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A Plan for Monitoring Carbon on Middlebury College Lands Biology 490

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Table of Contents Cover Sheet………………………………………………………………..………………………….…….1 Contents………………………………………………………………..…………….……...……………2 Acknowledgements………………………………………………………..………..………………………4 Executive Summary……………………………………..………………………..………………………5 Introduction………………………………………………………………..…………………………………6 History of Carbon at Middlebury………………………………………………………………..…………7

- History/overeview of Middlebury lands and land use………………………………………………………………..……………………….…….8

- Goals of this report…………………………………………..………………….……………..8 Study Areas ………………………………………………………………..……………………………….9

- Battell Research Forest…………………………….……………..…………………………10 - Breadloaf (Myhre Cabin)………………………………………………………..………......11 - Fertig Lot…...………………………………………………..……………………………......11 - Lussier Farm………………………………………………………………..………………...11 - Alternatives………………………………………………………………..…………………..11

Measuring Carbon in live biomass………………………………………………………………..……..12 Measuring Carbon in Woody Debris …………………………………………………………………....13

- Coarse Woody Debris………………………………………………………………..………………………...13 o Estimating CWD Volume

………………………………………………………………..……………………………13 o CWD Decay Classification………………………………………………………………14 o Determining Carbon Content of CWD…………………………………………………15

- Fine Woody Debris………………………………………………………………..………….15 o Subplots………………………………………………………………..……………...….15 o Measuring FWD………………………………………………………………..…...……15

- Repetition………………………………………………………………..…………………….16 - Unknowns and

Issues………………………………………………………………..…………………….…..16 Measuring Carbon in Soils and Forest Floor………………………………………………………...…16

- Field Methods………………………………………………………………..…………….…16 o Within Plot Sampling Design……………………………………………………………17 o Soil Sampling Design……………………………………………………………………18

- Lab Analysis……………………………………………………………………………….… 19 Opportunities for Community Involvement ……………………………………………………………..19 Conclusion………………………………………………………………..……………………………......20 Works Cited………………………………………………………………..……………………………....21 Appendix A ………………………………………………………………..……………………………….23 Appendix B………………………………………………………………..……………………………

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Figures 1 Locations of study areas……………………………………………………………….. 10 2 A sample 25 m2 permanent plot with two soil types. Soil sampling is represented by the

star shapes and will occur in each soil type within a plot, as is described in Section VI: Measuring C in Soils and Forest Floor. FWD sampling is described in Section V: Measuring Carbon in Woody Debris. The black dots represent the 5 randomly chosen subplots of 0.5x0.5 m from which FWD will be measured……………………………………………………………………………...…17

3 Soil sampling design. Each hash mark indicates a potential sampling point from which

samples will be selected randomly at every monitoring event. One sampling point is chosen from each cardinal direction, for a total of eight samples per monitoring event……………………………………………………………………………………..18

Tables 1 Sample data sheet that will be used for calculations of carbon in live biomass………...12 2 The stage of decay classes for CWD as defined by Harvard Forest (Harvard Forest CWD

Protocol)………………………………………………………….....…………………....14 3 A sample data sheet for the collecting information on course woody debris……………15 4 A sample datasheet for collecting information on fine woody debris…………………...16 5 Sample data sheet. Provides site, soil type, identification information, and room for field

observations of each soil sample…………………………………………………..……..18

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Acknowledgements

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Executive summary ● Earth’s climate has already warmed 0.6 C during the past century, and is expected to

continue to warm ● 1990 Kyoto Protocol was signed by major world leaders and put into effect by some, but

not others (i.e. the US) ● Although Kyoto didn’t happen, individual states and institutions have taken carbon

emissions reductions into their own hands ● RGGI was established in 2008 to reduce carbon emissions in the power sector by 10% by

2018 using a cap-and-trade system ● Middlebury college has set the goal to be carbon neutral by 2016 ● The College is the largest private land owner in Addison County, and therefore land

management could play a large role in achieving its carbon neutrality goals. ● Ten permanent, 625 m2 plots will be established at the Battell Research Forest, the

Breadloaf campus, and forested wetlands owned by the college in the Champlain Valley to monitor carbon storage.

● We propose protocols for monitoring carbon within live biomass, woody debris, and forest floors

● Woody debris will be split into two categories, coarse woody debris and fine woody debris, and measured every five years in each plot.

