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Harvard Forest Data Archive

HF271

Prospect Hill Forest Plots at Harvard Forest 2013

Related Publications

Data

Overview

  • Lead: Audrey Barker Plotkin, Danielle Ignace, Liza Nicoll, Jim Tang, Joshua Rapp
  • Investigators: Michael Dietze, David Foster, Glenn Motzkin, David Orwig
  • Contact: Audrey Barker Plotkin
  • Start date: 2013
  • End date: 2013
  • Status: completed
  • Location: Prospect Hill Tract (Harvard Forest)
  • Latitude: +42.527 to +42.552
  • Longitude: -72.194 to -72.169
  • Elevation: 300 to 400 meter
  • Taxa: Acer rubrum (red maple), Aralia nudicaulis (sarsaparilla), Maianthemum canadense (Canada mayflower), Osmunda cinnamomea (cinnamon fern), Pinus strobus (white pine), Quercus rubra (northern red oak), Tsuga canadensis (eastern hemlock)
  • Release date: 2016
  • Revisions:
  • EML file: knb-lter-hfr.271.2
  • DOI: digital object identifier
  • Related links:
  • Study type: long-term measurement
  • Research topic: biodiversity studies; large experiments and permanent plot studies; physiological ecology, population dynamics and species interactions
  • LTER core area: primary production, populations
  • Keywords: carbon, diameter at breast height, forest disturbance, forest ecosystems, hemlock, land use, maple, oak, pine, soil
  • Abstract:

    Northern temperate forests function as an important carbon sink in the global carbon budget. Eddy covariance and plot-based measurements indicate that the Harvard Forest carbon sink is substantial and that the rate of carbon sequestration has increased over the past 20 years. A synthesis effort is underway to better understand how forest development, response to disturbance, and dominant tree species drive the patterns and mechanisms of forest carbon dynamics at the Harvard Forest. The goal of this project is to sample and analyze forest carbon stocks and dynamics across a suite of permanent plots established in 1937 and re-measured in 1992 in order to understand how and why forest carbon stocks have changed between 1937 and 2013. Data from this suite of plots will give spatial and temporal context to a synthesis of detailed, site-specific permanent forest plot studies. Other goals, such as vegetation biodiversity, and a more detailed study of root patterns in soils and belowground carbon distribution, are of strong interest but not addressed in detail in this sampling effort.

  • Methods:

    Plot Selection

    Forest inventories of the three main tracts of the Harvard Forest have been completed every 10-30 years since 1907. The timing and methods used in the 1937 inventory make it a particularly valuable data set. The inventory covers the range of forest types and the three main tracts of the Harvard Forest land base. It was conducted one year before 75% of the standing timber at Harvard Forest was blown down by the Great 1938 Hurricane, so these measurements show maximum vegetation development since agricultural abandonment. The inventory used fixed-radius plots (0.025-0.1 ha) with measurements of individual trees and information on the understory flora, which allow direct comparisons of net change in forest biomass and species composition. Although the hurricane eliminated the plot markers, plot locations were carefully recorded on large-scale maps, allowing us to re-establish plots in the same location. In 1992, most of the plots on the Prospect Hill Tract were located and sampled (Motzkin et al. 1999). All data from 1937 and 1992 are available electronically (HF039, HF015).

    We considered the merits of focusing on the Prospect Hill Tract (PH) 1937/1992 plots versus a broader suite of plots from the 1937 forest inventory across the Harvard Forest. The PH plots would leverage and give context to the eddy-flux measurements and plots, and the 35-ha SIGEO plot. The broader suite would cover a larger spatial scale and perhaps additional forest types. For the 2013 sampling, we decided to focus on the PH plots sampled in both 1937 and 1992 (n=169).

    Since examining change in soil carbon stocks between 1992 and 2013 was a goal, we further refined our pool of plots to those that also had soils sampled in 1992 (n=137).

    We stratified these 137 plots based on key determinants of forest composition and structure by Motzkin et al. (Motzkin, G., Wilson, P., Foster, D. R., Allen, A. E. 1999. Vegetation patterns in heterogeneous landscapes: the importance of history and environment. Journal of Vegetation Science 10: 903-920). They identified historical land-use, soil drainage, and soil CN ratio as three key determinants of forest composition and structure in 1992, so we stratified the plots along these three gradients, and chose plots that spanned these gradients. From the 1992 data, we binned the plots based on land-use history (4 categories), soil drainage (6 categories) and quartiles of CN ratio (4 categories). Since finding the plots in the field was a nontrivial challenge, we also prioritized sampling plots that had been located prior to the 2013 field season.

