Forest carbon assessment for the Francis Marion National Forest

Alexa Dugan ([email protected]) and Duncan McKinley ([email protected])

July 22, 2019 1.0 Introduction

Carbon uptake and storage are some of the many ecosystem services provided by forests and grasslands. Through the process of photosynthesis, growing plants remove carbon dioxide (CO2) from the atmosphere and store it in forest biomass (plant stems, branches, foliage, roots) and much of this organic material is eventually stored in forest soils. This uptake and storage of carbon from the atmosphere helps modulate greenhouse gas (GHG) concentrations in the atmosphere. Estimates of net annual storage of carbon indicate that forests in the United States (U.S.) constitute an important carbon sink, removing more carbon from the atmosphere than they are emitting.1 Forests in the U.S. remove the equivalent of about 12-19 percent of annual U.S. fossil fuel emissions or about 206 teragrams of carbon after accounting for natural emissions, such as wildfire and decomposition.2, 3

The Intergovernmental Panel on Climate Change (IPCC) has summarized the contributions of global human activity sectors to climate change in its Fifth Assessment Report.4 From 2000 to 2009, forestry and other land uses contributed just 12 percent of human-caused global CO2 emissions.5 The forestry sector contribution to GHG emissions has declined over the last decade.4,6,7 Globally, the largest source of GHG emissions in the forestry sector is deforestation,1,4,8 defined as the removal of all trees to convert forested land to other land uses that either do not support trees or allow trees to regrow for an indefinite period.9 However, the United States is experiencing a net increase in forestland in recent decades because of the reversion of agricultural lands back to forest and regrowth of cut forests,10 a trend expected to

1 Pan, Y., R.A. Birdsey, J. Fang, R. Houghton, P.E. Kauppi, W.A. Kurz, O.L. Phillips, et al. 2011. A large and persistent carbon sink in the world’s forests. Science 333: 988–993. 2 US EPA. 2015. US inventory of greenhouse gas emissions and sinks: 1990 – 2013. Executive Summary. EPA 430-R15-004 United States Environmental Protection Agency. Washington, D.C. 27 pp. 3 Janowiak, M., W.J., Connelly, K. Dante-Wood, G.M. Domke, C. Giardina, Z. Kayler, K. Marcinkowski, T. Ontl, C. Rodriguez-Franco, C. Swanston, C.W. Woodall, and M. Buford. 2017. Considering Forest and Grassland Carbon in Land Management. Gen. Tech. Rep. WO-95. Washington, D.C.: United States Department of Agriculture, Forest Service. 68 p. 4 IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151. 5 Fluxes from forestry and other land use (FOLU) activities are dominated by CO2 emissions. Non-CO2 greenhouse gas emissions from FOLU are small and mostly due to peat degradation releasing methane and were not included in this estimate. 6 FAOSTAT (2013). FAOSTAT database. Food and Agriculture Organization of the United Nations, available at http://faostat.fao.org/. 7 Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, et al. 2014. Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 121 pp. 8 Houghton, R.A., J.I. House, J. Pongratz, G.R. van der Werf, R.S. DeFries, M.C. Hansen, et al. M.C. 2012. Carbon emissions from land use and land-cover change. Biogeosciences 9: 5125–5142. 9 IPCC 2000. Intergovernmental Panel on Climate Change (IPCC), Special Report on Land Use, Land Use Change and Forestry, Summary for Policy Makers, 2000. IPCC, Geneva, Switzerland. 20 pp. 10 Birdsey R, K. Pregitzer, and A. Lucier. 2006. Forest carbon management in the United States:1600-2100. J. Environ. Qual. 35:1461-1469.

1 continue for at least another decade.11,12

Forests are dynamic systems that naturally undergo fluctuations in carbon storage and emissions as forests establish and grow, die with age or disturbances, and re-establish and regrow. Forests release CO2 into the atmosphere when trees and other vegetation die, either through natural aging and competition processes or disturbance events (e.g., combustion from fires). This process transfers carbon from living carbon pools to dead pools, which also release carbon dioxide through decomposition or combustion (fires). Management activities include timber harvests, thinning, and fuel reduction treatments that remove carbon from the forest and transfer a portion to wood products. Carbon can then be stored in Box 1. Description of the primary forest carbon models used to commodities (e.g., paper, conduct this carbon assessment lumber) for a variable duration ranging from Carbon Calculation Tool (CCT) days to years, or, in the case of some structural Estimates annual carbon stocks and stock change from 1990 to timber, from many 2013 by summarizing data from two or more Forest Inventory decades to centuries. In and Analysis (FIA) survey years. CCT relies on allometric the absence of models to convert tree measurements to biomass and carbon. commercial thinnings, Forest Carbon Management Framework (ForCaMF) harvests, and fuel reduction treatments, Integrates FIA data, Landsat-derived maps of disturbance type forests will thin naturally and severity, and an empirical forest dynamics model, the from mortality-inducing Forest Vegetation Simulator, to assess the relative impacts of disturbances or aging, disturbances (harvests, insects, fire, abiotic, disease). resulting in dead trees ForCaMF estimates how much more carbon (non-soil) would decaying and emitting be on each national forest if disturbances from 1990 to 2011 carbon to the atmosphere. had not occurred. Following natural Integrated Terrestrial Ecosystem Carbon (InTEC) model disturbances or harvests, A process-based model that integrates FIA data, Landsat- forests re-establish and derived disturbance maps, as well as measurements of climate regrow, resulting in the variables, nitrogen deposition, and atmospheric CO2. InTEC uptake and storage of estimates the relative effects of aging, disturbance, regrowth, carbon from the and other factors including climate, CO2 fertilization, and atmosphere. Over the long nitrogen deposition on carbon accumulation from 1950 to term, through one or more 2011. Carbon stock and stock change estimates reported by cycles of disturbance and InTEC are likely to differ from those reported by CCT regrowth (if the forest because of the different data inputs and modeling processes. regenerates after the disturbance), net carbon flux (the balance from

11 USDA Forest Service. 2016. Future of America's forests and rangelands: update to the 2010 Resources Planning Act Assessment. General Technical Report WO-GTR-94. Washington, DC. 250 p. 12 Wear, D.N., R. Huggett, R. Li, B. Perryman, and S. Liu. 2013. Forecasts of forest conditions in regions of the United States under future scenarios: A technical document supporting the Forest Service 2010 RPA Assessment. US Department of Agriculture Forest Service, General Technical Report SRS-170.

2 accumulation and loss) is often zero. This happens when regrowth of trees accumulates the same amount of carbon as was emitted as a result of disturbance or mortality.13 Although disturbances, forest aging, and management are often the primary drivers of forest carbon dynamics in some ecosystems, environmental factors such as atmospheric CO2 concentrations, climatic variability, and the availability of limiting forest nutrients, such as nitrogen, can also influence forest growth and carbon dynamics.14, 15

In this section, we provide an assessment of the amount of carbon stored on the Francis Marion National Forest (baseline carbon stocks) and how disturbances, management, and environmental factors have influenced carbon storage overtime. This assessment primarily used two recent U.S. Forest Service reports: the Baseline Report16 and Disturbance Report.17 Both reports relied on Forest Inventory and Analysis (FIA) and several validated, data-driven modeling tools to provide nationally consistent evaluations of forest carbon trends across the National Forest System. The Baseline Report applies the Carbon Calculation Tool (CCT),18 which summarizes available FIA data across multiple survey years to estimate forest carbon stocks and changes in stocks at the scale of the national forest from 1990 to 2013. The Baseline Report also provides information on carbon storage in harvested wood products (HWP) for each Forest Service region.

