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Harvard Forest Data Archive


Specific Leaf Area in the Clearcut Site at Harvard Forest 2012

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  • Lead: Christopher Williams
  • Investigators: Myroslava Khomik, Richard MacLean
  • Contact: Information Manager
  • Start date: 2012
  • End date: 2012
  • Status: completed
  • Location: Prospect Hill Tract (Harvard Forest)
  • Latitude: +42.546
  • Longitude: -72.174
  • Elevation: 403 meter
  • Taxa:
  • Release date: 2015
  • Revisions:
  • EML file: knb-lter-hfr.229.2
  • DOI: digital object identifier
  • EDI: data package
  • DataONE: data package
  • Related links:
    • Study type: short-term measurement, modeling
    • Research topic: ecological informatics and modelling; forest-atmosphere exchange; physiological ecology, population dynamics and species interactions
    • LTER core area: primary production, disturbance
    • Keywords: canopy cover, foliage, leaf area index, leaves, plant physiology
    • Abstract:

      Clearcutting a forest ecosystem can result in a drastic reduction of the stand’s productivity. Despite the severity of this disturbance type, past studies have found that the productivity of young regenerating stands can quickly rebound, approaching that of mature undisturbed stands within a few years. One of the obvious reasons is increased leaf area with each year of recovery. However, a less obvious reason may be the variability in species composition and distribution during the natural regeneration process. The purpose of this study was to investigate to what extent the increase in GEP, observed during the first four years of recovery, in a naturally regenerating clearcut stand was due to 1) an overall expansion of leaf area, and 2) an increase in the canopy’s photosynthetic capacity stemming from either species compositional shifts or drift in physiological traits within species. We found that the multi-year rise in GEP following harvest was clearly attributed to the expansion of leaf area rather than a change in vegetation composition. Sizeable changes in relative abundance of species were masked by remarkably similar leaf physiological attributes for a range of vegetation types present in this early successional environment. Comparison of upscaled leaf-chamber to eddy-covariance-based light-response curves revealed broad consistency in both maximum photosynthetic capacity and quantum yield efficiency. The approaches presented here illustrate how chamber- and ecosystem-scale measurements of gas exchange can be blended with species-level leaf area data to draw conclusive inferences about changes in ecosystem processes over time in a highly dynamic environment.

    • Methods:

      The study site occupies roughly a 200 m x 400 m area (8 ha) near the top of Prospect Hill, within the Harvard Forest Long Term Ecological Research Site. Specific leaf area (SLA, in cm2/g) was estimated for the ten most abundant species at the site. These species represented some 80% of the vegetation in year 2012.

      More than 100 leaves were sampled for each species in August of 2012. Leaves were collected randomly along the five transects used for vegetation line-intercept measurements and analyzed within two days of collection. Individual leaves were scanned on the LI-3000 leaf area meter (LI-COR Biosciences, Lincoln, NE, USA) and their area (in cm2) recorded. The scanned area was corrected by the calibration curve for the machine. Scanned leaves were oven-dried for 48 hours (at 60oC). Dried leaves were weighed on a digital balance and specific leaf area was calculated for each leaf as follows:

      SLA = area (cm2)/weight (g)

      where area is the one-sided projected leaf area of a single leaf and weight is the dry weight of that leaf. A mean across all leaves sampled, for each species sampled, was used in leaf area index calculations using also the litterfall data (see HF230).

      Notes: Fern samples were scanned, but not retained for weighing - will need to resample in 2013. Raspberry and blackberry leaves are compound leaves, but we scanned and weighed the individual leaflets - not sure if that would cause errors. Beech and birch were not differentiated, so check if the bi data gives double peaks, which would indicate that we had two distinct groups of samples, bi and be, that should have been separated.

      Calibration curve for HF leaf area meter: y = 0.9664x - 0.5567, R2 = 1, where y = LI-3000 area (cm2) and x = area of standard (cm2).

    • Use:

      This dataset is released to the public under Creative Commons CC0 1.0 (No Rights Reserved). Please keep the dataset creators informed of any plans to use the dataset. Consultation with the original investigators is strongly encouraged. Publications and data products that make use of the dataset should include proper acknowledgement.

    • Citation:

      Williams C. 2015. Specific Leaf Area in the Clearcut Site at Harvard Forest 2012. Harvard Forest Data Archive: HF229 (v.2).

    Detailed Metadata

    hf229-01: specific leaf area

    1. id: unique data identifier
    2. transect: vegetation transect along which the leaf was collected
    3. species: species code
      • bb: blackberry
      • bi: birch (paper?)
      • oa: oak (red?)
      • ws: wild sasparilla
      • pc: pin cherry
      • rm: red maple
      • rb: raspberry
      • bc: black cherry
      • kw: knotweed
    4. common name of species sampled
    5. replicate: replicate of leaf collected of the same species at the site
    6. cumm.area: cumulative area on machine's counter as measured by machine while processing leaves (unit: squareCentimeters / missing value: NA)
    7. leaf.area: leaf area of the actual leaf, as measured by machine, often difference between previous and current area count on the machine (unit: squareCentimeters / missing value: NA)
    8. la.corrected: actual leaf area of the scanned leaf, corrected by the calibration curve for the machine (unit: squareCentimeters / missing value: NA)
    9. la.notes: notes on leaf area
    10. match.code: code used to match leaf areas with weights, the two were processed separately, these were the codes on the envelopes containing the leaf samples
    11. dry.weight: dry leaf weight (unit: gram / missing value: NA)
    12. sla.cm2: calculated specific leaf area (unit: squareCentimeterPerGram / missing value: NA)
    13. sla.m2: calculated specific leaf area (unit: squareMeterPerGram / missing value: NA)
    14. species identification code, used in further analysis
      • bb: blackberry
      • bi: birch (paper?)
      • oa: oak (red?)
      • ws: wild sasparilla
      • pc: pin cherry
      • rm: red maple
      • rb: raspberry
      • bc: black cherry
      • kw: knotweed
    15. notes: notes