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Effects of Management on Carbon Sequestration in Forest Biomass in Southeast Alaska


The Tongass National Forest (Tongass) is the largest national forest and largest area of old-growth forest in the United States. Spatial geographic information system data for the Tongass were combined with forest inventory data to estimate and map total carbon stock in the Tongass; the result was 2.8 ± 0.5 Pg C, or 8% of the total carbon in the forests of the conterminous USA and 0.25% of the carbon in global forest vegetation and soils. Cumulative net carbon loss from the Tongass due to management of the forest for the period 1900–95 was estimated at 6.4–17.2 Tg C. Using our spatially explicit data for carbon stock and net flux, we modeled the potential effect of five management regimes on future net carbon flux. Estimates of net carbon flux were sensitive to projections of the rate of carbon accumulation in second-growth forests and to the amount of carbon left in standing biomass after harvest. Projections of net carbon flux in the Tongass range from 0.33 Tg C annual sequestration to 2.3 Tg C annual emission for the period 1995–2095. For the period 1995–2195, net flux estimates range from 0.19 Tg C annual sequestration to 1.6 Tg C annual emission. If all timber harvesting in the Tongass were halted from 1995 to 2095, the economic value of the net carbon sequestered during the 100-year hiatus, assuming $20/Mg C, would be $4 to $7 million/y (1995 US dollars). If a prohibition on logging were extended to 2195, the annual economic value of the carbon sequestered would be largely unaffected ($3 to $6 million/y). The potential annual economic value of carbon sequestration with management maximizing carbon storage in the Tongass is comparable to revenue from annual timber sales historically authorized for the forest.

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W.W.L. received financial support from the Royce Fellowship program at Brown University. We thank Mike McClellan, Frances Biles, and Dave D’Amore at the US Forest Service Juneau Forestry Sciences Laboratory for invaluable help with data collection and manipulation. Mark Harmon, Linda Heath, and two anonymous reviewers provided insightful comments on an earlier version of the manuscript.

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Correspondence to Wayne W. Leighty.



USDA Forest Service personnel used global positioning system (GPS) units to locate the center point of each sampling plot and three additional points 36.6 m from the central point at azimuths of 360°, 120°, and 240°.

  1. 1.

    Trees more than 12.5 cm in diameter at breast height (dbh) were recorded at all four points in 14.6-m–diameter subplots.

  2. 2.

    Trees/saplings/seedlings less than 12.5 cm dbh were recorded at all four points in 4-m–diameter subplots.

  3. 3.

    Main Vegetation Types (MVT) were defined based on a combination of species cover and stature at all four points using the 14.6-m–diameter subplots.

  4. 4.

    Horizontal/vertical (HV) profiles of understory vegetation were used to estimate percent cover in two 11.3-m–diameter subplots within each MVT identified at the sampling location.

  5. 5.

    Three 11.3-m downed-wood transects intersecting at the center with azimuths of 360°, 120°, and 240° were constructed in each HV subplot.

  6. 6.

    One soil pit (no more than 50 cm deep) was dug at a representative location within each plot.


Table 4 Allometric Equations Used to Convert Forest Inventory Assessment (FIA) Grid Data to Carbon Stocks


Table 5 Decay Reduction Factors for Calculation of Carbon in Standing Dead Wood
Table 6 Visual Cues Used by Forest Inventory Assessment (FIA) Crews to Determine the Stage of Decay


The cubic volume (m3) of each piece of CWD was calculated as:

$$ V = [(\pi/8) \times (D_{S}^{2} + D_{L}^{2}) \times l]/10,000 $$

where D S is the small-end diameter (cm), D L is the large-end diameter (cm), and l is the length of the piece of CWD (m). The volume for each piece of CWD was then converted to an areal value (m3/ha) using the following equation:

$$ V_{a} = (\pi/(2 \times L)) \times (V/l) \times 10,000m^{2}/ha $$

where L is the total length of the transect line in the plot (m). Finally, the oven-dried biomass (kg/ha) for each piece of CWD was calculated as:

$$ B = V_{a} \times 1,000kg/m^{3} \times SpG \times DCR $$

where SpG is the specific gravity of the debris piece (varies by species, unitless), and DCR is the decay class reduction factor, calculated as 1−% decay (recorded to the nearest 5% in Forest Inventory Assessment [FIA] data), and expressed as a decimal. The mass per unit area was summed for all CWD recorded in the FIA data and multiplied by 0.5 to calculate the total carbon stock.

As an example of the methods used to calculate carbon stocks in SWD: Total carbon stock (kg/ ha) in Pacific silver fir SWD was calculated as:

$$ \eqalign { \{[{398.311.64} &\times {freq} \times {2.76} \times {0.4} \cr &\times 1.13 \times \sqrt{(1 + (({slp \over freq})/100)^{2})]/37} \} \cr &\times 0.5 \times (1 - 0.008 \times {decay \over freq})}$$

where freq is the total number of intersections with Pacific silver fir SWD recorded in the FIA data, slp is the sum of slope estimates (% slope) for all the records of Pacific silver fir SWD in the FIA data, and decay is the sum of decay estimates (amount of rotten or otherwise missing wood, recorded as a percent) for all records of Pacific silver fir SWD in the FIA data. The quantity (slp/freq) is an estimate of the average slope at the FIA sample point, and the quantity (decay/freq) is an estimate of the average amount of decay in Pacific silver fir SWD at the FIA sample point. The non-slash, non-horizontal correction factor and “composite” species composition factors described by Brown (1974) were used to develop species-specific equations. The specific gravity of hardwood SWD lacking species identification was assumed to be 0.363, the numerical average of all hardwood species found in southeast Alaska (US Forest Products Laboratory 1974). Estimates of carbon stocks in SWD calculated with species-specific equations, like the one described above, were summed across all species to estimate total carbon stock (kg/ha) in SWD at each FIA sample point.


Table 7 Organic Carbon in Soil Categories


Plotting soil carbon stock against aboveground carbon stock for each Aboveground Carbon Polygon Type (ACPT) shows a relationship between aboveground and soil carbon stocks that reflects general ecosystem characteristics. High soil carbon stocks in muskeg and deep saprist soils correlate with moderate to low aboveground carbon stocks (filled circles). A wide range in aboveground carbon stocks is supported by a well-defined range of soil carbon stock (filled triangles). Indicating the influence of other factors on aboveground carbon stocks. Rocky, icy, and otherwise harsh locations (filled squares) have little soil or aboveground carbon. ACPT 8 is an outlier (open diamond).

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Leighty, W.W., Hamburg, S.P. & Caouette, J. Effects of Management on Carbon Sequestration in Forest Biomass in Southeast Alaska. Ecosystems 9, 1051–1065 (2006).

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  • carbon sequestration
  • geographic information system
  • climate change
  • forest management
  • Alaska