    Field Methods

    Plot location and marking (hf271-01). The plots sampled in 1992 are each 22.5m x 22.5m (0.056 ha). In 1995, one corner of each of the 269 plots sampled in 1992 was marked by an iron pipe engraved with the plot number. Plot locations in a GIS file are approximate, so the true location of the plot corner was typically within 1-30m from the mapped location. In Spring 2013 we searched, flagged, and recorded the location of with a GPS for as many plots as possible. We located 80 plots prior to the field season. During the summer, we located an additional 14 plots that were sampling priorities based on the stratification scheme described above.

    We established the square 22.5 x 22.5m plot using a compass and tapes. The error tolerance was 1m (that is, if the square closed with greater than 1m error, we re-ran the lines until the plot closed with less than 1m error). In some cases, the plot corner that the iron pipe marked was ambiguous. First off, the plot file ‘orientation’ column indicates not the plot corner where the iron pipe is, but the orientation of the plot relative to the pipe (that is, a NE orientation means that the plot is located to the NE of the pipe, and therefore the pipe is in the SW corner of the plot). In the majority of cases the actual and recorded orientation is NE (that is, the pipe is in the SW corner). However, in several cases the orientation from the 1992 study is not NE, and it is ambiguous whether the final iron pipes were all (or mostly?) put in the SW corner, or if some of the pipes did end up at a different corner as recorded. The file from 1992 and the 1999 paper don’t match. Motzkin thought that they did place the iron pipe in the SW corner for all plots, but wasn’t sure.

    For these ambiguous plots, we used the 1992 tree data and any other clues (whether the plot would cross into a road or over a wall, for instance) to determine the correct plot orientation. We did find that most of the plots in question are oriented NE, but there were 3 plots that we decided were oriented as recorded in the 1992 file. Are you confused? We were too, but think we puzzled our way to the correct solution in most or all cases.

    We marked all 4 corners of each plot with pvc posts painted red and engraved with the plot number and corner (e.g. PH106-SW). The posts typically extend ~0.5m high. Combined with the more-accurate GPS locations of each plot, finding and laying out the plots for future sampling should be easy.

    From late May through mid-July, 2013, we sampled 60 plots: Plot layout and posts: all 60. Live and dead trees: all 60. Coarse and fine woody debris: all 60. Litter and soils: 46 of the 60 plots. Understory physiology: 33 of the 60 plots. Tree cores: 4 of the 60 plots.

    Barker Plotkin led the field sampling, along with Nicoll and assisted by REU students Sophie Bandurski, Pat O’Hara, Christine Pardo, Hannah Wiesner.

    Live and dead trees (hf271-02; hf271-03). We measured all live and dead trees (not woody shrubs) greater than 2.5cm diameter at breast height (dbh). We recorded species and status (L or D) for all. For live trees and intact dead trees (that is, those with unbroken tops and most branches), we measured diameter at breast height. For standing dead trees (minimum height 0.5m), we recorded base diameter, top diameter, height, and decay class (see below). For height estimates, we directly measured snags less than 2m, estimated height relative to a person of known height (up to 3-4 person-heights) and used a clinometer to estimate height for taller stems. Leaning stems were included if they were greater than 45 degrees relative to the ground.

    Coarse and fine woody debris (hf271-04; hf271-05). We measured downed coarse wood (minimum diameter = 7.5cm, minimum length = 1m) along two line-intercept transects (Harmon, M.E. and J. Sexton. 1996. Guidelines for measurements of woody detritus in forest ecosystems.) across the plot diagonals (total ~64m transect), recording species, the diameter in both directions and decay class. If we were unsure of the species, we attempted to classify it as ‘deciduous’ or ‘conifer’ but in some cases the species is recorded as ‘unknown.’ If the debris had a hollow, the diameter of the hollow was measured to be subtracted later. We tallied downed fine wood (FWD) along a random 3-m part of each transect. We tallied the number of FWD pieces in two diameter classes: 0.6-2.5cm; 2.5-7.5cm.

    Litter and soils (hf271-06; hf271-07). Samples archived: 4oz. containers of sieved (less than 2mm) mineral and organic soils archived.