● Soils will be monitored on a five-year sampling regime for each soil type at each plot ● Potential community involvement in a carbon monitoring program could help raise

awareness of climate change, sustainability, and forest management, and increase college-town dialogue

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I. Introduction Since the industrial revolution, atmospheric concentrations of CO2 and other greenhouse gases have increased substantially (Lai 2004). As a result, the earth’s climate has warmed by approximately 0.6oC during the last century (IPCC 2001). Along with a warming climate the IPCC predicts more variable precipitation, altered frequencies and intensities of extreme weather, and a rise in sea-level (2007). While many of these impacts are now unavoidable, the severity of some future impacts can be lessened through the mitigation of carbon emissions now (IPCC 2007). In 1990 leaders from countries across the globe came together in Kyoto, Japan to discuss targets and methods for green house gas (GHG) emissions reduction (UNFCCC 2011). The major feature of the Kyoto Protocol is that it sets binding targets for 37 industrialized nations and the European Community for GHG mitigation. The targets amount to a 5 percent decrease of GHG emissions from 1990 levels over a five-year period from 2008 to 2012 (UNFCCC 2011). Several market-based incentives were suggested for countries to achieve their goals; Emissions Trading, Clean Development Mechanisms, and Joint Implementation (FCCC 2011). While some signers of the Kyoto Protocol, such as the European Union, have made good progress toward their promised targets, others have not. The United States in particular, one of the largest carbon emitters in the world, signed the Kyoto Protocol but did not put any initiatives into action after Kyoto. While a setback to the global push for emissions reductions, some states and individual institutions within the United States have moved forward on setting their own goals and methods for carbon reduction. The Northeastern United States in particular has begun the Regional Green House Gas Initiative (RGGI). Established in 2008, the RGGI is an agreement between ten Northeastern and Mid-Atlantic states to reduce CO2 emissions from the power sector 10% by 2018 using a cap a trade system (RGGI 2011). The RGGI auctions off permits each quarter to large power companies and uses the money raised to invest in projects related to energy efficiency, renewable energy, and clean energy technologies (RGGI 2011). Regulated power plants can also use a limited number of CO2 offsets to meet up to 3.3% of their compliance obligations. These offsets are used to carry out projects in greenhouse gas emissions reductions or carbon sequestration outside the capped electricity sector (RGGI 2011). Individual institutions have also made strides toward reducing their carbon emissions. Middlebury College in particular has set the goal of becoming carbon neutral by 2016. In order to achieve this lofty goal, the college must have a good sense what their net carbon output is. For the past several years the college has worked hard to assess their emissions and develop a comprehensive plan for reductions. II. History of Carbon at Middlebury

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The most recent Middlebury College Campus Master Plan, written in 2008, puts a strong emphasis on sustainable development, as defined by the UN Brundtland Commission. The college sees itself obliged to minimize the deleterious effects of “the end of oil, global warming, environmental degradation, species extinctions, and increasing human population pressure on resources and the environment.” (Campus Master Plan 2008) In accordance to this obligation, the college launched five efforts regarding sustainability:

1. A comprehensive overview and assessment 2. A building energy audit 3. A transportation audit 4. A landscape and planning effort 5. A student-initiated and produced Carbon Initiative Study

The last effort on this list, the student-led Carbon Initiative Study, resulted in the adoption of the 2007 Carbon Neutrality Initiative. The initiative recommends “that the college achieve carbon neutrality relative to its ‘directly responsible footprint’ of 29,287 Millions of Tons of Carbon Dioxide Equivalent” by 2016. (Campus Master Plan 2008) Since the adoption of the initiative, the biggest effort towards carbon neutrality was the installation of a $12 million biomass gasification facility in 2009. The plant offsets about 1 million gallons of #6 fuel oil with 20,080 tons of woodchips annually. All of the woodchips for the biomass plant are acquired through a broker, which makes carbon accounting very difficult. As such, ENVS401 wrote an extensive report in the fall of 2009, aimed at better understanding the source of the woodchips. Because the college cannot account for the source of the woodchips, it cannot say whether the biomass plant significantly reduces CO2 emissions. A very detailed set of procurement standards are outlined in the report that would make carbon accounting possible (ES401 Fall 2009 Biomass Report). Additionally, the plant has not been running long enough to generate enough usage data. The college is as of yet unable to say how much biomass it needs in one year to run the plant. As an alternative, the college is testing the feasibility of growing its own biomass on college-owned lands and nearby farms. Together with the State University of New York College of Environmental Science and Forestry, the college set up tests plots growing willow shrubs. There have been preliminary results but more research needs to be done. In addition to biomass, the college has a few relatively minor projects in efficiency, wind, building design and solar thermal. Blatantly absent from the college’s carbon neutrality plan, however, is land management. Middlebury is the largest private land owner in Addison County, yet it has no land management plan for carbon sequestration.

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History/overview of Middlebury lands and land use Middlebury College owns a total of 2467 ha of land outside the main campus: 1300 ha in the Champlain Valley and 1167 ha in the Green Mountains (primarily around the Breadloaf Campus). Much of this land was given to the college in gift by a variety of benefactors; in the Green Mountains, the majority of the college’s land was given by Joseph Battell upon his death in 1915. The New York Times reported on March 22, 1915, that a “tract of more than 20,000 acres of forest land in this State is bequeathed to Middlebury College, to be preserved as a forest park forever, under the will of the late Joseph H. Battell. Mr. Battell, who was publisher of The Middlebury Register, was greatly interested in forest preservation, and had been acquiring virgin timber lands for more than forty years.” Ownership of most of the Battell lands was transferred to the U.S. Forest Service in the 1930s and 1950s, and it is now part of the Green Mountain National Forest. Battell’s deed stipulation that the lands be preserved remains the guiding management principle for the Battell Research Forest, a 42 ha patch of uncut hemlock forest in East Middlebury, VT. The remainder of the college’s lands, outside the Battell Research Forest, have been managed on a largely ad hoc, parcel-by-parcel basis. Small parcels around the Breadloaf campus are enrolled in Vermont Family Forests (VFF), and have been logged in recent years; most of the forest land, however, has been the subject of passive management. In 2008, the Board of Trustees approved a series of land stewardship guidelines, intended to guide the College’s land use decisions and to further the College’s commitment to environmental stewardship. The principles, which are designed as a set of voluntary guidelines, were designed to apply to college lands outside of the main campus, with exceptions made for lands with deeded restrictions on use, or those given in gift and intended for rapid turnover. The principles are as follows.

