Changes in Understory Vegetation and Soil Characteristics Following Silvicultural Activities in a Southeastern Mixed Pine Forest1
By Archer, Jessica K Miller, Deborah L; Tanner, George W
ARCHER, J. K. (Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611), D. L. MILLER (Department of Wildlife Ecology and Conservation, University of Florida West Florida Research and Education Center, 5988 Hwy 90, Bldg 4900, Milton, FL 32583), AND G. W. TANNER (Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611). Changes in understory vegetation and soil characteristics following silvicultural activities in a southeastern mixed pine forest. J. Torrey Bot. Soc. 134: 489-504. 2007.-A silvicultural chronosequence was studied in upland pine stands of Fort Benning, Georgia, to assess understory vegetation and soil characteristics following silvicultural disturbance activities. Hypotheses regarding patterns of understory vegetation distribution and abundance, and the impact of disturbance on soil properties were evaluated in 32 forest stands. The chronosequence encompassed various times following clear-cut regeneration: stand age (0-3 yrs), (8-10 yrs), (18-20 yrs), and (30-80 yrs). Soil pH, total carbon (C) and nitrogen (N) contents, soil texture, and bulk density were used to characterize soil conditions across the chronosequence. Foliar cover by species was used to characterize vegetation across this same chronosequence. Canonical correspondence analysis (CCA) was performed to determine the relationship between understory vegetation pattern and measured soil gradients and stand age. CCA identified stand age as the most important factor influencing distribution and abundance of understory vegetation. Herbaceous species composition and cover varied more with stand age than did understory woody species. Aside from a decrease in bulk density soil variables did not vary with recovery time. Indicator analysis identified Gaylussacia mosieri (Small) and Carya spp. as the only significant woody indicators of age class. Cyperus croceus (Vahl) and Bulbostylis barbata (Rottb.) C. B. Clarke were identified as herbaceous indicators of the 0-3 age class. Andropogon virginicus (L.), Dichanthelium sp. and Sporobolus junceus (Beauv.) Kunth were significant indicators of the 8-10 year age class. Significant indicators of the 15-20 year class were Pityopsis sp. and Tridens flavus (L.) A. S. Hitchc. Andropogon ternarius (Michx.), Schizachyrium scoparium (Michx.) Nash, Desmodium sp., Hieracium sp., and Rhynchosia tomentosa (L.) Hook. & Arn. were indicators of 30-80 year age class. Major changes in understory vegetation cover and composition continued for at least 15-20 years post clear-cut regeneration. Key words: disturbance, groundcover, longleaf pine, silviculture.
At small spatial scales, longleaf pine forests are considered one of the most species rich in the U.S. and perhaps worldwide (Walker 1993). The herbaceous understory may contain 40-75 species of vascular plants in a single 1 m^sup 2^ quadrat and 130 for a 0.1 ha plot (Walker and Peet 1984, Clewell, 1989). Plant and animal species associated with longleaf pine communities typically exhibit a high incidence of endemism (e.g., 191 species of rare plants) (Noss et al. 1995). Unfortunately, 97% of the original longleaf pine forests have been lost or degraded through anthropogemc disturbances and fire suppression (Frost 2006).
Historically, frequent (1-10 yrs) lightning fires maintained longleaf pine ecosystems. Fires were carried over large areas by a fairly continuous cover of perennial grasses and pine duff. Ten to thirty percent of the southeastern pinelands burned annually (Ferry et al. 1995). These frequent fires reduced litter accumulation and invasion of competing woody species. Pine seedlings and many of the grasses and forbs of longleaf pine communities are shadeintolerant, and many require bare mineral soil and reduced competition for germination and early growth. Many of these forests were converted to agricultural fields or forest plantation (most often of other pine species) or used in the Naval stores industry (turpentine or pitch); invariably, much of the herbaceous understory was disturbed and fragmented by the logging roads, agricultural fields and grazing. These alterations inhibit surface fires from carrying for long expanses. In the absence of fire, oak (Quercus spp.), hickory (Carya spp.), and other pine species replaced longleaf pine on the Coastal Plain (Stout and Marion 1993) leading to a decline in species associated with longleaf pine-dominated ecosystems.