The Disturbance Report provides a national forest-scale evaluation of the influences of disturbances and management activities, using the Forest Carbon Management Framework (ForCaMF).19,20,21 This report also contains estimates of the long-term relative effects of disturbance and non-disturbance factors on carbon stock change and accumulation, using the Integrated Terrestrial Ecosystem Carbon (InTEC) model.22, 23

The key findings from these reports are summarized here. See Box 1 for descriptions of the carbon models used for these analyses. To infer future forest carbon dynamics, information from additional reports, including the most recent Resource Planning Act (RPA) assessment11 and a

13 McKinley, D.C., M.G. Ryan, R.A. Birdsey, C.P. Giardina, M.E. Harmon, L.S. Heath, et al. 2011. A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications 21: 1902-1924. 14 Caspersen J.P., S.W. Pacala, J.C. Jenkins, G.C. Hurtt, P.R. Moorcroft, and R.A. Birdsey. 2000. Contributions of Land-Use History to Carbon Accumulation in U.S. Forests. Science 290: 1148-1151. 15 Pan Y, R. Birdsey, J. Hom, and K. McCullough. 2009. Separating effects of changes in atmospheric composition, climate and land-use on carbon sequestration of U.S. mid-Atlantic temperate forests. Forest Ecology and Management 259:151–164. 16 USDA Forest Service. 2015. Baseline estimates of carbon stocks in forests and harvested wood products for National Forest System Units, Southern Region. 45 pp. 17 Birdsey, R., Dugan, A.J., Healey, S., Dante-Wood, K., Zhang, F., Chen, J., Hernandez, A., Raymond, C., McCarter, J. In press. Assessment of the influence of disturbance, management activities, and environmental factors on carbon stocks of United States National Forests. Fort Collins, Colorado: Gen. Tech. Report RM-xxx. 18 Smith, J.E., L.S. Heath, and M.C. Nichols. 2010. U.S. Forest Carbon Calculation Tool: forest-land carbon stocks and net annual stock change. Revised. Gen. Tech. Rep. NRS-13. Newtown Square, PA: US Department of Agriculture Forest Service Northern Research Station. 2010; 34 p. 19 Healey SP, S.P. Urbanski, P.L. Patterson, and C. Garrard. 2014. A framework for simulating map error in ecosystem models. Remote Sensing of Environment 150: 207-217. 20 Healey, S P., C.L. Raymond, I.B. Lockman., A.J. Hernandez, C. Garrard, and C. Huang. 2016. Root disease can rival fire and harvest in reducing forest carbon storage. Ecosphere 7: e01569. 21 Raymond C.L., S.P. Healey, A. Peduzzi, and P.L. Patterson. 2015. Representative regional models of post-disturbance forest carbon accumulation: Integrating inventory data and a growth and yield model. Forest Ecology and Management 336: 21-34. 22 Chen, W., J.M. Chen, and J. Cihlar. 2000. Integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry. Ecological Modelling 135: 55-79. 23 Zhang, F., J.M. Chen, Y. Pan, R. Birdsey, S. Shen, W. Ju, and L. He. 2012. Attributing carbon changes in conterminous U.S. forests to disturbance and non-disturbance factors from 1901-2010. Journal of Geophysical Research 117: G02021.

3 regional vulnerability assessment,24 provide information on potential future conditions. Collectively, these reports incorporate advances in data and analytical methods, representing the best available science to provide comprehensive assessments of National Forest System carbon trends. The Francis Marion National Forest (FMNF) was administratively combined with the Sumter National Forest to form the administrative unit of the Francis Marion and Sumter National Forests (FMS). According to recent estimates from the latest FIA inventory, the FMNF accounts for about 41 percent of the forested area in the FMS. The model results presented here, including the baseline carbon stocks and impacts of disturbances and other factors, are available only for combined FMS. However, the following assessment uses information on the forested area, forest types and ages, and disturbances of the FMNF relative to the entire FMS to provide a reasonable interpretation of how these model results may apply more specifically to the FMNF.

1.1 Background The FMNF, located on the coastal plain of South Carolina, consists of 250,825 acres of forestland, mostly of the Loblolly-Shortleaf pine forest type group (per recent FIA estimates).25 The carbon legacy of FMNF and other national forests in the region is tied to the history of Euro- American settlement, land management, and disturbances. After the Civil War in the 1860s, agricultural expansion and large-scale timber extraction became the dominant driving forces of forest change in the South. The The following table provides a crosswalk Box 2. Carbon Units. timber industry among various metric measurements units used in the assessment of soon became carbon stocks and emissions. centered in the Tonnes Grams South, and by Multiple Name Symbol Multiple Name Symbol 1919 the region 0 10 Gram G was producing 37 3 10 kilogram Kg percent of U.S. 100 tonne t 106 Megagram Mg lumber.26 During 103 kilotonne Kt 109 Gigagram Gg this period, most 106 Megatonne Mt 1012 Teragram Tg of the remaining 109 Gigatonne Gt 1015 Petagram Pg primary forests of 1012 Teratonne Tt 1018 Exagrame Eg the South were 1015 Petatonne Pt 1021 Zettagram Zg harvested, and 1018 Exatonne Et 1024 yottagram Yg much of the 1 hectare (ha) = 0.01 km2 = 2.471 acres = 0.00386 mi2 previous 1 Mg carbon = 1 tonne carbon = 1.1023 short tons (U.S.) carbon forestland was 1 General Sherman Sequoia tree = 1,200 Mg (tonnes) carbon replaced with 1 Mg carbon mass = 1 tonne carbon mass = 3.67 tonnes CO2 mass agriculture and A typical passenger vehicle emits about 4.6 tonnes CO2 a year grazing.

24 McNulty, S., S. Wiener, E. Treasure, J. Moore Myers, H. Farahani, L. Fouladbash, D. Marshall, R. Steele, D. Hickman, J. Porter, S. Hestvik, R. Dantzler, W. Hall, M. Cole, J. Bochicchio, D. Meriwether, and K. Klepzig. 2015: Southeast Regional Climate Hub Assessment of Climate Change Vulnerability and Adaptation and Mitigation Strategies, T. Anderson, Ed., United States Department of Agriculture, 61 pp. 25 This estimate does not contain forested area in the Uwharrie or Croatan National Forests in North Carolina. Estimate obtained using EVALIDator (https://apps.fs.usda.gov/Evalidator/evalidator.jsp) which queries the Forest Inventory & Analysis database. 26 Williams, M. 1989. Americans and Their Forests: An Historical Geography. New York: Cambridge University Press.

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As the need for sustainable forest management became evident, the U.S. government began purchasing large areas of these overharvested and often submarginal lands in the eastern United States in the early and mid-20th century to be established as national forests.27 The FMNF was established in 1936. With the help of the Civilian Conservation Corps, millions of trees were planted across the region in an effort to restore forest vegetation and control erosion.27,28 In 1989, the forests of the FMNF were devastated by Hurricane Hugo, a powerful category 4 storm. This legacy of timber harvesting and early efforts to restore the forest, as well as the effects of Hurricane Hugo are still visible today, influencing forest age structures, tree composition, and carbon dynamics.29

2.0 Baseline Carbon Stocks and Flux 2.1 Forest Carbon Stocks and Stock Change 60 According to results of the Baseline Report,16 carbon 50 stocks in the FMS increased 40 from 36.7±6.1 teragrams of carbon (Tg C) in 1990 to 30 44.7±8.0 Tg C in 2013, a 22 20 percent increase in carbon (Tg) Stocks Carbon stocks over this period (Fig. 10 1).30 The FMNF alone contains about 21 Tg C of carbon,25 0 which is equivalent to the emissions from approximately Year 17 million passenger vehicles Figure 1. Total forest carbon stocks for the baseline period 1990 in a year. Despite some to 2013 for Francis Marion & Sumter National Forests bounded uncertainty in annual carbon by 95 percent confidence intervals (error bars) (Figure from Appendix A.I, USDA Forest Service 2015). stock estimates, reflected by the 95 percent confidence

27 Shands 1992. The Lands Nobody Wanted: The Legacy of the Eastern National Forests. The origins of the National Forests. Pinchot Institute for Conservation Studies. 28 Johnson, C. and D. Govatski. 2013. Forests for the people: the story of America’s eastern national forests. Island Press, Washington, D.C. 29 Birdsey R, K. Pregitzer, and A. Lucier. 2006. Forest carbon management in the United States: 1600-2100. J. Environ. Qual. 35:1461-1469. 30 This report uses carbon mass, not CO2 mass, because carbon is a standard unit and can easily be converted to any other unit. To convert carbon mass to CO2 mass, multiply by 3.67 to account for the mass of the O2. 1000 teragrams (Tg) =1 petagram (Pg) 1000 teragrams = 1 billion metric tonnes 1000 teragrams = 1 gigatonne 1 teragram = 1 million metric tonnes 1 teragram = 1 megatonne 1 megagram (Mg) = 1 metric tonne 1 metric tonne = 0.98 U.S. long ton 1 metric tonne per hectare = 0.4 U.S. long tons per acre carbon (C) mass * 3.67 = carbon dioxide (CO2) mass 5 intervals, there is a high degree of certainty that carbon stocks on the FMS have been stable or increased from 1990 to 2013 (Fig. 1). Soil carbon contained in organic material to a depth of one meter Aboveground live (excluding roots) is the largest Belowground live carbon pool, storing another 44 Understory 39% Standing dead percent of the forest carbon stocks Down dead (Fig. 2). About 39 percent of Forest floor forest carbon stocks in the FMS Soil are stored in the aboveground portion of live trees, which 44% includes all live woody vegetation 8% at least one inch in diameter. 5% Recently, new methods for 2% 0% 2% measuring soil carbon have found that the amount of carbon stored in Figure 2. Percentage of carbon stocks in each of the forest soils generally exceeds the carbon pools for the Francis Marion & Sumter National Forests in 2013. estimates derived from using the methods of the CCT model by roughly 12 percent across forests in the United States.31 4 The annual carbon stock change 3