○ The College recognizes the importance of applying principles of environmental sustainability to the stewardship of its lands beyond the Main Campus.

○ Land stewardship involves fiscally responsible decision-making. ○ The College recognizes that College lands are parts of broader ecosystems, and

promotes practices that improve the biological integrity of those ecosystems. ○ The College recognizes the value of the traditional Vermont landscape and

historically important land uses to Middlebury College and to the larger Vermont community.

○ The College recognizes that appropriate use of lands can help achieve broader sustainability goals, such as reduction of transportation impacts through development of land close to town centers, or reduction of carbon emissions through development of land for alternative energy sources.

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○ The College recognizes the value of existing teaching an research sites and acknowledges the tremendous opportunities for experiential education across disciplines that exist in its network of landholdings.

○ The College embraces the ideal of compatible uses, recognizing that conservation and fiscal prudence are not mutually exclusive objectives, and resolves that responsible stewardship will carefully consider all of these guiding principles.

○ The fifth goal, highlighted in bold, explicitly recognizes the contribution that land management can make to broader sustainability initiatives at Middlebury College. Although the college has opted not to include land-related offsets in its carbon accounting as it moves towards the goal of carbon neutrality, effective management strategies can increase the carbon sequestration potential of the land. A first step in managing college lands for carbon sequestration is monitoring their current carbon uptake; that task is the focus of this report. Goals of this report

1. Identify forested sites, owned by Middlebury College, on which to monitor carbon

sequestration. 2. Outline a protocol for measuring the major ecosystem carbon pools: live biomass, woody

detritus, and soils. 3. Propose an implementation plan for monitoring carbon pools in existing courses (BIOL

323, GEOL 257, others) III. Study Areas We will be monitoring C sequestration in the forested lands owned by Middlebury College. In particular, we will focus our research on the Battell Research Forest, the Breadloaf campus, and forested wetlands in the Champlain Valley (Figure 1). Our proposed sites, located in the Champlain Valley and the western escarpment of the green mountains, offer contrasting land use histories that we will compare in our monitoring of sequestered C. A total of ten 625 m2 permanent plots will be established to monitor carbon in live trees, coarse woody debris (CWD), fine woody debris (FWD), and soil. Live biomass will be measured annually. CWD, FWD, and soils will be monitored every 5 years on a rotating basis. Monitoring at Breadloaf and the Battell Research Forest began in 2010. We have proposed two additional study sites, and will work with the College’s Land Advisory Committee to determine the feasibility of using these sites.

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Figure 1. Locations of study areas. Battell Research Forest Located on the Green Mountains western escarpment at an elevation of ~690 – 1370ft, this 42-hectare old growth hemlock forest that also contains red pine, maple, beech, birch, and hickory. The oldest hemlocks and pines in the forest are roughly 300 years old, with many younger trees filling in areas that have been disturbed by fire or have experienced canopy gaps due to ice and wind storms (Lapin et al. 2010). 5 permanent plots will be established for monitoring:

A. Old-growth hemlock forest B. Red pine C. Second growth hardwood

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D. Wind gap of 1950 E. Ice storm gap of 1998.

Breadloaf (Myhre Cabin) Located approximately 4 miles east of Ripton, VT in the Green Mountians at an elevation of ~1500 ft. a second growth forest of mixed hardwoods and conifers. The history of landuse and disturbance is much more complicated than in the Battell Research Forest (Andrea Lloyd and Steve Trombulak pers.com). We will use a similar monitoring protocol, with permanent plots representing different forest types and ages. 3 permanent plots: A. Hardwood forest plot TBD B. Second hardwood forest plot TBD C. Red pine or norway spruce plantation Fertig lot Located in the Champlain valley, ~1 km south of the Battell Bridge in Middlebury on the eastern bank of Otter Creek. This site is a ~20 hectare floodplain forest dominated by red and silver maple, green ash, American elm, and swamp white oak. The area also contains some shrub swamp, marsh, and open water (Lapin et al. 2010). One permanent plot will be located at this site. Lussier Farm Located ~3.5 km south of the Battell Bridge in Middlebury and ~1 km west of Otter Creek. This site contains agricultural fields, pasture lands and a large portion of the Otter Creek Swamp. This area is characterized by red maple-green ash swamp vegetation with perennially saturated or supersaturated soils that are very rich in organic matter. Associated tree species include American elm, swamp white oak, trembling aspen, yellow birch, and black ash. Trees in most of this site rarely exceed 40 cm dbh (Lapin et al. 2010). One permanent plot will be located at this site. Alternatives Several other options for forested wetlands include Gorham Farm (swamp and floodplain forest), Jackson VIC Lot (successional wetlands), Howard Lot (Wetland conifer plantation), Palmer Lot (Swamp forest), and the Otter Creek Gorge (clayplain forest) (Lapin et al. 2010). Exact locations and shapes of the 10 plots will be determined as monitoring gets underway at each site. IV. Measuring Carbon in live biomass

