduction after short-term (<5 years) and long-term (>5 years) prescribed fire studies conducted throughout the eastern United States. Log response ratios significantly greater than zero indicate that the oak reproduction was taller than the mesophytic reproduction postfire. Log response ratios significantly less than zero indicate the opposite, and log response ratios not different from zero indicate that the heights of the two species groups were equivalent. The numbers refer to the prescribed fire projects in Table 2.

difference between the two species groups. Summary analysis also found no differences between the categories by season of burn, seedling status, or study duration because their P values ranged from 0.686 to 0.96.

Fifteen fire projects provided data on the establishment of new oak seedlings (Figure 5). Overall, the number of new oak seedlings increased by an average of 1,315 290 stems/ha during the course of these studies, resulting in agrand mean of 0.33 ln R. This effect size was not different from 0 because of the tremendous variability reported in the studies (individual log response ratios rangedfrom 1.02 to 0.94). Summary analysis showed no differences based on study duration (P 0.334).

Discussion

Forestry professionals identify periodic fire as a major reason for the historical occurrence of mixed-oak forests in eastern North America and the cessation of that fire regime in the early 20th century as one of the key factors in the current, widespread oak regeneration problem (Abrams 1992, Brose et al. 2001, Nowacki and Abrams 2008). Consequently, researchers have been engaged in trying to determine how to use prescribed fire to help solve this problem, and their efforts have produced dozens of studies and hundreds of publications replete with examples of when prescribed burning benefited oak reproduction, when it hindered forest renewal, and when it had a negligible impact on the regeneration process. Meta-analysis offers a means bywhich these divergent studies can be compared on a common basis to support or refute the notion that prescribed fire can help regenerate mixed-oak forests.

Prediction Testing

The results of our meta-analysis support the idea that prescribed fire can help regenerate mixed-oak forests in some situations. Prescribed burning reduced the density of midstory stems (prediction 1), oak reproduction sprouted postfire at higher rates than mesophytic reproduction (prediction 2), and postfire height growth of oak reproduction was comparable to that of mesophytic reproduction (prediction 4). In addition, establishment of new oak seedlings showed a trend toward greater density in burned areas relative to unburned control areas (prediction 5). Collectively and individually, all four of these findings indicate that fire moves an oak forest through the regeneration process in a manner consistent with sustaining that forest’s oak component in the future.

Further testing of these predictions and the nonsignificant outcome of prediction 3 (that the postfire proportion of oak reproduction will be greater than that of other hardwood species) illustrate some important caveats on using fire to promote oak regeneration. Reduction of midstory density (prediction 1) was dependent on the diameters of the stems and the number of fires. Single fires, especially those in the dormant season, decreased the number of small saplings, especially those less than 10 cm dbh but had virtually no

Figure 5. The establishment of new oak seedlings (log response ratio 95% confidence interval) after short-term (<5 years) and long-term (>5 years) prescribed fire studies. Log response ratios significantly greater than zero indicate an increase in the density of new oak seedlings, whereas log response ratios significantly less than zero indicate the opposite, and log response ratios not different from zero indicate no change in the density of new oak seedlings. The numbers refer to the prescribed fire projects in Table 2.

effect on larger diameter stems. This outcome is understandable because prescribed fires are conducted under predetermined fuel and weather conditions to minimize the risk of escape and damage to valuable crop trees. Once hardwoods have grown beyond 10 cm dbh, they are large enough and have thick enough bark to survive most prescribed burning, especially single, low-intensity, dormant-season fires. Multiple fires do eventually cause a reduction in the number of larger saplings and poles. Unfortunately, the multifire data came entirely from dormant-season fires so comparing them with growing-season burns was not possible. However, it is likely that growing-season fires would have a faster and greater impact than dormant-season burns on reducing the density of larger diameter stems.

The superior postfire sprouting ability of oak reproduction (prediction 2) was probably a result of their tendency to allocate carbon more to root development than to stem development in contrast with many of the mesophytic hardwood species (Gottschalk 1985, 1987, 1994, Kolb et al. 1990, Brose 2011). Superior oak sprouting was not universally observed, however; the status of the reproduction (released or suppressed) and fire seasonality (dormant or growing season) were major factors in the outcome. Growing-season fires involving released reproduction produced the largest advantage to oaks in postfire sprouting rates. Conversely, growing-season fires involving suppressed reproduction resulted in a postfire oak sprouting rate less than that of the competitors. This was probably the result of the suppressed oak seedlings having smaller roots and depleted carbohydrate reserves relative to the larger, well-established, shade-tolerant mesophytic species. For dormant season burns, the postfire sprouting rates of oaks were slightly but nonsignificantly higher than those of the competing mesophytic species, regardless of whether the oak reproduction was suppressed or had been released. The few dormant-season studies that showed a difference in sprouting rates between the two species groups had extenuating circumstances such as the competitor’s high susceptibility to fire or the use of several burns.

The superior postfire sprouting ability of oak did not translate into an increase in oak’s relative abundance in the regeneration pool (prediction 3). Generally, changes in oak relative abundance tended to follow the previously described patterns of oak sprouting. Prescribed growing season burns involving released oak reproduction resulted in greater oak relative abundance, whereas dormant-season fires or any fires involving suppressed oak reproduction usually showed decreased relative abundance or no appreciable change. The overall lack of change in oak relative
abundance is probably a result of new mesophytic seedlings germinating from the seed stored in the forest floor (Schuler et al. 2010) or disseminated from nearby trees or sprouts arising from root systems.

The equalizing of postfire height growth between oak and mesophytic reproduction (prediction 4) should be interpreted cautiously. First, the mesophytic group contained a wide variety of hardwood species, everything other than oak and hickory, so the mean heights used in the meta-analysis were tempered by the slower growing species. Unfortunately, many of the studies did not differentiate well enough among mesophytic species to allow us to focus on primary competitors such as yellow-poplar. Second, height growth of sprouting hardwoods after fire is a function of their prefire size and vigor, the degree of shading, and site quality. The 11 studies used in the meta-analysis repre-sented a diverse mix of prefire seedling conditions, canopy cover, and sites. Thus, the equal height growth of oak and mesophytic reproduction postfire may be an artifact of the inherent variability among the studies rather than a biological certainty that oak reproduction can match mesophyitc reproduction in height growth postfire.

Prediction 5, that fires facilitate the establishment of new oak seedlings, must also be interpreted cautiously. We intended to use only studies that tallied multiple stems arising from the same rootstock as one stem, but sometimes we could not determine from some of the projects whether this was how the reproduction was inventoried. Moreover, only a few of the publications mentioned the occurrence of an acorn crop, an essential precursor to establishment of new oak seedlings. It is not clear whether fires actually improve the germination success of acorns or whether the reported increases were the result of the inventorying procedures.

Management Implications

In even-aged stand management, the regeneration process for mixed-oak forests can last 10 to 25 years depending on numerous factors (Loftis 2004, Johnson et al. 2009). The process consists of three major phases, production of acorns, establishment of oak seedlings from those acorns, and development of those seedlings into competitive-sized oak reproduction, and an event, an adequate, timely release of that reproduction (Loftis 2004). Two intrinsic factors make the process inevitably slow: sporadic acorn produc tion and root-centered seedling growth. In addition, weather, interfering vegetation, wildlife, dense midstory shade, and other factors can slow or stall any of the three phases.

Based on this meta-analysis, prescribed fire appears to fit into two places in the oak regeneration process. The first is at the beginning of the regeneration process as a site preparation tool. The second is near the end of the regeneration process as a release tool. In either case, the first step in using fire is an inventory of the abundance and size of the oak reproduction, overstory conditions, and potential stand renewal obstacles such as competing and interfering vegetation, browsing pressure by white-tail deer (Odocoileus virginianus), and site limitations. The inventory may be a comprehensive examination as is done with stand prescription programs such as SILVAH (Brose et al. 2008) or less-intensive assessment of stand conditions. However, it must be done to determine whether there is enough oak reproduction to proceed with stand regeneration. The determination of the adequacy of oak reproduction is highly stand-specific; what is sufficient oak reproduction for one stand may be inadequate for another based on several extenuating factors such as site characteristics, composition of the competing species, and impact of white-tail deer.

Mature, closed-canopy oak stands that lack adequate oak reproduction are at the beginning of the regeneration process. Burning can decrease midstory density, thereby increasing understory light and can reduce the thickness of the forest floor, especially the litter layer, which can be a barrierto germination and seedling establishment (Korstian 1927, Barrett 1931, Carvell and Tryon 1961, Wang et al. 2005). Site preparation burning may also have a negative impact on populations of acorn pests such as weevils (Curculio spp.) (Wright 1986, Riccardi et al. 2004) and xerify the upper layers of the soil (Barnes and Van Lear 1998), making it a less hospitable seedbed for mesophytic hardwoods. This approach will probably take a decade or more because the benefits of burning are initially small and multiple burns are needed to create the desired understory conditions. This appears to be especially true with low-intensity fires conducted in the dormant season. In comparing winter and spring burns, Barnes and Van Lear (1998) concluded that three dormant-season fires were needed to equal the impact of one growing-season burn for intermediate-quality sites in the upper Piedmont region of western South Carolina. Regardless of fire seasonality and fire intensity, site preparation burning will probably be a long-term endeavor because oak seedling establishment is dependent on an acorn crop, and masting in oaks can be highly sporadic due to several intrinsic and extrinsic factors. Furthermore, leaf litter re-accumulates within a few years postburn so the benefit of litter reduction is short-lived. Our conclusion is that site preparation is a fair to good use of prescribed fire in oak management, but the time required to achieve satisfactory results may be a major disadvantage. Reducing midstory shade with herbicides (where permitted) may be a more efficient approach with less potential damage to residual canopy trees.

Oak stands with an adequate density of oak reproduction that have received a heavy partial cut or have been completely harvested are well into the regeneration process because the reproduction is no longer limited by shading. In this context, prescribed burning to release the oak reproduction from the competing mesophytic species appears to be an excellent use of fire as long as the competing stems are less than 10 cm dbh. Of the studies included in this meta analysis, those that occurred in stands that had been partly to completely harvested several years before the fires showed consistently strong positive benefits to the oak component. The oak reproduction survived at a higher rate than the mesophytic competitors, oak relative abundance increased postfire, and the oak sprouts grew at a rate comparable to that of the mesophytic hardwoods. In release burning, fire seasonality and fire intensity matter. The strongest benefits to oak were associated with moderate- to high-intensity growing-season fires. In practical application, when an oak stand has adequate oak reproduction to proceed with the regeneration process, we recommend harvesting the overstory via a two-cut shelterwood sequence or a final removal cut and then burning either between the shelterwood harvests or after the overstory is completely removed. The key is to wait several years after the harvest to burn so that the oak reproduction has adequate time to develop its root system and increase its probability of vigorous sprouting after future burns (Brose 2008, 2011).

Our review of fire-oak literature suggested several special circumstances that may alter or curtail burning plans. One is that prescribed fires can damage and kill overstory trees, some of which may be high-value crop trees. Although this negative effect has been known for years (Nelson et al. 1933, Paulsell 1957, Berry 1969, Wendel and Smith 1986), it is especially true for burning during a shelterwood sequence because of the elevated fuel loads (Brose and Van Lear 1999). In such cases, slash management (lopping, scattering, or removal from the bases of crop trees) is essential to prevent unacceptable losses. Another fire damage caveat is when an oak stand is in the stem exclusion stage of development. Sapling- and pole-size oaks are quite susceptible to fire scarring and subsequent value loss with little change in species composition (Carvell and Maxey 1969, Ward and Stephens 1989, Maslen 1989). Acorns appear to be quite susceptible to fire damage (Auchmoody and Smith 1993), so we advise against burning shortly after an acorn crop if the germinants from those acorns are needed to become oak advance reproduction. A closely related caveat pertains to small oak seedlings. Prescribed fires will kill suppressed oak reproduction, especially growing-season burns. Although this meta-analysis did not examine the influence of seedling size on the outcome of the studies, it was apparent from the few studies with detailed height data that sprouting rate was affected by size. Large oak reproduction sprouted postfire at consisently higher rates than small oak reproduction, especially when the fire occurred in the growing season, and initially larger stems grew taller after burning under any given overstory stocking and burn treatment. Initial diameter and size of oak reproduction are good indicators of its ability to survive fire and are good predictors of future competitive capacity (Brose and Van Lear 2004, Dey and Hartman 2005). Consequently, when the oak component of the regeneration pool is mostly small reproduction, land managers should consider using low-intensity dormant-season burns to minimize losses or opt for other silvicultural practices such as a shelterwood preparatory cut or individual stem herbicide treatments to move the oak stand forward in the regeneration process.

Two nonoak caveats are the presence of invasive species and deer browsing. Some plant species such as the native hay-scented fern (Dennstaedtia punctilobula) and the exotic tree of heaven (Ailanthus altissima) can spread rapidly after a fire (Rebbeck et al. 2010, Gottschalk et al. 2012) so their presence in or near the burn unit may require preemptive control measures to prevent their spread. Similarly, whitetail deer will be attracted to burned areas and excessive browsing can quickly turn a potential regeneration success into a failure. Potential deer problems should be identified and mitigated before burning.

Future Research Needs

Our collecting and reviewing of the fire-oak literature and our subsequent meta-analysis identified several Knowledge gaps that merit research. They are the following:

  1. The relationship between fire intensity and postfires prouting of hardwood reproduction. We had hoped to include fire intensity as one of the contributing factors, but this was not feasible because the studies had widely divergent approaches to measuring this variable. Some simply described fire intensity (cool, hot,or typical for the conditions) or placed it in broad classes (low, moderate, or high) or measured characteristics of the flaming front, but reported them at the stand or treatment level. Despite this variability, it was clear that relationships exist between fire intensity and postfire sprouting of hardwood reproduction. Fire in tensity and postfire sprouting need to be measured at the same scale.
  2. Fire effects on the establishment of new oak seedlings.Although our meta-analysis suggests that establishment of new oak seedlings increases postfire, wecannot be sure because some studies included in the analysis did not state exactly how the reproductionwas inventoried. Research is needed to determine whether fire promotes establishment of new oak seed-lings and to verify the sensitivity of acorns to fire.
  3. The impacts of fires on other oak ecosystem compo-nents. The vast majority of the fire-oak publications we found directly address regeneration concerns, but the fire effects on other ecosystem properties may be important indirect influences on oak reproduction and oak forest health. For example, oaks are ectomycorrhizal, whereas most of the mesophytic species are endomycorrhizal, and shoestring fungus (Armillaria mellea) is a common pathogen implicated in oak decline. How does fire affect these fungal communities? In addition, growing-season burns provide excellent control of competing mesophytic hardwoods, but they may adversely affect ground-nesting birds and herpetofauna in the short term via disrupted nesting or direct mortality. Do these short-term losses really occur or do such burns benefit the overall populations in the long-term by creating improved habitat? Knowing the impacts of fire on potentially sensitive species will help managers tailor their burning prescriptions.
  4. A comparison of fire with other silvicultural treat-ments and the sequencing of fire with other silvicultural treatments. The number of oak forests that could benefit from properly applied prescribed fire far exceeds what can be accomplished, even under the best of circumstances. Knowing the tradeoffs between prescribed fire and a fire surrogate such as herbicide application or mechanical site scarification will help foresters match the right tool with the job. Similarly, the exact sequencing of fire with other silvicultural practices merits more research because the more efficient and streamlined the oak regeneration process is, the more likely it is to succeed. Research on treatment efficiency would help managers make wiser use of their limited budgets.