1) can be used to evaluate whether a - 2 forest is a carbon sink or source in 1 a given year. Carbon stock change 0 is typically reported from the -1 perspective of the atmosphere. A negative value indicates a carbon -2

Stock change (Tg C yr C (Tg change Stock sink: the forest is absorbing more -3 carbon from the atmosphere -4 (through growth) than it emits (via decomposition, removal, and combustion). A positive value Year indicates a source: the forest is Figure 3. Carbon stock change from 1990-2012 for the Francis Marion & Sumter National Forests are bounded by 95 percent emitting more carbon than it takes confidence intervals (error bars). A positive value indicates a up. carbon source and a negative value indicates a carbon sink. (Figure from Appendix A.II, USDA Forest Service 2015). Annual carbon stock changes in FMS were roughly 0.43 ± 0.94 Tg C per year from 1990 to 1991, indicating the forest was a slight C source. This loss is likely due to decomposition and heightened respiration after Hurricane Hugo (Fig. 3). However, carbon

31 Domke, G.M., C.H. Perry, B.F. Walters, L.E. Nave, C.W. Woodall, and C.W. Swanston. 2017. Toward inventory-based estimates of soil organic carbon in forests of the United States. Ecological Applications 27: 1223-1235.

6 stocks began to increase indicating that the forest shifted to a weak carbon sink of 0.57±0.94 Tg C per year from 1993 to 2003. Although the rate of carbon stock increase slowed from 2004 to 2013, the FMS maintained a carbon sink. The uncertainty between annual estimates can make it difficult to determine whether the forest is a sink or a source in a specific year (i.e., uncertainty bounds overlap zero) (Fig. 3). However, the general trend of increasing carbon stocks over the 22-year period from 1991 to 2013 (Fig. 1) strongly suggests that the FMS are a modest carbon sink.

The change in carbon stocks over time depends on several factors, including growth rates, natural ecosystem dynamics, and management activities. Changes in forested area may also affect whether forest carbon stocks are increasing or decreasing. The CCT estimates from the Baseline Report are based on FIA data, which may indicate changes in the total forested area from one year to the next. According to the FIA data used to develop these baseline estimates, the forested area in FMS increased from 568,926 acres in 1990 to 592,847 acres 2013, a net change of 23,922 acres, with some variations in estimates over this period.32 When forestland area increases, total ecosystem carbon stocks typically also increase, indicating a carbon sink. The CCT model used inventory data from two different databases. This may have led to inaccurate estimates of changes in forested area, potentially altering the conclusion regarding whether or not forest carbon stocks are increasing or decreasing, and therefore, whether the National Forest is a carbon source or sink.33

Carbon density, which is an estimate of forest carbon stocks per unit area, can help separate the effects of changing forested area from the effects of growth and mortality on carbon stock trends. In the FMS, carbon density increased from about 64.5 metric tonnes of carbon per acre in 1990 to 75.4 metric tonnes per acre in 2013 (Fig. 4). This increase in carbon density suggests that total carbon stocks may have indeed increased and are not just an artefact of changes in forested area.

Carbon density is also useful for comparing trends among units or ownerships with different forest areas. Similar to FMS, most national forests in the Southern Region have experienced increasing carbon densities from 1990 to 2013. Carbon density in the FMS has been somewhat higher than the regional average for the 14 national forests in the Southern Region (this excludes El Yunque NF in Puerto Rico). Differences in carbon density between units may be related to inherent differences in biophysical factors that influence growth and productivity, such as climatic conditions, elevation, soils, and forest types. These differences may also be affected by disturbance and management regimes (see Section 3.0).

32 Forested area used in the CCT model may differ from more recent FIA estimates, as well as from the forested areas used in the other modeling tools. 33 Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nicholas. 2011. Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. General Technical Report NRS-88. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p.

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100 NFs in Alabama 90 Daniel Boone 80 Chattahoochee-Oconee Cherokee 70 NFs in Florida 60 Kisatchie NFs in Mississippi 50 George Washington 40 Ouachita Ozark and St. Francis 30 NFs in North Carolina

Carbon density (t/ac) density Carbon 20 Francis Marion-Sumter 10 NFs in Texas Jefferson 0 Other NFS areas

Year

Figure 4. Estimated carbon stock density (metric tonnes C per acre) across national forest units in the Southern Region from 1990 to 2013 (Figure 7 in USDA Forest Service 2015).

2.2 Uncertainty associated with baseline forest carbon estimates All results reported in this assessment are estimates that are contingent on models, data inputs, assumptions, and uncertainties. Baseline estimates of total carbon stocks and carbon stock change include 95 percent confidence intervals derived using Monte Carlo simulations34 and shown by the error bars (Figs. 1, 3). These confidence intervals indicate that 19 times out of 20, the carbon stock or stock change for any given year will fall within the green-highlighted zone. The uncertainties contained in the models, samples, and measurements can exceed 30 percent of the mean at the scale of a national forest, sometimes making it difficult to infer if or how carbon stocks are changing. However, despite relatively high annual error estimates for total carbon stocks in FMS, indicated by the 95 percent confidence intervals, it can be inferred with a high degree of certainty that carbon stocks have been stable or potentially increased from 1990 to 2013.

The baseline estimates that rely on FIA data include uncertainty associated with sampling error (e.g., area estimates are based on a network of plots, not a census), measurement error (e.g., species identification, data entry errors), and model error (e.g., associated with volume, biomass, and carbon equations, interpolation between sampling designs). As mentioned in Section 2.1, one such model error has resulted from a change in FIA sampling design, which led to an apparent change in forested area. Change in forested area may reflect an actual change in land use due to reforestation or deforestation. However, given that the FMS have experienced minimal changes in land use or adjustments to the boundaries of the national forests in recent years, the change in

34 A Monte Carlo simulation performs an error analysis by building models of possible results by substituting a range of values – a probability distribution – for any factor that has inherent uncertainty (e.g., data inputs). It then calculates results over and over, each time using a different set of random values for the probability functions.

8 forested area incorporated in CCT is more likely a data artefact of altered inventory design and protocols.35

The inventory design changed from a periodic inventory, in which all plots were sampled in a single year to a standardized, national, annual inventory, in which a proportion of all plots is sampled every year. The older, periodic inventory was conducted differently across states and tended to focus on timberlands with high productivity. Any data gaps identified in the periodic surveys, which were conducted prior to the late 1990s, were filled by assigning average carbon densities calculated from the more complete, later inventories from the respective states.35 The definition of what constitutes forested land also changed between the periodic and annual inventory in some states, which may also have contributed to apparent changes in forested area.

In addition, carbon stock estimates contain sampling error associated with the cycle in which inventory plots are measured. Forest Inventory and Analysis plots are resampled about every 5 years in the eastern United States, and a full cycle is completed when every plot is measured at least once. However, sampling is designed such that partial inventory cycles provide usable, unbiased samples annually but with higher errors. These baseline estimates may lack some temporal sensitivity, because plots are not resampled every year, and recent disturbances may not be incorporated in the estimates if the disturbed plots have not yet been sampled. For example, if a plot was measured in 2009 but was clear-cut in 2010, that harvest would not be detected in that plot until it was resampled in 2014. Therefore, effects of the harvest would show up in FIA/CCT estimates only gradually as affected plots are re-visited and the differences in carbon stocks are interpolated between survey years.35 In the interim, re-growth and other disturbances may mute the responsiveness of CCT to disturbance effects on carbon stocks. Although CCT is linked to a designed sample that allows straightforward error analysis, it is best suited for detecting broader and long-term trends, rather than annual stock changes due to individual disturbance events.