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In forest ecosystems, live biomass is the living herbaceous and woody vegetation that store carbon through photosynthesis (Vogt et al. 2007). Various methods are used worldwide to measure the carbon pools and fluxes in the above-ground woody biomass (AGWB). Three different approaches are most commonly used: biometry, remote sensing, and the eddy flux technique. Biometric field measurements allow for estimations of carbon in the AGWB through conversions using allometric regression equations (Brown 2002). Live biomass estimations provide insight for forest carbon storage through data collected on growth, health, and productivity. In each of the described plots, carbon in live biomass will be measured (See: Study areas). The AGWB will be estimated through field measurements of the living trees in each plot. Carbon storage will be calculated for trees larger than 5cm in diameter, as is convention in the literature. These calculations are based on the diameter at breast height (DBH) measurements and allometric regression equations developed for groups of similar species. For each tree within the plot, DBH and species will be recorded. Allometric regression equations will be obtained from Jenkins et al. (2004). In pilot studies, we found that the species-specific equations presented in Jenkins et al. varied in the components that could be predicted, and concluded that more consistent results could be obtained by using the generalized biomass equations presented in Tables 1 and 2 of that publication. However, this finding may change as more species-specific equations become available. Table 1. Parameters for estimating aboveground biomass for groups of hardwood and softwood species (referred to in this report as the “generalized equations”). Reproduced from Jenkins et al. (2003). Parameters are used to estimate biomass (bm; kg) using the equation bm=Exp(β0+ β1lndbh). Equations are valid for trees with a dbh of at least 2.5 cm. Species groups are defined in Table 4 of the original Jenkins et al. report.

Parameter Species group β0 β1

Data points

Max DBH R2

Hardwood Aspen/alder/cottonwood/willow -2.2094 2.3867 230 70 0.953 Soft maple/birch -1.9123 2.3651 316 66 0.958 Mixed hardwood -2.4800 2.4835 289 56 0.980 Hard maple/oak/hickory/beech -2.0127 2.4342 485 73 0.988 Softwood Cedar/larch -2.0336 2.2592 196 250 0.981 Douglas-fir -2.2304 2.4435 165 210 0.992 True fir/hemlock -2.5384 2.4814 395 230 0.992 Pine -2.5356 2.4349 331 180 0.987 Spruce -2.0773 2.3323 212 250 0.988 Woodland Juniper/oak/mesquite -0.7152 1.7029 61 78 0.938 Biomass can then be partitioned into components using the component ratios reported in Jenkins et al. (2003; Table 2).

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Table 2. Parameters for estimating biomass components (from Jenkins et al. 2003). Parameters are used to estimate the ratio of total biomass in each component using the equation ratio=Exp(α0+α1/dbh).

Parameter Biomass component α0 α1 Data points R2

(A) Hardwoods Foliage -4.0813 5.8816 632 0.256 Coarse roots -1.6911 0.8160 121 0.029 Stem bark -2.0129 -1.6805 63 0.017 Stem wood -0.3065 -5.4240 264 0.247 (B) Softwoods Foliage -2.9584 4.4766 777 0.133 Coarse roots -1.5619 0.6614 137 0.018 Stem bark -2.0980 -1.1432 799 0.006 Stem wood -0.3737 -1.8055 781 0.155 The sample data sheet (Table 3) notes the study site and biometric plot for biomass measurements. Trees will be tagged with identification numbers so they remain consistent for all sampling periods. Tree species and DBH are recorded for conversions to carbon content and notes will be added as necessary. Tree mortality and new recruitment will also be noted for overall live biomass carbon calculations. The survey each year will tag trees that have grown into the 5 cm diameter class. Table 3. Sample data sheet that will be used for calculations of carbon in live biomass.

Site Plot Tree ID# Species DBH (cm) Notes

BRF A 001 PIRE 34.6 Visible ice storm damage

BRF A 002

BRF B 003

BRF C 004

Live biomass measurements will be taken annually to measure carbon storage and flux in the BRF, Breadloaf, and Fertig and Lussier. Tree growth for the northern-hardwood New England species is notable on an annual time scale and yearly sampling will allow for comparisons of carbon storage between years.