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Fire effects on temperate forest soil C and N storage

Posted on June 29, 2011 by in Books/Articles

Fire effects on temperate forest soil C and N storage

LUCAS E. NAVE,1,2,5 ERIC D. VANCE,3 CHRISTOPHER W. SWANSTON,4 AND PETER S. CURTIS1

1Ohio State University, Department of Evolution, Ecology and Organismal Biology, Columbus, Ohio 43210 USA
2University of Michigan Biological Station, Pellston, Michigan 49769 USA

3National Council for Air and Stream Improvement, Research Triangle Park, North Carolina 27709 USA
4USDA Forest Service, Northern Research Station, Houghton, Michigan 49931 USA

Abstract.

Temperate forest soils store globally significant amounts of carbon (C) and nitrogen (N). Understanding how soil pools of these two elements change in response to disturbance and management is critical to maintaining ecosystem services such as forest productivity, greenhouse gas mitigation, and water resource protection. Fire is one of the principal disturbances acting on forest soil C and N storage and is also the subject of enormous management efforts. In the present article, we use meta-analysis to quantify fire effects on temperate forest soil C and N storage. Across a combined total of 468 soil C and N response ratios from 57 publications (concentrations and pool sizes), fire had significant overall effects on soil C ( 26%) and soil N ( 22%). The impacts of fire on forest floors were significantly different from its effects on mineral soils. Fires reduced forest floor C and N storage (pool sizes only) by an average of 59% and 50%, respectively, but the concentrations of these two elements did not change. Prescribed fires caused smaller reductions in forest floor C and N storage ( 46% and 35%) than wildfires ( 67% and 69%), and the presence of hardwoods also mitigated fire impacts. Burned forest floors recovered their C and N pools in an average of 128 and 103 years, respectively. Among mineral soils, there were no significant changes in C or N storage, but C and N concentrations declined significantly ( 11% and 12%, respectively). Mineral soil C and N concentrations were significantly affected by fire type, with no change following prescribed burns, but significant reductions in response to wildfires. Geographic variation in fire effects on mineral soil C and N storage underscores the need for region-specific fire management plans, and the role of fire type in mediating C and N shifts (especially in the forest floor) indicates that averting wildfires through prescribed burning is desirable from a soils perspective.

Keywords: carbon sinks; fire; forest management; meta-analysis; soil carbon; soil nitrogen; temperate forests.

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INTRODUCTION

Roughly half of Earth’s terrestrial C is in forests, and of this amount, about two-thirds is stored in soils (Dixon et al. 1994, Nave et al. 2010). Fire is one of the most important disturbances affecting forest soil C accumulation and loss, yet the effects of fire on soil C storage are poorly understood from a large-scale perspective. Fire effects on soil C storage are especially important within the temperate zone, since forests of this region are a major part of the terrestrial C sink that mitigates rising atmospheric CO2 and climate change (Schimel 1995, Liski et al. 2003). Temperate forests, especially in the northern hemisphere, are home to globally unique interactions between disturbance history, climate, and N cycling that make these ecosystems significant C sinks (Goodale et al. 2002, Luyssaert et al. 2008). Understanding the effects of disturbances like fire on soil C and N storage is consequently imperative to the science, policy, and practice of forest management in the temperate zone.

Manuscript received 30 March 2010; revised 26 August 2010; accepted 9 September 2010. Corresponding Editor: X. Xiao. 5 E-mail: lukenave@umich.edu

The management of fire in temperate forests is important not just because it impacts the global C cycle, but also because fire affects forest productivity and hydrology. Fire pyrolizes and volatilizes C and N from litter and soil organic matter (SOM), which are the principal storehouses of these elements in forest soils (Certini 2005). Fire also alters the composition and structure of remaining litter and SOM, leading to changes in C and N cycling processes that form the basis of plant nutrition (Wan et al. 2001, Gonzalez Perez et al. 2004). Consequently, through its effects on SOM amount, composition, and soil C and N cycling, fire may affect forest productivity (Jurgensen et al. 1997, Grigal and Vance 2000). Fire-induced litter and SOM losses, increased soil hydrophobicity, and shifts in soil C and N cycling drive hydrologic changes, including decreased soil water retention, increased surface runoff and sediment loading to surface water, and N export in surface and ground water (DeBano 1998, Neary et al. 1999, Shakesby and Doerr 2006). Predicting changes in soil C and N storage due to fire will therefore allow anticipation of changes in ecosystem services including water quality protection, C sequestration, and the supply of forest products.

Many sources of variability mediate the effects of fire on soil C and N storage, which limits the generality of conclusions drawn from individual studies. In addition to the inherent spatial and temporal heterogeneity of soil C and N storage (Magrini et al. 2000, Homann et al. 2001, 2008), variation in geographic features, fire characteristics, and soil structure and morphology may influence the observed effects of fire on forest soils. For example, in one study of prescribed burns in the Appalachian region of the United States, landscape position and fire intensity had significant effects on the magnitude of forest floor C and N losses, while mineral soils were unaffected by prescribed fire (Vose et al. 1999). Organic (forest floor) and mineral soil horizons have divergent responses to fire that have been noted throughout the literature, with forest floors typically showing greater C and N shifts than mineral soils (Binkley et al. 1992, Rothstein et al. 2004, Murphy et al. 2006, Johnson et al. 2007). Studies examining the role of fire intensity on soil processes and properties have found different levels of change following prescribed vs. wildfires, with prescribed fires either having smaller impacts, or mitigating the effects of wildfires (Choromanska and DeLuca 2001, Wan et al. 2001, Grady and Hart 2006). Finally, in addition to georaphic effects operating at fine spatial scales, such as within a study site (e.g., Vose et al. 1999), regional geography may also influence fire effects on forest soils. For example, Hatten et al. (2005) pointed to the interaction between seasonal precipitation deficits and thunderstorm activity as a driver of wildfire occurrence in the northwest United States, a region increasingly prone to severe fires (Bormann et al. 2008). In the present study, we sought to determine whether there is a consistent, overall effect of fire on temperate forest soil C and N storage, to quantify the magnitude of these changes, and to identify the most important sources of variability among studies of fire and temperate forest soils.

METHODS

In order to address the objectives of our study, we conducted a meta-analysis following the general methods of Curtis (1996), Johnson and Curtis (2001), and Nave et al. (2009). We searched the peer-reviewed and gray literature (i.e., government technical reports) using Boolean keyword searches within the online databases ISI Web of Science, BIOSIS, Agricola, and CAB Direct. Keyword search strings were permutations of terms including: forest, fire, burn, burning, management, soil C, and soil N. In the process of inspecting .6500 references returned by our literature searches, we found 57 publications that met our inclusion criteria of: (1) reporting control (unburned) and treatment (burned) soil C and N values, and (2) being conducted in a temperate forest (4-8 months of mean air temperature .108C [Koppen 1931]). Acceptable controls for un-burned forest soils were either pre-burn soil C and N values, or soil C and N observations from nearby reference stands that were not burned. The latter type of control value included both simultaneous measurements of burned and unburned soils, and chronosequences, in which case the oldest stand was treated as the control. As a minimum, control stands were those that had not been burned within the past 30 years, although some publications had control stands that had not been burned for 1-2 centuries. Therefore, our meta-analysis does not bear specifically on the consequences of longterm fire suppression, nor does it focus on the effects of frequent fires in ecosystems with short fire return intervals. Rather, our analysis includes many different temperate forest types with diverse fire regimes, sampled across a range of time scales. Although they did not meet the temperate climate requirement, we included several publications from the southeast United States due to the importance of this region to U.S. forest management. We accepted soil C and N concentrations and pool sizes as metrics of soil C and N, and used meta analysis to determine whether concentrations and pool sizes significantly differed in their responses to harvest. Among publications that reported both concentrations and pool sizes, we chose pool sizes as the response parameter, and we calculated soil C and N pool sizes for publications that reported concentrations and bulk densities. When used in reference to soil C and N, the term ‘‘storage’’ denotes C and N pool sizes only; we use the more general terms ‘‘soil C’’ and ‘‘soil N’’ when referring to soil C and N measurements that encompass both types of reporting units.

We extracted metadata (potentially useful predictor variables) from each publication, including temporal, climatic, soil chemical and physical data, measurement units, and treatment and analytical methods. One pertinent distinction in the soil physical data category was the soil layer sampled. We extracted data for organic and mineral soil layers separately, and coded the data so that we could test for differences between soil layers defined as forest floor (mostly organic horizons), surface mineral soil (uppermost 3-20 cm of mineral soil), deep mineral soil (20-100 cm), and whole mineral soil profile. We chose these coarsely defined layers based on the distribution of reported sampling depths during early literature assimilation with the goal of being able to detect small changes in soil C or N through high levels of within-layer replication. When initial meta-analyses revealed no significant differences between surface, deep, and whole mineral soils, we recoded the response ratios from these groups into a single category (mineral soil) for subsequent analyses. Regarding our classification of fire, we categorized studies as either prescribed burns or wildfires if meta-data were descriptive enough to ascertain which fire type occurred.

TABLE 1. Factors tested as predictor variables in the meta-analysis.

Factor Levels
Reporting units pool size; concentration
Soil layer forest floor; mineral soil (range: 3-100 cm)
Soil texture coarse (mostly sand); fine (mostly silt or clay)
Soil taxonomic order Alfisol; Andisol; Entisol; Inceptisol; Spodosol; Ultisol
Species composition coniferous; mixed conifer-hardwood
Geographic group northeast U.S.; northwest U.S.; southeast U.S.; southwest U.S
Fire type wildfire; prescribed fire
Time since fire continuous (yr)
Mean annual temperature continuous (8C)
Mean annual precipitation continuous (cm)

Notes: The levels listed within each categorical factor define the response ratio groups contrasted in Qb analysis in Table 2; factors without discrete levels were tested using continuous meta-analysis. Mineral soils only.

In addition to categorizing studies by fire type, we categorized fires according to whether they were of low or high intensity according to authors’ descriptions. In the literature we assimilated, fires were occasionally described in qualitative terms like ‘‘low-intensity’’ or ‘‘stand-replacing,’’ but quantitative measures of fire intensity were rarely reported. In the end, only one third of the soil C and N response ratios we collected had any associated meta-data that allowed attribution of fire intensity. We deemed this rate of reporting too low to include fire intensity as a categorical variable in our final analysis, since small sample sizes that are based on a limited number of studies risk detecting significant effects that are in reality confounded with other factors specific to those studies. The complete list of factors by which we categorized the response ratios in the database before final analysis appears in Table 1.

Meta-analysis estimates the magnitude of change in a parameter (i.e., the ‘‘effect size’’) in response to an experimental treatment, which may be applied across a wide range of experimental systems and conditions. We used the ln-transformed response ratio R to estimate treatment effect size:

lnðRÞ ¼ lnð XT= XCÞ ð1Þ

where XT is the mean soil C or N value of treatment (burned) observations and XC is the mean soil C or N value of control observations for a given set of experimental conditions. The number of response ratios (k) from a given publication depends on how many sets of experimental conditions are imposed. For example, one publication with soil N storage data from a control soil and from two different levels of fire (prescribed and wild) would yield k ¼ 2 response ratios, or ‘‘studies.’’ Because it is unitless, the effect size R is a standardized metric that allows comparison of data between experiments reporting responses in different units (Hedges et al. 1999). After back transformation (eln(R)), R can be conceptualized as the proportional or percentage change in soil C or N relative to its control value. When error terms and sample sizes are reported for each XT and XC, a parametric, weighted meta-analysis is possible, but many publications we found did not report these data. Therefore, in order to include as many studies as possible, we used an unweighted meta-analysis, in which all studies in the data set are assigned an equal variance (1). In an unweighted meta-analysis, the distributional statistics of interest (mean effect sizes and confidence intervals) are generated with the nonparametric statistical method known as bootstrapping. Bootstrapping estimates a statistic’s distribution by permuting and resampling (with replacement) the data set hundreds of times. Since it generates a statistic’s distribution from the available data, bootstrapping is not subject to the assumptions of parametric tests, and typically produces wider, more conservative confidence intervals (Adams et al. 1997). We performed analyses using MetaWin software (Sinauer Associates, Sunderland, Massachusetts, USA), with 999 bootstrap iterations.

One of our primary goals in this analysis was to identify which commonly reported factors were the best predictors of variation in soil C and N responses to fire. Accomplishing this task with meta-analysis is similar to using ANOVA to partition the total variance of a group of observations (Qt, the total heterogeneity) into two components: within- and between-group heterogeneity (Qw and Qb, respectively; Hedges and Olkin [1985]). In such a Qb analysis, a categorical factor that defines a group of response ratios with a large Qb is a better predictor of variation (or heterogeneity) than a categorical factor associated with small response-group Qb. In order to determine which categorical factors were the ‘‘best’’ predictors of variation, we followed the hierarchical approach detailed in Curtis (1996) and Jablonskiet al. (2002). Briefly, we performed the following steps independently for soil C and soil N data sets. First, we ran meta-analysis on the entire data set to determine which categorical factor among those in Table 1 had the lowest P value, and then divided the database into the categorical groups defined by the levels of that factor (e.g., soil layer had the lowest P value, so we subsequently divided the database into forest floor and mineral soil groups). Then, within each of these groups,

TABLE 2. Between-group heterogeneity (Qb) among the k studies comprising each response parameter.

 

Response parameter k Reporting units Soil layer Soil texture Soil taxonomic order Species composition Geographic group Fire type Time MAT MAP
Overall soil C 240 6.7** 29.0** NA 11.2** 4.2* 3.4 1.2 0.03 1.5* 0.01
Forest floor C storage 72 5.9* NA NA 8.5** 7.4** 3.8 4.2* 4.5** 1.5 5.2**
Mineral soil C storage 73 0.5* NA 0.01 0.8* 0.01 0.6** 0.04 0.5* 0.5* 0.2
Overall soil N 228 1.8* 14.0** NA 6.9* 3.4* 3.7 3.9** 0.1 0.02 0.5
Forest floor N storage 64 4.9* NA NA 2.2 10.7** 8.4* 8.3** 2.9* 2.2 1.6
Mineral soil N storage 75 0.8* NA 0.1 1.1** 0.05 0.6* 0.01 0.1 0.1 0.4*

Notes: Overall soil C and N responses to fire include all studies in the database, regardless of reporting units (concentration or pool size). Forest floor and mineral soil C and N storage responses are pool sizes only, except for the reporting units column, which demonstrates significant differences between concentrations and pool sizes. Note that the values for continuously varying factors (time, MAT, MAP) represent Qm, which is conceptually similar to but statistically distinct from Qb. See Table 1 for the predictor variables tested in Qb analysis. NA means ‘‘not applicable.’’ Predictor variables showing statistically significant Qb are denoted by asterisks.