Logging, with associated high levels of soil disturbance, impacts stability of certain soil minerals, alters soil structure, increases bulk density, and leads to altered vegetation species composition (Congdon and Herbohn 1993). Heavy machinery used in logging operations can impact soil bulk density and infiltration. Depending on the amount of compaction and soil type, recovery is possible, especially for surface soils (Gayoso and Iroume 1991), yet reported recovery times vary widely. Following logging, bulk density in a loblolly pine forest was thought to take 18 years to recover (Hatchell et al. 1970) while compaction remained significantly elevated 32 years after logging in Douglas fir stands in the northwest (Wert and Thomas 1981).
Soil scarification, mechanical break up of the soil surface during silviculture site preparation, can result in a process where exposed mineral soil becomes encrusted and compacted by rainfall, thus impeding seedling root penetration (Pierce et al. 1993). High levels of compaction can significantly inhibit root growth and thus shoot growth. Mycorrhizal mycelial growth, seed germination, and seedling emergence also maybe restricted by increased compaction (Greacen and Sands 1980).
Understory plants have been recognized as indicator species of potential timber site productivity since the 1920s (Clements 1928, Hunter 1990), yet many of the studies on anthropogenic impacts on southern pine forests focus on wildlife (Ercelawn 1999, Fimbel et al. 2001) and tree species (Huth and Ditzer 2001, Stearns and Likens 2002) recovery rates. Understory herbaceous vegetation often is left out of the equation even though it is likely to be the most sensitive of the three groups (Duffy and Meier 1992). In the southeastern longleaf pine ecosystems, where understory vegetation is a critical factor in determining locally high faunal diversity and silvicultural activity is prevalent, the importance of such studies has been underestimated.
In forest systems not dominated by southern pines, several recent studies examined the impact of silviculture on herbaceous understory growth, composition and diversity (Halpern and Spies 1995, Roberts and Gilliam 1995, Elliott et al. 1997, Gilliam 2002). Other work reported on the interacting effects of complete deforestation and herbicide on groundcover vegetation recovery (Kochenderfer and Wendel 1983, Reiners 1992). Still others looked at impacts of logging on vegetation patterns through comparisons of second-growth and old-growth forests (Goebel et al. 1996, Qian et al. 1997). While these studies focus primarily on herbaceous vegetation diversity, our study focused on composition and abundance of understory vegetation (herbaceous and woody) and soil changes following silvicultural practices in a southeastern mixed pine forest once dominated by longleaf pine.
There were four specific questions addressed in this study: 1) Do certain soil characteristics change following clear-cutting? We hypothesize that bulk density will decrease over time. 2) Are plant assemblages differentially affected by logging depending on soil texture? Since plant growth can be inhibited by increased bulk density and the clay content of a soil affects the degree to which the soil can be compacted, we believe that the texture of the soil wiU affect plant assemblages. 3) What are the rate and pattern of changes in distribution and abundance of herbaceous groundcover and understory woody species following clear-cut regeneration (hereafter, clear-cutting)? Certain species known to be particularly sensitive to disturbance will likely increase over recovery time. 4) Can we identify species that are indicators of assemblages of plants characterizing increasing length of time following clear-cutting? Identification of pattern and rate of understory recovery following clearcutting will aid in identification of sensitivity and rate of return of herbaceous species following low to moderate levels of disturbance and further separate natural variation from variation attributed to anthropogenic disturbance. Recovery time may be reduced through improved management. Silviculture can be managed spatially and temporally so that forest ecosystems do not further depart from natural states, but instead move towards recovery.