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Though live biomass estimates and calculations are useful for accurate measurements of carbon pools in the forest, they are difficult to attain and monitor for larger, landscape-scale estimates. For this study, however, biometric measurements and carbon calculations in the plots may be representative of the BRF and similar northern-hardwood New England forests. One issue with live biomass measurements are the time and resource constraints of ground field work. Thus, the Biology Department will devote one or two lab classes from BIOL0323 to complete the AGWB measurements. V. Measuring Carbon in Woody Debris Woody debris (WD) accounts for a large amount of the carbon stored in an ecosystem, and has been estimated to hold about 18% of the entire ecosystem carbon in temperate forests (Pregitzer and Euskrichen 2004). While the majority of this tends to be held in coarse woody debris (CWD), fine woody debris (FWD) also plays an important role (Currie and Nadelhoffer 2002). We define CWD as any dead biomass with a diameter greater than 10 cm, while the diameter of FWD falls between 6 mm and 10 cm. Measuring these pools is a multistep process. First, estimate the volume of all WD and second, assign it to a decay class. Based on these decay classes, estimate the density and carbon content of the various pieces. Coarse Woody Debris Estimating CWD Volume First, it is important to tag each piece of CWD to allow for comparisons over time, where possible, also attempt to identify each piece to species. However, if this is too difficult, classify it as hard or soft wood. For downed CWD within our plots we will use the Newton Method of Volume Estimation as described by Harmon and Sexton (1996). This requires three diameter measurements along the length of the log, one on each end and one in the middle, as well as a measure of total length. Volume can then be estimated using the equation: V = L(Ab+4Am+At)/6 Where L represents volume, and A represents base area, middle area, and top area respectively. Base area and top area are interchangeable. If the log is hollow it is important to measure the diameter of the hollow as well and subtract the estimated volume of the hollow from the log’s volume. Logs that lie partially outside of our plot will be treated as though they end at the edge of

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the plot. Stumps, defined as standing woody debris that cannot be estimated effectively using an allometric equation, will be measured in the same way. If stumps are too tall to measure At , At

may be estimated. Dead standing snags, defined as standing woody debris where allometric equations effectively estimate C content, will be treated as live trees (see Measuring C in live biomass), noting whether or not branches are present. CWD Decay Classification The stage of decay of each piece of CWD will be measured using Harvard Forest’s five class system (Harvard Forest CWD Protocol). Table 4. The stage of decay classes for CWD as defined by Harvard Forest (Harvard Forest CWD Protocol).

Decay Class

Characteristics

1 Recently dead, bark intact, twigs present, wood solid

2 Wood solid, some branches present, “significant” signs of weathering, no sloughing

3 May be sloughing, wood not very solid (but, nails still must be hammered into the wood)

4 Nails can be forcefully “pushed” into the wood, some sloughing/friability of the wood

5 Sap & heartwood friable, barely holding shape, not quite S.O.M., bark not present (except birches)

CWD Data Sheet Table 5. A sample data sheet for the collecting information on course woody debris.

Site Plot CWD ID#

Species Basal Diameter

Middle Diameter

Top Diameter

Length (m)

Decay Class

Notes

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BRF A 101 FAGR 13cm 10cm 6cm 3.4 2 Hollow 1.4m long, 3cm diameter

BRF C 102

BRF C 103

BRF D 104

CWD Decay Class Dependent Density Biomass calculations require estimates of the density of wood in different decay classes. Ideally, such estimates could be found in the literature. The alternative seems to be subsampling and determining density of our own wood. Determining Carbon Content of CWD Once the density and volume of each CWD piece is known it is easy to determine the mass. (Density x volume = mass) With mass we will use the established carbon content values for each species type (from Measuring C in live biomass) to determine how much carbon is stored in each piece. Fine Woody Debris Subplots FWD will be measured in 5 variable subplots within each of our large plots. In order to randomly select these plots year to year the 25x25m plot will be subdivided into 25 5x5m plots, each of which will be assigned a number between 1 and 25. Five of these subplots will be chosen using a random number generator each year. The center 0.5x0.5m of each of these subplots will then be sampled for FWD (Fig. 2). Measuring FWD

All FWD (anything with a diameter between 6mm and 10cm) will be collected from each of these subplots and brought back to the lab, where they will be dried. Once dry, each subplots sample will be weighed, and we will assume that 50% of the dry biomass is carbon. FWD Data Sheet

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Table 6. A sample datasheet for collecting information on fine woody debris.

