Development of ecosystem structure and function on reforested surface-mined lands in the Central Appalachian Coal Basin of the United States

Published Date
November 2015, Volume 46, Issue 5, pp 683–702

Title 

Development of ecosystem structure and function on reforested surface-mined lands in the Central Appalachian Coal Basin of the United States

• Author 

• Bethany N. Avera, 
• Brian D. Strahm, 
• James A  Burger, 
• Carl E. Zipper

• Abstract 

• Surface mining in the Appalachian Coal Basin drastically disturbs the landscape. Post-mining reforestation efforts have reached reliable tree survivability and growth; however, it is unclear whether these reforestation efforts also restore the ecological functions associated with the native forest ecosystem. The objectives of this study were to quantify the rate at which certain key ecosystem functions return to the landscape, and to relate the development of those functions to structural attributes of the ecosystem. A chronosequence of four reclaimed and reforested stands (ages 5, 11, 21 and 30 years) and unmined reference stands representing pre-mining conditions, were identified on the Appalachian Plateau in southwestern Virginia. Total soil nitrogen (N) and component (mineral soil, forest floor, root, and aboveground biomass) ecosystem carbon (C) pools were quantified. Throughout the growing season, monthly samples for soil gas fluxes [i.e., carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4)], available inorganic-N [nitrate (NO3 −) and ammonium (NH4 +)], and total and active microbial biomass were measured. As expected, soil organic C (SOC) and total ecosystem C returned to the mined landscape, although at levels still less than half of the unmined reference after 30 years. Ecosystem C accumulation was significantly correlated with soil N (r = 0.80; p = 0.0003) as well as total and active microbial biomass (r = 0.92; p = <0.0001 and r = 0.86; p = <0.0001, respectively). Surprisingly, available inorganic-N and gas fluxes of CO2 and N2O showed no significant differences among any of the mined and unmined stands; however, the reforested mined soils showed a significantly diminished capacity for CH4 uptake, where upland soils typically represent the largest global biogenic sink of atmospheric CH4. Thus, although many ecosystem components (e.g., forest and microbial biomass) and functions (e.g., N cycling), rapidly returned to the reclaimed landscape, some critical ecosystem functions (e.g., methanotrophy) exhibited a fundamentally different rate of return, if present at all. Our results indicate that reforestation of native hardwoods on reclaimed surface mined lands is largely successful at restoring many ecosystem functions, but that certain functional attributes are decoupled from the observed redevelopment of ecosystem structure. Thus, reforestation and forest ecosystem restoration are not necessarily the same thing, and a better understanding of potential disconnects between the two concepts can be critical in guiding both the science and the practice into the future.

• Introduction 

• Forests play a vital role in maintaining environmental quality in many world regions. They sequester and store C, cycle nutrients, provide habitat for diverse flora and fauna, produce wood and non-wood products, provide aesthetic value, improve soil quality, and modulate water quantity and quality, among a myriad of other ecosystem services (FAO 2010; Burger and Zipper 2011; Zipper et al. 2011). Within the temperate deciduous forest biome of the eastern US, the Appalachian mixed mesophytic forest ecoregion reaches from northwest Alabama to southwestern Pennsylvania (Bailey 1983; Omernik 1987). The Appalachian mixed mesophytic forest is among the most biologically diverse temperate forest ecosystem in the world with up to 30 distinct canopy tree species and a rich understory composition of plant, animal and microbial communities (Braun 1950; Muller 1982; McEwan et al. 2005).
  Geologically, the Appalachian mixed mesophytic forest region occurs within the Appalachian Coal Basin, which is the predominant coal resource in the eastern US (Ruppert 2001). The resultant surface coal mining has deforested and disturbed over 1.1 million ha within the central Appalachian region (Bernhardt and Palmer 2011). Appalachian surface mining uses explosives to remove geologic materials, called overburden, that overlie coal seams. Once the overburden is removed, coal is extracted. On large mines, the iterative process of removing overburden and coal may proceed vertically downward to remove multiple coal seams. The overburden is then used for reclamation to reconstruct the landscape to approximate original contour (AOC) to the extent feasible, unless an AOC variance has otherwise been obtained (Zipper et al. 1989). Coal mine reclamation in the US is regulated by a federal law, the Surface Mine Control and Reclamation Act (SMCRA), which requires soil salvage and replacement unless a variance from this requirement is also obtained. In Appalachia, miners are often able to receive such variances; hence, blasted overburden is often used as a topsoil substitute, essentially inverting the soil-geologic system as a starting point for reforestation.
  As a result of this reclamation process, Appalachian mine soils are often very rocky, with coarse fragments frequently exceeding 50 % (Torbert and Burger 2000). The overlapping rocks can create bridging voids that alter the hydrologic flow through the soil; potentially causing lower water storage capacity and differential settling (Sencindiver and Ammons 2000). Soil compaction, often to ensure slope stability during reclamation, causes mine soils to have high bulk densities, a condition that inhibits forest tree establishment and growth, likely due to effects such as lower soil aeration, root penetration, and water infiltration (Davidson et al. 1984; Torbert et al. 1988; Andrews et al. 1998; Jones et al. 2005). Furthermore, reclamation practices often emphasize rapid establishment of fast-growing vegetative cover, that depresses survival and growth of planted trees (Torbert and Burger 2000; Angel et al. 2005). Such challenges are not unique to the Appalachian region, however. Globally, studies cite challenges such as soil toxicity, nutrient deficiencies, low organic matter content, low water holding capacity, and oxidation of acid-forming materials, as common issues in the post-mining reclamation process (e.g., Hüttl and Weber 2001; Seo et al. 2008).
  Methods have been developed to overcome many of these challenges and allow for successful reforestation of mine sites. Both in the US and abroad, reforestation efforts have largely focused on site preparation, topsoil replacement practices, and species-specific suitability for growth and survival based on local mine site conditions (Tacey and Glossop 1980; Knowles and Parrotta 1995; Parrotta and Knowles 2000; Bradshaw and Hüttl 2001). Similarly, irrespective of region, reclamation scientists routinely evaluate plant performance (e.g., establishment, survival, growth, foliar nutrient content) to evaluate reclamation success (Parrotta and Knowles 2000; Koch and Samsa 2007; Seo et al. 2008; Pietrzykowski et al. 2013). Specifically, in the Appalachian region of the US, efforts have focused on the establishment and productivity of a diverse native hardwood forest representative of the pre-mining condition. Site condition and species trials have been conducted within the Appalachian region of the US to determine the most successful methods to restore native hardwood species to the post-mining landscape (Torbert et al. 1985; Burger et al. 2005a, b; Burger et al. 2008; Burger and Fannon 2009). As a result, scientists and practitioners in the Appalachian region have developed the Forestry Reclamation Approach (FRA), a set of best management practices to guide the reforestation of Appalachian mine sites with native hardwood trees (Burger et al. 2005a, b; Zipper et al. 2011).
  While these studies have been critical in determining the best site preparation methods, topsoil substitutes or amendments, and tree species to promote reforestation, an underlying assumption in many mined-land reforestation efforts is that the return of trees (e.g., species composition and density, components of ecosystem structure) will lead to the development of a forest ecosystem that mimics the functionality of the local unmined forest ecosystem. Even where such relationships are tested, they often focus too heavily on either aboveground (e.g., Parrotta and Knowles 2000) or belowground (e.g., Hüttl and Weber 2001; Chodak et al. 2009; Chodak and Niklińska 2010) aspects of the ecosystem as a focal point for evaluating the efficacy of reclamation. To this end, the Australian Centre for Mining Environmental Research (previously the Australian Centre for Minesite Rehabilitation Research) has developed the Ecosystem Function Analysis (EFA) as a tool to evaluate mine-site reclamation success more holistically. The EFA focuses on landscape functions analysis (soil resource condition), vegetation dynamics (species composition, species similarity to an analogous unmined site, presence and growth of target species) and habitat complexity (Bell 2001); however only infers ecosystem function based on more readily observable properties. While it is impossible to characterize the full suite of ecosystem properties and processes in any study, a more robust approach with multiple elements of both above- and belowground structure and function is essential in evaluating the restoration of an ecosystem. Thus, in this study we investigated the development of a select suite of critical forest ecosystem structural (e.g., aboveground and microbial biomass) and functional (e.g., C sequestration, N cycling, biosphere-atmosphere regulation) attributes on a chronosequence of reforested mine sites (ages 5, 11, 21, and 30 years) as well as a reference unmined forest. Our goals were to characterize the rates of return of these properties and processes, and determine if commonly measured metrics of forest structure (e.g., biomass, stem density) adequately represent the return of selected key ecosystem functions (e.g., elemental cycling).