Materials and Methods. STUDY AREA. The 72,900 ha Fort Benning Military Reservation is located in Muscogee, Marion, and Chattahoochee Counties, Georgia and Russell County, Alabama (32[degrees] 21′ N, 84[degrees] 58′ W). The study was conducted on the portion of the reservation found within the Upper Coastal Plain Ecoregion (Bailey 1995) with nearly level to gently rolling topography and a maximum elevation of 240 m. The semi-tropical climate averages 124 cm of rainfall annually with average daily temperature ranging from -1 [degrees]C to 37 [degrees]C (Dale et al. 2002). The study was restricted to upland, sandhill areas on droughty, infertile entisols and ultisols with loamy sand to sandy loam surface horizons. Major soil associations included Cowarts- Nankin, Troup-Cowarts-Nankin, Troup-Lake-land, Ailey-Troup- Vaucluse, Dothan-Orangeberg-Esto, and Troup-Vaucluse-Pelion (USDA NRCS 1983, USDA NRCS 1997). These sandhills are believed to have been longleaf pine forests (Cooper 1989) but are now dominated by mixed stands of Pinus palustris P. Mill, (longleaf pine), P. taeda L. (loblolly pine), P. echinata^. Mill, (shortleaf pine), andP. elliottii Engelm. (slash pine), with understories consisting of Quercus laevis Walt, (turkey oak), Q. marilandica Muenchh. (blackjack oak), Q. incana Bartr. (bluejack oak), Nyssa sylvatica Marsh, (blackgum), Diospyros virginiana L. (persimmon), Liquidambar styraciflua L. (sweetgum), Morella cerifera (L.) Small (wax myrtle), and Cornus florida L. (dogwood) (King et al. 1998). The groundcover consists mainly of Andropogon virginicus L. (broomsedge), Andropogon ternarius Michx. (splitbeard), Schizachyrium scoparium (Michx.) Nash (little bluestem), Rubus spp. (blackberry), Gaylussacia dumosa Small (dwarf huckleberry), Vaccinium spp. (blueberry), and Pteridium aquilinum (L.) Kuhn (bracken fern) (Myers 1990, King et al. 1998).
Most of the reservation was farmed until 1918 when the western part of the present Fort Benning was established under military control (Kane and Keeton 1998). In 1941, a mixture of row crop farming, pasture, and forest remnants was added on the eastern half of the reservation and were subjected to management practices including clear-cutting, selective thinning (on only the oldest sites), use of heavy machinery, creation of haul roads, use of log decks and skid trails, drum chopping, planting of pine seedlings (loblolly or longleaf). Prescribed burning was used in both growing and dormant seasons on a three year cycle but herbicides were not used. Military activity in the study area was low (restricted to small-scale troop bivouacs). Areas of the reservation with tank and vehicle maneuvers, large-scale troop movements, and ordnance activity were not included.
EXPERIMENTAL DESIGN. A chronosequence of sites was selected based on age since clearcut. Prior to site selection, criteria were established to minimize the variation among sites for environmental and disturbance variables other than logging history. Potential replicates were restricted to parts of the reservation identified as having the potential to support sandhill vegetation (longleaf pine, deciduous oaks, and bluestems), similar history of farming and fire, minimum size of 5 ha, slopes of 0-6%, and minimal military activity. To explore effects of differences in soil texture, four pine stands with loamy and four with sandy surface soils (according to soil survey maps) were randomly selected in each of the following age classes: 0-3 years, 8-10 years, 18-20 years, and 30-80 years. All sites were planted to pine plantation after clear-cutting. The 0-3 year sites were longleaf plantations, with no overstory and generally high groundcover. The 8-10 year sites were either longleaf or loblolly plantations as most of the existing plantations established before 1996 were loblolly. These sites generally had no overstory cover above 4 m. While all sites were originally clear- cut at year 0, an additional thinning generally around 15-20 year post harvest had been conducted on 30-80 year sites.
DATA COLLECTION. Five points were randomly located within each of the 32 sites. Overstory canopy cover was measured with a concave spherical densiometer by averaging the readings of the four cardinal directions from the center of each point (Lemmon 1956). From the center point, 3 m transects were established at 0[degrees], 120[degrees], and 240[degrees]. Along each transect, woody species (
An undisturbed soil core (upper 20 cm, 8 cm diameter) was taken adjacent to each herbaceous quadrat for laboratory bulk density (BD) determination of oven-dried soil (Blake 1965). To analyze for soil texture, pH, total nitrogen, and total carbon, four surface soil samples (upper 20, 4 cm diameter) were collected at the terminus of each transect and at each center point. These samples were mixed, homogenized and considered as a composite sample for each subplot. The percentage of sand, silt and clay was determined using the hydrometer method (Bouyoucos 1927). Soil pH was measured using a 1:1 DDI water soil slurry on an Orion SA720 pH meter (Fisher Scientific, Fair Lawn, NJ). Total C and N content was determined on dried, ground soil and detritus samples using a Carlo-Erba NA-1500 CNS Analyzer (Haak-Buchler Instruments, Saddlebrook, NJ).