* P , 0.05; ** P , 0.01.
Soil C response data were reported as either concentrations or pool sizes.

we ran meta-analysis again for each remaining categorical factor, and identified the one with the lowest P value. We performed this variance-partitioning exercise twice as described above, at which point we felt it prudent to go no further due to limited sample sizes and possible confounding relationships. When, during the course of these Qb iterations, we found multiple categorical variables with the same P value, we selected the one with the highest Qb. Categorical groups with k , 5 were included in overall meta-analyses of fire effects on soil C and N, but were not included in the iterative Qb analyses, since these poorly replicated groups sometimes had outlying effect sizes that artificially inflated the Qb values. For example, while our database included studies from the United States, Europe, Asia, Australia, and South America, geographic group analyses were conducted only on U.S. regions.

In addition to identifying categorical variables that influenced soil responses to fire, we tested several continuously varying factors (e.g., time and climatic variables) as predictors of variation using continuous meta-analyses. Continuous meta-analysis is similar to the variance-partitioning process of Qb analysis, in that the heterogeneity among k observations is partitioned into that which is explained by a linear regression model (Qm), and that which constitutes the residual error variance (Qe). In this way, continuous meta-analysis is analogous to the ANOVA F test for significance of linear regression models (Hedges and Olkin 1985). Continuous meta-analysis also estimates the coefficients for the intercept and slope terms of linear models, allowing estimation of linear relationships between predictor variables and response parameters. In all tests, including overall, hierarchical Qb, and continuous meta analyses, we accepted test results with P , 0.05 as statistically significant.

While our literature search was not exhaustive, the database we developed for this analysis is quite large, comprising 468 soil C and N response ratios from 57 papers published between 1975 and 2008. These publications correspond to studies of forest fire conducted in temperate forests around the world, and the full data set is available online.6

RESULTS

Overall effects and principal sources of variation Fires significantly reduced soil C ( 26% 6 6%) and soil N ( 22% 6 6%) in the temperate forests included in this analysis, although many sources of variation mediated this overall effect (Table 2). Fires had significantly different effects on pool sizes vs. concentrations of soil C and soil N, demonstrating that the units of measurement used to report soil C and N values are an important source of variation. Fires reduced both pool sizes and concentrations, but with significantly greater reductions in pools. On average, soil C storage declined by 35% following fire, and soil C concentrations decreased by 9%. Fires reduced soil N storage by 28%, while soil N concentrations declined by 12%. Fire had fundamentally different impacts on forest floors and mineral soils. Indeed, soil layer was the strongest of all predictor variables tested in our analyses, in terms of both level of significance and Qb values. The significant effect of soil layer (P , 0.01) explained 25% of the variation among soil C response ratios (Qb ¼ 29.0, Qt ¼ 115.6), and 14% of the total heterogeneity among soil N response ratios (P , 0.01, Qb ¼ 15.6, Qt ¼ 106.2).

Variation in fire effects within soil layers Forest floors.—In a pattern similar to that observed in the overall analysis, the effects of fire on forest floors depended on the units used to report C and N values (Table 2; P , 0.01 for soil C, P , 0.05 for soil N). However, forest floors differed from the overall analysis in that neither C nor N concentrations changed in response to fire (Fig. 1). Forest floor C and N storage both declined significantly, with mean effect sizes of  6 ( http://www.nrs.fs.fed.us/niacs/tools/soil_carbon/ )

 

FIG. 1. Changes in soil C and N due to forest fires, overall and by soil layer. All points are mean effect sizes with bootstrapped 95% confidence intervals, with the number of studies (k) in parentheses. Groups with confidence intervals overlapping the dotted reference line (0% change) show no significant change in soil C or N due to fire. At the top of each panel, the solid diamond shows the overall effect of fire, including C and N pool sizes and concentrations from forest floors and mineral soils. Within each soil layer, mean effect sizes are shown separately for C and N pool sizes (storage; solid symbols) and C and N concentrations (open symbols).

59% and 50% for the two response parameters, respectively. Since we were primarily concerned with changes in C and N storage due to fire, we restricted further forest floor analyses to those studies reporting C and N pool sizes (and those reporting sufficient data to calculate pool sizes). Among these studies, fire effects were impacted most by species composition (Table 2, Fig. 2), with mixed hardwood-conifer forests losing significantly less C and N ( 37% and 12%, respective spite of the large magnitude of these fire-induced C and N losses, reductions in forest floor C and N storage did not appear to be permanent. Continuous meta-analyses demonstrated that time was a significant  redictor of variation among forest floor C and N storage response ratios (Table 2). For these two elements, linear models generated through continuous meta-analysis suggested recovery times of 100-130 years (Fig. 3).

 

FIG. 2. The effects of fire on forest floor C and N storage, overall and by species composition group. All points are mean effect sizes with bootstrapped 95% confidence intervals, with the number of studies (k) in parentheses. Groups with confidence intervals overlapping the dotted reference line (0% change) show no significant change in forest floor C or N storage due to fire.

 

FIG. 3. Recovery of forest floor (A) carbon and (B) nitrogen pools following forest fires. Each point represents one response ratio. Some response ratios in the database could not be assigned a time value; these studies are not  plotted.

Mineral soils.—As with the overall analysis, and forest floors, fire effects varied significantly according to the units used to report mineral soil C and N data (Table 2). Fire did not change mineral soil C or N storage, but %C and %N declined by an average of 11% and 12%, respectively (Fig. 1). Soil taxonomic order and geographic location explained more of the variation among mineral soil C and N storage response ratios than any other predictor variables, but because these two predictors were not independent in our data set, we chose to explore and interpret variation among C and N response ratios according to only one of them. To determine which variable was a stronger predictor of variation in fire effects on mineral soil C and N storage, we aggregated the response ratios from both response parameters, which had statistically indistinguishable responses to fire. Tests of the two predictors on the aggregated C and N response ratios subsequently demonstrated that geographic location was a more important determinant of C and N storage shifts (Qb ¼ 3.9, P , 0.01) than soil taxonomic order (Qb ¼ 1.7, P , 0.01). When considered in a geographic context, fires had a significant impact only on mineral soil C pool sizes in forests of the northwest United States, where C storage declined by an average of 19% (Fig. 4). While other geographic groups differed from one another in their responses to fire, none showed significant changes in mineral soil C or N storage.

Variation in fire effects due to fire type

Fire type was another important source of variation in fire effects on soil C and N (Table 2). While fire type was not among the most important sources of variation in the overall analysis, the distinction between wildfires and prescribed burns was significant for forest floor C storage (P , 0.05) and forest floor N storage (P , 0.01). In both cases, wildfires caused greater declines than prescribed fires (Fig. 5). Wildfires reduced forest floor C storage by 67%, compared to an average of 46% for prescribed burns, and the effect was quite similar for forest floor N storage ( 69% vs. 45%).

FIG. 4. The effects of fire on mineral soil C and N storage, overall and by geographic group. All points are mean effect sizes with bootstrapped 95% confidence intervals, with the number of studies (k) in parentheses. Groups with confidence intervals overlapping the dashed reference line (0% change) show no significant change in forest floor C or N storage due to fire. Geographic groups shown are from the United States. The small numbers of observations from Australian, European, and South American geographic groups are not plotted.

FIG. 5. Changes in soil C and N storage due to forest fires, by soil layer and fire type. All points are mean effect sizes with bootstrapped 95% confidence intervals, with the number of studies (k) in parentheses. Groups with confidence intervals overlapping the dashed reference line (0% change) show no significant change in soil C or N storage due to fire. Within each soil layer, mean effect sizes are shown separately for wildfires (solid symbols) and prescribed fires (open symbols).

Neither type of fire affected mineral soil C or N storage (Fig. 5), but wildfires reduced mineral soil %C and %N by 17% and 18%, respectively (Table 4). Prescribed fires had no effect on mineral soil %C or %N.

Soil C and N budgets

The effects of fire on soil C and N budgets were driven not only by the magnitude of the changes, but also by the relative pool sizes of C and N in the forest floor vs. the mineral soil (Table 3). Fires caused forest floors to lose substantial amounts of their C and N pools, but the impacts of these losses on overall soil C and N budgets were tempered by the relatively small proportion of total soil C and N stored in the forest floor in these forests. In unburned forests, forest floor C and N storage constituted approximately one-third of total soil C and N pools. Following fire, forest floors accounted for only ;15% of total soil C and N storage. On average, fires reduced forest floor C storage from 18 to 7 Mg/ha, although the lack of any change in the mineral soil meant that the relative decline in total soil C storage was much less: 55 Mg C/ha in the control and 46 Mg C/ha in the burned forests. Forest floor and mineral soil N pools were much smaller, but the impacts were quite similar to those on C pools. Fire decreased forest floor N storage from an average of 0.5 to 0.2 Mg/ha, but the lack of any change in mineral soil N storage meant that the soil profile total changed from an average of 1.6 to 1.3 Mg/ ha following fire.

TABLE 3. C and N budgets for unburned (control) and burned (treatment) soils included in the meta-analysis.

Parameter and soil layer k Control (Mg/ha) Burned (Mg/ha)
Mean 95% CL Mean 95% CL
C storage
Forest floor 72 18 13,23 7 6,9
Mineral soil 73 37 25,49 37 35,40
Sum 55 38,72 46 43,49
N storage
Forest floor 64 0.5 0.4,0.6 0.2 0.2, 0.3
Mineral soil 75 1.1 0.9,1.3 1.1 1.1, 1.2
Sum 1.6 1.3,1.9 1.3 1.2, 1.5

Notes: The number of observations in each response parameter-soil layer group is the same as in Table 2. Unburned means and 95% confidence limits were calculated directly from the control data provided by papers included in the meta-analysis. Burned means and 95% CLs were calculated as products of the unburned means and the (eln(R)) and 95% CL values calculated by meta-analysis and described in Methods.

C and N budgets for the two soil layers are derived from various publications with different levels of sampling and replication. These differences preclude direct comparisons of C budgets to N budgets.

DISCUSSION

Overall effects and primary sources of variation Soil C and N changes frequently are reported in primary studies of forest fire, although the magnitude of these changes varies substantially within and among studies (e.g., Baird et al. 1999, Boerner et al. 2005, Ferran et al. 2005, Gundale et al. 2005). By using meta analysis to synthesize the results of many individual studies across temperate forests, we demonstrate that fires have relatively consistent effects on soil C and N at the global scale, even as site-to-site exceptions do occur (see Plate 1). This is even the case for temperate forest floors, which we expected to have more dynamic responses to disturbance than mineral soils due to their exposed position at the top of the soil profile, which make them susceptible to direct combustion and postfire erosion, as well as their relatively small organic matter mass and sensitivity to litter and detritus inputs (Robichaud and Waldrop 1994, Binkley and Giardina 1998, Currie 1999). These differences probably underlie the highly  significant distinction between forest floor and mineral soil responses to fire implicated in our analysis (Table 2). In particular, since forest floors are exposed and mineral soils are insulated from all but the most extreme surface fires, combustion probably has a much stronger direct effect on forest floor organic matter. Furthermore, the smaller organic matter pool of forest floors (Table 3) means that losing a small absolute quantity of organic matter has a larger proportional effect on C and N storage in this component of the soil profile than in the mineral soil. If we had been able to populate soil layer categories of finer vertical resolution with a sufficient number of response ratios, it is possible that near-surface mineral soils would have shown significant postfire changes in C and N storage as well. Nonetheless, the results of our analysis suggest that mineral soils generally do not exhibit net changes in C or N storage following fire (Fig. 1). In this regard, the effects of fire on soil C and N storage are distributed throughout the soil profile in a very similar way to the effects of forest harvesting on soil C storage, which reduces C storage in the forest floor but not the mineral soil (Nave et al. 2010).

Variation in fire effects within soil layers Forest floors.—While combustion was probably the most important process directly influencing forest floor C and N reductions among the studies included in our analysis, other mechanisms likely contributed as well  (Certini 2005). For example, postfire stimulation of decomposition and N cycling rates suggest that microbial action may be responsible for some forest floor C and N losses (Fernandez et al. 1997, Fierro et al. 2007). On the other hand, pyrolysis is known to produce organic compounds highly resistant to microbial and chemical action (‘‘black carbon’’), which may subsequently be lost from the forest floor and exported to deeper horizons by soil water percolation, mesofauna activity, and other causes. (Schmidt and Noack 2000, Gonzalez-Perez et al. 2004, 2008). Forest floor C and N reductions may also occur due to erosion by wind or water (Swift et al. 1993, Murphy et al. 2006). In the case of fires that kill vegetation, postfire reductions in aboveground litterfall can have major effects on forest floor C and N pools (Belanger et al. 2004, Rothstein et al. 2004). However, it is important to consider that while tree mortality may reduce leaf litterfall, dead trees produce substantial woody detritus that typically is not sampled as a forest floor component. Coarse woody debris may cover 25-60% of the forest floor following stand-replacing fires, although it is not certain how much of this material ultimately persists as soil organic matter (Hely et al. 2000, Tinker and Knight 2000, Spears et al. 2003, Turner et al. 2003).

Litterfall plays a fundamental role in recovering and maintaining forest floor C and N pools after fire, but it also influences the magnitude of fire-induced C and N losses. It is likely that the relationship between litterfall and fire C and N losses is driven by fuel type effects, since mixed hardwood-conifer forests lost significantly less C and N than forests dominated solely by conifers (Fig. 2). In addition to producing high C:N litter that resists decomposition and accumulates on the forest floor (Finzi et al. 1998, Cote et al. 2000, Silver and Miya 2001), litter and wood produced by many coniferous tree species contain flammable resinous organic compounds (Schwilk and Ackerly 2001, Kozlowski and Pallardy 2002). Whether present within a matrix of conifers at the patch or landscape scale, hardwoods mitigate fire intensity by producing less flammable foliage, litter, and woody detritus (Gustafson et al. 2002, Sturtevant et al. 2002, Kennedy and Spies 2005, Ryu et al. 2007, Nowaki and Abrams 2008, Lee et al. 2009).