Site Plot Subplot (1-25) FWD Dry Weight (g)

Notes

BRF A 3

BRF A 7

Repetition WD in plots will be measured every five years, two plots per year on a ten year rotation. Unknowns & Issues These methods don’t take a few specific sources of carbon into account, including suspended detritus (hanging dead branches), the roots of stumps, and buried CWD. We exclude these sinks because they are difficult to measure and likely to only contribute a small amount of carbon to the overall pool. Chojnacky et al. (2003) used data sets from the FIA (Forest Inventory and Analysis National Program) to estimate carbon content held in WD in various forest types. Their methods were interesting, and may be worth considering if direct measurements of WD carbon are deemed too difficult. VI. Measuring Carbon in Soils and Forest Floor Field Methods All field methods were derived from suggestions given by Steve Apfelbaum (Pers. Comm. 3/20/2011). One soil sampling plot should be established per soil type within each larger research plot (Figure 2). Subplots will be established in the center of the soil type, within the 25 m x 25 m study area. Soils are defined as less than 6 mm diameter twigs and 2 mm diameter roots, and layers of duff, humus, topsoil, and subsoil. The center of each plot is marked with a buried metal pin, to ease relocation of the plot with a metal detector. Three meters from the center, true north is permanently pinned. The cardinal and ordinal directions are marked on the same three meter radius from the center. Points are identified at quarter meter intervals along each cardinal and ordinal line (Figure 3). Every quarter meter point is considered as a potential sampling point.

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Within Plot Sampling Design

Figure 2. A sample 25 m2 permanent plot with two soil types. Soil sampling is represented by the star shapes and will occur in each soil type within a plot, as is described in Section VI: Measuring C in Soils and Forest Floor. FWD sampling is described in Section V: Measuring Carbon in Woody Debris. The black dots represent the 5 randomly chosen subplots of 0.5x0.5 m from which FWD will be measured. On a 5-year sampling regime, eight points are chosen randomly from the available array of quarter meter points. This means that one point will be selected along each transect each sampling year. Points are sampled with a 0’ 1-5/8” sampling probe. The strata is characterized, and subsamples are collected from each stratum (Table 7). Bulk density is measured between the quadrant radials using a bulk density canister and these data are then converted to mass using the formula: [(dryweight – canweight) / volume] * % C Soil Sampling Design

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Figure 3. Soil sampling design. Each hash mark indicates a potential sampling point from which samples will be selected randomly at every monitoring event. One sampling point is chosen from each cardinal direction, for a total of eight samples per monitoring event. Table 7. Sample data sheet. Provides site, soil type, identification information, and room for field observations of each soil sample.

Site Soil Type

Plot ID

Sample ID

Soil horizon descriptions

Notes

duff/humus topsoil subsoil …

BRF Lymen 1 1

1 2

1 …

1 8

1 Bulk Density

BRF Berkshire 2 2

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Breadloaf … 3 3

Lab Analysis In the lab total soil carbon, inorganic carbon, and organic carbon should be measured from each sampling point. In addition, if we have the necessary tools we recommend measuring percent nitrogen, phosphorous, and pH. Standard lab analysis uses a C-N-S Analyzer to calculate carbon content (Ellert et. al 2002, McCarty et. al 2002). The Middlebury College Geology Department owns both a Thermo Flash EA1112 Soil Combustion C-N-S Analyzer, and a Leco TGA-701, which can be used for loss on ignition (LOI) analysis of carbonate content. Another option for lab technique is mid-infrared spectroscopy (MIRS), which is quick, adaptive, nondestructive, non-reagent consuming (McCarty et. al 2002). MIRS is more accurate, when compared to C-N-S analyzer results, than near-infrared spectroscopy. On this topic we defer to the Middlebury College Geology Department, as they are more familiar with the available lab equipment. GEOL 0257 or other similar geology classes have expressed interest in participating in soil sampling on Middlebury College lands, and may edit these methods as necessary to achieve precise, repeatable results. VII. Opportunities for Community Involvement One option to facilitate data collection lies in the involving the local community, either through community events or by enlisting the assistance of a middle or high school science class. Citizen science has always been integral to research, and has experienced a resurgence in recent years (Silvertown 2009). Engaging local communities in research increases their understanding of science and fosters a deepened sense of place (Brossard et al. 2005, Evans et al. 2005), while providing research assistance. Citizen science efforts have been found to be reasonably accurate when compared to results gathered by hired biology technicians across many fields, including ornithology, snail studies, moth studies, ladybug projects, mushroom ID, invasive species surveys, all-taxa inventories, and marine science (Evans et. al 2005, Delaney et. al 2007, Silvertown 2009). Inclusion of a local high school class or a community carbon monitoring program could help raise awareness of climate change, sustainability, and forest management, increase college-town dialogue, and provide needed monitoring workforce.

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VIII. Conclusion At present there are no formal management plans for the College’s lands and in an effort to achieve the 2016 Carbon Neutrality goals we think that carbon storage on these lands should be considered. Our proposal would set up a multi-year carbon monitoring protocol that will help to better understand the dynamic processes at work on our forest lands. With the data we collect, we will be better able to account for the College’s carbon emissions and storage potential. All actions toward understanding and increasing carbons sequestration and storage on Middlebury’s lands could potentially be funded by the Regional Greenhouse Gas Initiative and other funding sources. Carbon monitoring is a relatively simple process. Field measurements and lab analysis allow for an accurate estimation of forest carbon. Through continued monitoring of live biomass, woody debris, and the soil, C fluxes and pools in Middlebury College lands can be effectively observed. Live biomass calculations derive from tree-specific allometric equations and diameter measurements. Woody debris surveys estimate the amount of carbon stored by measuring the volume of decaying dead wood, while soil measurements will be estimated in partnership with the College’s geology department. These three components are integral attributes of the carbon monitoring program and will allow the college to effectively quantify the C sequestered and stored in the biometric plots. Our results can also inform college land management decisions. As the administration understands the benefits of carbon sequestration better, it will need to reevaluate college land utilization. Many agricultural lands today yield marginal benefits at best, and may be converted to forests instead. As the carbon neutrality deadline approaches, the college may also find that forest carbon sequestration is a cheap alternative to more expensive steps towards carbon neutrality. For example, building renovations can cost upwards of a million dollars, while managing forests for carbon sequestration can pay for itself with the help of RGGI credits.