DATA ANALYSIS. Differences in edaphic variables among the four age classes were analyzed using a generalized linear model (PROC GLM) procedure (SAS 8.0). Based on analysis of soil texture, each site was assigned to either a sandy (sand) or loamy (sandy loam or loamy sand) textural group. There was a factorial arrangement of treatments with age since clear-cut (4 levels, 0-3, 8-10, 15-20, 30- 80) and textural soil group (2 levels, sandy or loamy) as main factors. The interaction of age since clear-cut and textural group was included in the model. Pearson’s conelation coefficient was used to determine the relationship among edaphic variables.
Vegetation data were separated into woody and herbaceous species using Integrated Taxonomic Information System (www.itis.gov) as a guide for the purposes of the analysis. Patterns of woody and herbaceous species composition and canopy cover in relation to the measured environmental and edaphic variables were analyzed separately with canonical correspondence analysis (CCA; CANOCO 4.0, Ter Braak 1998). For purposes of analysis, the original number of environmental variables was reduced to eight (age since clearcut (0- 3 yr, 8-10 yr, 15-20 yr), pH, total N, % canopy cover, total C, % clay, and bulk density) due to strong autocorrelations (the variables removed from analysis were 30-80 year, bare ground, C/N ratio, % sand, and % silt). Actually values for % clay were used for each site. Also removed from the analyses were the species Pinus taeda and P. palustris as most or all present were artificially planted on the sites and do not represent natural regeneration.
Statistical validity of the resulting environmental axes was evaluated by an unrestricted Monte Carlo permutation test based on 199 random trials (Ter Braak 1998). The forward-selection option was used to determine the minimal set of environmental variables that could explain the largest amount of variation in the species composition data. At each step, the statistical significance of the environmental variable added in the course of the forward selection was tested by means of a Monte Carlo permutation test. Variables were significant if the permutation test derived P
The Dufrene and Legendre (1997) method of calculating species indicator values was used to describe the usefulness of individual species for indicating time since clear-cut. This method produces an indicator value between zero (no indication) and 100 (perfect indication). To receive a perfect score, the presence of a species would point to one of the age classes without error (always present and exclusive to that group). A randomization test was used to test for significance of the indicator value.
Results. SOIL CHARACTERISTICS. Texture analysis revealed discrepancies in the original classification of sites as sandy or loamy based on soil survey information. Twelve sites were misclassified. After reclassification based on % sand from laboratory textural analysis, three 15-20 year sites and five 30-80 year sites were categorized as loamy sand or sandy loam (loamy) while five 15-20 year sites and three 30-80 year sites were classified as sand (sandy). This new site classification was used in all analysis of variance. Percent sand ranged from 52.6% to 91.3% while clay ranged from 2.3% to 23.7%, with no significant difference among age classes (P = 0.698).
Table 1. Mean and stan error (SE) for built density (BD), pH, total carbon (TC), and total nitrogen (TN) for four age classes representing years adter a clear-cut divide into sandy and loamy surface soils groups.