Materials and Methods

Study location

This study was conducted at the 450 ha Powell River Project, a cooperative research and education center on the Appalachian Plateau physiographic province in Wise County, Virginia. Specifically, this study focused on a chronosequence of reclaimed and reforested mined lands across four age classes (ages 5, 11, 21, and 30 years), as well as unmined reference areas within the Powell River Project boundary that had not been significantly disturbed since logging occurred commonly throughout the region in the early 1900s. Local vegetation is of the Appalachian mixed mesophytic forest ecotype on unmined lands. Local climate is characterized by a mean annual precipitation of 1230 mm with the lowest mean monthly temperatures occurring in January (5.4 °C), and the warmest temperatures occurring in July and August (27 °C).

Each reclamation age class in the chronosequence leveraged previously established, independent, replicated studies that are part of the long history of reclamation research at the Powell River Project (Torbert et al. 1985; Burger et al. 2008; Burger and Fannon 2009; Fields-Johnson et al. 2012 for the 30-, 21-, 11-, and 5-year-old treatments, respectively). In all cases, the stands for the chronosequence age classes occurred on areas that were surface-mined and reclaimed following SMCRA requirements. Reclamation practices included recontouring slopes and using a mix of weathered and unweathered sandstone and siltstone overburden (with some shale and coal fragments) as topsoil substitutes; although the 21- and 30-year-old sites did contain less weathered material than more contemporary topsoil substitutes. In selecting studies/treatments to use for this chronosequence, other relevant site characteristics (e.g., slope, initial revegetation seed mix, grading/compaction) were constrained whenever possible (Tables 1, 2). Each of the reclaimed stands was intentionally planted with native hardwoods following reclamation (Table 2). Detailed information on the land use history of the unmined stands was not available, although the area was commonly logged in the early 1900s.

Table 1

Site characteristics of chronosequence stands at time of sampling including mean slope (%), mean stem density of all woody vegetation categorized relative to a 2 cm diameter at breast height (dbh), and mean basal area given for all stems >2.5 cm at dbh

Stand age (year)	Aspect	Slope (%)	Stem density		Basal area (m2 ha−1)
						<2.5 cm at dbh (stems ha−1)	>2.5 cm at dbh* (stems ha−1)
5	S	63 ± 0.6	2460 ± 1420	255 ± 157	0.51 ± 0.1
11	SW, SE, NE	32 ± 4.9	722 ± 417	934 ± 539	19.7 ± 2.6
21	S, W	28 ± 2.8	1060 ± 613	2250 ± 1300	20.0 ± 1.5
30	NW	33 ± 2.0	1440 ± 833	1870 ± 1080	23.2 ± 3.8
Unmined	W, SE	24 ± 2.3	340 ± 196	976 ± 564	36.8 ± 7.9

Variance given as ± one SE. * Footnote gives list of species contributing to >2.5 cm stem density across all replicates of each stand age class

Age 5: Acer saccharum, Alnus glutinosa, Cornus florida, Elaeagnus angustifolia, Fraxinus americana, Juglans nigra, Liriodendron tulipifera, Prunus serotina, Pinus stobus, Quercus alba, Quercus prinus, Quercus rubra; Age 11: Acer saccharum, Fraxinus americana, Liriodendron tulipifera, Pinus strobus, Quercus alba, Quercus prinus, Quercus rubra, Robinia pseudoacacia; Age 21: Acer rubrum, Acer saccharum, Amelanchier arborea, Malus coronaria, Platanus occidentalis, Populus deltoides, Prunus serotina, Quercus rubra, Robinia pseudoacacia; Age 30: Acer rubrum, Acer saccharum, Alnus glutinosa, Cercis canadensis, Liriodendron tulipifera, Platanus occidentalis, Quercus prinus, Robinia pseudoacacia; Unmined: Acer saccharum, Aesculus hippocastanum, Liriodendron tulipifera, Quercus prinus, Rhododendron maximum

Table 2

Reclamation treatments and planted species of each chronosequence age

Stand age	Reclamation treatment	Hydroseed cover crop mix	Planted tree species
5	Loose-graded Hydroseeded Fertilized with 22 kg ha−1 N, 68 kg ha−1 P and 18 kg ha−1 K 1680 kg ha−1wood cellulose fiber	Rye grain (Secale cereal) Orchard grass (Dactylis glomerata) Perennial ryegrass (Lolium perenne) Korean lespedeza (Lespedeza cuneata) Birdsfoot trefoil (Lotus corniculatus) White (Ladino) clover (Trifolium repens) Redtop (Agrostis gigantea) Weeping lovegrass (Eragrostis curvula)	Crop trees: white ash (Fraxinus americana), white oak (Quercus alba), sugar maple (Acer saccharum), black cherry (Prunus serotina), red oak (Quercus rubra), chestnut oak (Quercus prinus), black oak (Quercus velutina), yellow poplar (Liriodendron tulipifera), and white pine (Pinus strobus) Wildlife/Nurse trees: Gray dogwood (Cornus racemosa), red mulberry (Morus rubra) Redbud (Cercis canadensis), and shagbark hickory (Carya ovata)
11	Lightly graded and left uncompacted Hydroseeded	Orchard grass Birdsfoot trefoil Timothy grass (Phleum pratense) Red clover (Trifolium pratense L.)	Crop trees: white ash, white oak, sugar maple, red oak, chestnut oak, yellow poplar, and white pine Wildlife/Nurse trees: Silky dogwood (Cornus amomum), crab apple (Malus spp.), and bristly locust (Robinia hispida)
21	Smooth-graded and tracked Hydroseeded	Kentucky-31 tall fescue (Festuca arundinaceaSchreb.) Orchard grass Redtop Perennial ryegrass Red clover Serecia lespedeza	Crop trees: red oak, white oak, white ash), black walnut (Juglans nigra), eastern cottonwood (Populus deltoidesBartram ex Marsh), American sycamore (Platanus occidentalis), and yellow poplar
30	Smooth-graded and tracked Hydroseeded Fertilized with 560 kg ha−1 of 10–20–20 (N–P–K) 1681 kg ha−1wood fiber mulch	Kentucky-31 tall fescue Birdsfoot trefoil Redtop White (Ladino) clover Annual rye (Lolium multiflorum)	Crop trees: black locust (Robinia psuedoaccacia), European black alder (Alnus glutinosa), black walnut, chestnut oak, Chinese chestnut (Castanea mollissima), yellow poplar, American sycamore, eastern cottonwood, and red oak

Site and stand characterization

Within the plot boundaries of three replicate treatments from the original studies (Torbert et al. 1985; Burger et al. 2008; Burger and Fannon 2009; Fields-Johnson et al. 2012), one 5 m radius circular plot was established for site and stand characterization. Sample plots were randomly located inside of a 5 m buffer from the original plot/stand boundary using exclusion criteria (e.g., atypical canopy cover, noticeably different slope, uncharacteristic understory vegetation) to avoid areas that were not representative of the stand as a whole.

In the spring of 2013, three quantitative soil pits were excavated 5 m from each plot center (Vadeboncoeur et al. 2012), at 0°, 120° and 240° relative to an azimuth of 0°. Within a 25 cm × 25 cm sampling frame, the soil O horizon was clipped and removed. Mineral soils were excavated by depth increment (0–5, 5–10, and 10–25 cm). A flat-edged tool was used to scrape the sides and bottom of the pits to the precise dimensions. Soil was brushed off of large rocks excavated from the pit and the rocks were left in the field. Likewise, roots that were too thick to be cut with a small folding saw were brushed off and left in situ, potentially negatively biasing subsequent root biomass estimations. Collected material was oven-dried at 60 °C. Dried mineral soils were gently broken up manually and passed through a 2-mm sieve to remove rocks, roots, and other debris. Of the >2 mm fraction, roots and coal were separated for further analysis. Volumetric density (VD) of the fine, soil fraction (<2 mm) was calculated for each depth increment by dividing the dry mass of the fine fraction by the total volume of the excavated depth increment.