In contrast, the Disturbance Report (Section 3.0) integrates high-resolution, remotely-sensed disturbance data to capture effects of each disturbance event the year it occurred. This report identifies mechanisms that alter carbon stocks and provides information on finer temporal scales. Consequently, discrepancies in results may occur between the Baseline Report and the Disturbance Report.36

2.3 Carbon in Harvested Wood Products Although harvest transfers carbon out of the forest ecosystem, most of that carbon is not lost or emitted directly to the atmosphere. Rather, it can be stored in wood products for a variable duration depending on the commodity produced, as the HWP model illustrated (Fig. 5). Harvested wood can be used for products such as lumber, panels, and paper that account for a significant amount of off-site carbon storage. Estimates of the contribution of carbon stored in HWP are important for both national-level accounting and regional reporting.37,38 Wood products

35 Woodall CW, J. Smith, and M. Nichols. 2013. Data sources and estimation/modeling procedures for National Forest System carbon stocks and stock change estimates derived from the US National Greenhouse Gas Inventory. USDA Forest Service. 36 Dugan AJ, R. Birdsey, S.P. Healey, Y. Pan, F. Zhang, G. Mo, J. Chen, C. Woodall, A.J. Hernandez, K. McCullough, J.B. McCarter, C.L. Raymond, and K. Dante-Wood. 2017. Forest sector carbon analyses support land management planning and projects: assessing the influence of anthropogenic and natural factors. Climatic Change 144: 207-220. 37 Skog K.E. 2008. Sequestration of carbon in harvested wood products for the United States. Forest Products Journal 58: 56-72. 38 Bergman, R., M.E. Puettman, A. Taylor, and K.E. Skog. 2014. The carbon impacts of wood products. Forest Products Journal 64: 220-231.

9 can also be substituted for other products, such as concrete and steel, that emit more GHGs in manufacturing, thus lowering net emissions and creating a substitution effect.13,39,40 Harvested wood and residues may also be burned to produce heat or electrical energy, or converted to liquid transportation fuels and chemicals that would otherwise come from fossil fuels, also resulting in a substitution effect. Much of the harvested carbon that is initially transferred out of the forest can also be recovered with time as the affected area regrows.

The Baseline Report provides an assessment of carbon stored in the wood products sector for all national forests within each Forest Service region from 1912 to 2013. Carbon accounting for HWP was conducted by incorporating data on harvests on national forests documented in cut- and-sold reports within a production accounting system.41,42 This approach tracks the entire cycle of carbon, from harvest to timber products to primary wood products to disposal. Harvested wood products carbon pools include both products in use and products that have been discarded to solid waste disposal sites, such as landfills and dumps.

As more commodities are produced and remain in use, the amount of carbon stored in products increases. As more products 30 are discarded, the carbon stored in solid waste disposal 25 sites increases. Products in 20 Products in use solid waste disposal sites may Products in SWDS continue to store carbon for 15 many decades. In national forests in the Southern 10 Region, harvest levels remained low until after the HWP Carbon Stocks (Tg) Stocks Carbon HWP 5 start of World War II in the late 1930s and early 1940s, 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 when they began to increase, Year continuing to increase through the late 1970s.16 This Figure 5. Estimated cumulative total carbon stored in HWP increase in harvests also sourced from national forests in the Southern Region. Carbon in HWP includes products that are still in use and carbon stored at caused an increase in carbon solid waste disposal sites (SWDS).41 (Figure 9 in USDA Forest storage in HWP from Service 2015). national forests in the Southern Region (Fig. 5).

39 Gustavsson, L., R. Madlener, H.F. Hoen, G. Jungmeier, T. Karjalainen, S. Klöhn, et al. 2006. The role of wood material for greenhouse gas mitigation. Mitigation and Adaptation Strategies for Global Change 11: 1097–1127. 40 Lippke, B., E. Oneil, R. Harrison, K. Skog, L. Gustavsson, and R. Sathre. 2011. Life cycle impacts of forest management and wood utilization on carbon mitigation: knowns and unknowns. Carbon Management 2:303-333. 41Loeffler, D., N. Anderson, K. Stockmann, K. Skog, S. Healey, J.G. Jones, J. Morrison, and & J. Young. 2014. Estimates of carbon stored in harvested wood products from United States Forest Service Southern Region, 1911-2012. Unpublished report. Missoula, MT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Forestry Sciences Laboratory. 27 p. 42 Smith, J.E., L.S. Heath, Linda, K.E. Skog, and R.A. Birdsey. 2006. Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. Gen. Tech. Rep. NE-343. Newtown Square, PA: U.S. Department of Agriculture Forest Service, Northeastern Research Station. 216 p.

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Timber harvesting and subsequent carbon storage then declined in the early 1980s, but soon increased rapidly and peaked in 1986. Storage in products and landfills reached about 25 Tg C in 2001 (Fig. 5). However, because of a significant decline in timber harvesting in the harvesting in the 1990s and early 2000s (to 1940s levels), carbon accumulation in the product sector has slowed, and carbon storage in products in use has declined slightly since the late 1990s. In the Southern Region, the contribution of national forest timber harvests to the HWP carbon pools exceeds the decay of retired products, causing a net increase in product-sector carbon stocks from 1912 to 2013. In 2013, the carbon stored in HWP was equivalent to roughly 2.7 percent of total forest carbon storage associated with national forests in the Southern Region.

2.4 Uncertainty associated with estimates of carbon in harvested wood products As with the baseline estimates of ecosystem carbon storage, the analysis of carbon storage in HWP also contains uncertainties. Sources of error that influence the amount of uncertainty in the estimates include: adjustment of historic harvests to modern national forest boundaries; factors used to convert the volume harvested to biomass; the proportion of harvested wood used for different commodities (e.g., paper products, saw logs); product decay rates; and the lack of distinction between methane and CO2 emissions from landfills. The approach also does not consider the substitution of wood products for emission-intensive materials or the substitution of bioenergy for fossil fuel energy. The collective effect of uncertainty was assessed using a Monte Carlo approach.34 Results indicated a ±0.05 percent difference from the mean at the 90 percent confidence level for 2013, suggesting that uncertainty is relatively small at this regional scale.4141

3.0 Factors Influencing Forest Carbon 3.1 Effects of Disturbance The Disturbance Report builds on estimates in the Baseline Report by supplementing high- resolution, manually-verified, annual disturbance data from Landsat satellite imagery.43 The Landsat imagery was used to detect land cover changes due to disturbances including fires, harvests, insects, and abiotic factors (e.g., wind, ice storms). The resulting Landsat-derived disturbance product indicates that harvesting followed by fires have been the dominant disturbance types detected on the FMNF from 1990 to 2011, in terms of the total percentage of forested area disturbed over the period (Fig. 6a). It is possible that additional disturbances or harvests occurred on the Forest, but were not identified because they were smaller than the 30 meter resolution of Landsat data, or removed so few trees that a change in canopy cover could not be detected. For example, Landsat record may exclude some low-severity prescribed fires that burned only along the forest floor and did not affect the overstory. Harvesting affected on average 0.5 percent of FMNF forest area annually. Over the 21 year period, harvests affected in total about 10.7 percent (27,000 acres) of the 250,825 acres. The satellite imagery indicates that fire (prescribed and wildfire) was the second most common disturbance on the FMNF. Fires also affected on average about 0.5 percent of the forested area per year. Fires affected the greatest area of forestland in 2006 and 2008, though still disturbed only about 2.0 percent of the forested area in FMNF during each of those years. In total fires burned about 9.4 percent of the FMNF or approximately 24,000 acres. Although during most

43 Healey, S. P., W.B. Cohen, Z. Yang, B.C. Brewer, E.B. Brooks, N. Gorelick, et al. 2018. Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment 204: 717–728.

