The fire monitoring program described in this handbook allows the National Park Service to document basic information, detect trends, and ensure that each park meets its fire and resource management objectives.
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
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; email@example.com. Daniel C. Dey, USDA Forest Service—firstname.lastname@example.org. Ross J. Phillips, USDA Forest Service—email@example.com. Thomas A. Waldrop, USDA Forest Service—firstname.lastname@example.org
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:
- Fire will reduce the density of midstory trees of all species.
- Oak reproduction will basal sprout after prescribed fires at a higher rate than the reproduction of mesophytic hardwood species.
- The proportion of oak reproduction relative to that of mesophytic hardwood species will increase postfire.
- Oak reproduction will be at least as tall as the reproduction of mesophytic hardwood species postfire.
- 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.
|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.
- Differential sprouting: The difference in postfire basal sprouting rates between oak reproduction ( 2.5 cm dbh) and those of mesophytic hardwood species.
- 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.
- Oak relative height: The height of the oak reproduc-tion compared with that of mesophytic hardwood species at the end of the study.
- 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|
|4||State Game Land 29||PA||Rel||Gro||1||3||2|
|6||Clear Creek SF||PA||Sup||Gro||1||3||3|
|9||Chilton Creek Tract||MO||Sup||Dor||1,3,4||5||5|
|13||Red River Gorge||KY||Sup||Dor||2,3||10||3|
|14||University of MO Forest||MO||Sup||Dor||10||10||2|
|16||Vinton Furnace EF||OH||Sup||Dor||2,4||7||4|
|25||Ft. Indiantown Gap||PA||Sup||Dor||3,4||1||4|
|27||University of TN Forest||TN||Rel||Dor||10||10||6|
|29a||Green River WMA||NC||Rel||Dor||2||5||3|
|29b||Green River WMA||NC||Sup||Dor||2||5||3|
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.
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).
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.
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.
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:
- 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.
- 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.
- 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.
- 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
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
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.
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: email@example.com
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.
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.
|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 ). 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
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|
|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|
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.
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|
|Mineral soil %C|
|Prescribed burn||21||4||11, 22|
|Mineral soil %N|
|Prescribed burn||21||1||12, 11|
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).
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.
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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References providing data for the fire/soil C meta-analysis (Ecological Archives A021-054-A1).