Soil bulk density ranged from 0.73 g cm^sup -3^ in a sandy loam > 30 year site to 1.31 g cm^sup -3^ in a sandy 15-20 year site and decreased significantly (P = 0.0045) with clay content. While only marginally significant differences were found among age classes (P = 0.0785), BD declined by 12.5% with time since clear-cut on sandy sites and only 0.8% on loamy sites. No significant differences were found among age classes for pH, total carbon (TC) and total nitrogen (TN)- (Table 1). Mean canopy cover of 3%, 31%, 69%, and 70% for most recent to oldest clear-cut sites increased significantly (P
VEGETATION CHARACTERISTICS-WOODY SPECIES. A total of 47 woody species were encountered (Table 2; see Archer 2003). Species richness in age classes from most recently clear-cut to the oldest sites was 36, 31, 37, and 32, respectively. The most abundant and frequent species in all age classes was Rubus sp. Indicator analysis identified Gaylussacia mosieri (P = 0.028) and Carya sp. (P = 0.072) as the only significant indicators of age class. Gaylussacia mosieri occurred most frequently with highest cover in the 30-80 year old age class but occurred infrequently in younger age classes. Carya sp. was an indicator of the 15-18 year age class. Canonical correspondence analysis (CCA) of woody species and environmental variables identify only marginally significant gradients in species abundance and distribution in relation to environmental variables measured (0-3 yr, 8-10 yr, 15-20 yr, % clay, BD, pH, TC, TN) (P = 0.070 first CCA axis; P = 0.090 all CCA axes). The first axis was most highly correlated with BD, canopy cover (Canopy) and 8-10 year age (Table 3; Fig. 1a) while pH and 0-3 year were highly correlated with the second axis and are closely associated in ordination space. Length and direction of arrow in the woody environmental plot illustrate the strength and directions of these relationships (Fig. 1a). The most significant of these was the % canopy cover which captured 17% of the total variance explained by the previously subset variables, followed by BD (16%). These two variables captured 33% of the total variance originally explained by all eight environmental variables. The cumulative variation explained by the first three axes of the species-environment relationship in the CCA was 61.5%. Thus, additional factors not measured contribute to woody species distribution patterns.
Table 2. Mean percent cover +- standard error for woody species in four age classes indicating years post clear-cut and two textural groups (sandy and loamy) with three to five replications for each. Dominant species and indicator species only are included in the table. For complete values for all species found see Archer 2003.
Relationships among age classes can be inferred from relative position within the CCA diagram. The 0-3 year and 8-10 year age classes are located in close association in ordination space indicating species are not well distinguished between these two age classes. Further, the location of the 15-20 year age class near the ordination origin suggests a unique woody vegetation community does not dominate this age class. Many woody species are located close to the origin in the species ordination further illustrating the poor differentiation of woody species in association with any of the environmental variables measured (Fig. 1b). The 30-80 year age class, although not in the analysis, would be placed in close proximity to the “C30″ and “S30″ sites found in Fig. 1c, which corresponds to the placement of the environmental variable canopy; increases in % canopy cover are associated with the older sites. Also of note is the relative placement of the oldest and youngest sites (Fig. 1c). Since they are located furthest from each other and in opposite quadrants of the plot, it can be inferred that species distribution differs most between these sites.
VEGETATION CHARACTERISTICS: HERBACEOUS SPECIES. One hundred fifty- eight herbaceous species were encountered. (Tables 4 and 7). Species richness for age classes from most recently clear-cut to oldest sites was 80, 61, 79, and 71 per 80 m^sup 2^, respectively. Many species were rarely encountered with a total of 57 (approximately 36% of all herbaceous spp.) occurring once in 32 sites, 22 (~ 14%) twice, and 12 (~ 8%) three times. Mean species richness (10 m^sup 2^ per site), total cover, and total forb cover did not differ significantly among age classes. However, mean total grass cover of 36% for 30 year sites was greater than that of 0-3 and 15-20 age classes. Mean grass cover of 22%, 32 %, and 21 % for 0-3, 8-10, and 15-20 year classes, respectively, did not differ significantly. Mean percent bare ground decreased significantly 15 years after clear- cut (35%, 24 %, 7%, and 7 % for 0-3, 8-10, 15-20, and 30 year sites, respectively) and was a significant indicator of the 0-3 age class (P = 0.050). Cover of individual herbaceous species also differed across age classes (Table 4). After removal of a single outlier (site with low sand content 52%), indicator analysis identified several species representative of each of the four age classes. Analysis based on 31 sites with % sand ranging from 67-91% identified Cyperus croceus (P = 0.034) and Bulbostylis barbata (P = 0.0630) as significant indicators of the 0-3 age class. Andropogon virginicus var. virginicus (P = 0.002), Dichanthelium sp. (P = 0.012), unidentified moss (P = 0.008) and Sporobolus junceus (P = 0.044) were significant indicators of the 8-10 year age class. Andropogon virginicus var. virginicus occurred almost exclusively in the 8-10 year age class. Desmodium sp. (P = 0.034), Pityopsis sp. (P = 0.020) and Tridens flavus (P = 0.048) were indicators of 15-20 year class. Andropogon temarius (P = 0.028), Schizachyrium scoparium (P = 0.011), Hier actum sp. (P = 0.024), and Rhynchosia tomentosa (P = 0.088) were indicators of 30-80 year sites. Schizachyrium scoparium and Andropogon ternarius are difficult to differentiate in field sampling when floral parts are unavailable. Therefore, values for these two species when not differentiated as either, they were summed. Indicator analysis found this complex is a significant indicator of the 30-80 year age class (P = 0.019).