Mineral soil organic C (SOC) and mineral soil N concentrations were quantified at each depth increment using an Elementar vario Micro Cube elemental analyzer connected to an Elementar IsoPrime100 isotope ratio mass spectrophotometer (IRMS) (Elementar, Hanau, Germany). A two end-member mixing model, based on the different δ13C isotopic compositions of geogenic (i.e., coal and coal-like forms) and pedogenic (i.e., more recent biogenic) C sources, was used to correct total organic C (TOC) concentrations, as geogenic organic C (GOC) in these soil and overburden materials are derived from coal and are not considered to be a portion of the cycling SOC pool. Following Acton et al. (2011), O horizon and coal were used as end-members in the analysis. Coal samples were collected within each plot from the surface of the forest floor at the time of soil sampling. O horizon samples were pre-ground using a Wiley Mill (Thomas Scientific Model 4 Miley Mill, Swedesboro, NJ) and coal samples were ground using a mortar and pestle. Coal samples from the three plots within each stand were homogenized as they were ground. A subsample of each of the mineral soil, forest floor, and coal samples were individually ground to a fine powder using a Retsch MM-200 ball mill (Retsch, Haan, Germany). Following the method of Harris et al. (2001), finely ground soil samples were weighed into silver (Ag) capsules and were fumigated with 100 mL of 12 M hydrochloric acid (HCl) in a vacuum desiccator for 24 h to remove inorganic-C (i.e., carbonates) that would otherwise obscure the soil δ13C signature. These soil samples were then dried at 60 °C for 48 h to remove any residual chloride prior to combustion (Harris et al. 2001). Silver capsules were closed and packaged within tin (Sn) capsules to preserve sample integrity. O horizon and coal samples were weighed directly into Sn capsules. The isotope mass balance determination of SOC concentration was calculated using the following equations:

δ13CTOC=PSOC×δ13CSOC+PGOC×δ13CGOC$${\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">\delta </span></span>}}^{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">13</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">T</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">P</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">S</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">\delta </span></span>}}^{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">13</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">S</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">P</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">G</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">\delta </span></span>}}^{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">13</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">G</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}$$

(1)

1=PSOC+PGOC$$\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">1</span></span>}{\text{<span style="font-family: -webkit-standard;"><span style="background-color: rgba(255, 255, 255, 0);">P</span></span>}}_{\text{<span style="font-family: -webkit-standard; font-size: small;"><span style="background-color: rgba(255, 255, 255, 0);">SOC</span></span>}}{\text{<span style="font-family: -webkit-standard;"><span style="background-color: rgba(255, 255, 255, 0);">P</span></span>}}_{\text{<span style="font-family: -webkit-standard; font-size: small;"><span style="background-color: rgba(255, 255, 255, 0);">GOC</span></span>}}$$

(2)

%CSOC=PSOC×%CTOC$$\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">\%</span></span>}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">S</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">P</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">S</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">\%</span></span>}{\mathrm{<span\; style="font-family:\; -webkit-standard;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}_{\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">T</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">O</span></span>}\mathrm{<span\; style="font-family:\; -webkit-standard;\; font-size:\; small;"><span\; style="background-color:\; rgba(255,\; 255,\; 255,\; 0);">C</span></span>}}$$

(3)

where δ 13 C TOC is the isotopic value of the total organic carbon (TOC) in each sample (i.e., air-dried, sieved, untreated) and δ 13 C SOC and δ 13 C GOC are the delta values (‰) of the soil/pedogenic organic carbon (SOC) and geogenic/coal organic carbon (GOC) stable isotope end-members, respectively. P SOC and P GOC represent the proportional amounts of TOC accounted for by the soil/pedogenic and geogenic/coal sources of organic carbon, respectively. Concentrations of SOC (%C SOC ) were calculated by multiplying the measured concentration of TOC (%C TOC ) by the determined proportion of SOC (P SOC ). Soil C and N content (Mg ha−1) was then calculated from corrected carbon concentrations using measured VD for each depth increment.

All living trees within the 5 m plot radius and having a diameter greater than 2.54 cm at breast height (1.4 m) were measured for diameter at breast height (DBH). For woody plants smaller than these criteria, ground line diameter (GLD) was recorded. Basal area was estimated as the sum of the cross-sectional area for stems at DBH (when diameter >2.54 cm at DBH) and at GLD (for stems when diameter <2.54 cm at DBH). Aboveground woody biomass (Mg oven-dry weight ha−1) for each plot was calculated using region- and species-specific DBH- and GLD-based allometric equations (Bickelhaupt et al. 1973; Day and Monk 1974; MacLean and Wein 1976; Brenneman et al. 1978; Ker 1984; Williams and McClenahen 1984; Clark and Schroeder 1986; Elliot and Clinton 1993; Ter-Mikaelian and Korzukhin 1997; Jenkins et al. 2004). Aboveground woody biomass was then converted to aboveground biomass C (expressed as Tree C) content by multiplying biomass by a factor of 0.5.

Soil dynamics

Measurements of total and active microbial biomass, inorganic N availability, and gas fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were taken monthly from spring (April/May) to fall (October/November) of 2013. All monthly samples were duplicated within each plot to account for spatial heterogeneity, but averaged prior to statistical analysis to provide one value per replicate plot per sampling interval. Collars for gas flux measurements were installed in two random locations within each plot and all other monthly samples were co-located with these locations. Mineral soil samples (0–10 cm) were collected for total and active microbial biomass C and each sample was a composite of three locations adjacent to the corresponding gas flux measurement location. Soil samples for microbial biomass and substrate induced respiration were transported on ice and stored at 4 °C prior to analysis. Each sample was homogenized and the rocks and roots were manually removed and discarded. Microbial biomass assays were completed within 1 week, using field moist soils.

Chloroform fumigation extractions were used to quantify the total microbial biomass C (i.e., chloroform-sensitive fraction; Wardle and Ghani 1995), following the method of Beck et al. (1997). Two 25 g replicates of field-moist soil were weighed out from each 10 cm mineral soil sample. One replicate of each soil sample was fumigated with chloroform (CHCl3) in a vacuum desiccator for 24 h. The second replicate was not fumigated. Post fumigation, both replicates were shaken on the reciprocal shaker with 100 mL 0.5 M K2SO4 for 1 h at 200 revolutions m−1. After settling, each K2SO4-extract was then filtered through Whatman #2 filter paper and the filtrate was collected in scintillation vials. Filtrates were sent to the North Carolina State University Environmental and Agricultural Testing Service for soluble TOC analysis on a Shimadzu TOC/TN Analyzer (Shimadzu Scientific Instruments, Inc., Columbia, Maryland). Total microbial biomass C (mg C g−1 dry soil) was then calculated following Parkinson and Paul (1982) and Beck et al. (1997) using a correction factor of 0.45.

Substrate-induced respiration (SIR) was used to quantify the metabolically active (i.e., glucose-responsive) fraction of the microbial biomass C (Wardle and Ghani 1995). Ten g of field moist soil were placed into glass vials. d-Glucose (Dextrose anhydrous, F.W. 180.16) solution of 1 g glucose g−1 soil was dissolved in deionized water (DI H2O) and 20 mL glucose solution was added to each sample. Vials were then sealed with rubber septa (West and Sparling 1986; Fierer et al. 2003). Samples were incubated at room temperature (~20–25 °C) while mixed on a reciprocal shaker at 200 rev min−1. Headspace CO2 concentrations were determined using a Li-Cor LI-6250 CO2 Analyzer (LI-COR Biosciences Inc., Lincoln, Nebraska) immediately after all vials were capped and at subsequent 1 h intervals for the duration of the 4 h incubation. The CO2-C flux rate was calculated using the slope of CO2-C concentrations evolved over the 4 h incubation period. To convert to final units of μg CO2-C g−1 soil h−1, field moist mass was converted to dry mass equivalent using measured gravimetric water content (Parkinson and Paul 1982; Bailey et al. 2002).