    We collected the surface litter from three, 50cm x 50cm quadrats in the plots in which we sampled soils. We chose sample locations based on probing soils to find a rock-free area (not so easy!) and to spread the samples across the plot. In the 50 cm *50 cm quadrat where litter is collected, we cut a 15cm x 15cm quadrat into the organic layer based on a wooden frame. Peel the organic layer and measure the depth. Put the organic layer into the bag. After the organic layer was removed, we pounded in a hammer corer (5cm in diameter) into soils (15 cm depth) and collected mineral soil. We had some concerns that the hammer corer sleeve often did not fill all the way, although the sample hole was indeed 15cm deep. If the soils was getting compressed in the corer, that would be okay. If the soil was getting compacted at the bottom of the hole, then the bulk density calculations may be too low.

    Lab Methods

    Live and Dead Trees. For live trees, we used species-specific allometric equations to estimate aboveground biomass. A list of allometric equations used can be found in the script ‘U:\PH-FOREST\functions\biomass.R’. For dead trees, we estimated biomass based on allometries if the tree was intact. If not, we used the height, top diameter, and base diameter measurements to estimate the volume as a frustrum of a cone, then used decay-class and species-group specific density values (from Liu, W.H., D.M. Bryant, L.R. Hutyra, S.R. Saleska, E. Hammond-Pyle, D. Curran and S.C. Wofsy. 2006. Woody debris contribution to the carbon budget of selectively logged and maturing mid-latitude forests. Oecologia 148:108-117) to estimate biomass.

    Snag volume and mass calculations depended on the data available, split into 5 methods: VOLUME FORMULAE - per piece:

    (1) frustrum of cone. volume = (height_m*(((3.14159*((baseD/200)^2)) +( ((3.14159*((baseD/200)^2))*(3.14159*((topD/200)^2)))^0.5)+(3.14159*((topD/200)^2))))/3

    (2) frustrum of cone. volume = (height_m*(((3.14159*((dbh_cm/200)^2)) +( ((3.14159*((dbh_cm/200)^2))*(3.14159*((topD/200)^2)))^0.5)+(3.14159*((topD/200)^2))))/3

    (3) cylinder. volume = 3.14159*((dbh_cm/200)^2)*height_m

    (4) allometry. step 1. figure out biomass based on dbh_cm and species. Step 2. get volume by dividing mass in grams by density

    (5) not enough information to calculate volume (18 trees)

    Then, to get mass: Mass in g/m2 = (volume*(10,00,000*dens). Density units are g/cm3, so multiply by 10,00,000 to get density as g/m3.

    Coarse and fine woody debris. Volume of coarse woody debris and fine woody debris were calculated based on equations and constants found in Harmon and Sexton. For CWD, species and decay-specific densities from Liu et al. (2006) were used to estimate mass from the volumes.

    Add details as we work through any issues – especially with the FWD. Outstanding FWD questions include:

    (1) Is d2q in the lookup table in cm2 or m2?

    (2) Which values for d2q should we use? One for all plots, or vary depending on dominant species in the plot?

    (3) Double-check density values and choose what to use – again, one value for all plots, or vary depending on dominant species in the plot?

    Litter and soils:

    (1) Oven-dry the soils until they are dry enough to easily pass through a sieve. Sieve the soil (2mm), and separate the rocks. Estimate the volume of total rocks by putting into a measuring flask with water. The increased volume of water is the volume of the rocks. Subtract the rock volume from the total soil volume, which is the core area multiplied by the soil depth.

    (2) We did not separate fine or coarse roots from the soil in this study, although that would have been a valuable (and time-consuming) addition. Most roots would be in the greater than 2mm fraction.

    (3) Record the oven-dry mass of each greater than 2mm and less than 2mm fraction of the soil sample.

    (4) Grind a sub-sample of soil and pack for CN analysis. CN analysis done by lab manager Patel using the Elementar CHN Analyzer in the Torrey Lab.

    (5) Calculate soil bulk density (dry soil, g/m3). The volume is calculated from the depth of the soils cores and the area of the auger.

    (6) Archive a 4oz container of the less than 2mm fraction of the mineral and organic soils. Discard the rest of the soils.

    (7) Dry the litter for 2 days at 70oC. Weigh the oven-dry samples, then discard.

  • Use:

    This dataset is released to the public under Creative Commons license CC BY (Attribution). Please keep the designated contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.