Table 3. Weighted correlation matrix for species axes, environmental axes, and environmental variables for woody species, using average measures for the 32 sites.
Because of the large number of rarely encountered species, CCA of herbaceous species and environmental variables (age, pH, % canopy cover, total N, total C, % clay, and bulk density) was performed with and without down-weighting. Both analyses showed overall significance between the environmental variables and species composition (all species P = 0.045; down weighted P = 0.010). In comparison to the woody ordination plots (Figs, 1a-c), herbaceous species (Fig. 2b) show many more species towards the outer edges of the ordination diagram indicating that environmental variables had a greater effect on the distribution of herbaceous species than they did on the woody species.
For both CCA analyses, the cumulative variation explained by the first three axes of the species-environment relationship was less than 40%. The high correlations between the environmental and species axes do indicate, however, that the environmental variables reasonably explained the first two ordination axes (Table 5, Figs. 1a and 2a).
In comparison to woody ordination plots (Figs, 1a-c), the length and direction of arrows associated with various age classes (Fig. 2a), occurrence of many more herbaceous species towards the outer edges of species plots (Fig. 2b), better clustering of similar age plots, and segregation of dissimilar aged plots (Fig. 2c) in the herbaceous plots suggest age since clear-cut was more influential in herbaceous species distribution. Forward selection and unrestricted Monte Carlo permutation tests indicated three (8-10 yr, 0-3 yr, and BD) of the nine environmental variables previously selected made statistically significant contributions to explaining the variance in the herbaceous vegetation data. The most significant of these was the 8-10 year which captured 20% of the total variance explained by the previously subset variables, followed by 0-3 year (16%) and bulk density (13%). These three variables thus captured 49% of the total variance originally explained by aU nine environmental variables. The first CCA axis separated 8-10 year sites from all other sites while the second axis separated 0-3 year sites for the most part from older sites. Increased bulk density was closely related to 0-3 year age class.
FIG. 1. Woody species: (a) Canonical correspondence analysis (CCA) ordination plot showing the location, length, and direction of edaphic variables, (b) CCA ordination plot showing the species occurrence in relation to edaphic variables. Code names for the species are located in Table 2. (c) CCA ordination plot showing the approximate locations of sample sites. Sites are coded with c = loamy, s = sandy, 0 = 0-3 yr, 8 = 8-10 yr, 15 = 15-20 yr, and 30 = 30-80 yr.
Discussion. In our study, most of the edaphic variables measured did not differ significantly with stand age which is consistent with other published studies. Soil properties were similar between Appalachian hardwood stands 20 and 80 years post clear-cut (Gilliam and Turrill 1993). Soil pH, texture, Ca, and Mg was comparable between uncut and 50 year post clear-cut sugar maple sites in Michigan (Albert and Barnes 1987). However, as predicted, bulk density (BD) decreased from the 0-3 to the 30-80 year sites. Also, BD was significantly related to soil texture with a much greater decrease in BD on sandy than loamy sites as age since clear-cut increased. A relationship between BD and soil texture has been found in other studies (Will et al. 2002) and recovery to pre-harvest BD has been shown to differ with respect to soil texture. Much higher BD was found in sandy loam soils compared to clay soils after multiple pass attempts of skidder tires (Koger et al. 1985). Clay soils that swell and shrink may partially recover with subsequent wetting and drying (Barzegar et al. 1995), whereas recovery in sandy soils can be slow or even non-existent (Greacen and Sands 1980). However, a study in Colorado forests reported variation in soil texture, but no significant effects of harvest on bulk density (Whitecotton et al. 2000).