Fires caused forest floors to lose significant amounts of C and N, although these pools appear to replenish with time (Fig. 3). On average, forest floor C and N storage in burned forests returned to pre-burn levels within 128 and 103 years, respectively, although there were legitimate exceptions to these point estimates of recovery time. In particular, as shown in Fig. 3, some  forest floors showed a complete net recovery of C and N pools within 40 years of fire. Since we estimated this recovery time from net changes in forest floor C and N pools compared to unburned forests, this duration probably represents the postfire time period during which the accumulation of litter inputs equilibrates with losses of forest floor organic matter through decomposition. The variables controlling the balance of these two fluxes are very complex, and include forest productivity, litter quality, and climate, as well as spatial variation in the effects of fire on these variables (Facelli and Pickett 1991, Berg 2000, Gholz et al. 2000, Raich and Tufekcioglu 2000). Results from our data set suggest an influence of productivity, because net changes in forest floor C storage following fire were positively correlated with mean annual precipitation (i.e., more precipitation Response parameter meant smaller C losses; Table 2). Since measures of and fire type k Mean 95% CL precipitation also are positively correlated with litter decomposition rates (Gholz et al. 2000), the fact that forests with higher precipitation showed smaller reduc- tions in forest floor C pools suggests that these forests may have recovered forest floor organic matter pools more quickly due to moister soils and higher productivity (Haxeltine and Prentice 1996). An additional explanation for this result, not mutually exclusive to the first, could be that abundant precipitation had the direct effect of mitigating forest floor organic matter losses by increasing the moisture content of available fuel (Neary et al. 1999). Variability in recovery times may be due to different levels of fire intensity, as prescribed burns lost less forest floor C and N and would presumably require less time to recover those pools than forests affected by wildfire (Fig. 5). However, due to a general lack of longterm prescribed fire studies, there were too few data to conduct a conclusive, separate assessment of recovery times for prescribed burns and wildfires. As scientific and social awareness of prescribed burning as an alternative to wildfires increases, long-term prescribed fire studies hopefully will become more prevalent and allow future analyses to compare the effects of these two burning regimes over multidecadal time scales.

Mineral soils.—Fire did not significantly affect the net storage of mineral soil C or N (Fig. 1). However, declines in the concentrations of the two elements suggest that counteracting processes may be masking underlying complexity (Table 4). In order for mineral soil C and N storage to show no net change in spite of decreased %C and %N, there must have been a compensating increase in the bulk density of the increment of soil that was sampled. The increase in bulk density could have been caused by direct combustion or postfire microbial decomposition of SOM and consequent degradation of soil structure, soil loss through wind or water erosion, or some combination (Shakesby and Doerr 2006, Bormann et al. 2008). In each case, increment sampling would result in the sampling of a deeper portion of the soil profile after fire than before. Since bulk density increases, and %C and %N generally decrease with depth in forest soils, the result could be lower concentrations of C and N, but similar amounts.

Geographic setting significantly influenced the effects of fire on mineral soil C and N storage (Table 2). While there was no significant change in either parameter across temperate forests as a whole (Fig. 1), regional variation pointed to consistent mineral soil C losses in forests of the northwest United States (Fig. 4). This suggests that fires are particularly intense in this region, possibly due to interactions between high forest productivity, abundant coniferous fuels, and strong seasonal droughts that combine to create the conditions for severe fires (Miller et al. 2009). The mountainous topography of the region likely augments erosion, which could exacerbate mineral soil C losses (Wondzell and King 2003). In a broader sense, the significance of geographic location as a predictor variable indicates that effects of fire on soil C pools must be considered in a regional context. If soils are to be included in policies or management plans that promote terrestrial C sequestration, then this analysis demonstrates the need for a regional perspective on fire management.

TABLE  4.   Effects of fire on mineral soil C and N concentrations, by fire type

 

Response parameter and fire type k Change
Mean 95% CL
Mineral soil %C
Prescribed burn 21 4 11, 22
Wildfire 55 17 26, 8
Mineral soil %N
Prescribed burn 21 1 12, 11
Wildfire 52 18 31, 3

Note: Groups with 95% confidence limits overlapping 0% change were not significantly affected by fire.

One factor important to consider in our analysis of how mineral soils varied in their C and N responses to fire involves the way we approached response ratio assimilation and coding during database development. As described in the Methods, we extracted separate response ratios for surface, deep, and whole mineral soils from publications whenever possible, in order to test for differences between mineral soil layers. Upon finding no such significant differences in the overall analysis, we recoded all of these response ratios as generic mineral soils in order to achieve maximum use of the data we had collected. In doing so, we violated a strict interpretation of the assumption of independent observations in meta-analysis. However, reanalyzing the mineral soil effect sizes and confidence intervals presented in this paper using only one of the mineral soil layers (surface mineral soils, which had the largest k) changes none of the results we present here. In other words, this internal sensitivity analysis showed that all significant findings regarding mineral soil C and N in this manuscript are robust to the violation of the independence assumption.

The importance of fire type

Fire type had a significant effect on C and N shifts in forest floors (pool sizes; Fig. 5) and mineral soils (concentrations; Table 4), with wildfires causing greater C and N declines than prescribed fires. Mineral soil C and N storage revealed no net changes after either type of fire, but wildfires significantly decreased mineral soil C and N concentrations, indicating that the biogeochemistry or nature of the C and N in these soils may have changed. Such changes

PLATE 1. Matrix of burned and unburned ground following the 1998 treatment at the University of Michigan Biological Station (USA) burn plot chronosequence. Spatial variation in fire intensity and soil organic matter content can obscure significant site-level soil C and N responses to fire, but a well-replicated sampling strategy surmounts this problem of heterogeneity. In similar fashion but on a much larger scale, meta-analysis constrains the effects of fire on soil C and N storage in temperate forests by testing hundreds of accumulated responses from dozens of  tudies, indicating with confidence that these effects are generally consistent and predictable based on site-level characteristics. Photo credit: Laura L. White, archived by the University of Michigan Biological Station.

in C and N chemistry and pool sizes are relevant to the capability of forests to maintain valuable ecosystem services such as nutrient retention, quantitative and qualitative water treatment, tree recruitment, and in some cases, forest productivity and C sequestration (Neary et al. 1999, Grigal and Vance 2000). Unfortunately, the mechanisms that underlie the greater C and N losses due to wildfire than prescribed fire are not clear from our analysis. One  possibility is that wildfire studies more commonly originate from forests subjected to long-term fire suppression, which have greater aboveground fuel accumulation and an increased risk of severe fire (Stephens 1998, Schoennagel et al. 2004). Conversely, it may be that prescribed fires tend to be implemented under less extreme fuel and weather conditions than wildfires, and represent an effective tool for reducing above ground fuel loads while mitigating the soil C and N losses that would occur in wildfire. Wildfires have increased in frequency in response to climate change and human land use practices (Attiwill 1994, Pinol et al. 1998, Kurz and Apps 1999, Westerling et al. 2006), and will continue to occur in temperate forests that have experienced them for millennia. Therefore, regardless of the underlying reasons for greater C and N losses with wildfire, the significant differences between the two types of fire suggest that proactive management, such as the prudent use of prescribed fire or other management tools, may be a preferable management alternative to losing larger quantities of C and N in wildfire. At the same time, expert judgment in the appropriate use of prescribed fire will be as important as ever, since some areas prone to severe wildfires rarely if ever provide the opportunity for a successful, contained prescribed fire.

Our findings differ from those presented in Johnson  and Curtis (2001), which suggested that wildfires increase mineral soil C and N. These changes were attributed to the input of charcoal to the soil C pool, the downward transport of hydrophobic organic matter and its subsequent stabilization with mineral cations, and the frequent colonization of burned sites by N fixing vegetation. Some of the divergence between these two meta-analyses arises from differences in sampling strategy. Specifically, in addition to considering elemental concentrations and pool sizes separately, and focusing solely on temperate forests, we used different depth categories than Johnson and Curtis (2001). An additional factor that differentiates the two analyses is the large increase in data availability since 1998, the year of the most recent paper included in Johnson and Curtis (2001). For example, the estimated soil C effect sizes of prescribed vs. wildfires from Johnson and Curtis (2001) were based on response ratios from 6 and 3 papers, respectively, while our present analysis includes prescribed fire response ratios from 24 papers and wildfire response ratios from 30 papers. Ultimately, the difference between these two meta-analyses illustrates the benefit of conducting meta-analysis as a cumulative process; as new data are published and added to the analysis, they increase the likelihood that this technique can detect the true, overall effect of fire on forest soils.

Soil C and N budgets

The absolute reductions in total soil C and N storage following fire were relatively small, since the soil layer most affected (the forest floor) was a small component of total soil C and N pools (Table 3). Furthermore, our analysis shows that fire-induced forest floor C and N losses are not permanent, but may require 100-130 years to recover. Since the forest floor plays vital roles in nutrient cycling and water retention (Tietema et al. 1992, Attiwill and Adams 1993, Schaap et al. 1997, Currie 1999), forest floor C and N losses may reduce soil productivity (and possibly new litterfall C and N inputs to soil) over the recovery period. The combination of direct C and N reductions, the length of C and N recovery, and the potential for reduced soil productivity should be considered in C and N management and accounting plans. Forest floor recovery may be accelerated somewhat by additions of C and N from coarse woody debris and tree mortality, although these inputs will often have a large C:N ratio and correspondingly low N availability. However, it is important to note that we did not include forest floor or mineral soil C:N ratio in this meta-analysis, and attempting to assess fire effects on either of those response parameters based on the C and N pool sizes in Table 3 would produce misleading conclusions. This is because the data available for calculating those pool sizes come from a diverse literature, and not all publications provide estimates of all pool sizes. For example, the mineral soil data in Table 3 include several publications with whole mineral soil profile C storage (large values), without a corresponding number of publications that include whole mineral soil profile N storage values. Hence, the mineral soil C:N ratios implied in Table 3 are rather high (.32).

Conclusions

In temperate forests, fires significantly reduced soil C ( 35%) and N ( 28%) storage, principally through effects on forest floors, which lost 59% and 50% of their C and N pools, respectively. Mineral soil C and N storage showed no overall changes in response to fire, in spite of significant declines in C ( 11%) and N ( 12%) concentrations. Prescribed fires caused smaller reductions in forest floor C and N storage than wildfires, and the presence of hardwoods also mitigated fire effects on forest floor C and N storage (compared to purely coniferous stands). In general, forest floors required 100-130 years to recover lost C and N pools. Among mineral soils, prescribed fires had no effect on C or N concentrations, while both of these parameters declined in wildfires. Finally, geographic variation in fire effects on mineral soil C and N storage indicate the need for region-specific fire management plans.

ACKNOWLEDGMENTS

This research was supported by the USDA-Forest Service Northern Research Station through Cooperative Agreement No. 06-JV-11242300. The National Soil Carbon Network also supported this work. We acknowledge John Clark, Jim Le Moine, and Robert Sanford for helpful conversations during the preparation of the manuscript, and Alex Friend, who helped define the scope of our larger meta-analysis project at its initiation.

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APPENDIX

References providing data for the fire/soil C meta-analysis (Ecological Archives A021-054-A1).

 

Fire Science Brief Issue-62-August 1st, 2009

Posted on August 01, 2009 by in News

The immediate effects of fire, within seconds or minutes of combustion, hold many clues to the future of burned areas. Credit: Bob Keane.

FOFEM: The First-Order Fire Effects Model Adapts to the 21st Century

Summary

Technology is playing an increasingly pivotal role in the efficiency and effectiveness of fire management. The First Order Fire Effects Model (FOFEM) is a widely used computer application that predicts the immediate or ‘first-order’ effects of fire: fuel consumption, tree mortality, emissions, and soil heating. FOFEM’s simple operation and comprehensive features have made it a workhorse for fire and resource professionals who need to be able to predict, assess and plan for fire’s effects. Over the last decade FOFEM has undergone several upgrades as developers  continue to improve function and expand applicability to meet the growing needs of managers, planners and analysts.

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Latest First Order Fire Effects Model (FOFEM) upgrades include:

  • The FOFEM Mapping Tool which allows the import or input of spatial data layers,
  • Extensive additions to tree mortality models that expand scope and application,
  • A tree-mortality modification specifically for use in southeastern longleaf pine ecosystems, and
  • A web-based version with expanded function, storage, connectivity and customization options.

What’s the status?

Patches of flame and smoldering fuel linger as a fire nears its end. Combustion has consumed fuel and generated heat and smoke. But how much fuel and how much smoke? How hot did it get, and where? What does this all mean for trees, soil and air—right here, right now—and why does it matter?

The imprint of fire on an ecosystem doesn’t end when the flames go out. It’s easy to think of fire as an isolated event, but it’s actually a process. Fire’s effects reverberate through an ecosystem over time. The immediate effects, known as first-order fire effects, influence the way a burned area will respond and regenerate over the coming days, months, decades and even centuries. Effects that occur over these longer time spans are called second-order fire effects. First-order fire effects drive and shape second-order fire effects, creating a roadmap to a burned area’s future and helping to define fire’s role in natural ecosystem processes.

First-order fire effects drive and shape second-order fire effects, creating a roadmap to a burned area’s future and helping to define fire’s role in natural ecosystem processes.

Connecting the dots

First-order effects include plant injury and mortality, soil heating, fuel consumption and smoke production. Second-order effects include vegetation succession, erosion and the eventual atmospheric concentration and dispersion of smoke. Each individual first-order fire effect is an intersection of information connected to the others, and the future, by possibilities. What has taken place and which direction will things go from here?

Credit: Missoula Fire Sciences Laboratory.

Fuel consumption is an important first-order effect that’s intricately linked to the others, as well as to the future of the ecosystem. Fire consumes fuel, in turn producing smoke and generating heat. The intensity and duration of heat determine the degree of vegetation mortality and soil heating. The amount of plant mortality and soil heating influence vegetation dynamics after fire. The composition and structure of this post-fire vegetation community influence the behavior and extent of the next fire.

The details of this dot-to-dot picture are vitally important to resource managers. It’s their job to know about the past, current and future conditions of the ecosystems under their purview, and to be able to use that information to shape plans and guide decisions.

Over the years, the Forest Service and science communities have developed different methods and procedures for estimating first-order fire effects. Before the computer age, managers had to rely on experience and knowledge to make general estimations of fire’s immediate effects. Today, FOFEM provides managers with consistent and quantitative prediction methods.

FOFEM provides managers with consistent and quantitative prediction methods.

A quick study

In 1989, a new tool emerged from the Forest Service Fire Science Laboratory in Missoula, MT. Bob Keane, Elizabeth Reinhardt and Jim Brown created the first version of a simple computer program that would forever change the process of predicting and planning for the immediate effects of fire—the First Order Fire Effects Model known as FOFEM. It’s an all-purpose, easy to use, free, downloadable software package that allows users to quantify the immediate effects of fire. It differs from other first-order effects models in that it combines and integrates results from multiple empirical fire effects studies into one program. Keane explains that their design criterion, as put forth by Brown, was straightforward. “Learn it in and hour, run it in a minute,” he says. And apparently, they succeeded.

Since its inception FOFEM has been used by thousands of fire and land managers across the country from a broad spectrum of agencies. It’s been endorsed by the National Wildfire Coordinating Group and sponsored by the Washington Office of Fire and Aviation Management. It’s used for environmental assessment, fire severity assessment, development of fire and silvicultural prescriptions, and preparation of timber salvage guidelines.

The most significant version of FOFEM (v.4.0) was developed by Reinhardt and Keane in 1997 with Joint Fire Science support. It included the Albini BURNUP model which simulates heat transfer to fuels, consumption rate and resulting heat. BURNUP can also model heat transfer to other ecosystem components like the living tissue under tree bark, the mineral soil underneath the fire, or the smoke above it. “BURNUP is the heart of the whole model,” says Keane. “We run it for almost everything now.”