  • Citation:

    Barker Plotkin A, Ignace D, Nicoll L, Tang J, Rapp J. 2016. Prospect Hill Forest Plots at Harvard Forest 2013. Harvard Forest Data Archive: HF271.

Detailed Metadata

hf271-01: plots

  1. plot.92: plot number (1-269; see pipe.92 & plot.37 for alternative plot identifiers)
  2. comp: compartment of Prospect Hill tract in which plot is located (1-9)
  3. pipe.92: the pipes marking one corner of each plot in the field are engraved with the set of Tract-Compartment-Pipe Number (e.g. P-1-1), so this is the third part of the unique pipe identifier
  4. plotno.37: plot number assigned in 1937
  5. lat.gps: latitude of plot, based on Garmin GPS in 2013 (degrees N; 94/269 plots located) (unit: degree / missing value: NA)
  6. long.gps: longitude of plot, based on Garmin GPS in 2013 (degrees W; 94/269 plots located) (unit: degree / missing value: NA)
  7. lat.92: approximate location (latitude, degrees N) (unit: degree / missing value: NA)
  8. long.92: approximate location (longitude, degrees W) (unit: degree / missing value: NA)
  9. veg.37: trees and understory vegetation sampled in 1937
    • 1: yes
    • 0: no
  10. veg.92: trees and understory vegetation sampled in 1992
    • 1: yes
    • 0: no
  11. soils.92: soil cores sampled and analyzed in 1992
    • 1: yes
    • 0: no
  12. veg.13: trees and dead wood pools sampled in 2013
    • 1: yes
    • 0: no
  13. soils.13: soil cores sampled and analyzed in 2013
    • 1: yes
    • 0: no
  14. phys.13: ecophysiology measurements of plants in 2013
    • 1: yes
    • 0: no
  15. treecore.13: tree cores sampled from all stems greater than or equal to 10 cm dbh
    • 1: yes
    • 0: no
  16. orient.92: orientation recorded from the 1992 inventory. The plot is oriented in this direction relative to the iron pipe (e.g. NE means that the pipe is in the SW corner of the plot)
  17. orient.13: plot is oriented in this direction relative to the iron pipe when plot was set out in 2013 (e.g. NE means that the pipe is in the SW corner of the plot). In 2013, all 4 corners of each plot (in which veg.13 = 1) were marked with painted pvc pipes.
  18. notes.13: notes relevant to plot set-up in 2013
  19. aspect: aspect measured by compass (degrees, 0-359), from 1992 survey (unit: degree / missing value: NA)
  20. slope: slope (degrees, not percent), from 1992 survey (unit: degree / missing value: NA)
  21. position: landscape position (factor with levels: LOW, MID LOW, MID, MID LEV, UPPER). Documentation of the level meanings not found, from 1992 survey.
  22. relief: microrelief, probably related to prevalence of pits/mounds and stumps (factor with levels 1-4). Documentation of the level meanings not found, from 1992 survey.
  23. rocks: cover-abundance of rocks, from 1992 survey
    • 0: none
    • 1: less than 1%
    • 2: 1-3%
    • 3: less than 5%
    • 4: 5-15%
    • 5: 16-25%
    • 6: 26-50%
    • 7: 51-75%
    • 8: greater than 75%
  24. hurricane: 1938 hurricane damage (1-6) , from Rowlands (1941) maps
    • 0: no damage
    • 1: 1-10% damage
    • 2: 11-25% damage
    • 3: 26-50% damage
    • 4: 51-75% damage
    • 5: 76-90% damage
    • 6: >90% damage
  25. fire1957: 1957 fire, based on HF archive map
    • 1: yes
    • 2: no
  26. abandon: abandonment date for sites that were cleared for agriculture. After Foster map of abandonment in HF Archives.
  27. series: soil series, from 1992 survey
    • 1: Canton loam
    • 2: Whitman loam
    • 3: Montauk fine sandy loam
    • 4: Scituate loam
    • 5: Charlton loam
    • 6: Brookfield loam
    • 7: peat
    • 8: Agawam fine sandy loam
    • 9: Windsor loamy sand
    • 10: rock outcrop
    • 11: shallow peat
    • 12: made land
    • 13: Freetown muck
    • 14: Chatfield fine sandy loam
  28. use: most intensive historical land-use, from 1992 survey
    • 1: cultivated
    • 2: improved pasture
    • 3: unimproved pasture
    • 4: woodlot
  29. drainage: soil drainage class, from 1992 survey
    • 1: somewhat excessively drained
    • 2: well drained
    • 3: moderately well drained
    • 4: poorly drained
    • 5: very poorly drained
    • 6: somewhat poorly drained
  30. plantation: plantation (planted trees)
    • 1: yes
    • 0: no