Table 4. Mean aerial cover (%) +- standard error for herbaceous species in four age classes indicating years after clear-cut with three to five replications. Dominant and indicator species only are included in the table. For complete values of all species see Archer 2003.
FIG. 2. Herbaceous species: (a) Canonical correspondence analysis (CCA) ordination plot showing the location, length, and direction of edaphic variables, (b) CCA ordination plot showing the species occurrence in relation to edaphic variables. Note that some species located near the origin of the plot were left out for ease of viewing. Code names for the species are located in Table 4. (c) CCA ordination plot showing the approximate locations of sample sites. Sites are coded with c = loamy, s = sandy, 0 = 0-3 yr, 8 = 8-10 yr, 15 = 15-20 yr, and 30 = 30-80 yr. Since plant growth can be inhibited by increased BD and the clay content of a soil affects the degree to which the soil can be compacted, we predicted that the texture of the soil would affect plant assemblages. Our analysis indicated that the distribution and abundance of both understory woody and herbaceous species were influenced by BD. Our sites lacked relief, and while impacts of silviculture (time since clear-cut, % canopy cover, and BD) were more important than soil texture, the potential interacting effect of compaction (BD) and soil texture may have contributed to changes in woody and herbaceous species distribution and root growth in the upper soil layers. Cowell (1995) found that woody species in Piedmont forests were influenced most by topography and then by the amount of sand fraction in the surface soil.
Our results also suggest composition and cover of herbaceous species are more indicative of recovery time after clear-cutting than woody species. In a similar study to ours, Conde et al. (1983) found that of the woody species only Rubus spp. and Hypericum spp. increased in abundance with pine removal although their study only followed recovery for two years. Herbaceous species may be more sensitive than trees and shrubs to local edaphic variation (Drewa et al. 2002), and thus possibly to disturbances that alter soil characteristics. Generally, compared to herbaceous species, woody species are more broadly distributed, animal dispersed, and have underground root systems that facilitate rapid aboveground regrowth and vegetative spread. This allows greater adaptation to disturbance and thus less responsiveness to change (Olson and Piatt 1995, Gile et al. 1997).
Table 5. Weighted correlation matrix for species axes, environmental axes, and environmental variables for herbaceous species, with down-weighting performed by CANOCO.
Differences in composition and cover of herbaceous species were apparent among the 0-3 year, 8-10 year, and older age classes. However, to a great extent herbaceous species composition and cover was similar in the two oldest age classes. Others have reported little change in herbaceous species composition after 20 years of recovery from disturbance because of quick re-colonization by groundcover species (Davison and Forman 1982, Kochenderfer and Wendel 1983, Gilliam and Turrill 1993). However, similar studies to ours report the absence or near absence of some herbaceous species even after more than 65 years of recovery from disturbance (Provencher et al. 2001, Kirkman et al. 2004).
Table 6. Woody species other than dominant or indicator species found in this study and their corresponding code. For values for these species see Archer 2003.
We predicted certain species known to be particularly sensitive to disturbance would be likely to increase over recovery time. Several recent studies have noted the response of individual herbaceous species to various disturbance types in southern forest. Hedman et al. (2000) compared herbaceous composition among southeast forests of differing ages, management strategies, and land-use histories. Provencher et al. (2001) conducted a similar chronosequence study to ours in Northwest Florida. Dale et al. (2002) also working at Fort Benning reported on understory species response to disturbance associated with military activity. Kirkman et al. (2004) identified species with low re-colonization potential by comparing ground-cover species of a 64-year old slash pine plantation with a natural longleaf pine savanna (reference site). Our findings supports these studies as to the response of many of the species identified but further distinguished species associated with sites representing age classes post clearcut. Comparison of the response of individual herbaceous species to disturbance in our study and those mentioned above can be found in Table 8. Of the nine herbaceous species found exclusively on reference sites (no military activity) by Dale et al. (2002), five species were also identified on our sites. However, all but one of these herbaceous species was found with recent clear-cutting.