What it does

FOFEM has continued to evolve and is now in version 5.2. FOFEM fire effects analyses can guide prescribed fire activities and wildfire response, help design and evaluate treatments for desired and potential fire effects, and compare the potential ecological consequences of varying alternatives. It can simulate effects of different prescribed treatment alternatives and can be used to customize prescribed fire treatments to meet specific objectives.

FOFEM v5.2 can be downloaded to computers running a Microsoft Windows environment. Users can select analysis for tree mortality, fuel consumption, smoke or soil. A different input interface appears with each choice. Realistic default values are provided, or you can enter your own custom information. It generates results in reports or graph that are appropriate for inclusion in planning documents.

How it does it

Fuel loads

FOFEM provides default fuel loads by fuel component (such as litter, duff, and woody fuel by size class). Default values depend on cover type and on fuel type (i.e., natural or slash fuels). The defaults are based on an extensive literature search summarized in the Mincemoyer Fuels Database, which is a major component of FOFEM. The defaults can be adjusted or replaced. Because fuels vary so much within cover type, Reinhardt recommends entering fuel loads directly if you can.

Tree mortality

To predict tree mortality, users enter a tree list or stand table that lists species, diameter, height, crown ratio and trees per acre. Tree mortality increases with increasing crown scorch, and decreases with increasing bark thickness, so FOFEM uses bark thickness and the percentage of crown volume scorched for prediction. Bark thickness is derived from species and tree diameter. Crown volume scorch is calculated using tree height and crown base height, and scorch height or flame length.

Reinhardt recommends avoiding flame length as an input when possible, however. This is because scorch height will actually decrease for a given flame length at higher wind speeds typical of many wildfires. Entering flame length may cause over-prediction of scorch height—and therefore tree mortality. Using scorch height is especially appropriate when using FOFEM to predict fire effects after the fact, when scorch height can be directly observed.

When predicting stand mortality, FOFEM assumes that the fire is continuous across the entire area of concern. In the case of discontinuous or patchy fire, the user can estimate the proportion of area burned to adjust estimated tree mortality per acre.

To predict tree mortality, FOFEM users enter a tree list or stand table of species, diameter, height, crown ratio and trees per acre.

Fuel consumption

Here again the model assumes continuous fire over the entire area of concern. As with tree mortality, if you’re dealing with patchy fire you should estimate the percentage of area burned and adjust per acre estimates. FOFEM predicts the quantity of fuel consumed by prescribed or wildfire for six different fuel components and predicts mineral soil exposed by fire as a result of duff and litter consumption. Consumption of different fuel types is  predicted using a mix of empirical equations, rules of thumb and modeling. Although herbaceous fuels are generally a small component of fuel load, they are calculated by FOFEM because of their contribution to emissions. Calculated by rule of thumb, FOFEM assumes that 100 percent of herbaceous fuels are consumed. The exception is when spring is selected as the burning season, and grass selected as the cover type. Consumption then drops to 90 percent.

Shrub fuels are also modeled with rules of thumb. For example, if the cover type is sagebrush and the season is fall, shrub consumption is predicted at 90 percent. For all other seasons it drops to 50 percent.

When predicting canopy fuel consumption, FOFEM requires the user to estimate the proportion of a given stand that will be affected by crown fire. The consumption of crown fuels is represented for the purposes of estimating smoke production or carbon budget. FOFEM does not predict whether a crown fire will occur or if canopy layers will be consumed.

Fuel moisture

You can select very dry, dry, moderate or wet burn conditions. FOFEM applies default moisture percentages for each. You can enter fuel moistures directly for duff, 0.25 to 1 inch, and greater than 3 inch woody fuels. If you want to set fuel moistures for all woody fuel size classes, or separate moistures for sound and rotten fuel, you can bypass the FOFEM interface and use BURNUP.

BURNUP bonus

BURNUP physically models heat transfer and burning rate of woody fuel particles as they interact over the duration of a burn. It estimates total fuel consumption by size class, as well as consumption rate and fire intensity over time. FOFEM uses BURNUP to predict woody fuel litter consumption (100 percent of litter is generally consumed) and smoke/emissions predictions.

BURNUP estimates flaming and smoldering consumption simultaneously in each time step. A fuelbed may produce flames in local concentrations of woody fuels at the same time that duff and isolated woody fuels burn in smolder combustion. Flaming and smoldering combustion burn with different combustion efficiencies and produce emissions at different rates. By modeling the two processes separately and simultaneously, BURNUP is able to take both into account in estimating emissions.

Fire intensity is derived from combustion of fuels in each time step, in turn determining fuel temperatures and combustion rates for the next time step. Immediately after ignition, intensity increases as the finest fuel burn. This generates more and more heat, progressively igniting larger and wetter fuel. As the smaller fuel burns up, intensity drops. The fire is assumed to go out when fire intensity is too low to sustain further combustion.

A sample graph of emissions production over time generated by FOFEM.

BURNUP computes different species of smoke emissions (chemical and particulate) in each time step. The FOFEM/BURNUP includes a graph and Smoke Emissions Report listing total emissions of PM2.5, PM10, CH4, CO, CO2, nitrogen oxide and sulfur dioxide. This information can then be used in other modeling systems to predict smoke dispersion and concentrations.

Soil heating

FOFEM predicts soil temperature over time at the soil surface and several depths below. It predicts expected average soil heating across the area because soil heating varies considerably within a burn unit. The model has been set up so that heat from surface fire (as modeled in prediction of fuel consumption) is used as the source of soil heat. If duff is present then the model assumes the duff is the source of soil heat. Duff fires have low intensity and spread much slower than flaming fire, but heating of deep soil layers is often greater in duff fires because they burn for a longer time in direct contact with the surface of mineral soil.

The graphic format of the soil heating report plots temperature vs. time, and displays temperature at several depths. Temperatures that exceed the 60°C (considered the lethal temperature for living organisms) are highlighted so you can identify which burning scenarios exceed this temperature at various soil depths. This lethal temperature is the default for the highlight, but you can change that by simply typing in a new number. The graph also includes the maximum temperature reached at the soil surface, the amount of duff consumed, the soil type, and starting soil temperature. The soil heating report contains a complete summary of FOFEM pre- and post-burn conditions, in addition to all the information displayed in the graph, which is useful for comparing scenarios.

A sample soil-heating prediction graph from FOFEM.

New features and functions

FOFEM mapping tool

The new FOFEM MT (FOFEM Mapping Tool) will have the capacity to automatically import LANDFIRE spatial data layers. You will also have the option of entering any spatial data layers you want, from any source. This feature can be used to calculate fire effects across the landscape for use in fire and fuel hazard analysis. This is useful for prioritizing fuel treatment or burn recovery activities.

FRAMES online portal

FOFEM will soon be functional as an online tool that allows users to skip downloading and installation. It will be accessible via FRAMES (Fire Research and Management Exchange System) which adapts fire research and management tools for use in a web-based environment. FRAMES project manager Greg Gollberg selected FOFEM as one of the models to be modified for online ease of use. “It’s a solid, simple program. A lot of people are familiar with it and it’s been around for a while,” he says. “It gave us the chance to start out small and see how it goes. We have hopes for adding more function, data storage and exchange, and new customization options.” The FRAMES website will house not only the web version of FOFEM but all of the downloadable versions too.

Expanded scope for tree mortality

The FOFEM tree mortality module was developed with data from western conifer forests. Although it works well in the western U.S., it’s commonly acknowledged that problems can arise with over-prediction of tree mortality when FOFEM is applied to other regions and forest types.

But plenty of work is underway to change that. Managers in the southeast will benefit from a new tree mortality model developed by Geoff Wang, with support from the Joint Fire Science Program and Clemson University. Wang created a modified version of FOFEM for use in longleaf pine and longleaf/slash cover types in the Southeastern U.S. Keane is hopeful that this will happen for other parts of the country too. “We constantly scour the literature for new tree mortality equations,” he says. “I’ve got file cabinets full of this stuff. When anyone does work on a new species it goes right into in the next revision.”

In addition, a team led by Sharon Hood at the Fire Modeling Institute at the Rocky Mountain Research Station in Missoula, is analyzing fire injury data on more than 16,000 trees from 82 wild and prescribed fires from 5 western states. They’re testing existing tree mortality models and developing new ones where necessary and incorporating it all into FOFEM.

Keeping pace with management needs

“The beauty of FOFEM is that it combines all the first-order effects under one roof,” says Keane. “The more we can add to it, the better off managers will be. It’s still the same nuts and bolts model. It still has the same guts. It’s just that it’s been repackaged again with new capacities. Next, we want to put in more emission elements and even more tree mortality. That will continue to increase applicability.”

So stay tuned for what are sure to be more changes and features that will keep FOFEM in the top tray of the fire planning and prediction toolbox well into the 21st century.

“The more we can add to it, the better off managers will be. It’s still the same nuts and bolts model. It still has the same guts. It’s just that it’s been repackaged again with new capacities.”

Publications and Web Resources
FOFEM download and tutorials:

http://www.fire.org/index.php?option=com_content&t ask=view&id=58&Itemid=31

Fire Modeling Institute, Missoula Fire Sciences Lab: http://www.fs.fed.us/fmi/index.html

FOFEM MT (mapping tool), Don Helmbrecht /Fire Modeling Institute / 406-829-7370 FMI:

dhelmbrecht@fs.fed.us
http://www.fs.fed.us/fmi/projects/abstracts/Helmbrecht_SpatialFOFEM_abstract.html

FRAMES Fire Research and Management Exchange System, Greg Gollberg / 208-885-9756, gollberg@ Uidaho.edu: http://frames.nbii.gov/portal/server.pt

Delayed Tree Mortality Following Fire in Western Conifers, Sharon Hood / Fire Modeling Institute / 406-329-
4818: shood@fs.fed.us

http://www.firelab.org/index.php?option=com_content &task=view&id=690&Itemid=262

Modifying FOFEM for use in the Coastal Plain Region of the U.S., Geoff Wang / Principle Investigator/ 864- 656-4864: gwang@clemson.edu http://www.firescience.gov/projects/05-4-3-06/05-4-3-06_final_report.pdf

Scientist Profiles

 

Fire history and the establishment of oaks and maples in second-growth forests

Posted on July 24, 2008 by in Books/Articles

Fire history and the establishment of oaks and maples in second-growth forests

Todd F. Hutchinson, Robert P. Long, Robert D. Ford, and Elaine Kennedy Sutherland

Abstract: We used dendrochronology to examine the influence of past fires on oak and maple establishment. Six study units were located in southern Ohio, where organized fire control began in 1923. After stand thinning in 2000, we collected basal cross sections from cut stumps of oak (n = 137) and maple (n = 204). The fire history of each unit was developed from the oaks, and both oak and maple establishment were examined in relation to fire history. Twenty-six fires were documented from 1870 to1933; thereafter, only two fires were identified. Weibull median fire return intervals ranged from 9.1 to 11.3 years for the period ending 1935; mean fire occurrence probabilities  (years/fires) for the same period ranged from 11.6 to 30.7 years. Among units, stand initiation began ca. 1845 to 1900, and virtually no oak recruitment was recorded after 1925. Most maples established after the cessation of fires. In several units, the last significant fire was followed immediately by a large pulse of maple establishment and the cessation of oak recruitment, indicating a direct relationship between fire cessation and a shift from oak to maple establishment.

Résumé : Nous avons eu recours à la dendrochronologie pour étudier l’influence du feu dans le passé sur l’établissement  du chêne et de l’érable. Six unités expérimentales ont été localisées dans le sud de l’Ohio où la lutte organisée contre les  feux a débuté en 1923. Après que des peuplements eurent été éclaircis en 2000, nous avons collecté des sections radiales  sur des souches de chêne (n = 137) et d’érable (n = 204). Dans chaque unité, l’historique des feux a été établi à partir des  chênes et l’établissement du chêne et de l’érable a été étudié en lien avec l’historique des feux. Vingt-six feux ont été documentés de 1870 à 1933; par la suite, seulement deux feux ont été identifiés. L’intervalle médian de Weibull entre les feux variait de 9,1 à 11,3 ans pour la période se terminant en 1935; la probabilité moyenne d’occurrence de feux (années/ feux) pendant la même période variait de 11,6 à 30,7 ans. Parmi les unités, l’origine des peuplements remonte aux environs de 1845 à 1900 et pratiquement aucun chêne n’a été recruté après 1925. La plupart des érables se sont établis après que les feux eurent cessé. Dans plusieurs unités, le dernier feu important a immédiatement été suivi d’une importante vague d’établissement de l’érable et de l’arrêt du recrutement du chêne, indiquant qu’il y a une relation directe entre la cessation des feux et le changement marqué par l’établissement de l’érable au lieu du chêne.

[Traduit par la Rédaction]

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Introduction

Across much of the eastern United States, red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), and other mesophytic and (or) shade-tolerant species have become abundant in historically oak-dominated landscapes, threatening the continued dominance of oak (Lorimer 1984; Abrams 1992). Fire-control policies instituted ca. 1910 to 1930 often are considered a primary cause of these successional trends (e.g., Lorimer 1993).

Oaks are considered to be better adapted than maples to a regime of periodic fires primarily because of their relatively thick and, thus, fire-resistant bark; their ability to compartmentalize wounds caused by fire; and the capacity of established seedlings to continue to sprout after being top-killed  repeatedly (Smith and Sutherland 1999; Johnson et al. 2002;  Van Lear and Brose 2002). Periodic anthropogenic fire is widely considered to have promoted and sustained eastern  oak ecosystems throughout their postglacial history  (Abrams 2002). However, specific knowledge of past fire  regimes, which can be obtained by analysis of fire-scarred  trees, is limited to relatively few areas. Several studies  show that fire was frequent in oak ecosystems prior to  (Cutter and Guyette 1994; Guyette et al. 2003; Shumway  et al. 2001) and after Euro-American settlement (Sutherland 1997; Schuler and McClain 2003; Guyette and Stambaugh 2004; Soucy et al. 2005) until fire control was instituted.

Received 8 August 2007. Accepted 8 November 2007. Published on the NRC Research Press Web site at cjfr.nrc.ca on 26 April 2008.
T.F. Hutchinson,1 R.P. Long, and R.D. Ford. USDA Forest Service, Northern Research Station, 359 Main Road, Delaware, OH 43015, USA.
E.K. Sutherland. USDA Forest Service, Rocky Mountain Research Station, 800 Block East Beckwith, P.O. Box 8089, Missoula, MT 59807, USA.

1Corresponding author (e-mail: thutchinson@fs.fed.us).

Much more is known about long-term patterns of tree establishment in old-growth oak forests. These studies often suggest a strong influence of fire cessation on tree recruitment (e.g., Abrams and Downs 1990; Abrams and Copenheaver 1999; Aldrich et al. 2005). Oak recruitment is known to have occurred for up to several hundred years but decreased or ceased around the time that fire control began. Several of these studies also document greatly increased recruitment of maples and other nonoak species during the same period (e.g., Abrams and Downs 1990; Abrams and Copenheaver 1999).