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IX. Works Cited Brossard, D., B. Lewenstein, and R. Bonney. 2005. Scientific knowledge and attitude change: the impact of a citizen science project. International Journal of Science Education 27: 1099-1121. Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution 116: 363-372. Chojnacky, D.C., R.A. Mickler, and L.S. Heath. 2003. Carbon in down woody materials of eastern U.S. forests. Second Annual Conference on Carbon Sequestration. Alexandria, VA. Currie, W.S. and K.J. Nadelhoffer. 2002. The imprint of land-use history: Patterns of carbon and nitrogen in downed woody debris at the Harvard Forest. Ecosystems 5: 446-460 Delaney, D. G., C. D. Sperling, C. S. Adams, and B. Leung. 2008. Marine invasive species: validation of citizen science and implications for national monitoring networks. Biological Invasions 10: 117-128.. Eller, B. H., H. H. Janzen, and T. Entz. 2002. Assessment of a method to measure temporal change in soil carbon storage. Soil Science Society of America 66: 1687 – 1 ENVS401 Fall 2009 Report. Biomass Procurement at Middlebury College : Assessments and Recommendations. 695. Evans, C., E. Abrams, R. Reitsma, K. Roux, L. Salmonsen, and P. P. Marra. 2005. The neighborhood nestwatch program: participant outcomes of a citizen-science ecological research project. Conservation Biology 19: 589-594. Harmon, M.E. and J. Sexton. 1996. Guidelines for measurements of woody detritus in forest ecosystems. Publication No. 20. U.S. LTER Network Office: University of Washington, Seattle, WA, USA. 73 pp. Intergovernmental Panel on Climate Change (IPCC). 2007. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Eds. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller. Cambridge University Press, Cambridge, United Kingdom. Lai 2004

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Lapin, M., C. Crosby, J.Valen, and A. Oberg. 2010. Middlebury College lands ecological and agroecological evaluation: Champlain Valley and Green Mountains escarpment. Unpublished. RGGI Inc. 2011. Regional greenhouse gas initiative: an initiative of the Northeast and Mid-Atlantic States of the U.S. http://www.rggi.org/home McCarty, G. W., J. B. Reeves III, V. B. Reeves, R. F. Follett, and J. M. Kimble. 2002. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sceicne Society of America 66: 640-646. Middlebury College Campus Master Plan 2008. Pregitzer, K.S. and E.S. Euskrichen. 2004. Carbon cycling and storage in world forests: biome patterns related to forest age. Global Change Biology 10: 1-26. Silvertown, J. 2009. A new dawn for citizen science. Trends in ecology and evolution 24: 467-470. United Nations Framework Convention on Climate Change (UNFCCC). 2011. Kyoto Protocol. http://unfccc.int/kyoto_protocol/items/2830.php Vogt 2007