Two ion exchange membranes (IEMs) (GE Osmonics, Inc., Trevose, PA), one anion and one cation, were used in situ to quantify nitrate (NO3 −) and ammonium (NH4 +) (Subler et al. 1995; Bowatte et al. 2008; Duran et al. 2013). IEMs were cut to a size of 50 cm2 (5 × 10 cm) and a 7 mm diameter hole was punched at the top. IEMs were submerged in a 1 M solution of sodium chloride (NaCl) to fill all exchange sites with readily exchangeable sodium (Na+) or chloride (Cl−) ions. Anion and cation membranes were stored in separate containers, each in 1 M NaCl solution at 4 °C prior to field deployment. Immediately prior to field deployment, IEMs were thoroughly rinsed with DI H2O. The first sets of IEMs were installed in April and IEMs were replaced approximately every 4 weeks, with the exact duration of field deployment recorded. To install in the field, a narrow slit was cut into the soil at a 45° angle using a soil knife. Each IEM pair (anion and cation) were gently placed in the slit with no wrinkles or overlaps between them. Nylon string was tied to the hole punched in each IEM and to a pin flag to identify the location of the membranes. Upon removal from the field each IEM pair was stored in its own small plastic bag and transported on ice back to Virginia Tech. IEMs were stored at 4 °C for less than 7 days until inorganic-N was extracted. To extract inorganic-N, IEMs were gently rinsed with DI H2O to remove soil particles from the surface. Each IEM was individually submerged in 50 mL of 1 M potassium chloride (KCl) and placed on a reciprocal shaker for 1 h at 200 rev m−1 (Subler et al. 1995; Hangs et al. 2003). Extracts were analyzed for NO3 −-N and NH4 +-N concentration on a TrAAcs 2000 Analytical Console (Bran + Luebbe, Analyser Division, Norderstedt, Germany) connected to an XY2 Auto sampler (SEAL Analytical, Mequan, Wisconsin).

Gas fluxes were measured using vented, non-steady state static chambers (Holland et al. 1999). The collars were installed at least 1 month prior to sampling in order to diminish impacts of soil disturbance associated with installation on the measured gas fluxes. Polyvinyl chloride (PVC) collars with a 23.5 cm diameter were situated approximately 5 cm into the ground, with ~12 cm of height aboveground forming the chamber. Volume calculations were made based on chamber-specific measurements where aboveground chamber height was measured at four points on the inside of each collar. At the time of sampling, the collars were capped to allow gas accumulation (Holland et al. 1999). Four 7 mL samples were taken from each chamber. Each sample was taken using a 30 mL plastic syringe fitted with a stopcock and 21 gauge needle, then ejected into a glass vial that had been sealed with a rubber septa, purged with dinitrogen gas and evacuated. The first sample was taken immediately after capping to establish the gas concentration prior to accumulation. Subsequent samples were taken at approximately 20, 40 and 60 min, with exact times recorded. During each measurement period, soil temperature and moisture were measured immediate adjacent to each plot. Soil temperature (10 cm) was monitored using a digital soil thermometer (Model HI 145, 12 cm probe, Hanna Instruments) and soil moisture was also integrated across 0–12 cm using a HydroSense time domain reflectometry probe (Campbell Scientific).

Concentrations of CO2, CH4 and N2O in each of the soil gas flux samples were analyzed simultaneously using a GC-2010 Gas Chromatograph (Shimadzu Scientific Instruments, Inc., Columbia, Maryland) with an AOC-5000plus Autosampler (Shimadzu Scientific Instruments, Inc., Columbia, Maryland). Following the method of Holland et al. (1999), gas concentrations were converted to mass and corrected for field conditions. A flux rate of each individual gas was calculated based upon the change in concentration over time, relative to the chamber volumes and surface area of the collar.

Statistical analyses

For all statistical analysis, data were first averaged at the plot level (n = 3) for all age stands. Differences in volumetric density, C and N concentrations and pool sizes, and C sequestration rates between the stands were determined by using a one-way analysis of variances (ANOVAs) with R (R Team 2013). For the parameters found to have significant differences (p < 0.1), multiple comparisons were made using Tukey’s HSD post-hoc test (α = 0.1) to identify which age stands were significantly different from each other, calculated with Agricolae R package (R Core Team 2013; de Mendiburu 2014). Non-normally distributed data were transformed when appropriate prior to statistical analysis. Differences between the stands of the monthly measurements were analyzed through ANOVA with repeated measures, paired with Tukey’s HSD post-hoc analysis sliced by time to detect differences between the stands within each month using SAS 9.3 (SAS Institute 2011). For repeated measures ANOVA, all non-normal monthly data was transformed using logarithmic transformations. Correlation coefficients for all parameters using Spearman’s nonparametric rank correlation method with untransformed data were calculated in SAS 9.2 (SAS Institute 2011).

Results

Ecosystem (i.e., cumulative mineral soil, O horizon, root, and aboveground tree) C and total soil N (TN) are accumulating on the mined reforested landscape (Table 3). Overall, C accumulation showed a rapid increase early in stand development that tapered off after the 11-year stands. This trend was most noticeable in tree C which increased from 0.3 ± 0.1 Mg C ha−1 at the 5-year stands to 53.8 ± 8.3 Mg C ha−1 at the 11-year stands. Coincident with these observations, the highest C sequestration rate was measured in the 11-year stands. Total ecosystem C increased from 9.5 ± 1.7 Mg C ha−1 in the 5-year stands to 83.5 ± 16.3 Mg C ha−1 in the 30-year stands; after 30 years, 47 % of the 178.9 Mg C ha−1 of the unmined ecosystem C had returned (Table 3). For all stands other than the 5-year, aboveground tree C was the greatest proportion of ecosystem C, accounting for an average of 73 % of the total ecosystem C. Mineral soil was the second largest pool for these stands, representing 12–16 % of the total ecosystem C for all mined stands and 20 % for the unmined stands. In the 5-year stands, the O horizon accounted for 48 % of the total ecosystem C, mineral soil organic C (SOC) 26 %, and tree C just 3 %.

Table 3

Mean (±SE; n = 3) carbon and nitrogen pools (Mg ha−1) and sequestration rates (Mg ha−1 year−1) for each chronosequence age and ecosystem component

Age	5	11	21	30	Unmined
Component
Soil N pools (Mg ha−1)
O Horizon	0.18 ± 0.02(ab)	0.18 ± 0.02(a)	0.34 ± 0.04(b)	0.21 ± 0.04(ab)	0.21 ± 0.05(ab)
Mineral soil	0.72 ± 0.05(a)	1.09 ± 0.10(ab)	1.37 ± 0.07(bc)	1.73 ± 0.16(bc)	2.54 ± 0.19(c)
Total soil	0.9 ± 0.05(a)	1.26 ± 0.11(ab)	1.72 ± 0.09(bc)	1.94 ± 0.20(c)	2.75 ± 0.23(d)
Ecosystem C pools (Mg ha−1)
Tree	0.27 ± 0.06(a)	53.80 ± 8.28(bc)	48.86 ± 7.20(b)	64.27 ± 13.40(bc)	125.71 ± 37.93(e)
O Horizon	4.57 ± 0.48(a)	5.85 ± 0.41(ab)	9.11 ± 1.05(b)	6.86 ± 1.40(ab)	6.53 ± 1.39(ab)
Mineral soil	2.45 ± 1.16(a)	7.67 ± 0.72(b)	12.24 ± 1.38(b)	9.76 ± 1.75(b)	36.32 ± 0.88(c)
Root	2.22 ± 0.19(a)	1.63 ± 0.24(a)	5.24 ± 0.49(b)	2.64 ± 0.51(a)	10.38 ± 2.92(b)
Total ecosystem	9.51 ± 1.74(a)	68.95 ± 8.15(b)	75.46 ± 5.07(b)	83.53 ± 16.33(b)	178.89 ± 39.71(c)
C Sequestration rate (Mg ha−1 year−1)
Tree	0.05 ± 0.01(a)	4.89 ± 0.75(b)	2.33 ± 0.35(c)	2.14 ± 0.45(c)	–
O Horizon	0.91 ± 0.10(a)	0.53 ± 0.04(b)	0.43 ± 0.05(bc)	0.23 ± 0.05(c)	–
Mineral soil	0.49 ± 0.23(a)	0.70 ± 0.07(a)	0.58 ± 0.07(a)	0.33 ± 0.06(a)	–
Roots	0.44 ± 0.04(a)	0.15 ± 0.02(bc)	0.25 ± 0.02(b)	0.09 ± 0.02(c)	–
Total ecosystem	1.90 ± 0.35(a)	6.27 ± 0.74(b)	3.59 ± 0.24(a)	2.78 ± 0.54(a)

Letter groups indicated significant differences between ages within each depth (p < 0.1)

Following trends in aboveground tree biomass, SOC and mineral soil N concentrations also increased with stand age. The unmined stands had the highest concentration of both SOC and mineral soil N at all three depths (Fig. 1). Mineral soil N and TN increased more gradually than the tree or total ecosystem C pools, with a marked increase from 5- to 11-years and then few significant differences otherwise (Table 3). In general, mineral soil N correlated strongly with ecosystem C (Fig. 2). The strongest correlations were between mineral soil N and SOC (ρ = 0.93; p < 0.0001) and mineral soil N and tree C (ρ = 0.66; p = 0.007).