Shumway et al. (2001) were the first to document both the fire history and patterns of establishment for oak and other species in an old-growth oak-dominated stand in western Maryland. The authors showed that fire and oak recruitment were frequent from the early 1600s through the early 1900s. Both the cessation of oak recruitment and the increased recruitment of red maple and black birch (Betula lenta L.) coincided with reduced fire frequency ca. 1930. Soucy et al. (2005) showed that oak-hickory stands in the Arkansas Ozarks originated following harvesting or fire ca. 1900 and that fires were frequent through the 1930s. As fires became much less frequent after ca. 1940, oak recruitment ceased, and other shade-tolerant, nonoak species, such as flowering dogwood (Cornus florida L.), red maple, and blackgum (Nyssa sylvatica Marsh.), became established (Soucy et al. 2005).

The unglaciated ‘‘hill country’’ of southeastern Ohio was dominated by oak forests ca. 1800, just prior to Euro-American settlement (Beatley 1959; Gordon 1969; Dyer 2001). After nearly all of the forests in the region were cut over in the 19th century, many stands regenerated to oak dominance (Goebel and Hix 1997; Dyer 2001; Yaussy et al. 2003), and oak remains abundant in the region today (Griffith et al. 1993). However, as with most areas in the eastern United States that historically were oak dominated, the continued abundance of oak is threatened by increasing densities of maples and other species (e.g., blackgum and beech (Fagus grandifolia Ehrh.)) and poor oak regeneration. Dendrochronology fire histories indicate that fires occurred frequently in the region from ca. 1870 to 1935 (Sutherland 1997; McEwan et al. 2007b). It is hypothesized that this fire regime sustained oak dominance as second-growth forests developed (McEwan et al. 2007b) and that fire control directly facilitated the establishment of the now-abundant maples and other competitors (Sutherland et al. 2003).

To better understand how past fires were related to tree establishment, we conducted a dendrochronology study at the Ohio Hills site of the national Fire and Fire Surrogate Study (FFS). Our study was carried out on three replicate sites, each containing two separate units (*20 ha each) that here thinned. The second-growth forests were dominated by oak in the overstory, but maples and other shade-tolerant species were abundant in the midstory and understory. We collected basal cross sections from cut stumps of both oaks and maples to document stand fire histories and tree establishment. We hypothesized that, within a stand, temporal patterns of oak and maple recruitment would be closely related to the occurrence of past fires. Specifically, we hypothesized that (i) fires were frequent prior to the initiation of fire control, (ii) oak establishment occurred primarily before the initiation of fire control, and (iii) maples established primarily during the fire-control era. By studying units within three spatially separated replicate sites that likely had different fire histories, we also hoped to better understand how variability in past fire regimes affected oak and maple establishment. To our knowledge, this is the first study that directly examines the relationship between specific historic fire events and oak and maple recruitment events. A better understanding of how past fire regimes affected the pattern and pace of recruitment during stand development could provide new insights for the use of prescribed fire to manage oak forests.

Methods

Study area and site descriptions

The study area is in southern Ohio within the Southern Unglaciated Allegheny Plateau (McNab and Avers 1994). The topography is highly dissected, consisting of sharp ridges, steep slopes, and narrow valleys. The bedrock geology is predominantly sandstones and shales that produce well-drained and acidic soils.

The Ohio Hills FFS study site has three replicates (hereafter sites): one each in the Raccoon Ecological Management Area (REMA), Zaleski State Forest, and Tar HollowState Forest. The REMA site (39812’34@N, 82823’07@W) is in Vinton County and within the Vinton Furnace Experimental Forest; owned by Forestland Group, LLC, and comanaged with the USDA Forest Service Northern ResearchStation. The Zaleski site (39821’22@N, 82821’59@W), also in Vinton County, is 19 km north of the REMA site. The Tar Hollow site (39819’ 47@N, 82846’11@W) is in Ross County, 35 km west of the Zaleski site. Soils at both REMA and Zaleski are predominantly Steinsburg and Gilpin series silt loams (Typic Hapludalfs); Tar Hollow soils are predominantly Shelocta-Brownsville complex sandy loams (Typic Hapludalfs and Typic Dystrochrepts, respectively) (Boerner et al. 2007). Both state forests are managed by the Ohio Department of Natural Resources’ (ODNR) Division of Forestry.

Human land use has had a major effect on these forests. Both the REMA and Zaleski sites are located near charcoal iron furnaces that operated in the 1800s. REMA is <2 km from Vinton Furnace and Zaleski is <4 km from Hope Furnace; these were in operation from 1853 to 1883 and from 1854 to 1874, respectively (Stout 1933). Forests at both sites presumably were harvested at least once to provide charcoal for iron smelting. The Tar Hollow site was not affected by the iron industry since it was more than 25 km from the nearest furnace. Land deeds show direct human occupation in small parcels within the Tar Hollow study site until the mid-1930s (ODNR, Division of Forestry, District Office, Chillicothe, Ohio).

The forests generally were similar in structure and composition across the three sites. Prethinning data collected in 2000 showed that mean stand basal area at REMA was 28 m2/ha; white oak (Quercus alba L.) accounted for 21% of the basal area; black oak (Quercus velutina Lam.), 17%; chestnut oak (Quercus montana Willd.), 15%; and scarlet oak (Quercus coccinea Muenchh.), 12% (D.A. Yaussy, USDA Forest Service, Delaware, Ohio, unpublished data).

Mean basal area at Zaleski was 27 m2/ha and was dominated by chestnut oak (31%), followed by white oak (22%), red maple (13%), and black oak (13%). At Tar Hollow, mean basal area was 32 m2/ha, and the dominant species were chestnut oak (33%), white oak (20%), and black oak (16%). Oak site indices (base age 50 years) are variable across the landscape because of the dissected topography, ranging from about 17 m (55 ft) on upper south-facing slopes to 24 m (80 ft) on lower north-facing slopes (D.A. Yaussy, USDA Forest Service, Delaware, Ohio, personal communication). On all sites, the sapling layer (1.4 m tall to 9.9 cm diameter at breast height (DBH)) and the midstory (trees 10-25 cm DBH) were dominated by shade-tolerant trees, the most abundant of which were red maple, sugar maple, blackgum, and beech (Albrecht and McCarthy 2006). Shade-tolerant trees >25 cm DBH occurred at low densities on all sites.

The mean annual temperature and precipitation are 11.3 8C and 1024 mm, respectively. Precipitation is distributed fairly evenly throughout the year with no months averaging <60 mm. Today, most fires occur during the early spring (March and April) and fall (October and November), when vegetation is predominantly dormant; spring dormant season fires are the most frequent (Haines et al. 1975; Sutherland et al. 2003), and nearly all fires are anthropogenic in origin.

At each FFS site, four treatment units (19-26 ha) were established: an untreated control (control), mechanical thinning (thin), prescribed fire (burn), and a combination of thinning and fire (thin+burn). Our dendrochronology study was conducted on the two thinned units (thin and thin+burn) at each site. Midstory thinning occurred from November 2000 to April 2001 and favored the retention of dominant and codominant oaks. However, to meet commercial thinning objectives, some dominant and codominant oaks were harvested. Across sites, stand density (trees ‡10 cm DBH) was reduced by 32% from a mean of 400 to 269 trees/ha, and tree basal area was reduced by 30% from a mean of 29 to 20 m2/ha.

Sampling design and field methods
At the REMA site, the two thinned units (hereafter, REMA 2 and REMA 3) were separated by a triangular wedge of untreated forest that ranged in width from several meters to 275 m. The Zaleski thinned units (Zaleski 2 and Zaleski 3) were contiguous, and the boundary between units was an intermittent stream drainage. The Tar Hollow thinned units (Tar Hollow 2 and Tar Hollow 3) also were contiguous, but the boundary did not follow a major topographic feature. Despite the contiguous units at Zaleski and Tar Hollow, we treated the units separately for summary and  nalyses, because 50% (14 of 28) of the fires that we document were recorded only in a single unit.

Ten 0.1 ha plots were established in each unit to monitor vegetation and soils for the FFS study. Plot corners were georeferenced with global positioning system (GPS) technology. The plots were distributed across the landscape to represent a continuous range of soil moisture conditions from dry (upper south-facing slopes) to mesic (lower north-facing slopes). In 2000, all overstory trees (‡10 cm DBH) were tallied by species and DBH on each plot prior to treatments. We focused our collection of oak and maple basal cross sections on the plots to utilize the tree data and georeferenced locations.

Full basal cross sections were cut from stumps with a chainsaw from December 2000 to May 2001, soon after thinning operations had been completed in each unit. All oak stumps in a plot were examined for the presence of wounds (discoloration, seams, staining, and wound wood ribs) that might indicate a fire event (Smith and  Sutherland 1999). We attempted to locate at least two oak stumps within or adjacent to each plot. We collected 137 oak cross sections across all units and recorded the upslope position on each. Samples included 64 white oak, 40 chestnut oak, 22 black oak, 7 scarlet oak, and 4 northern red oak (Quercus rubra L). All samples were cut at a height of about 5-10 cm aboveground. The mean basal diameter of the oak samples was 47.4 cm and ranged from 23.1 to 98.1 cm. We mapped the approximate location of each sample based on its position within or adjacent to a georeferenced vegetation plot.

To determine the temporal pattern of maple establishment, our objective was to collect basal cross sections from three stumps in and (or) adjacent to each plot. Our goal was to collect three maples for every two oaks, because maples were approximately 1.5 times as abundant as oaks in the midstory, where the thinning treatment was focused. Our first priority was to obtain larger maples to document the time when establishment began, but we sampled across a range of maple stump diameters. We collected 30-39 maples per unit for a total of 204 (142 red maple and 62 sugar maple). Nearly all sugar maple samples were collected in three units: Tar Hollow 2 (n = 27), Tar Hollow 3 (n = 20), and REMA 3 (n = 15). The mean basal diameter of maple samples was 27.5 cm and ranged from 10.1 to 58.4 cm.

Laboratory methods
Cross sections were planed and sanded to enhance ring boundaries and facilitate dating. Each oak sample was crossdated using skeleton plots (Stokes and Smiley 1968) against a previously established master chronology for the region (Sutherland 1997). Maple cross sections ‡70 years old also were skeleton plotted and cross-dated. Younger maple samples were ring-counted along two to four radii and crossdated by identification of key stress years. Several factors contributed to make the exact pith dates for maples somewhat less precise than that for the oaks. Firstly, the crossdating was less clear for maples than for the oaks, i.e., key stress years were not as consistent among the maples. Secondly, a small proportion of the maples had decay or incipient decay in or near the pith, which obscured the ring boundaries. Thirdly, some maples had rings that were locally absent in a portion of their circumference, a common phenomenon documented by Lorimer et al. (1999) for suppressed sugar maple trees. Several samples that we felt could not be reliably dated (primarily small suppressedstems) were omitted from further analyses.

We required at least three scarred samples per unit per year to classify a wound event as a fire scar and, consequently, a year as a fire year. If only two wounds were present in a unit in a given year but the adjacent unit showed evidence of a fire in the same year (three or more samples scarred), we recorded a fire for the unit with only two scars (this occurred once). These criteria were applied to limit the likelihood that wounds caused by other factors (e.g., logging, falling trees and branches, or animals) were recorded as fire scars (see McEwan et al. 2007a). The seasonality of each fire event was determined by examining where the wounds intersected the annual growth ring. For dormant-season scars (located between annual growth rings) it was not possible to determine whether the scar occurred in the fall after the previous growing season or in the late  Hutchinson et al. winter – early spring prior to the upcoming growing season (Sutherland 1997). Because fires in our region are most frequent in the early spring dormant season (March-April), we assigned dormant-season wounds to the calendar year of the upcoming growing season. For example, a dormant-season wound located between the 1922 and 1923 annual growth rings was recorded as a 1923 wound.

To better examine how the relative intensity and (or) extent of fires may have affected tree establishment, we defined fires as ‘‘significant’’ if (i) ‡33.3% of the samples exhibited wounds and (ii) at least five samples had wounds. Since little is known about the relationship between fire intensity and scarring in oaks (see Smith and Sutherland 1999; Guyette and Stambaugh 2004; McEwan et al. 2007a), this definition provides a relative measure of the intensity and extent of the fires within this study. We mapped the location of all samples (oaks and maples) in all fire years at REMA to visualize the spatial pattern of fire scars and tree establishment across the landscape. We used the ArcView version 3.2a geographic information system to map samples based on our field maps that showed locations within or adjacent to a georeferenced vegetation plot. We selected eight of the nine fire years at the REMA study site to illustrate spatial patterns of fire scarring and tree establishment.

Ring widths were measured on the oaks so that growth dynamics and potential release events could be determined. Oak cross sections were scanned and measured using WINDENDRO (Regent Instruments Inc., Ste-Foy, Que.). Two radii approximately 1808 apart were measured in each cross section. Radii were located to minimize the influence of wounds and associated wound wood on growth measurements. Ring-width measurements and crossating were verified with the program COFECHA 2.1 (Holmes 1983; Grissino-Mayer 2001b). The program ARSTAN (Cook and Kairiukstis 1990) from the Lamont Doherty Earth Observatory’s Tree-Ring Laboratory, was used to detrend measurements with a negative exponential curve or linear regression line. This standardization procedure removes the growth trend associated with age and produces dimensionless indices that can be averaged to create a master chronology for a site (Fritts 1976). A master chronology was created for each of the six sampled sites.

Potential releases associated with disturbance events were identified in each master chronology with the JOLTS program (Holmes 1999) from the International Tree-Ring Data Bank Dendrochronology Program Library. Major releases were those where there was a >100% increase in growth expressed as the mean chronology ring-width index over a 15 year period compared with the mean chronology ringwidth index in the preceding 15 year period. Minor releases were those where growth increased 50% over a 10 year period compared with a previous 10 year period (Lorimer and Frelich 1989; Soucy et al. 2005). We report release events only when there were at least 10 trees present in the master chronology. Fire-return interval analyses Data on fire history derived from oak cross sections were analyzed using the FHX2 program (Grissino-Mayer 2001a, 2004). Because fires were so infrequent after 1935, it was not possible to statistically compare fire frequency between pre- and post-fire suppression periods. Instead, for each study unit, we calculated fire intervals from the first fire to 1935 (before and during the early fire-control period) and compared these with fire intervals from the first fire to 2000. Both mean fire intervals (MFI) and Weibull median fire intervals (WMFI) were calculated. The latter is considered a better estimator of central tendency for the typically nonnormal fire-interval distributions (Grissino-Mayer and Swetnam 1997; Grissino-Mayer et al. 2004).

For each unit, we also calculated the mean fire occurrence probability (MFOP) for two periods (stand origination to 1935 and to 2000). Defined by Guyette et al. (2006), MFOP is the number of years divided by the number of fires in a chronological period. Guyette et al. (2006) calculated the MFOP to account for the fire-free period prior to the first recorded fire. For each site, we defined the stand origination as the first year in which at least four samples were present that could potentially record a fire.