hf271-02: live trees

  1. year: year, 2013
  2. plot: plot number (matches plot.92)
  3. treenum: tree number within each plot. Re-starts at ‘1’ in each plot. Trees are not tagged in field.
  4. species: tree species code
    • ACPE: Acer pensylvanicum
    • ACRU: Acer rubrum
    • ACSA: Acer saccharum
    • ACSC: Acer spicatum
    • BEAL: Betula alleghaniensis
    • BELE: Betula lenta
    • BEPA: Betula papyrifera
    • BEPO: Betula populifolia
    • BESP: Betula, species not determinded (either BELE or BEAL)
    • CADE: Castanea dentata
    • FAGR: Fagus grandifolia
    • FRAM: Fraxinus americana
    • NYSY: Nyssa sylvatica
    • PIRE: Pinus resinosa
    • PIRU: Picea rubens
    • PISP: Picea, species not determined
    • PIST: Pinus strobus
    • POGR: Populus grandidentata
    • PRSE: Prunus serotina
    • QUAL: Quercus alba
    • QURU: Quercus rubra
    • TSCA: Tsuga canadensis
  5. dbh.cm: diameter at breast height (unit: centimeter / missing value: NA)
  6. notes: tree-specific notes. If a 5-digit number is recorded, that matches to the 35-hectare SIGEO plot (HF253)

hf271-03: standing dead trees

  1. year: year, 2013
  2. plot: plot number (matches plot.92)
  3. species: tree species code
    • ACPE: Acer pensylvanicum
    • ACRU: Acer rubrum
    • ACSA: Acer saccharum
    • ACSP: Acer, species not determined
    • BEAL: Betula alleghaniensis
    • BELE: Betula lenta
    • BEPA: Betula papyrifera
    • BEPO: Betula populifolia
    • BESP: Betula, species not determined
    • CADE: Castanea dentate
    • CONF: conifer
    • DECD: deciduous (hardwood)
    • FAGR: Fagus grandifolia
    • FRAM: Fraxinus americana
    • PIRE: Pinus resinosa
    • PISP: Picea, species not determined
    • PIST: Pinus strobus
    • PRSE: Prunus serotine
    • PRSP: Prunus serotine
    • QUAL: Quercus alba
    • QURU: Quercus rubra
    • TSCA: Tsuga Canadensis
    • UNKN: unknown
  4. method: method for determining volume, based on measurements available
    • 1: calculate volume as frustrum of cone inputting baseD; topD; height_m
    • 2: calculate volume as frustrum of cone with slightly too-small base diameter inputting dbh, topD, height_m
    • 3: calculate volume as cylinder inputting dbh_cm and height_m
    • 4: calculate MASS with species-specific allometric equation, then use the INVERSE of the DENSITY to calculate volume
    • 5: do not calculate volume: too much missing information (18 trees total)
  5. decay: decay class
    • 1: solid wood, recently fallen, bark and twigs present
    • 2: solid wood, significant weathering, branches present
    • 3: wood not solid, bark may be sloughing but nail still must be pounded into wood
    • 4: wood sloughing and/or friable, nail may be forcibly pushed into wood
    • 5: wood friable, barely holding shape, nail may be easily pushed into wood
  6. type: category of dead wood
    • intact: intact tree, most branches/crown remaining
    • snag: standing dead tree with branches and part of crown missing
  7. treenum: tree number within each plot. Re-starts at ‘1’ in each plot. Trees are not tagged in field.
  8. dbh.cm: diameter at breast height for the dead tree (unit: centimeter / missing value: NA)
  9. base.d: diameter at the base of the down wood piece (unit: centimeter / missing value: NA)
  10. mid.d: diameter at the midpoint of the down wood piece (unit: centimeter / missing value: NA)
  11. top.d: diameter at the midpoint of the down wood piece (unit: centimeter / missing value: NA)
  12. ht.direct.m: height of wood piece in meters, measured directly with tape or estimated with pole (unit: meter / missing value: NA)
  13. clinht.d: if height determined with clinometer, the distance measurement (unit: meter / missing value: NA)
  14. clinht.t: if height determined with clinometer, the top measurement (percent) (unit: dimensionless / missing value: NA)
  15. clinht.b: if height determined with clinometer, the bottom measurement (percent) (unit: dimensionless / missing value: NA)
  16. height.m: height of snag (meters), either directly measured, or calculated by clinomter (ht = (clinht.t-clinht.b)*clinht.d) (unit: meter / missing value: NA)
  17. dens.gcm3: density, grams per cubic cm, based on species (species group) and decay class values from Liu, W.H., D.M. Bryant, L.R. Hutyra, S.R. Saleska, E. Hammond-Pyle, D. Curran and S.C. Wofsy. 2006. Woody debris contribution to the carbon budget of selectively logged and maturing mid-latitude forests. Oecologia 148:108-117. (unit: gramPerMeterCubed / missing value: NA)
  18. volume.m3tree: calculated volume based on method specified, m3 (for the individual snag). For method 4, volume is back-calculated using biomass calculated from allometries and the inverse of density, ie., divide mass (g) by density (g/1000000*cm3) (unit: cubicMeter / missing value: NA)
  19. vol.m3m2: volume of snag, m3 per m2, based on snag volume and plot size (506.25m2). Sum by plot to get total volume per unit area. (unit: meterCubedPerMeterSquared / missing value: NA)
  20. biomass.g.tree: for method 4 only, mass of snag calculated by allometry (unit: gram / missing value: NA)
  21. mass.gm2: calculated mass in g/m2, based on volume per area (m3/m2) and density (g/cm3). Sum by plot to get total mass per unit area. (unit: gramsPerSquareMeter / missing value: NA)
  22. notes: snag-specific comment. If a 5-digit number is recorded, that matches to the 35-hectare SIGEO plot (HF253)