Fig. 1

Mean (±SE; n = 3) soil organic C and total soil N concentrations (%) within each age by depth increment (0–5, 5–10 and 10–25 cm). Letter groups indicated significant differences between ages within each depth (p < 0.1)

Fig. 2

Bivariate correlations of mean (n = 3) total soil N (Mg ha−1) with total ecosystem C and ecosystem C components. Displayed on each graph are the Pearson’s correlation coefficients (r) and significance values (p)

Ecosystem C pools also correlated strongly with both total and active microbial biomass (Fig. 3). The unmined stands had significantly more total microbial biomass C than all of the mined stands from April-September, with the exception of May when the 21-year was not different from unmined (Fig. 4). Similarly, active microbial biomass C was significantly higher in the unmined than in the mined stands from April-August (Fig. 4). The strongest individual correlations for both total and active microbial biomass were tree C (r = 0.89; p = <.0001 and r = 0.80; p = 0.0003, respectively) and SOC (r = 0.81; p = 0.0002 for both). Total ecosystem C was highly correlated with both total and active microbial biomass C (ρ = 0.92; p = < 0.0001 and ρ = 0.86; p < 0.0001, respectively).

Fig. 3

Bivariate correlations of mean (n = 3) total microbial biomass C (left) and active microbial biomass C (right), correlated with total ecosystem C and ecosystem C components. Displayed on each graph are the Pearson’s correlation coefficients (r) and significance values (p)

Fig. 4

Mean (±SE; n = 3) of total microbial biomass C (mg C g−1 dry soil) and active microbial biomass C (μg CO2-C mg−1 soil h−1) within each age measured across the seven sampling months. Letter groups below the graph significant differences between ages within each month (p < 0.1)

Interestingly, significant differences in microbial biomass were not observed for many of the measured ecosystem attributes that are, at least in part, microbially mediated. There were no significant differences among any of the stands for available soil NO3 −, available soil NH4 +, or total inorganic-N (available soil NO3 − + NH4 +; Fig. 5). Likewise, neither the soil CO2 nor N2O flux varied significantly among age stands (Fig. 6). As anticipated, CO2 flux peaked during mid-growing season (June–August), corresponding to the peak in soil temperatures (July–September; Figs. 6, 7); however, no clear temporal trend in N2O fluxes were observed. In almost all cases from June-October, the unmined stands consumed significantly more atmospheric CH4 than the mined stands (Fig. 6). During the June-October period, CH4consumption in the unmined stands averaged 70.9 ± 8.1 μg CH4-C m−2 h−1, whereas CH4consumption in the mined plots averaged 93 % less, at only 5.2 ± 1.2 μg CH4-C m−2 h−1(Fig. 6).

Fig. 5

Mean (±SE; n = 3) of soil nitrate (NO3 −), ammonium (NH4 +) and inorganic N (NO3 − and NH4 +) availability (mg N cm−2 day−1) within each age measured across the seven sampling months. Letter groups below the graph significant differences between ages within each month (p < 0.1)

Fig. 6

Mean (±SE; n = 3) of CO2 (mg CO2-C m−2 h−1), CH4 (μg CH4-C m−2 h−1) and N2O (μg N2O-N m−2 h−1) fluxes within each age measured across the seven sampling months. Letter groups below the graph significant differences between ages within each month (p < 0.1)

Fig. 7

Mean (±SE; n = 3) of soil temperature (°C) and volumetric soil moisture (%) within each age measured across the seven sampling months. Letter groups below the graph significant differences between ages within each month (p < 0.1)

Soil temperature and moisture both generally followed a seasonal pattern. Soil temperature showed a clear seasonal pattern, ranging from 10.1 to 26.8 °C (Fig. 7). Notably, the 5-year was significantly warmest in all months, similar only to the 11-year in April to May, which was the next warmest stands. The remaining stands, with more-closed canopies and more northerly aspects, had fewer significant differences in soil temperature. Volumetric soil moisture ranged from 8 to 43 % across the age stands during the sampling months (Fig. 7). Corresponding with warmer temperatures, the 5-year stands were generally the driest sites while the 30-year stands were most moist in all months.

Discussion

Ecosystem C and N accrual

The establishment and growth of trees on the post-mining landscape has significant implications for ecosystem C. Within the 11-, 21-, and 30-year mined stands and the unmined stands, aboveground tree C accounted for approximately 70–73 % of the total ecosystem C, demonstrating the importance of tree establishment and productivity in ecosystem C accrual (Table 3). Amichev et al. (2008) reported similar results of at least 75 % of total ecosystem C contributed by aboveground tree C on reforested mined land. In this regard, the C pools in the 11-year and older stands scale with the unmined forests. Mineral-soil SOC pools also help drive the overall trend in ecosystem C accumulation. SOC content tripled from the 5-year to the 11-year stands, with the most rapid SOC sequestration rate of 0.7 ± 0.1 Mg SOC ha⁻¹ year⁻¹ measured in the 11-year stands. The SOC sequestration rates measured in this study are on par with others reported on eastern-USA reclaimed and reforested mine site (Sperow 2006). It is worth noting that sampling bias (small plots and incomplete capture of all root material) likely underestimates the contribution of belowground root biomass to the ecosystem C values presented.

Ecosystem C accrual was significantly correlated with mineral soil N (ρ = 0.93, p < 0.0001 for mineral SOC; ρ = 0.66, p = 0.007 for tree C) (Fig. 2), suggesting that productivity on reforested mined sites is driven by N, as is often expressed in most other temperate deciduous forest ecosystems (Lebauer and Treseder 2008). Thus, the return of soil N to the reclaimed mined landscape is critical for meeting reforestation goals in terms of productivity (Bradshaw 1983), and in turn, driving ecosystem C accrual.

Ecosystem structure and gas fluxes

As most soil microorganisms are heterotrophic, it is logical that our observations showed strong positive correlations of microbial biomass with ecosystem C (Fig. 3). However, there were no notable differences among stand ages for plant-available inorganic-N (Fig. 5) despite the differences in both total and active microbial biomass (Fig. 4). The IEMs provide a cumulative net measure of available NH₄ ⁺ and NO₃ ⁻; as such, they are not indicative solely of microbially mediated processes (e.g., N mineralization and nitrification). These results must also be considered in the context of plant uptake, which can be assumed to increase proportionally with plant biomass. The same caveat cannot be applied to soil-atmosphere fluxes of N₂O, however. N₂O is a byproduct of nitrification and denitrification, and N₂O fluxes suggested that such microbially mediated N cycling rates are occurring similarly across all of the stands (Fig. 6) despite differences in total N capital (Table 3).

Like N₂O, soil-atmosphere CO₂ efflux is controlled by both microbial (heterotrophic respiration) and plant (autotrophic root respiration) processes. Given increases in plant biomass and soil C with site ages, we expected CO₂ efflux to scale with age. Despite this, we found few differences among different aged stands, even though we observed significant differences in microbial biomass and ecosystem C pools. Although respiration generally correlates with plant productivity, it is also influenced by soil temperature and moisture (Raich and Schlesinger 1992; Holland et al. 1999: Schlesinger and Andrews 2000). Of these potential drivers, our soil CO₂ flux measurements correlated most strongly with temperature (ρ = 0.588; p < 0.0001), giving a clear seasonal pattern for CO₂ efflux.