Results

Twenty-eight fires were recorded, of which 26 occurred from 1870 to 1933 (Table 1). Twelve fires scarred five or more samples and were classified as significant fires (‡33.3% of the samples were scarred); these fires occurred from 1877 to 1923. Most wounds attributed to fire were recorded on small-diameter trees; for all fires, the basal diameter of oaks at the time of wounding was 12.7 ± 0.7 cm (mean ± SE). In most of the fires (n = 24), all wounds were located between annual growth rings, indicating occurrence in the dormant season (September to early April). In fire years, 147 of the 213 total wounds (69%) were on the uphill portion of the stem (3008 clockwise to 608), based on the uphill position recorded on the sample in the field. Of the 204 maple samples, only one exhibited a fire scar (a wound in a fire year); that maple, from Zaleski 2, had a wound in 1965.

Fire histories of the study units

REMA 2 had the greatest number of fires (n = 7) and significant fires (n = 5) (Table 1); fires were documented from 1877 to 1933. The 1917 significant fire had both dormant and earlywood scars, suggesting an early growing season fire. In the 1933 fire, most wounds were present in the late earlywood; in that fire, all seven scarred trees were young and small, having established in 1923 or 1924 and averaged only 5.1 cm in basal diameter (Table 1). We recorded six fires at REMA 3, three of which were significant, from 1878  1923 (Table 1; Fig. 1). As with REMA 2, the wounds in the 1917 fire indicate an early growing season fire; in the 1906 fire, all three wounds intersected the earlywood.

Zaleski 2 had evidence of six fires; five occurred from 1870 to 1928, and a sixth occurred in 1965 (Table 1). Although only the 1923 fire was classified as significant, it wounded 83% (15 of 18) of the samples; none of the other fires wounded more than three samples. No fires were recorded at Zaleski 2 during a 25 year period from stand origination (1844 to 1869). At Zaleski 3, the chronology was shorter, dating from 1880 to 2000. Although only three fires were recorded, both the 1917 and 1923 fires were significant. Again, the 1923 fire wounded a high percentage (61.9%) of the samples. All fire scars in both Zaleski units were in the dormant season.

Table 1. Summary data for the 28 fires documented on the six study units.

Diameter scarred (cm)

Study site, sample size,and chronologya Fire year Fire seasonb Scarred tree (%) No. of scarred trees Total no of trees Mean Range
REMA 2
n = 29
1855-2000		 1877
1885
1895
1900
1917
1923
1933		 D
D
D
D
D and E
D
E		 50.0
66.7
25.0
50.0
50.0
50.0
20.7		 6
8
3
6
6
9
7		 12
12
12
12
12
18
29		 8.6
13.0
14.4
18.1
22.0
13.1
5.1		 7.0-10.7
9.5-17.2
13.5-15.2
15.4-23.1
18.0-28.8
3.3-25.8
2.7-7.5
REMA 3
n = 22
1858-2000		 1878
1885
1895
1906
1917
1923		 D
D
D
E
D and E
D		 23.1
38.5
57.1
23.5
33.3
18.2		 3
5
8
3
7
4		 13
13
14
17
21
22		 7.4
11.0
14.2
18.4
11.7
16.8		 4.6-10.5
6.7-14.5
5.5-19.5
12.1-21.5
3.9-21.7
8.6-25.0
Zaleski 2
n = 24
1844-2000		 1870
1897
1917
1923
1928
1965		 D
D
D
D
D
D		 75.0
33.3
16.7
83.3
12.5
12.5		 3
3
3
15
3
3		 4
9
18
18
24
24		 4.7
5.0
13.3
16.0
11.1
22.5		 4.2-5.4
1.2-8.3
6.3-18.1
7.5-29.8
4.0-15.6
16.8-33.7
Zaleski 3
n = 25
1880-2000		 1914
1917
1923		 D
D
D		 22.2
35.0
61.9		 4
7
13		 18
20
21		 5.2
8.6
10.3		 3.8-6.8
4.9-13.6
6.3-17.2
Tar Hollow 2
n = 22
1844-2000		 1883
1900
1926		 D
D
D		 71.4
26.7
22.7		 5
4
5		 7
15
22		 7.9
4.4
13.4		 5.7-13.7
1.5-8.0
8.0-24.0
Tar Hollow 3
N = 17
1899-2000		 1900
1912
1984		 D
D
D		 50.0
25.0
17.6		 2
3
3		 4
12
17		 6.4
7.2
40.7		 3.0-6.8
3.7-10.7
36.3-47.0

Note: All data are from the oak samples. Years in bold type indicate fires that scarred five or more trees and ‡33.3% of samples.
aSample size is the total number of oak samples. Chronology period begins with the first year when four or more samples were present to record fires.
bD, dormant season; E, earlywood; D and E, both dormant and earlywood wounds were present in the samples.

We recorded three fires at Tar Hollow 2 from 1883 to 1926 (Table 1). No fires were documented in the 39 years from stand origin (1844) to the 1883 fire; that fire was the only significant fire, wounding five of seven trees. Tar Hollow 3 had the shortest chronology (1899-2000) of all units; fires were documented in 1900, 1912, and 1984. Only two trees were scarred in 1900, but this is included as a fire because of its concordance with the four trees scarred in 1900 in Tar Hollow 2. Only three trees were scarred in the 1912 and 1984 fires at Tar Hollow 3. As with Zaleski, all fire scars were located between annual growth rings, indicating dormant-season fires.

Fire-return intervals
In the period before active fire control and ending in 1935, composite mean fire intervals (MFI) only could be calculated at three of the six units (REMA 2, REMA 3, and Zaleski 2). At these units, MFI ranged from 9.0 to 14.5 years (Table 2). Likewise, the composite Weibull median fire interval (WMFI) ranged from 9.1 years at REMA 2 to 11.3 years at Zaleski 2. For the same pre-1936 period, the mean fire occurrence probabilities (MFOP; Guyette et al. 2006), which also take into account the period of time prior to the first fire, ranged from 11.6 and 12.2 years at REMA 2 and REMA 3, respectively, to 30.7 years at Tar Hollow 2. For the five units originating in 1880 or before (all but Tar Hollow 3), there was a period of at least 20 years from stand origination to the first recorded fire. As only two fires were documented from 1936 to 2000, fire-interval calculations that end in 2000 are longer (Table 2). The WMFI ranged from 12.7 and 13.9 years at REMA 2 and REMA 3, respectively, to 35.4 years at Tar Hollow 2.

Fire and the establishment of oaks and maples
At REMA 2, all oak samples established prior to 1924, and nearly every maple recruited after the 1923 fire (Fig. 2a).

Fig. 1. Fire history diagram for REMA 3. The broken horizontal lines represent the growth years for the 22 oak samples. The solid triangles are wounds that were in a recorded fire year; vertical bars are wounds present in years not recorded as fire years. The six fire years are indicated by the vertical lines located above the timeline.

 

 

Study site Period ending in1935 Period ending in 2000
MFI WMFI (87.5%-12.5%) MFOP (years/fires) MFIa WMFI (87.5%-12.5%)a MFOP (years/fires)
REMA 2 9.3 9.1 (4.8-14.1) 11.6 (81/7) 17.6 12.7 (2.8-36.1) 20.9 (146/7)
REMA 3 9.0 9.2 (6.7-11.3) 12.2 (73/6) 20.3 13.9 (2.6-42.4) 23.8 (143/6)
Zaleski 2 14.5 11.3 (2.8-28.8) 18.4 (92/5) 21.7 18.2 (5.5-40.3) 26.2 (157/6)
Zaleski 3 – – 18.7 (56/3) 28.7 14.0 (1.5-63.2) 40.3 (121/3)
Tar Hollow 2 – – 30.7 (92/3) 39.0 35.4 (13.3-68.2) 52.3 (157/3)
Tar Hollow 3 – – 18.5 (37/2) 33.3 27.3 (7.6-64.2) 34.0 (102/3)

Note: A dash indicates that there were an insufficient number of fire events to calculate the interval.
aFire interval calculations in these columns are based on a final incomplete interval ending in 2000.

After an initial period of oak establishment (1852 to 1865), presumably after harvesting for the charcoal iron industry, there was a 51 year period (1866 to 1916) when no oak recruitment was recorded. Thereafter, two pulses of oak establishment were documented immediately after the significant fires of 1917 and 1923. No maples predated the 1917 fire, and several maples established between the 1917 and 1923 fires. In 1923, immediately after the last significant fire, 15 maples recruited. Thereafter, 17 maples established from 1924 to 1938.

At REMA 3, initial oak establishment occurred from 1849 to 1860. As with REMA 2, no establishment was recorded after 1924 (Fig. 2b). After 1860, there were no large pulses of oak establishment, but there were 7 years from 1885 to 1924 in which pith dates were recorded for one or two oaks. The oldest maple dated to 1921, and a pulse of 11 stems established in 1923, immediately after the last fire. An additional 12 maples established from 1924 to 1949.

The temporal patterns of fire and establishment at the Zaleski units were similar to those of REMA, remarkably so for the initiation of maple establishment and the corresponding cessation of oak recruitment. At Zaleski 2, four oaks established in the 1840s (Fig. 2c). There was a period of oak recruitment from 1872 to 1880, with a pulse of eight stems in 1879 and 1880, following the 1879 fire. After 1880, we record virtually no oak recruitment for 42 years (1881 to 1922). Oaks then established in 1923 and 1924, immediately after the 1923 fire which scarred 15 of 18 oaks. Maple establishment began in 1922 (n = 4), just before the 1923 fire; four others dated to 1923. Thereafter, 22 maples established from 1924 to 1965, with a maximum of three stems in a single year.

 

Fig. 2. (a–f) Temporal establishment of oaks and maples for the six study units. Fires are indicated by vertical lines above the timeline; significant fires, those with ‡33.3% of samples wounded, are indicated by vertical arrows.

 

At Zaleski 3, we recorded 5 oaks that established before 1900 (primarily ca. 1880); then, 10 trees established in 1902 (Fig. 2d). The 1902 pulse of oak recruitment was not associated with a fire. As in unit 2, there was another period of oak establishment (n = 5) in 1923 and 1924, immediately following the 1923 significant fire; thereafter, we recorded only a single oak that established in 1954. As in unit 2, maple recruitment initiated in 1922 (n = 4), and eight trees established in 1923, directly after the significant fire. From 1926 to 1928, 11 maples established, and 11 others had pith dates from 1937 to 1959.

Tar Hollow 2 exhibited an early period of oak establishment (n = 8) from 1835 to 1851, six of the eight trees had pith dates of 1842 and 1843 (Fig. 2e). We recorded no oak establishment from 1852 to 1885; 15 trees established from 1886 to 1919. There was a small pulse (n = 3) of oak recruitment in 1900 after the fire of that year. Unlike REMA and Zaleski, maple establishment began nearly 40 years earlier at Tar Hollow 2. Maples (both red and sugar) recruited for 70 years (1881 to 1951) in a fairly continuous manner but did not exhibit the large pulses recorded at REMA and Zaleski.

The oldest oak recorded in Tar Hollow 3 dated to 1851, but no other samples predated 1894 (Fig. 2f). We record fires in 1900 and 1912. The 1900 fire scarred two of the four samples, and there was a pulse of oak recruitment (n = 6) that year. Thereafter, six oaks had pith dates from 1902 to 1924; no more than one tree was recorded in any single year. Maple recruitment at Tar Hollow 3 spanned from 1897 to 1963. As in Tar Hollow 2, there were no large establishment events. At both Tar Hollow units, despite different patterns of fire and an earlier initiation of maple establishment compared with REMA and Zaleski, oak establishment ceased at the same time at all sites (ca. 1920 to 1925).

Spatial distribution of fire scars and tree establishment at REMA

Fires that occurred in 1885, 1895, 1917, and 1923 were recorded in units 2 and 3 (Fig. 3). The 1885 and 1917 fires were classified as significant in both units. By contrast, the presence of fire-scarred trees was limited to one unit in the other five fire years (1877, 1878, 1900, 1906 [not shown], and 1933). Presumably, these fires did not burn across the intermittent stream drainage separating the two units. Similarly, the stream drainage in the center of unit 2, running southwest, appears to have limited fire spread in several years when trees were scarred only northwest (1895 and 1900) or southeast (1933) of the drainage.

For all fire years, scarred oaks were located near oaks that were not scarred. The trees most prone to exhibiting fire scars were in the northern portion of unit 2, near the top of the ridge; two or more of the seven trees that had established there before the first fire in 1877 were scarred in all unit 2 fires prior to 1933.

Maple establishment is first shown in both units on the 1923 fire map (Fig. 3g); these trees established from 1917 to 1922 and survived the 1923 fire. In unit 3, most of the maples that established before 1923 were in areas where fire scars were not recorded on oaks in 1923, suggesting that those small trees were in unburned patches. In unit 2, all three maples predating 1923 are within 5-20 m of an oak with a 1923 fire scar; however, all three oaks with fire scars were small in 1923, each having established immediately after the 1917 fire. By the time of the 1933 fire (Fig. 3h), maples had established across most of the landscape. All of the maples in the 1933 fire map were relatively near scarred oaks and thus escaped that fire; none of these samples had 1933 wounds. However, the low-intensity and perhaps patchy nature of the 1933 spring growing season fire is suggested by the fact that only small oaks (mean basal diameter 5.1 cm) that established after the 1923 fire were scarred.

Radial growth and releases

The master chronologies show growth that is typical of trees from forest interior sites and show only several sustained release events (Fig. 4). Growth releases were identified at only two of the six units; however, none of these releases coincided with a fire. Zaleski 2 had a major release beginning in 1896, perhaps coinciding with a harvest based on its magnitude. No oak recruitment was associated with this release. A moderate release also was identified at this site for 1906. REMA 3 had a major release in 1864  (Fig. 4), although considerable variability in early growth associated with the small sample size may partially account for this release event. No growth releases were identified at Zaleski 3, REMA 2, or at the Tar Hollow units. At REMA 3, some oak recruitment preceded the 1864 release event, but there is no evidence that these were related (Figs. 2 and 4). These analyses, based on the standardized mean ring width chronologies, indicate that fires were of insufficient intensity to cause standwide mortality and the release of surviving oaks.

Discussion

Historic fire regime

Generally, fires were frequent from ca. 1870 to 1935 as stands developed but were uncommon thereafter, reflecting the regional postsettlement history of anthropogenic fire and its suppression. A 1920-1922 forest survey of 10 southern Ohio counties reported that 25% of all forested land showed visible evidence of having burned at least once within the previous decade (ODNR, Ohio Division of Forestry, Columbus, Ohio). Data from the same survey indicated that 5% to 7% of forested land burned annually (Ohio Experiment Station 1922). Organized fire control was instituted in 1923, and its infrastructure and effectiveness developed rapidly.  By 1935, 19 fire lookout towers had been erected in 8 southern Ohio counties, and 447 fire wardens were employed (Leete 1938). From 1926 to 1935, the mean annual forest acreage burned had been reduced to 0.8% (Leete 1938); from 1950 to 2000, it was further reduced to only 0.1% per year (ODNR, Division of Forestry, Columbus, Ohio).