hf271-04: coarse woody debris

  1. year: year of measurement
  2. plot: number of the 22.5 m x 22.5 m plot (matches plot.92)
  3. tran: transect number. Aggregate by plot & transect for volume or mass of cwd per unit area
  4. num: sequence of cwd pieces measured, start at 1 for each transect; just to keep spreadsheet in order
  5. tlength: transect length (unit: meter / missing value: NA)
  6. species: tree species code
    • ACPE: Acer pensylvanicum
    • ACRU: Acer rubrum
    • BEAL: Betula alleghaniensis
    • BELE: Betula lenta
    • BEPA: Betula papyrifera
    • BEPO: Betula populifolia
    • BESP: Betula (species not identified)
    • CADE: Castanea dentata
    • CASP: Carya (species not identified)
    • CONF: conifer
    • DECD: deciduous (hardwood)
    • FRAM: Fraxinus americana
    • PIRU: Picea rubens
    • PIST: Pinus strobus
    • PRSE: Prunus serotina
    • QURU: Quercus rubra
    • TSCA: Tsuga canadensis
    • UNKN: unknown
  7. decay: decay class
    • 1: solid wood, recently fallen, bark and twigs present
    • 2: solid wood, significant weathering, branches present
    • 3: wood not solid, bark may be sloughing but nail still must be pounded into wood
    • 4: wood sloughing and/or friable, nail may be forcibly pushed into wood
    • 5: wood friable, barely holding shape, nail may be easily pushed into wood
  8. diam1: diameter of wood where it crosses the transect. A negative number measures the hollow within a log in the same transect (unit: centimeter / missing value: NA)
  9. diam2: second measurement of diameter taken perpendicular to first. Sometimes a second diameter isn’t possible to measure, if the log is pressed into the ground. (unit: centimeter / missing value: NA)
  10. diamavg: elliptical average diameter; if only one diameter recorded, it is in this column (linetran) (unit: centimeter / missing value: NA)
  11. volume.m3m2: volumem3m2=9.869*(((diamavg/100)^2)/(8*tlength)) (unit: meterCubedPerMeterSquared / missing value: NA)
  12. density.gcm3: density, grams per cubic cm, based on species (species group) and decay class values from Liu, W.H., D.M. Bryant, L.R. Hutyra, S.R. Saleska, E. Hammond-Pyle, D. Curran and S.C. Wofsy. 2006. Woody debris contribution to the carbon budget of selectively logged and maturing mid-latitude forests. Oecologia 148:108-117. (unit: gramPerMeterCubed / missing value: NA)
  13. mass.gm2: mass=volume*(dens*1000000) (unit: gramsPerSquareMeter / missing value: NA)
  14. notes: notes