The three gases for which soil-atmosphere fluxes were measured (CO₂, CH₄, N₂O) all absorb shortwave radiation and, hence, influence global climate (IPCC 2013). Of these fluxes, CH₄fluxes, showed the largest decoupling from forest ecosystem structural measures (e.g., microbial and plant biomass). Upland soils are the only known global biogenic sink for atmospheric CH₄, and forest soils have been found to be the most efficient soil sinks (Keller et al. 1983; Crill 1991; Keller and Reiners 1994; Castro et al. 1995; Le Mer and Roger 2001). In this study, the reforested mine sites are almost completely lacking in methanotrophy. Even after 30 years of development, the highest measured CH₄ uptake rate was 13.649 μg CH₄-C m⁻² h⁻¹, contrasted with an uptake rate in excess of seven times higher in the unmined reference stands (98.037 μg CH₄-C m⁻² h⁻¹) during the same month (September). Seasonality is known to mediate CH₄ soil-atmospheric fluxes (Crill 1991), yet no clear seasonal patterns explained the lack of methanotropy in the mined sites, where there were no significant trends soil temperature and moisture (Fig. 7). Research to-date investigating CH₄fluxes under disturbance scenarios has focused on secondary successional environments (e.g., conversion of agricultural pastures or grasslands to forest), not on scenarios of primary succession such as have been documented here. Thus, these observations represent an important point of future study regarding soil functional development on the more than 1 million ha of surface mined land in Appalachia as well as extensive mining disturbances in other parts of the world.

Implications

Reforestation in the post-surface mining context is often focused on reestablishing forest structure and productivity. Understanding these methods’ effectiveness at restoring forest ecosystems to post-mining landscapes, however, also requires an evaluation of forest ecosystem functions. This research shows that the reforestation of native hardwoods in the Central Appalachian Coal Basin is largely effective in re-establishing ecosystem C and N pools, as well as a myriad of the selected ecosystem functions, such as C sequestration and N cycling. It also appears that these reforestation efforts are largely successful at re-establishing many microbially mediated processes; however, the critical function of CH₄uptake is essentially absent from these mined soils. Thus, our results highlight that many commonly observed measures of ecosystem structure, especially those that focus on aboveground plant properties, are not always reliable indicators of the restoration of the full suite of ecosystem services that existed on the pre-mining landscape.

Acknowledgements

We thank the Powell River Project and the Department of Forest Resources and Environmental Conservation at Virginia Tech for funding. Additionally, funding for this work was provided in part by Virginia Agricultural Experiment Station and the McIntire-Stennis Program of the National Institute of Food and Agriculture, US Department of Agriculture.

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How to Stretch for Cheerleading Flyers

Stretches for flyers Photo Credit ViewApart/iStock/Getty Images

Flyers perform many types of movements within a stunt. From turning and bending to twisting and contorting, flexibility is important for any flyer. Without a solid stretching routine, you are at risk of injury from muscle tears. It's also important for a flyer to build a solid base by doing a good strength-training routine. If you're too flexible without building a good support, or muscle, system, you can damage your body by hyperextending your joints.

Stretching for Flyers

Step 1

Lightly jog or do some light jump rope exercises to prepare the muscles for stretching. Warm up for 10 to 15 minutes before beginning a stretching exercise.

Step 2

Do some back stretches to loosen your lower back. Incorporate yoga poses, such as alternating between downward-facing dog and child's pose, to increase low back flexibility.

Step 3

Kneel on one knee. Point the toe of your front leg and slowly push forward, lowering your body into a split position. Stretch for 30 seconds and twist your torso, facing forward into a center split position. Hold for 30 more seconds and repeat on the opposite leg.

Step 4

Stand facing a wall with your hands on the wall for support. Stand with both legs parallel, knees straight, feet together and lift your right leg behind you without shifting your hips. Hold for 30 seconds and repeat on the left leg.

Step 5

Sit on the floor with your feet in front of you, keeping your knees straight. Bring your chest to your knees, stretching your legs without bending your knees. Keep your back straight and chin up. Hold for 30 seconds and relax.

Heel Stretches and Scorpion Flexibility

Step 1

Place your right hand against the wall for balance, with your feet parallel. Slowly draw your left leg up your right leg through a passe position while keeping your back straight. When your foot is high enough to grab your heel without hunching your back, gently pull the heel up until you can look at your knee. Hold for 30 seconds and repeat on the right leg.

Step 2

Lie on your stomach and push up into a yoga cobra position. Bend your knees, bringing your toes toward the back of your head. If you are comfortable, reach behind your head and grab your feet for an additional stretch. Hold for 30 seconds. Begin with one leg at a time if stretching both legs is too difficult in the beginning.

Step 3

Stand facing a wall with your hands against the wall for balance. Slowly extend your right foot into arabesque, the same way as in Step 4 from above, but this time bend the knee of the leg you are lifting behind you and point your toe as high to the ceiling as possible. When your foot is high enough, reach overhead behind you and grab the foot with your right hand and hold for 30 seconds.

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Dumbbell Exercises for Lower Back

A woman is exercising with dumbbells. Photo Credit Purestock/Purestock/Getty Images

Overview

Weak lower back muscles often result in pain and discomfort after standing or carrying objects. Dumbbells are generally used to work the muscles of your arms and legs, but also to strengthen the lower back. If your doctor gives you her nod of approval, keep these muscles strong by performing two to three sets of 8 to 12 repetitions of dumbbell exercises on three nonconsecutive days a week.

Dumbbell Stiff Leg Deadlift

This exercise contracts the deep spinal muscles on either side of your spinal column. Stand with your feet hip-width apart, tilt your tailbone back to keep your spine straight and hold a dumbbell in each hand. Contract your abdominal muscles and stabilize your spine. Keeping your back and legs straight, bend forward at your hips to lower the weights toward the floor with your arms extended. Continue down as far as possible, then contract your back and gluteal muscles to return to the standing position. To increase the intensity of the exercise, stand on a box so you can lower the weights past your toes.

Dumbbell Deadlifts

Dumbbell deadlifts work not only your lower back, but also several leg and hip muscles. Place two dumbbells on the floor and stand between them with your feet hip-width apart. Bend your knees and hips to lower down until your thighs become parallel with the floor. Tilt your tailbone back to keep your spine straight. With your arms extended, grasp the dumbbells with your palms facing in. Brace your abdominal and spinal muscles and push your chest out slightly. Press through your heels to lift the weights by extending your knees and hips to come to a full standing position. Pause at the top of the movement then slowly lower back to the starting position.

Good Mornings

This exercise develops the spinal erectors and the quadratus lumborum of the lower back. Stand with your feet shoulder-width apart, tilt your tailbone back to keep your spine straight and hold a dumbbell in each hand. Bend your arms to lift the weights to shoulder level, resting them on your shoulders, if possible. Contract your abdominal muscles and stabilize your back. Keeping your back and legs straight, slowly bend forward at your waist as far as possible or until you feel a stretch in your hamstrings. Contract your gluteal muscles and lower back to return to the standing position.

Tips

Form is vital to preventing lower back injury and injury can occur quickly if trying to move too much weight too soon. Use slow and controlled movements rather than speed and momentum; and warm up and stretch prior to any lower back exercises.

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How Often Should I Do Dumbbell Side Bends?

A fit woman is holding dumbbells at her side. Photo Credit bondarchik/iStock/Getty Images

Dumbbell side bends work the muscles of the trunk, particularly the deep spine muscles, explains Len Kravitz, PhD, Program Coordinator of Exercise Science and researcher at the University of New Mexico. Although they can help contribute to a stronger core, reduce the risk of back pain and improve functional movements such as a serving a tennis ball or carrying a heavy object on one side of the body, dumbbell side bends won't create a slim and toned midsection -- only diet and cardiovascular exercise can help you do that. If you do include dumbbell side bends in your routine, do them at least two times per week on nonconsecutive days.

Why Not Everyday?

The main muscle activated during dumbbell side bends is the quadratus lumborum, which is located in the lower back on either side of the spine. This muscle, just like any muscle, needs rest between workouts to recover and grow stronger. When you add dumbbells to the side bend, you overload the muscle more and break down the fibers. Strength and function improve when these fibers are repaired by the body. The muscles need 48 to 72 hours to do this. So, at most, you can work them every other day.

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Do Weighted Side Bends Reduce Back Fat?