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3 3 (a) (b) 4 Insects 3 2 Harvest 2 Fire 2 1 1 1

Percentage of forest disturbed forest of Percentage 0 0

Year Year Figure 6. Percentage of the forested area disturbed in the Francis Marion National Forest by (a) harvests, insects, and fire, and (b) by magnitude classes of Disturbance characterized by Percent Change in Canopy Cover (CC). 1) 0-25 percent CC, 2) 26-50 percent CC, 3) 51-75 percent CC, and 4) 76-100 percent CC. Disturbed area estimates do not include disturbances or forested area in the Sumter NF. years the forest experienced a negligible impact by insects in terms of area affected, in 2011 insects affected roughly 1.3 percent of the forested area. In total all disturbances combined affected about 22 percent (about 55,000 acres) of the forested land on the FMNF (Fig. 6a). Although disturbances varied in proportion of trees removed or killed (Fig. 6b), they generally removed less than 50 percent of canopy cover (low to moderate in magnitude). The Forest Carbon Management Framework (ForCaMF) incorporates Landsat disturbance maps summarized in Figure 6, along with FIA data in the Forest Vegetation Simulator (FVS).44 The FVS is used to develop regionally representative carbon accumulation functions for each combination of forest type, initial carbon density, and disturbance type and severity (including undisturbed).21 The ForCaMF model then compares the undisturbed scenario with the carbon dynamics associated with the historical disturbances to estimate how much more carbon would be on each national forest if the disturbances and harvests during 1990-2011 had not occurred. ForCaMF simulates the effects of disturbance and management only on non-soil carbon stocks (i.e., vegetation, dead wood, forest floor). Like CCT, ForCaMF results supply 95 percent confidence intervals around estimates derived from a Monte Carlo approach.19

Timber harvesting on the FMS was the most significant disturbance influencing carbon stocks from 1990 to 2011 (Fig. 7). Harvesting accounted for 73 percent of the total non-soil carbon lost from the forest due to disturbances, whereas fire accounted for 21 percent, and insects 6 percent.16 These estimates also include the Sumter National Forest, which did not experience much fire, thus these value may indicate a higher percentage of fire when considering only the FMNF. The ForCaMF model indicates that, by 2011, FMS contained 2.6 metric tonnes per acre less non-soil carbon (i.e., vegetation and associated pools) due to harvests since 1990, as

44 Crookston, N.L. and G.E. Dixon. 2005. The forest vegetation simulator: A review of its structure, content, and applications. Computers and Electronics in Agriculture 49: 60-80.

12

compared to a hypothetical 1 undisturbed scenario

(Fig. 7). This is 1) - 0 equivalent to roughly 645,800 metric tonnes C (0.65 Tg C) -1 in total for the approximately -2 All disturbances 27,000 acres that Fire only were harvested on Harvest only the FMNF. As a -3 Insect only

Lost Potential Storage (t C ac C (t Storage Potential Lost result, non-soil Wind only carbon stocks in the FMS would have -4 1990 1993 1996 1999 2002 2005 2008 2011 been approximately 5.3 percent higher in Year 2011 if harvests had Figure 7. Lost potential storage of carbon (non-soil) as a result of fire, insects, harvest, and wind (abiotic) on Francis Marion & Sumter National not occurred since Forests for the period 1990–2011. (Figure 2d in Appendix A, USDA Forest 1990 (Fig. 8). Service, in review). Although the FMNF accounts for about 41 percent of the forested area in FMS, about 47 percent of the harvests between 1990 and 2011 occurred in this area. Thus, the impact of harvests on carbon storage on the FMNF alone is likely somewhat greater than what is represented in Figs. 7 and 8, which include all FMS. Fires were the second most common disturbance detected on the FMNF (Fig. 6a). Overall, the fires detected over this 21-year period resulted in the loss of approximately 0.73 metric tonnes per acre of non-soil carbon on the FMS by 2011, or about 1.8 percent of non-soil carbon stocks (Fig. 7, 8). Approximately 98 percent of the fires on the FMS occurred on the FMNF, indicating that the effects of fires on carbon storage on the FMNF alone are likely greater than what is depicted in Figure 8. Given that the FMNF contains 250,825 acres, this loss is equivalent to about 183,000 metric tonnes C in total for the approximately 24,000 acres that burned in the FMNF. Despite some more significant disturbances over the past decade, losses due to disturbances and management over the 21-year period represent only a small fraction of the roughly 21 Tg C (21 million metric tonnes C) stored on the FMNF. Across all national forests in the Southern Region, harvest has been the most significant disturbance affecting carbon storage since 1990, causing non-soil forest ecosystem carbon stocks to be 2.4 percent lower by 2011 (Fig. 8). Considering all national forests in the Southern Region, by 2011, fires accounted for the loss of 0.9 percent of non-soil carbon stocks, insects 0.2 percent, and abiotic factors (wind, ice storms) 0.1 percent. The ForCaMF analysis was conducted over a relatively short time. After a forest is harvested or burned, it will eventually regrow and recover the carbon removed from the ecosystem in the harvest. However, several decades may be needed to recover the carbon removed depending on the magnitude of the disturbance (e.g., clear-cut versus partial cut, wildfire versus surface fire),

13

NFs in Texas

Francis Marion-…

NFs in North Carolina Wind Insects Ozark and St. Francis Harvest Ouachita Fire All disturbances George Washington…

NFs in Mississippi

Kisatchie

NFs in Florida

Cherokee

Chattahoochee-…

Daniel Boone

NFs in Alabama

REGION 8

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 2011 Non-soil Carbon Storage Reduction due to 1990-2011 Disturbances

Figure 8. The degree to which 2011 carbon storage on each national forest in the Southern Region was reduced by disturbances from 1990 to 2011 relative to a hypothetical baseline with no disturbance. Results include all non-soil ecosystem pools (i.e., vegetation). Wind disturbances include all abiotic effects, such as storm, ice damage, and flooding (Figure 1 from Birdsey et al., in press). as well as the conditions prior the disturbance (e.g., forest type and amount of carbon). The time required for a forest to reach pre-disturbance stocking generally increases with both increased removal of biomass and the amount of pre-harvest aboveground live-tree carbon.21 The ForCaMF model also does not track carbon stored in harvested wood after it leaves the forest ecosystem. In some cases, removing carbon from forests for human use can result in lower net contributions of GHGs to the atmosphere than if the forest was not managed, when accounting for the carbon stored in wood products, substitution effects, and forest regrowth.13,38,45,46 Therefore, the IPCC recognizes wood as a renewable resource that can provide a mitigation benefit to climate change.9

ForCaMF quantifies and compares the effects of different disturbance trends for each forest from 1990 to 2011, providing an estimate of how much more carbon the forest would have if observed fire, harvest, or insect trends had not occurred. This estimate helps to identify the greatest local

45 Skog, K.E., D.C. McKinley, R.A. Birdsey, S.J. Hines, C.W. Woodall, E.D. Reinhardt, and J.M. Vose. 2014. Chapter 7: Managing Carbon. In: Climate Change and United States Forests, Advances in Global Change Research 57, pp. 151-182. 46 Dugan, A.J., R. Birdsey, V.S. Mascorro, M. Magnan, C.E. Smyth, M. Olguin, and W.A. Kurz. 2018. A systems approach to assess climate change mitigation options in landscapes of the United States forest sector. Carbon Balance and Management. 13: doi: 10/1186/s13021-018-0100-x.

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influences on continued carbon 30% storage and puts the recent effects (a) Maple/Beech/Birch of those influences into perspective. 25% Elm/Ash/Cottonwood Oak/Gum/Cypress However, factors such as stand age, 20% Oak/Hickory drought, and climate may affect Oak/pine overall carbon change in ways that 15% Lobolly/Shortleaf pine are independent of disturbance Longleaf/Slash pine trends. The purpose of the InTEC 10%

Percentage of forest of Percentage work was to reconcile recent 5% disturbance impacts with these other factors (Sections 3.2, 3.3). 0% 3.2 Effects of Forest Aging InTEC models the collective effects of forest disturbances and 14 (b) management, aging, mortality, and 12 subsequent regrowth on carbon

stocks from 1950 to 2010. The 1)

- 10 model uses inventory-derived maps 1 yr 1

- 8 of stand age, Landsat-derived disturbance maps (Fig. 6), and 6 equations describing the 4 relationship between net primary NPP (t C ha C (t NPP productivity (NPP) and stand age. 2 Stand age serves as a proxy for past 0 disturbances and management activities.47 When a forested stand is disturbed by a severe, stand- Stand age (years) replacing event, the age of the stand Figure 9. (a) Stand age distribution in 2011 and (b) net resets to zero and the forest begins primary productivity (NPP)-stand age curves for forest type to regrow. Thus, peaks of stand groups in Francis Marion National Forest. Stand age establishment can indicate stand- distribution does not include stands from the Sumter replacing disturbance events that National Forest. promoted regeneration.

Stand-age distribution for the FMNF derived from 2010 forest inventory data indicates a pulse of stand establishment (roughly a quarter of the forested stands) occurring 20 to 30 years before 2010 (1980s) (Fig. 9a). This period of elevated stand regeneration is related to recovery after Hurricane Hugo in 1989 ravaged the forests of coastal South Carolina. This pulse may also reflect regrowth following an increase in timber removals across all national forests in the Southern Region.16 Although the Forest experienced extensive mortality due to the hurricane, it still contains remnants of an earlier period of elevated stand establishment occurring roughly 70 to 80 years ago (1930-1940). Stand age trends for the FMS, which includes the more inland Sumter NF that did not experience hurricane impacts, also indicates this early 1900s pulse of

47 Pan, Y., J.M. Chen, R. Birdsey, K. McCullough, L. He, and F. Deng. 2011. Age structure and disturbance legacy of North American Forests. Biogeosciences 8:715-732.