Our study adds to the growing body of dendrochronological evidence that fire was frequent in the central hardwood region prior to organized fire control; examples include oak and oak-pine community types in the Missouri and Arkansas Ozarks (Cutter and Guyette 1994; Guyette et al. 2002; Guyette and Spetich 2003; Soucy et al. 2005); pine-oak communities in the southern Appalachians (Brose and Waldrop 2006); post oak (Quercus stellata Wang.) barrens in Indiana (Guyette et al. 2003) and Tennessee (Guyette and Stambaugh 2004); and oak forests in southern Ohio (Sutherland 1997; McEwan et al. 2007b), Maryland (Shumway et al. 2001), and West Virginia (Schuler and McClain 2003). The fire-return intervals that we calculated for the REMA and Zaleski sites, ranging from 9.1 years prior to 1936 to 18.2 years overall (WMFI), are within the range reported in those studies (2-24 years), despite our more conservative criteria for classifying fire years. However, the 35 year firereturn interval at Tar Hollow 2 exceeds the range in the other studies.

In the central hardwoods region, dissected topography is known to have limited the spread of fires historically (Guyette et al. 2002). In our study, mapped fire-scarred trees suggest that even relatively small intermittent stream drainages limited fire spread in some years, resulting in some fires that were recorded on, and presumably burned, only a portion of the 20 ha units. By contrast, several of the significant fires spanned two units, scarring trees as far as 900 m apart.

Fig. 3. (a–h) Spatial distribution of oaks and maples at REMA in eight fire years. Solid circles indicate oaks scarred by a fire in the year associated with the map, and open circles show oaks that were not scarred in that year. The shaded triangles indicate the location of maples that had established by the time of the 1923 (Fig. 3g) and 1933 (Fig. 3h) fire years. (No maples were documented to have established at the time of the 1917 fire or before.)

 

Fig. 4. Master tree ring chronologies (ARSTAN) showing fire events (vertical arrows) and the point when a minimum of 10 trees were averaged (vertical line) into the mean chronology. Major (M) and moderate releases (m) were only noted in the Zaleski 2 and REMA 3 chronologies.

As other dendrochronological fire-history studies in the region have shown (e.g., Sutherland 1997; Shumway et al. 2001; McEwan et al. 2007b), the great majority of fires occurred in the dormant season (September to early April). Only fires in 1906, 1917, and 1933 at REMA had wounds located in the earlywood. In southern Ohio, radial growth (earlywood production) in oak begins in middle to late April, during bud-swelling and leaf unfolding (Phipps 1961). Oak cross sections collected in early May clearly show earlywood production, whereas samples from mid June show latewood production (R.W. McEwan, Department of Forestry, University of Kentucky, Lexington, Ky., unpublished data). Thus, we estimate that fires exhibiting both dormant and earlywood scars likely occurred in mid-April at the onset of radial growth (e.g., the 1917 REMA fire). The single fire (REMA 2 in 1933) that exhibited wounds intersecting the late portion of the earlywood probably occurred in May.

Sutherland (1997) and McEwan et al. (2007a) showed that historic fire occurrence in this region was not related strongly to monthly climatic conditions. Similarly, we recorded fires in both wet and dry periods. However, for the years in which fires occurred at more than one study site (1900, 1917, and 1923), all exhibited two or more months of drought conditions (Palmer drought severity index more negative than -1.5) the previous fall (1900, 1917, 1923) or also in the spring of the recorded fire year (1900).

The intensity of fires as these stands developed is difficult to determine with certainty because research is lacking that directly relates fire intensity to scarring in oak. However, several studies that have examined patterns of scarring in oak following prescribed fires provide some insight. Smith and Sutherland (1999) found that 14 of 18 small oak trees (4-23 cm DBH) had at least one fire scar after two low-intensity prescribed fires (flame lengths generally <50 cm with no overstory tree mortality). Guyette and Stambaugh (2004) showed that 35% to 65% of mostly small post oak trees (10-25 cm basal diameter) were scarred during three separate prescribed fires in an oak community in Tennessee. These fires burned 72%-93% of the area and reduced stand density (mostly small-diameter trees) by 35%. However, in both studies, only trees with visible bark char were selected for sampling. In our study, we found that, on average, 40% of the oak samples, most of which were small at the time (5 to 25 cm basal diameter) were scarred in the historic fires. These scarring percentages suggest that the fires would have been similar in intensity to the prescribed fires reported by Smith and Sutherland (1999) and Guyette and Stambaugh (2004). Although pulses of oak establishment immediately after some historic fires in our study suggest abundant resprouting after top kill, there is no evidence of high-severity stand-replacement fires even in these relatively young, regenerating stands.

McEwan et al. (2007a) reported that during 15 separate prescribed fires that were similar in intensity to those in Smith and Sutherland (1999), the scarring rate was much lower (12.6%) in white oak. However, because the sample trees in that study were much larger (most were >20 cm DBH), it is difficult to compare those scarring percentages with the historic scarring of small trees in our study.

Five years after a prescribed fire, Wendel and Smith (1986) found that 66% of overstory trees (all species, >12.7 cm DBH) exhibited fire scars visible on the exterior of the stem as exposed wood with callous tissue. The fire in their study was higher in intensity, reducing stand basal area by nearly 20%. The high scarring percentage of larger trees in their study suggests a higher intensity fire than was typical of the historic fires in our study.

Fire, land use, and tree establishment
At REMA and Zaleski, periods of oak and maple establishment were related to specific fire events as these stands developed. The establishment and subsequent survival of maples generally began immediately after the cessation of significant fires, i.e., fires that wounded at least one-third of the oak samples. The final oak establishment event also occurred directly after the last significant fire at three of the four REMA and Zaleski units. Because maples seldom were recorded as witness trees in upland forests just before Euro American settlement (Beatley 1959; Dyer 2001), these results lend support to the hypothesis that organized fire control facilitated the invasion of maples into the uplands from the more fire-protected lowlands (Abrams 1998).

The temporal patterns of fire history and maple establishment were similar at all four units at REMA and Zaleski. All units had periodic fires from ca. 1870 to 1925, and all units burned in both 1917 and 1923; in each of those years, fires were significant in three units. The initiation of maple establishment was similar in that none was documented in any units before the 1917 fires. Limited establishment was documented between the 1917 and 1923 fires, and large pulses occurred immediately after the 1923 fires followed by continuous establishment into the 1960s. The large pulses of maple establishment after the 1923 fires suggest resprouting from previously established individuals. Red maple, which accounted for 89% of the maple samples at REMA and Zaleski, has thin bark and is highly susceptible to top kill by fire (Harmon 1984; Regelbrugge and Smith 1994; Hutchinson et al. 2005); however, it also sprouts prolifically after topkill (Albrecht and McCarthy 2006; Blankenship and Arthur 2006). Maples probably began recruiting into these stands earlier than we document, perhaps much earlier, but presumably were being killed or top-killed until the cessation of fires.

The limited establishment and survival of maples before the 1923 fires (1917 to 1922) in all units may have resulted from several factors. Firstly, wildfires usually burn in a mosaic pattern, particularly in dissected landscapes, resulting in variable fire intensities and including unburned patches. Established maples may have escaped the 1923 fires in unburned patches. We also speculate that the initiation of maple establishment at REMA and Zaleski may have been facilitated by reduced anthropogenic land use, particularly by livestock in open-range woodland livestock grazing (Green 1907). The human population of Vinton County declined steadily with the demise of the iron furnace industry from a maximum of 17 223 in 1880 to 10 287 by 1930 (Vinton County Ohio Genealogy 2005). During the same period, farmland in the county decreased from 93283 to 61559 ha, and most of these lands reverted to forest (Bromley 1934a). Woodland grazing also likely decreased during this period, which would have favored the recruitment of trees, including maples. Brose and Waldrop (2006) showed that the cessation of livestock grazing in the Great Smoky Mountains National Park contributed to increased tree recruitment there in the 1920s and 1930s.

Oak also exhibited pulses of establishment immediately after some fires, suggesting resprouting from previously established stems. However, some fires were not followed by pulses of oak establishment. The final period of oak establishment occurred immediately after the 1923 fires; thereafter, we recorded virtually no additional oak stems. These data suggest that the large increase in maple establishment after the cessation of fires contributed via competition to the lack of subsequent oak recruitment. The absence of fire  after 1923, probably coupled with reduced woodland grazing, also likely facilitated the development of higher stand densities. The resulting closed-canopy conditions that developed would have greatly limited the ability of the relatively shade-intolerant oaks to establish from seed (Beck 1970) but not the shade-tolerant red maple, which can persist for long periods beneath a canopy (Tift and Fajvan 1999). Thus, further oak recruitment from seed, followed by growth and survival, probably was limited by a combination of shading and competition from both overstory trees and understory maples after fires ceased (e.g., Aldrich et al. 2005)

The history of fire and tree establishment at Tar Hollow differed from that of REMA and Zaleski in several aspects. First, at Tar Hollow, there were fewer historical fires (n = 5) and only one was significant. In all, we recorded only 19 historic fire scars at Tar Hollow compared with 48 at Zaleski and 75 at REMA.

The temporal pattern of maple establishment also differed, beginning nearly 40 years earlier (1881) and not exhibiting the distinct pulses after fire cessation that occurred at REMA and Zaleski. Tar Hollow also differed in that there was a long period of fairly continuous oak establishment (ca. 1890 to 1925), that coincided with the continuous recruitment of maples. These differences in fire and regeneration among sites may have resulted from different human land use.

Timber harvesting in the 1800s at Tar Hollow was not associated with charcoal production as at REMA and Zaleski and, thus, may have differed in intensity and extent. Perhaps more important is the evidence of greater and more varied human land use at the Tar Hollow site. In the 1930s, several Land Utilization Project (LUP) areas were established in which the State of Ohio purchased submarginal farmlands and then resettled the occupants (Bromley 1934a, 1934b). The REMA and Zaleski study sites were located near LUP areas while the Tar Hollow site was within the Ross-Hocking LUP. Land titles and appraisals that included detailed ownership and land-use maps from the time of purchase (ca. 1935) indicate that the Tar Hollow site consisted of a number of small parcels of mixed-ownership (ODNR, Division of Forestry, Regional Office, Chillicothe, Ohio). The maps indicate a patchy mixture of cover types: the most abundant was ‘‘forest land (including woodland pasture),’’ but it also included some areas of ‘‘grazing land (grazing or open pasture)’’ and a smaller portion as ‘‘crop land (including orchard and hay meadows).’’ The more varied land uses at Tar Hollow likely would have created a more patchy distribution of disturbances (fire, grazing, and harvesting). In particular, the patchy ownership and land use might have limited fire spread, resulting in the lower observed occurrence of fire. In turn, fewer fires probably facilitated the recruitment and survival of maples beginning much earlier at this site. At Tar Hollow, woodland grazing may have been more prevalent for a longer period with direct human occupation into the mid-1930s, potentially limiting the large pulses of maple establishment that occurred at REMA and Zaleski. Although fires generally were less frequent and wounded fewer trees at Tar Hollow, these units also developed into oak-dominated forests.

Other potential factors affecting tree establishment In addition to fire and human land use (particularly woodland grazing), the regeneration of oak forests was surely influenced by dramatic changes in wildlife populations. The decline and extirpation of the acorn-consuming white-tailed deer (Odocoileus virginianus (Zimmermann)), wild turkeys (Meleagris gallopavo L.), and passenger pigeons (Ectopistes migratorius (L.)) occurred from the mid-1800s to the early 1900s, as these stands were developing (Chapman 1938). Although these declines could have benefited oak regeneration from seed, the consumption of acorns and grazing of seedlings by domestic livestock probably were widespread during this period. No deer were present in Ohio from 1904 to 1922 when a restocking program was initiated (Chapman 1938). By 1938, it was estimated that only 2000 deer were in Ohio (Chapman 1938); the current estimate is 650 000 (ODNR, Division of Wildlife, Columbus, Ohio). Thus, excessive deer browsing clearly would not have contributed to the cessation of oak recruitment ca. 1925. In fact, a lack of deer browsing, the cessation of fire, and a decrease in livestock grazing may have facilitated the dramatic increase in maple establishment during the 1920s and 1930s.

It also is unlikely that American chestnut (Castanea dentata (Marsh.) Borkh.) mortality, caused by the chestnut blight fungus, facilitated the initial recruitment of maple in these stands. Even at REMA and Zaleski where maple recruitment began later (ca. 1920), it predated the arrival of chestnut blight, which caused mortality in Vinton County primarily from 1928 to 1936 (Beatley 1959). Also, chestnut accounted for only 4%-6% of witness trees ca. 1800 (Beatley 1959).

Selective harvesting was common after the mid-1930s on the Zaleski and Tar Hollow State Forests and at the REMA, then owned by D.B. Frampton and Co. (Beatley 1959). Previous work on sites near our REMA sites showed some growth releases suggestive of selective harvesting (Hutchinson et al. 2003). However, our data indicate that, in the absence of periodic fire, canopy disturbances after ca. 1925 did not facilitate oak recruitment. Similarly, during the fire control era, small openings in closed-canopy stands would have favored the growth of maples, which can respond with rapid growth even after long periods of suppression (Tift and Fajvan 1999).

Implications for oak regeneration today
Our results show the past importance of periodic fire in sustaining oak establishment and in limiting maple recruitment as stands developed. However, in many oak forests, there is now an abundance of maples in the midstory that are large enough to be fire resistant. Also, many forests that remain dominated by overstory oaks may be too dense to support oak regeneration even if the maple midstory could be removed with fire. Research has shown that simply returning low-intensity prescribed fires to fully stocked stands does not open the canopy sufficiently to improve the competitive status of oak regeneration (Hutchinson et al. 2005; Blankenship and Arthur 2006). Similar to the past importance of fire in early stand development, oak regeneration has improved when prescribed fire was applied to openstructured stands that developed after partial harvest (Kruger and Reich 1997; Brose and Van Lear 1998; Iverson et al. 2008). However, other studies have shown that the timing and intensity of the mechanical treatments and fire are critical to their success (Franklin et al. 2003; Albrecht and McCarthy 2006). Although fire was important in sustaining oak forests in the past, the legacy of prolonged fire exclusion necessitates research to refine oak regeneration prescriptions that incorporate canopy disturbances, fire, and other tools (Brose et al. 2006).

Acknowledgements

We thank David Hosack, Kristy Tucker, Brad Tucker, Bill Borovicka, Tim Fox, Joan Jolliff, and Zachary Traylor for field and laboratory assistance. We thank Patrick Brose, James Rentch, Ryan McEwan, Marty Jones, and two anonymous reviewers for providing many valuable suggestions on previous drafts of the manuscript. We thank the Ohio Department of Natural Resources, Division of Forestry, for supporting this research on Tar Hollow and Zaleski State Forests; we thank Bob Boyles and Michael Bowden of the Division of Forestry for assistance with historical documents. We also thank Forestland Group, LLC, for supporting this research on the Vinton Furnace Experimental Forest. This is publication No. 172 of the Fire and Fire Surrogate Network Project funded by the Join Fire Sciences Program.

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