A modified side bend can assist in targeting back fat. Photo Credit Jupiterimages/Polka Dot/Getty Images

Many people struggle with back fat -- those pesky fat deposits that look like rolls on the sides of your mid -- and lower back. Weighted side bends will help strengthen the muscles under the back fat, but they don’t specifically reduce the fat that resides above those muscles. To burn fat overall, you have to burn more calories than you consume.

Dumbbell Side Bends

Choose a light weight -- no more than 10 pounds -- and hold it in your left hand. Let your left arm hang down against your side. Bend your waist to the opposite side of the dumbbell until you feel a stretch. Slowly return to an upright position, and continue the movement and bend to the same side that the weight is on. That completes one rep. Do two sets of 12 to 15 repetitions.

Muscles Worked

The weighted side bend works the obliques, which are responsible for lumbar flexion, lumbar rotation, lateral flexion of the lumbar spine. They also increase intra-abdominal pressure. The exercise also recruits the quadratus lumborum, or deep low back lateral flexor; the iliopsoas, or hip flexors; erector spinae muscles, upper and middle trapezius, levator scapulae, gluteus medius, and gluteus mimimus.

Spot Reduction Myth

There is no such thing as spot reduction exercise -- in other words, there is no one exercise that will burn the fat off your back, or your thighs, or your abs. Although many people claim they have a “magic secret” to banish back fat, there is no substance to such claims. Exercises that work those areas only work the muscles under the fat -- which may actually make you look bigger.

Lose Fat

To lose one pound of fat, you must burn 3,500 calories. You may do this by reducing your caloric intake and creating a deficit, or by exercising to burn those extra calories. A combination of the two works well; you could do it by dieting alone, but adding some cardiovascular exercise and some weight training gets your body into better shape. The weighted side bend is a good exercise to include in your workout because it uses a lot of muscles and improves your core strength.

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How to Lose Fat Off Your Back

A young woman is doing lateral pull downs. Photo Credit prudkov/iStock/Getty Images

Back fat is embarrassing and unsightly, but it can be dealt with by doing a little hard work. Strong back muscles are good for your posture, relieve many types of common back pain and help you look good in a bathing suit. Do exercises targeted toward your back and core to strengthen and tone back muscles. You can also bust back fat by losing weight throughout your whole body. This is accomplished by reducing the total number of calories you eat each day, or burning more calories than you consume through exercise. A combination of diet and exercise is often the best and fastest way to lose troublesome weight on your back.

Diet and Lifestyle

Step 1

Reduce the total number of calories you eat each day by 250 to 500. This will help you lose 1/2 to 1 pound per week. This weight will come off your entire body, so do not expect major changes to your back fat right away if you do this without exercise.

Step 2

Eat healthy, whole, unprocessed foods. Choose your diet mainly from lean proteins, healthy fats, low-fat dairy, whole grains, and fruits and vegetables. Drink plenty of water, as this helps everything in your body work efficiently and makes you feel a bit more full.

Step 3

Do aerobic exercise three to five times per week for 30 to 90 minutes per session, depending on your current fitness and health. Choose from moderate-intensity activities such as brisk walking, jogging, cycling, taking a kick boxing class or swimming.

Exercises to Strengthen and Tone

Step 1

Lie on top of a stability or exercise ball with your belly on the ball, your back flat, your legs straight out behind you and your feet firmly planted on the ground. Raise your chest off the ball and let your arms hang straight down from your shoulders. Lift your arms at a 30-degree angle from your body, so they form a Y-shape above you. Pause for a few moments before slowly lowering them back down. Do 10 repetitions three times per week.

Step 2

Stay with the stability ball and roll your body forward so your legs and feet are lifted off the ground, your arms are pointing down toward the floor in front of the ball and your hands are firmly planted on the ground in front of the ball. Walk your hands forward until you begin to feel slightly unsteady, and then walk them back and place your feet back on the floor behind the ball. Repeat this exercise 10 times three times per week.

Step 3

Pick up a pair of dumbbells. Bend forward at the waist until your torso is parallel to the floor. Let your arms, which are holding the dumbbells, hang straight down from your shoulders with your palms facing each other. Raise your arms straight out to the sides and hold for a few moments. Breathe deeply. Slowly return your arms to the starting position. Do 12 repetitions three times per week.

Warnings

• Speak with your physician before beginning a new diet and fitness regimen. Do not attempt to lose more than 2 pounds per week at a maximum, as more than that can be dangerous for your body. Use proper technique when lifting weights to avoid injury. If you do not know how to use free weights, ask a trainer at your gym for help.

Things You'll Need

• Large exercise stability ball
• Dumbbells

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How to Lose Your Side & Back Fat

To help trim side and back fat, one must make healthier food choices. Photo Credit B. Sporrer/J. Skowronek/StockFood Creative/Getty Images

You carry a spare tire in your car in case of an emergency, but you don't need the one encircling your torso. This layer of fat that sits around your waist, back and sides, is likely visceral fat -- which is a metabolically active form of fat that greatly raises your risk of developing certain diseases. Although targeting a specific part of your body for weight loss is futile -- you can't cherry pick where weight loss occurs -- trying the traditional weight-loss methods of diet and exercise will usually reduce back and side fat quickly.

The Trouble With Visceral Fat

Visceral fat -- also called intra-abdominal fat -- lies deep inside the abdomen. It encases the internal organs and releases inflammatory compounds that make you more vulnerable to heart disease, type 2 diabetes and -- in women, breast cancer and the need for gall bladder surgery. Visceral fat is often referred to as belly fat, but fat around your middle can include both visceral and subcutaneous types of fat, and can expand your tummy to the front and sides. Subcutaneous fat -- which sits just beneath the skin -- doesn't create the same health risks that visceral fat does, however.

Side and Back Fat Is Responsive to Exercise

A physically active lifestyle is one of the most effective strategies for combating visceral fat, explains Rush University Medical Center. The first weight you lose with exercise is belly fat. Get at least the 30 minutes per day, five days per week, of moderate-intensity cardiovascular exercise, as recommended by the Centers for Disease Control and Prevention. If you can fit in more exercise, you'll experience greater weight loss all over -- including side and back fat -- and have better health results.

Strength training provides critical support for a visceral fat-loss program. You build muscle -- which burns more calories at rest than fat does -- so your overall body's energy usage improves. A study published in a 2007 issue of the American Journal of Clinical Nutrition showed that consistent strength training two times a week for two years prevented increases in visceral fat and total body fat in women. A paper in Current Sports Medicine Reports from 2012 noted that strength training's positive effects on decreasing visceral fat help prevent and manage type 2 diabetes. Adopt a program that targets every major muscle group at least twice per week.

A Dietary Way to Address Fat

Exercise alone isn't enough to trim your back and side fat. You must reduce calories and make healthier food choices. A pound of fat is equal to 3,500 calories; burn 500 to 1,000 calories more than you consume every day to lose 1 pound of fat a week. For most people, increasing physical activity helps contribute to this deficit, as does trimming the number of calories consumed. How many calories you need to consume to lose your back and side fat depends on your age, gender, activity level, hormones and size. Use an online calculator or consult with your medical provider to estimate your calorie needs.

Until then, changes in the way you eat can help. Eliminate refined grains -- including white bread and pasta -- as well as extra sugar. Soft drinks and baked goods are foods to eliminate first. Make your meals using lean protein -- such as white fish, skinless poultry, lean steak or tofu -- and watery, green vegetables. Starchy vegetable or whole grain servings should not exceed about 1/2 to 1 cup per meal. Skip processed snack foods such as crackers, cereal bars and pretzel mixes. Instead, snack on plain yogurt, nuts, fresh fruit or low-fat cheese.

Lifestyle Changes for Fat Reduction

An overly stressed lifestyle contributes to the accumulation of fat around your back and sides. Having too many work deadlines, trying to keep up with paying your bills and meeting your family obligations can create a chronic state of stress. Learn to deal with stress healthfully by confiding in friends, engaging in yoga or meditation or seeking help from a behavioral therapist. Stress may induce mindless eating, which causes weight gain, and incites your body to release the hormone cortisol, which encourages fat to accumulate around your middle.

Stress can lead to poor sleep, which also contributes to the development of visceral fat. Aim to get 7 to 9 hours most nights. Getting too little or too much sleep often results in overeating and poor food choices because you're too tired to focus.