15 establishment but it accounts for a greater percentage of the forest and spanned from 1910 to 1940. This pulse of stand regeneration came after decades of intensive logging and the conversion of forest to agriculture. However, the establishment of the Francis Marion NF and Sumter NF in 1936 as well as policies focusing on restoring forests after decades of overharvesting and conversion of forest to agriculture enabled these stands to establish, survive, and accumulate carbon. Similar age trends indicating the legacy effects of historical management and land use have been widely observed in eastern U.S. forests.10

Stands regrow and recover at different rates depending on forest type and site conditions. Forests are generally most productive when they are young to middle age, then productivity peaks and declines or stabilizes as the forest canopy closes and as the stand experiences increased respiration and mortality of older trees.47,48 The relationship between forest productivity and stand age are quantified in NPP-age curves (Error! Reference source not found. 9b), derived in part from FIA data.49 The predominately Loblolly-Shortleaf pine stands that established in the 1980s are currently highly productive. Since the 1980s, stand establishment on the FMNF has been relatively low.

The InTEC model results show that the FMS were most rapidly accumulating carbon from the 1950s through 1970s (Fig. 10) (positive slope) as a result of regrowth following disturbances and the reversion of agriculture to forest in the early 1900s. This resulted in heightened productivity of the young to middle-aged forests by the mid-20th century. As stand establishment declined and more stands reached slower growth stages around the1970s and 1980s along with mortality associated with Hurricane Hugo in 1989, the rate of carbon accumulation slightly declined (negative slope). However, the InTEC results include forests from the Sumter NF which did not experience the hurricane and also contain higher percentages of older stands. Thus the forests in the FMNF potentially experienced a larger decline in carbon accumulation in the late 1980s, followed by a more rapid increase in carbon accumulation due to recovery. Although the results of ForCaMF indicate that recent disturbances (1990 to 2011) have had a small direct effect on carbon stock losses, InTEC results indicate that the legacy effects of historical land management and recent disturbances, reflected by the age structure, continue to drive carbon dynamics today. These negative effects of disturbance and aging on carbon accumulation have been completely offset by non-disturbance factors that influence forest growth rates and subsequently carbon accumulation.

3.3 Effects of Climate and Environment The InTEC model also isolates the effects of non-disturbance factors including climate (temperature and precipitation), atmospheric CO2 concentrations, and nitrogen deposition on forest carbon stock change and accumulation. The modeled effects of variability in temperature and precipitation on carbon stocks has varied from year-to-year.50 According to results of the InTEC model, climate has had a positive effect on changing carbon stocks and accumulation

48 Pregitzer, K.S., and E.S. Euskirchen. 2004. Carbon cycling and storage in world forests: biome patterns related to forest age. Global Change Biology 10: 2052-2077. 49 He, L., J.M. Chen, Y. Pan, R. Birdsey, and J. Kattge. 2012. Relationships between net primary productivity and forest stand age in U.S. Forests. Global Biogeochemical Cycles 26: GB3009, doi:10.1029/2010GB003942. 50 Figures 3a-3b, Appendix C4. USDA Forest Service. In review. Assessment of the Influence of Disturbance, Management Activities, and Environmental Factors on Carbon Stocks: Southern Region, Pp 104.

16

from roughly the 1950s through the 1970s, which further enhanced the 10 carbon sink and amplified carbon accumulation (Fig. 10) However, the 5 strength of the climate effect declined slightly in the 1980s and 1990s. This 0 may have been due to some notably All effects warmer years and a drought in the mid- Climate late 1980s. This negative climate impact

Accumulated C (Tg C) (Tg C Accumulated -5 CO2 has continued into the 2000s due to N deposition warming temperatures. Warmer Disturbance\aging -10 temperatures can increase forest carbon 1950 1960 1970 1980 1990 2000 2010 emissions through enhanced soil Year microbial activity and higher Figure 10. Accumulated carbon in the Francis respiration,51,52 but warming Marion & Sumter National Forests due to temperatures can also reduce soil individual disturbance/aging, non-disturbance moisture through increased factors, and all factors combined for 1950–2010, evapotranspiration, causing lower forest excluding carbon accumulated pre-1950 (Figure growth.53 4d in Appendix C.4, USDA Forest Service, in review). In addition to climate, the availability of CO2 and nitrogen can alter forest growth rates and subsequent carbon uptake and accumulation.14,15 Increased fossil fuel combustion, expansion of agriculture, and urbanization have caused a significant increase in both CO2 and nitrogen emissions. The InTEC model incorporated annual global estimates of atmospheric CO2 concentrations54 and national forest-scale observations of annual nitrogen deposition (National Atmospheric Deposition Program) to estimate the effects of CO2 and nitrogen availability on carbon stocks.22,23

According to the InTEC model, higher CO2 has consistently had a positive effect on carbon stocks in FMS, tracking an increase in atmospheric CO2 concentrations worldwide (Fig. 10). However, a precise quantification of the magnitude of this CO2 effect on terrestrial carbon storage is one of the more uncertain factors in ecosystem modeling.55,56 Although it has been suggested that elevated CO2 increases productivity and aboveground biomass in young forests, it

51 Ju, W.M., J.M. Chen, D. Harvey, and S. Wang. 2007. Future carbon balance of China’s forests under climate change and increasing CO2. Journal of Environmental Management 85: 538–562. 52 Melillo J.M., S.D. Frey, K.M. DeAngelis, W.J. Werner, M.J. Bernard, et al. 2017. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358: 101-105. 53 Xu, W., W. Yuan, W. Dong, J. Xia, D. Liu, and Y. Chen. 2013. A meta-analysis of the response of soil moisture to experimental warming. Environmental Research Letters 8: 044027. 54 Keeling, R.F., S.C. Piper, A.F. Bollenbacher, and S.J. Walker. 2009. Atmospheric CO2 records from sites in the SIO air sampling network, in Trends: A Compendium of Data on Global Change, Carbon Dioxide Information Analysis Center, Oak Ridge Natl. Lab., U.S. Dep. of Energy, Oak Ridge, Tenn. 55 Jones, A.G., J. Scullion, N. Ostle, P.E. Levy, and D. Gwynn-Jones. 2014. Completing the FACE of elevated CO2 research. Environment International 73: 252–258. 56 Zhang, F., J.M. Chen, Y. Pan, R. Birdsey, S. Shen, W. Ju, and A.J. Dugan. 2015. Impacts of inadequate historical disturbance data in the 20th century on modeling recent carbon dynamics (1951-2010) in conterminous US Forests. Journal of Geophysical Research: Biogeosciences 120: 549- 569. 17 remains uncertain whether this effect is sustained as forests age.57,58,59,60 Long-term studies examining the relationship between increased atmospheric CO2 and soil nitrogen availability show that forests initially respond with higher productivity and growth, but the effect is greatly diminished or lost within 5 years in most forests.58 There has been considerable debate regarding the effects of elevated CO2 on forest growth and biomass accumulation, thus warranting additional study.55,58,61,62

Modeled estimates suggest that overall nitrogen deposition had a positive effect on carbon accumulation in the FMS (Fig. 10). Like CO2, the actual magnitude of this effect remains uncertain. Estimates from inventory data in the northeast and north-central United States confirm that nitrogen deposition has enhanced growth among most tree species, subsequently increasing forest carbon accumulation.63 However, elevated nitrogen deposition can also decrease growth in some species for a variety of reasons, such as leaching of base cations in the soil, increased vulnerability to secondary stressors, and suppression by more competitive species.6363 Some regional studies have documented negative effects on forest productivity associated with chronically high levels of nitrogen deposition in the eastern United States.64,65,66 Policies limiting nitrous oxide emissions may have led to a decline in nitrogen deposition since the 1990s.67 The InTEC model simulated that rates of carbon accumulation associated with nitrogen deposition decreased as deposition rates declined. Overall, the InTEC model suggests that CO2 and nitrogen fertilization offset the declines in carbon accumulation associated with historical disturbance, aging, and regrowth, and climate.