hf271-05: fine woody debris

  1. year: year of measurement (2013)
  2. plot: plot identifier (matches plot.92)
  3. transect: transect number (1 or 2)
  4. distance.m: transect length fwd tallied (m) (unit: meter / missing value: NA)
  5. size.class: size class of fwd
    • 1: 0.65 - 2.4 cm
    • 2: 2.5 - 7.5 cm
  6. count: number of fwd pieces along the transect (unit: number / missing value: NA)
  7. d2q: squared average quadratic mean diameter, m2, by species/species type and size class (Harmon and Sexton 1996) (unit: squareMeter / missing value: NA)
  8. a: species-specific constant used to calculate volume (Harmon and Sexton 1996) (unit: dimensionless / missing value: NA)
  9. dens: species-specific density, g/cm3, used to calculate mass (Harmon and Sexton 1996) (unit: gramsPerCubicCentimeter / missing value: NA)
  10. volume: volume, m3/m2, calculated with the equation [V= 9.869*count*a*(d2q/(8*distance.m))], where V is volume per unit area, d is the quadratic mean piece diameter for a size class, and a is the average secant piece along the transect (Harmon and Sexton 1996). (unit: gramsPerSquareMeter / missing value: NA)
  11. mass: mass, g/m2, calculated from the equation dens*(1/0.000001))*volume}. Do not sum mass for a plot; each transect stands alone, so you'd average if there is >1 mass per plot. (unit: gramsPerSquareMeter / missing value: NA)

hf271-06: litter mass

  1. year: year of sample (2013)
  2. plot: plot identifier (matches plot.92)
  3. sample: in most cases, three samples of litter (and soils) taken from each plot
  4. mass.g: oven dry mass of the litter (unit: gram / missing value: NA)
  5. litter.gm2: mass of litter on a per-m2 basis (unit: gramsPerSquareMeter / missing value: NA)
  6. note: there were a few litter samples that may have been mis-labeled; these and other comments are here

hf271-07: soils

  1. year: year of sample (2013)
  2. plot: plot identifier (matches plot.92)
  3. sample: in most cases, three samples of litter (and soils) taken from each plot (45 plots had soil sampled in 2013), and separated into mineral and organic horizons
  4. horizon: soil horizon
    • M: mineral
    • O: organic
  5. depth.cm: depth of the sample (15cm for M; variable depth for O) (unit: centimeter / missing value: NA)
  6. core.area.cm2: O layer collected with a 15cm by 15cm frame (225 cm2); M layer collected with a cylindrical corer with inside diameter of 5 cm (2 in.) but also with a plastic sleeve insert, making the effective diameter 4.7cm (unit: squareCentimeters / missing value: NA)
  7. core.vol.cm3: calculated based on depth.cm and core.area.cm2 (unit: centimeterCubed / missing value: NA)
  8. rock.vol.cm3: volume of rocks in the sample, estimated by water displacement (unit: centimeterCubed / missing value: NA)
  9. soil.vol.norock.cm3: core volume after removing rock volume is the soil volume (core.vol.cm3 – rock.vol.cm3) (unit: centimeterCubed / missing value: NA)
  10. mass.fine.g: oven-dry mass of soil that passed through a 2mm sieve (unit: gram / missing value: NA)
  11. mass.coarse.g: oven-dry mass of soil too large to pass through a 2mm sieve (unit: gram / missing value: NA)
  12. mass.tot.g: mass.fine.g + mass.coarse.g (unit: gram / missing value: NA)
  13. bulk.dens.gcm3: bulk density, based on mass.tot.g/soil.vol.norock.cm3 (unit: gramsPerCubicCentimeter / missing value: NA)
  14. c.percent: Carbon content based on ground subsample prepared for Elementar CHN Analyzer (unit: dimensionless / missing value: NA)
  15. c.gg: conversion from percent carbon to carbon on a gC per g soil basis (C.percent/100) (unit: dimensionless / missing value: NA)
  16. carbon.gm2: soil carbon stock on an area basis (g carbon per m2). Formula = bulk.dens.gm3 * C.gg * 1,000,000 * (depth.cm/100) (unit: gramsPerSquareMeter / missing value: NA)
  17. n.percent: Nitrogen content based on ground subsample prepared for Elementar CHN Analyzer (unit: dimensionless / missing value: NA)
  18. note: notes