Table 7. Herbaceous species other than dominant or indicator species also found in this study and their corresponding code. For values of these species see Archer 2003.
We wanted to identify species that are indicators of assemblages of plants characterizing increasing length of time following clearcutting. Generally, only a few species stand out as possible indicators of an age class post clear-cut. Bulbostylis barbata was identified as indicator of younger sites. Several herbaceous species of Bulbostylis and Eupatorium have been found to be absent from mature forest older than 55 years (Greenberg et al. 1995). In agreement with our findings, Andropogon virginicus and many species of Dichanthelium and Aristida have been found to be more abundant soon after a disturbance, followed by a slow decrease in frequency and abundance over time (Lemon 1949, Grelen 1962, Greenberg et al. 1995). Provencher et al. (2001) grouped Aristida spp., Andropogon spp., and 11 species of Dichanthelium together as concomitant with soil disturbance and decreasing over recovery time.
Table 8. Recent studies in southeastern pine forest which compared herbaceous species occurrence and abundance in recently logged sites (0-10 yrs) and/or with high intensity of soil disturbance to sites without recent logging (15-80 yrs) and/or with low intensity of soil disturbance. Species listed were identified as disturbance indicators.
We found Schizachyrium scoparium and Andropogon ternarius were indicators of 30-80 year sites. Schizachyrium scoparium is considered a mid-late successional plant throughout its range. While these two species occurred in all age classes, both increased with recovery time and had higher frequency and cover values on the oldest sites. Provencher et al. (2001) also found Schizachyrium scoparium and Andropogon ternarius to be associated with mid- to late-successional stages, increased with recovery time and peaked 50 years after disturbance. Further, Schizachyrium scoparium and Rhynchospora grayi Kunth were the only species they identified as indicating a recovered condition and perhaps high quality groundcover.
Species richness did not differ among age classes for either woody or herbaceous species, while species distribution and abundance did. Studies on disturbance report increases, decreases, and no change in species richness (Conde et al. 1983, Halpern and Spies 1995, Roberts and Gilliam 1995, Wender et al. 1999, Ford et al. 2000). At least in Fort Benning, it is clear that individual species rather than diversity will be more informative for revealing landscape recovery following clear-cuts.
It is critical to note however, that indicator species found to be useful in the Fort Benning landscape may not be generalized. A regional synthesis of longleaf pine vegetation revealed high variation in understory vegetation structure and composition due to fire regime, soil moisture, soil texture, geographic location, and anthropogenic disturbances (Rodgers and Provencher 1999). Further, sandhills may have highly heterogeneous soil depth and moisture, possibly contributing to plant species richness and community composition (Provencher et al. 1997). Thus, further studies are necessary in order to verify the success of potential indicators outside of this localized area. Rodgers and Provencher (1999) suggest the use of controlled studies to adequately assess historical species distributions and recovery pathways following disturbance.
1 Funding was provided by the Strategic Environmental, Research and Development Program (SERDP), Ecosystem Management Program (SEMP), Project CS-1114A.
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Jessica K. Archer
Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611
Deborah L. Miller2 3
Department of WildUfe Ecology and Conservation, University of Florida West Florida Research and
Education Center, 5988 Hwy 90, Bldg 4900, Milton, FL 32583
George W. Tanner
Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611
2 Jeff Mullahey, Jack Putz, and Mack Thetford provided insightful comments on early drafts of the manuscript. Bill DeBusk aided with the experimental design. Kenneth Portier assisted with the statistical analysis. Mica Schneider, Cynthia WiIkerson, Jason Tritt, Dwight Dindial, Joe Prenger, and Jennifer Donze all provided assistance with fieldwork. Hugh Westbury, Mark Byrd, Scott Long, Darrell Odom, and Bob Larimore provided logistical support at Fort Benning. David Hall assisted with taxonomic identification.
3 Author for correspondence. Email. email@example.com
Received for publication June 1, 2006, and in revised form September 11, 2007.
Copyright Torrey Botanical Society Oct-Dec 2007
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