3.4 Uncertainty associated with disturbance effects and environmental factors As with the baseline estimates, there is also uncertainty associated with estimates of the relative effects of disturbances, aging, and environmental factors on forest carbon trends. For example, omission, commission, and attribution errors may exist in the remotely-sensed disturbance maps used in the ForCaMF and InTEC models. However, these errors are not expected to be significant given that the maps were manually verified, rather than solely derived from automated methods. In addition to uncertainty in map products, ForCaMF results may also incorporate errors from the inventory data and the FVS-derived carbon accumulation functions.21

57DeLucia, E.H., J.G. Hamilton, S.L. Naidu, R.B. Thomas, J.A. Andrews, et al. 1999. Net primary production of a forest ecosystem under experimental CO2 enrichment. Science 284:1177 – 1179. 58 Norby, R.J., J.M. Warren, C.M. Iversen, B.E. Medlyn, and R.E. McMurtrie. 2010. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Science 107: 19368-19373. 59 Schimel, D., B.B. Stephens, and J.B. Fisher. 2015. Effect of increasing CO2 on the terrestrial carbon cycle. Proceedings of the National Academy of Sciences 112: 436-441. 60 Anderson-Teixeira, K.J., A.D. Miller, J.E. Mohan, T.W. Hudiburg, B.D. Duval, and E.H. DeLucia. 2013. Altered dynamics of forest recovery under a changing climate. Global Change Biology 19: 2001–2021. 61 Zhu, Z., S. Piao, R.B. Myneni, M. Huang, Z. Zeng, J.G. Canadell, et al. 2016. Greening of the Earth and its drivers. Nature Climate Change 6: 791–795. 62 Körner, C., R. Asshoff, O. Bignucolo, S. Hättenschwiler, S.G. Keel, S. Peláez-Riedl, et al. 2005. Carbon flux and growth in nature deciduous forest trees exposed to elevated CO2. Science 309: 1360–1362. 63 Thomas, R.Q., C.D. Canham, K.C. Weathers, and C.L. Goodale. 2010. Increased tree carbon storage in response to nitrogen deposition in the US. Nature Geoscience 3: 13–17. 64 Boggs, J.L., S.G. McNulty, M.J. Gavazzi, and J. Moore Myers. 2005. Tree growth, foliar chemistry, and nitrogen cycling across a nitrogen deposition gradient in southern Appalachian deciduous forests. Canadian Journal of Forest Research 35:1901-1913. 65Aber, J., W. McDowell, K. Nadelhoffer, A. Magill, G. Berntson, M. Kamakea, S. McNulty, W. Currie, L. Rustad, and I. Fernandez. 1998. Nitrogen Saturation in Temperate Forest Ecosystems. BioScience 921-934. 66 Pardo, L.H., M.J. Robin-Abbott, and C.T. Driscoll. 2011. Assessment of Nitrogen Deposition Effects and Empirical Critical Loads of Nitrogen for Ecoregions of the United States. Gen. Tech. Rep. NRS-80. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 291 p. 67 Strock, K.E., S.J. Nelson, J.S. Kahl, J.E. Saros and W.H. McDowell. 2014. Decadal trends reveal recent acceleration in the rate of recovery from acidification in the northeastern U.S. Environmental Science & Technology 48: 4681-4689. 18

To quantify uncertainties, the ForCaMF model employed a Monte Carlo-based approach34 to supply 95 percent confidence intervals around estimates.1919

Uncertainty analyses such as the Monte Carlo approaches applied to the baseline estimates, HWP estimates, and ForCaMF results are not commonly conducted for spatially explicit, process- based models like InTEC because of significant computational requirements. However, process- based models are known to have considerable uncertainty, particularly in the parameter values used to represent complex ecosystem processes.68 InTEC is highly calibrated to FIA data and remotely-sensed observations of disturbance and productivity, so uncertainties in these datasets are also propagated into the InTEC estimates. National-scale sensitivity analyses of InTEC inputs and assumptions56 as well as calibration with observational datasets23 suggest that model results produce a reasonable range of estimates of the total effect (e.g., Fig. 10, “All factors”). However, the relative partitioning of the effects of disturbance and non-disturbance factors as well as uncertainties at finer scales (e.g., national forest scale) are likely to be considerably higher.

Furthermore, results from the InTEC model may differ substantially from baseline estimates (CCT), given the application of different datasets, modeling approaches, and parameters.36 The baseline estimates are almost entirely rooted in empirical forest inventory data, whereas the InTEC model involves additional data inputs and modeling complexity beyond simply summarizing ground data. Therefore, although InTEC also provides information on carbon stock change and accumulation, the baseline estimates derived from CCT (section 2.1) are the recommended estimates of carbon stocks and stock changes.

4.0 Future Carbon Conditions 4.1 Prospective Forest Aging Effects The retrospective analyses presented in the previous sections can provide an important basis for understanding how various factors may influence carbon storage in the future. For instance, the combination of historical disturbances, aging, and regrowth have had a relatively significant effect on carbon trends on the FMS since 1950. The FMNF age structure shows that over 70 percent of the forested stands are young to middle-aged (less than 80 years) and few stands are older (Fig. 9a). This suggests that the Forest may continue to act as a carbon sink for several more decades. However, as the Forest continues on this aging trajectory, eventually more stands will reach a slower growth stage (Fig. 9b), potentially causing the rate carbon accumulation to decline and the Forest may eventually transition to a steady state in the future. However, although yield curves indicate that biomass carbon stocks may be approaching maximum levels (Fig. 9b), ecosystem carbon stocks can continue to increase for many decades as dead organic matter and soil carbon stocks continue to accumulate.69 Furthermore, while past and present

68 Zaehle S, S. Sitch, B. Smith, and F. Hatterman. 2005. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Glob Biogeochem Cycles 19:GB3020 69 Luyssaert, S., E.-D. Schulze, A. Börner, A. Knohl, D. Hessenmöller, B.E. Law, P. Ciasis, and J. Grace. 2008. Old-growth forests as global carbon sinks. Nature 455: 213–215.

19 aging trends can inform future conditions, the applicability may be limited, because potential changes in management activities could affect future stand age and forest growth rates.70,71 The RPA assessment provides regional projections of forest carbon trends across the United States.11 These RPA estimates are based on a new approach that uses the annual FIA system developed in the early 2000s to estimate carbon stocks retrospectively to 1990 and forward to 2060.72 The RPA assessment considers economic factors affecting potential land-use change and associated effects on rates of carbon accumulation or loss. For example, the RPA reference scenario assumes forest area will continue to expand at current rates until 2022, when it will begin to decline as forests are increasingly converted to other uses. However, because these projections are conducted regionally, they include all forestland ownerships, including federal, tribal, state, and private lands. National forests tend to have higher carbon densities than private lands and may have land management objectives and practices that differ from those on other lands.

For RPA’s South Region (equivalent to Forest Service’s Southern Region boundary, but includes all land ownerships), projections indicate that the rate of carbon sequestration has been declining since about 2010, due to the loss of forestland (i.e., land-use change) and to a lesser extent through forest aging,

Figure 11. Projections of forest carbon stock changes in the disturbances, growth, and South Region (equivalent to the boundaries of Southern Region, mortality (net R9, but includes all land tenures) for the RPA reference sequestration) (Fig. 11). At scenario (Figure 8-8 in USDA Forest Service 2016). Actual net the global and national sequestration of forests is the total carbon stock change minus scales, changes in land losses associated with land-use change. Solid green line: net use—especially the sequestration; brown dashed line: land use transfer; blue- conversion of forests to dashed line: total carbon stock change. non-forest land (deforestation)—have a substantial effect on carbon stocks.1,8 Converting forest land to a non-forest use removes a large amount of carbon from the forest and inhibits future carbon sequestration. However, national forests tend to experience low rates of land-use change, and thus, forest land area is not expected to change substantially within the FMNF in the future. Therefore, on national forest lands, the projected carbon trends may closely resemble the “net sequestration” trend in Fig. 11, which

70 Davis, S.C., A.E. Hessl, C.J. Scott, M.B. Adams, and R.B. Thomas. 2009. Forest carbon sequestration changes in response to timber harvest. For. Ecol. Manage. 258:2101–2109. 71 Keyser, T.L. and S.J. Zarnoch. 2012. Thinning, age, and site quality influence live tree carbon stocks in upland hardwood forests of the Southern Appalachians. Forest Science 58:407-418. 72 Woodall C.W., J.W. Coulston, G.M. Domke, B.F. Walters, D.N. Wear, J.E. Smith, H-E Anderson, B.J. Clough, W.B. Cohen, D.M. Griffith, S.C. Hagan, I.S. Hanou, M.C. Nichols, C.H. Perry, M.B. Russell, J.A. Westfall, and B.T. Wilson. 2015. The US forest carbon accounting framework: stocks and stock change 1990–2016. Gen Tech Rep NRS-154. Newtown Square: USDA Forest Service, Northern Research Station.