To further boost your daily calorie burn, get up and move more, even when you're not formally exercising. Fidget, do household chores, choose stairs over the elevator or park farther away from your destination.

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Exercises to Get Rid of Spare Tire

A man is running on a treadmill. Photo Credit muratemre/iStock/Getty Images

You need one in your car, just in case -- but there's no "just in case" requirement for that spare tire that surrounds your midsection. If you're hoping to work off that unsightly abdominal fat, you'll be doing yourself a favor in not only appearance, but also in preventing chronic diseases. The abdominal fat that's surrounding your organs, called visceral fat, puts you at greater risk of problems like heart disease and diabetes -- while the under-the-skin fat, including the spare tire, is just generally unsightly. By adopting a healthy routine, you should be able to cut down on both types of fat around your waist and hips.

Burn Off the Tire

Instead of focusing on an ab-strengthening routine, commit to calorie-burning workouts -- namely, cardiovascular exercises. To get the most benefit in the shortest amount of time, choose exercises that burn the highest number of calories. Among the big calorie burners: running or jogging, jumping rope, high-impact aerobics and rowing, all which will help a 155-pound person burn between 250 and 300 calories during a 30-minute session. If those exercises are not desirable, choose any other activity that gets your heart pumping -- try to keep up the pace for 30 minutes a day, five days a week.

High-Speed Chase

The spare tire at the sides of your abdomen mostly consists of subcutaneous or "below the skin" fat. According to the American Council on Exercise, you can blast away more subcutaneous fat by doing high-intensity interval training or HIIT. Try it once or twice a week, on days when you don't have time for a longer workout. You can perform HIIT with nearly any type of cardio. Warm up by walking or jogging for about five minutes, and then speed up your pace -- running, swimming, cycling or jumping rope at about 90 percent of your maximum speed for 30 seconds to one minute. Then slow down to about 50 percent of max for another 30 seconds to one minute. Repeat each interval a total of six to eight times, and then cool down.

Add Some Muscle

A balanced exercise routine shouldn't overlook strength training. Weight training burns calories and helps you get stronger and more toned muscles. And since it takes more energy to maintain a pound of muscle than it does to maintain a pound of fat, you'll burn more calories by replacing muscle with fat. Start out with a simple routine, using weight machines at your gym to do exercises that work all the major muscle groups: back, legs, arms and chest. You can also do a simple routine at home using a set of dumbbells. Try bench presses, overhead presses, biceps curls and triceps curls. For your legs, hold the dumbbells and do squats, lunges and step-ups.

The Big Belly Blasters

Part of your strength-training routine can also include a few abdominal exercises aimed at the obliques at the sides as well as the rectus abdominis or "six-pack" at the center of the torso. Among the most beneficial exercises for the obliques is the "captain's chair," the bicycle crunch and the reverse crunch, according to the American Council on Exercise. For the rectus abdominis, the bicycle crunch and captain's chair are also key, according to ACE, as well as crunches on an exercise ball. Try doing three abdominal exercises two or three days a week, on the same day as your other strength-training exercises, or when you're sitting in front of the TV. Limit yourself to one or two sets -- the idea here is to strengthen the muscles, but not to bulk them up too much, or you'll end up with an even thicker midsection.

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Exercise for Upper Belly Fat

Friends jogging on treadmills next to one another smiling. Photo Credit Antonio_Diaz/iStock/Getty Images

Losing upper belly fat can challenge even the most industrious athlete, but it’s easier to lose than you think. A combination of diet and exercise to lose weight everywhere is the only way to trim your middle -- spot reduction doesn’t exist. And while vanity drives many couch potatoes to put down the TV remote and head to the gym, getting rid of the deadly visceral fat hidden under that spare tire should take priority. It encases internal organs, creating inflammation that contributes to heart disease, type 2 diabetes and colon cancer.

Be Rid of the Roll with Cardio

Aerobic exercise melts fat away, and weight training develops muscles. You first need to lose weight before you’ll be able to see the ab muscles underneath. Ride a bike, jog, take a brisk walk or go for a swim. The key to losing weight all over, including the upper belly region is to work out regularly within your target heart zone, also known as the fat-burning zone.

Measure Your Max

Determining your maximum heart rate -- MHR -- is one of the best ways to customize workouts for best results. Target heart rate zones are expressed as percentages that are calculated from your maximum heart rate for your age so you or your trainer can tailor workouts for weight loss. Find your estimated MHR by subtracting your age from 220 if you’re male; subtract your age from 226 if you’re female. For example, a 45-year-old male has an estimated MHR of 175 beats per minute, or bpm.

Get In the Zone

You’ll burn fat when you exercise within 65 percent and 75 percent of your MHR. For the 45-year-old male, that means he needs to keep his heart rate within 114 bpm and 131 bpm for overall weight loss, including upper belly fat. Once you’re in your fat-burning zone, stay there for at least 12 minutes to burn fat at a faster rate.

Pump It Up

A fast metabolism burns fat faster than a sluggish one. Boost your upper belly weight loss results with a resistance-training program that builds lean muscle. Lean muscles burn calories even when you’re at rest, which helps maintain your weight loss gain through your aerobic efforts. According to Pennsylvania State University, a basic resistance-training program to help burn fat performed every other day should include bench presses, pull-downs, military presses and squats where you gradually add weight.

Dump the Junk

Refined sugars and carbs pack on pounds where you don’t want them to go. Complex carbs, such as fresh fruits, vegetables and whole grains satisfy hungry and keep the stress hormone cortisol balanced. Sudden spikes in blood sugar caused by high glycemic foods, including processed Franken-foods laden with sugar and artificial ingredients, release cortisol and pack on pounds. So in addition to regular aerobic exercise and resistance training, be sure to exercise good judgment with your food.

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Are There Exercises for Losing a Spare Tire?

A woman is warming up. Photo Credit Eric Gevaert/Hemera/Getty Images

Extra fat around your tummy can not only be hard to get rid of, it can be dangerous to your health. The visceral fat that makes up a significant portion of belly fat is linked to high cholesterol, high blood pressure, diabetes, cancer and insulin resistance. Although spot reduction isn't possible -- you can't solely target stomach fat -- there are many exercises to burn fat throughout your body and tone the core muscles. This will help you deflate the spare tire and sculpt firmer abs.

Cardio

To lose the spare tire, you have to burn fat throughout your body by creating a calorie deficit, yp burn more calories than you consume. This forces your body to use stored fat as fuel. Running, jogging, swimming, bicycling and dancing are all forms of cardiovascular exercise that can help you burn fat, including fat in your midsection. A 140 pound woman can burn around 277 calories bicycling for 30 minutes at 12 to 14 miles per hour. That same woman can burn about 319 calories running at 6 mph in that same half hour. The more high intensity cardio you do, the more calories you burn and the more weight you will lose. The Centers for Disease Control and Prevention recommends doing no less than 150 minutes of moderate, or 75 minutes of vigorous, aerobic activity each week to lose weight.

Abdominal Exercises

Training your abdominal muscles with abdominal holds, isometric ab squeezes, situps, crunches, prone planks, ab rollouts and knee raises helps melt fat and gives you a toned look.The American Council on Exercise suggests that doing these core strengthening exercises two to three times a week will strengthen your ab muscles, stabilize your pelvis and improve the appearance of your waistline.

Oblique Exercises

If you carry love handles, that extra fat that hangs just above your waist, doing core exercises that target your obliques can help tone the sides of your midsection and fill out some of the lose skin that may be left there once you lose weight and your waistline slims. Exercises that work your obliques include side planks, side crunches, Russian twists, side bends, twisting knee raises and ballet twists. Incorporate these into your core strengthening routine at least twice a week.

Overall Strength Training

Incorporating full-body strength training into your exercise routine will help you lose more body fat than doing cardio alone. According to Harvard Health, doing an hour of weight training twice a week can reduce your proportion of body fat by up to 4 percent and can help you keep off the visceral fat that bulges your belly. In addition to core exercises, perform strength training exercises for your arms and legs. Do pushups, pullups, biceps curls, triceps dips, presses and flys to improve muscle tone in your upper body. Do squats, lunges, calf raises, leg lifts, hamstring curls and deadlifts to strengthen the muscles in your lower body. You can use weights, resistance bands or body weight for strength training exercises.

www.livestrong.com