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Appendix A R scripts Script 1: Calculates biomass components for the species group “mh” (mixed hardwood). #This line reads in the data file, and creates an R object called "mh". mh<-read.csv("mh.csv") #These lines simplify the process of referring to the variable names in the object mh-- they mean that you can just type the variable name instead of calling it, e.g., mh$dbh.2010. attach(mh) names(mh) #First we create a matrix that will hold our data. mh.mass<-matrix(nrow=nrow(mh), ncol=26) #Next we name the columns-- first by creating a vector that lists the name, and then by applying that vector to the matrix we just made. mh.mass.names<-c("tag", "dbh.2010", "ag.2010", "foliage.ratio.2010", "roots.ratio.2010", "bark.ratio.2010","stemwood.ratio.2010", "foliage.2010", "roots.2010", "bark.2010", "stemwood.2010", "branches.2010", "wholetree.2010", "dbh.2011", "ag.2011", "foliage.ratio.2011", "roots.ratio.2011", "bark.ratio.2011","stemwood.ratio.2011", "foliage.2011", "roots.2011", "bark.2011", "stemwood.2011","branches.2011", "wholetree.2011", "change.tree") colnames(mh.mass)<-mh.mass.names #Now we copy in the DBH & tree ID data from the original dataset. mh.mass[,1]<-mh[,1] mh.mass[,2]<-mh[,4] mh.mass[,14]<-mh[,5] #And now we calculate biomass-- the for loop cycles through each tree individual, and applies the formula. I have used the equations in Table 1 from the Forest Service General Technical Report NE-319 to calculate aboveground biomass, and then calculated individual components using the equations in Table 2. The results are placed in the empty matrix we just created. for (i in 1:nrow(mh)){ mh.mass[i,3]<-exp(-2.4800+2.4835*log(mh.mass[i,2])) #ag.2010 eqn from gte-ne-319 table 1 mh.mass[i,4]<-exp(-4.0813+5.8816/mh.mass[i,2])#foliage.ratio.2010 eqn from gte-ne-319 table 2 mh.mass[i,5]<-exp(-1.6911+0.8160/mh.mass[i,2])#roots.ratio.2010 eqn from gte-ne-319 table 2 mh.mass[i,6]<-exp(-2.0129-1.6805/mh.mass[i,2])#bark.ratio.2010 eqn from gte-ne-319 table 2 mh.mass[i,7]<-exp(-0.3065-5.4240/mh.mass[i,2])#stemwood.ratio.2010 eqn from gte-ne-319 table 2 mh.mass[i,8]<-mh.mass[i,3]*mh.mass[i,4]#foliage.2010 multiplies ag.2010 by the biomass ratio mh.mass[i,9]<-mh.mass[i,3]*mh.mass[i,5]#roots.2010 multiplies ag.2010 by the biomass ratio mh.mass[i,10]<-mh.mass[i,3]*mh.mass[i,6]#bark.2010 multiplies ag.2010 by the biomass ratio mh.mass[i,11]<-mh.mass[i,3]*mh.mass[i,7]#stemwood.2010 multiplies ag.2010 by the biomass ratio mh.mass[i,12]<-mh.mass[i,3]-mh.mass[i,8]-mh.mass[i,10]-mh.mass[i,11]#branches.2010 calculated by subtraction mh.mass[i,13]<-mh.mass[i,3]+mh.mass[i,9]#whole tree=ag.2010+roots.2010 mh.mass[i,15]<-exp(-2.4800+2.4835*log(mh.mass[i,14]))#ag.2011 eqn from gte-ne-319 table 1 mh.mass[i,16]<-exp(-4.0813+5.8816/mh.mass[i,14])#foliage.ratio.2011 eqn from gte-ne-319 table 2 mh.mass[i,17]<-exp(-1.6911+0.8160/mh.mass[i,14])#roots.ratio.2011 eqn from gte-ne-319 table 2 mh.mass[i,18]<-exp(-2.0129-1.6805/mh.mass[i,14])#bark.ratio.2011 eqn from gte-ne-319 table 2 mh.mass[i,19]<-exp(-0.3065-5.4240/mh.mass[i,14])#stemwood.ratio.2011 eqn from gte-ne-319 table 2 mh.mass[i,20]<-mh.mass[i,15]*mh.mass[i,16]#foliage.2011 multiplies ag.2010 by the biomass ratio mh.mass[i,21]<-mh.mass[i,15]*mh.mass[i,17]#roots.2011 multiplies ag.2010 by the biomass ratio mh.mass[i,22]<-mh.mass[i,15]*mh.mass[i,18]#bark.2011 multiplies ag.2010 by the biomass ratio mh.mass[i,23]<-mh.mass[i,15]*mh.mass[i,19]#stemwood.2011 multiplies ag.2010 by the biomass ratio mh.mass[i,24]<-mh.mass[i,15]-mh.mass[i,16]-mh.mass[i,18]-mh.mass[i,19]#branches.2011 calculated by subtraction mh.mass[i,25]<-mh.mass[i,15]+mh.mass[i,21]#whole tree biomass=ag.2011+roots.2011 mh.mass[i,26]<-mh.mass[i,25]-mh.mass[i,13] } #The results are then output as a CSV file, in case we want to look at them in Excel. write.csv(mh.mass, file="mh_biomass.csv")

Script 2: Calculates biomass totals ##use biomass outputs to calculate accum. sums #read in mb.biomass data mb.biomass <- read.csv("/Users/lsanchez/desktop/mb_biomass.csv", header = T, row.names = NULL, as.is = T) mo.biomass <- read.csv("/Users/lsanchez/desktop/mo_biomass.csv", header = T, row.names = NULL, as.is = T) mh.biomass <- read.csv("/Users/lsanchez/desktop/mh_biomass.csv", header = T, row.names = NULL, as.is = T) tf.biomass <- read.csv("/Users/lsanchez/desktop/TSCA_biomass.csv", header = T, row.names = NULL, as.is = T)

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#create matrix in which to place biomass totals biomass.accu <- as.data.frame(matrix(NA, nrow = 27, ncol = 4),) dimnames(biomass.accu)[[2]]<- c("mb","mh","mo","tf") rownames(biomass.accu)<- rownames.biomass #place accu data into matrix biomass.accu[,1] <- colSums(mb.biomass) biomass.accu[,2] <- colSums(mh.biomass) biomass.accu[,3] <- colSums(mo.biomass) biomass.accu[,4] <- colSums(tf.biomass) #output to .csv file write.csv(biomass.accu, file="/Users/lsanchez/desktop/biomass.accu.csv")