20 isolates the effects of forest aging, disturbance, mortality, and growth from land-use transfers and indicates a small decline in the rate of net carbon sequestration through 2060.

4.2 Prospective Climate and Environmental Effects The observational evidence described above and in previous sections highlights natural forest development and ecological succession along with atmospheric concentrations as the major drivers of historic and current forest carbon uptake and storage on FMNF and elsewhere in the region. Climate change introduces additional uncertainty about how forests—and forest carbon uptake and storage—may change in the future. Climate change causes many direct alterations of the local environment, such as changes in climate variables like temperature and precipitation, and it affects a wide range of ecosystem processes.73 Furthermore, disturbance rates are projected to increase with climate change,74 making it challenging to use past trends to project the effects of disturbances on forest carbon dynamics.

According to regional projections from the National Climate Assessment,75 the southeastern United States is expected to experience more intense tropical storms, extreme heat events, increased wildfire frequency, and decreased water availability over the next few decades as a result of climate change.24 Forests in the FMNF, which is located on the coast, are at especially high risk of flooding due to sea-level rise as well as damage from extreme weather events such as more frequent and intense hurricanes that are projected under a warming climate.24 Summer temperatures are projected to increase substantially, and summer precipitation is expected to fluctuate across the region. Model simulations project an increase in the number of hot days with maximum temperatures above 95oF. Average annual precipitation in the Southeast is projected to increase, with the largest increases occurring in the winter, and the number of wet days increasing.24 24

Despite higher precipitation, elevated temperatures may increase soil respiration and reduce soil moisture through increased evapotranspiration, which would negatively affect growth rates and carbon accumulation.51,52 Modeled results of recent climate effects using the InTEC model indicate that years with elevated temperatures have generally had a negative effect on carbon uptake in the FMS (Fig. 10). Heat stress is also projected to limit growth and carbon uptake of some Southern pines and hardwood species.2424Error! Bookmark not defined. Elevated temperatures and moisture stress may cause forests in the region to be more susceptible to the spread of insects, invasive species, and pathogens76, which can significantly reduce carbon uptake.77,78,79

73 Vose, J.M., D.L. Peterson, T. Patel-Weynand. 2012. Effects of Climatic Variability and Change on Forest Ecosystems: A Comprehensive Science Synthesis for the U.S. Forest Sector. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 265 p. 74 Joyce, L. A., S.W. Running, D.D. Breshears, V.H. Dale, R.W. Malmsheimer, R.N. Sampson, B. Sohngen, and C. W. Woodall, 2014: Ch. 7: Forests. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 175-194. doi:10.7930/J0Z60KZC. 75 Carter, LM, Jones, JW, Berry, L, Burkett, V, Murley, JF, Obeysekera, J, Schramm, PJ, & Wear, D. (2014). Southeast and the Caribbean. In J. M. Melillo, T. T. C. Richmond, & G. W. Yohe (Eds.), Climate Change Impacts in the United States: The Third National Climate Assessment (pp. 396-417). Washington D.C.: U.S. Global Change Research Program. 76 Ayres, M.P. and M.J. Lombardero. 2000. Assessing the Consequences of Global Change for Forest Disturbance from Herbivores and Pathogens. Science of the Total Environment 262: 263-286. 77D’Amato, A.W., J.B. Bradford, S. Fraver, and B.J. Palik. 2011. Forest management for mitigation and adaptation to climate change: Insights from long-term silviculture experiments. Forest Ecology and Management 262: 803–816. 78 Healey, S P., C.L., Raymond, I. Lockman, I.B., A.J. Hernandez, C. Garrard, and C. Huang. 2016. Root disease can rival fire and harvest in reducing forest carbon storage. Ecosphere. 7: e01569. 79 Kurz, W.A., C.C. Dymond, G. Stinson, G.J. Rampley, E.T. Neilson, A.L. Carroll, T. Ebata, and L. Safranyik. 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature 452: 987–990. 21

Altered temperature, precipitation, and growing season may affect the ability of some northern hardwood species to germinate and survive. Higher temperatures may allow species from lower elevations to move upslope to higher elevations, changing the composition of forest communities. In addition, climate-driven failures in species establishment may reduce the ability of forests to recover carbon lost after mortality-inducing events or harvests. Although future climate conditions also allow for species that are better adapted to a warmer climate to increase, it is uncertain how well these species will be able to take advantage of changing conditions.80

Carbon dioxide emissions are projected to increase through 2100 under even the most conservative emission scenarios.4 Several studies point to the beneficial effects of carbon dioxide fertilization, particularly in deciduous forests. Several models, including the InTEC model (Fig.10), project greater increases in forest productivity when the CO2 fertilization effect is included in modeling.15,23,60 However, as previously mentioned, the effect of increasing levels of atmospheric CO2 on forest productivity is transient and can be limited by the availability 58 nitrogen and other nutrients. Productivity increases under elevated CO2 could be offset by losses from climate-related stress or disturbance.

Given the complex interactions among forest ecosystem processes, disturbance regimes, climate, nutrients, and management, it is difficult to project how forests and carbon trends will respond to novel future conditions. Furthermore, the effects of future conditions on forest carbon dynamics may change over time. As climate change persists for several decades, critical thresholds may be exceeded, causing unanticipated responses to variables such as temperature and CO2 concentrations. The effects of changing conditions will almost certainly vary by species and forest type. Some factors may enhance forest growth and carbon uptake, whereas others may hinder the ability of forests to act as a carbon sink, potentially causing various influences to offset each other. Monitoring of forest responses to these changes will help inform decisions that contribute to sustainable forest management in a warmer climate. 5.0 Summary An assessment of forest carbon stocks and factors that influence carbon accumulation is critical for understanding how forests may respond to changing environmental and management conditions. The long-term capacity of forest ecosystems and wood products to sequester and store carbon depends in large part on their resilience and adaptive capacity, as well as how forests are managed to foster these attributes while providing ecosystem services.13 Under a changing climate, forests are expected to be increasingly at risk to a variety of stressors, including moisture stress, forest insects, invasive species, and extreme weather events.24,73,79,81

Forests in the FMS, which includes the FMNF, are maintaining a carbon sink. Forest carbon stocks have increased by about 22 percent between 1990 and 2013, and negative impacts on carbon stocks caused by disturbances and environmental conditions have been modest and exceeded by forest growth. According to satellite imagery, timber harvesting and fire have been

80 McKenney, D.W., J.H. Pedlar, K.Lawrence, K.Campbell, and M.F. Hutchinson. 2007. Potential impacts of climate change on the distribution of north American trees. BioScience 57:939-948. 81Allen, C.D., A.K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, and N. Cobb. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259: 660–684.

22 the most prevalent disturbances types detected on FMNF since 1990. However, disturbances during this period have been relatively small, harvesting and fires each affected on average just 0.5 percent of the FMNF forest area annually, and they were characterized as mostly low to moderate intensity (less than 50 percent reduction in canopy cover). Forest carbon losses on the FMS associated with harvests have been small compared to the total amount of carbon stored in the Forest, resulting in a loss of about 5.3 percent of non-soil carbon from 1990 to 2011. However, these estimates represent an upper bound, because they do not account for continued storage of harvested carbon in wood products or the effect of substitution. Carbon storage in HWPs (in use and landfills) sourced from national forests in the Southeast increased since the early 1900s. However, recent declines in timber harvesting have slowed the rate of carbon accumulation in the product sector.

Recent disturbances including Hurricane Hugo as well as the legacy of stand recovery following intensive timber harvesting and land clearing for agriculture during the 19th century have had a significant effect on forest carbon tends on the FMNF. As a result of Hurricane Hugo in 1989 stands on the FMNF are now mostly young to middle aged (Fig. 4). Young and middle aged forests can uptake carbon more rapidly due to higher growth rates as compared to older stands. Thus, the age structure of the FMNF suggests that the Forest may continue to act as a carbon sink for several decades as forests continues to recover from the hurricane.

Climate and environmental factors, including elevated atmospheric CO2 and nitrogen deposition, have also had a significant influence carbon accumulation on the FMNF. Climate has had the least and most variable effect on forest carbon accumulation among all factors assessed. Recent warmer temperatures and precipitation variability may have stressed forests, causing climate to have a negative impact on carbon accumulation in the 2000s. Conversely, increased atmospheric CO2 and nitrogen deposition may have enhanced growth rates and helped to counteract ecosystem carbon losses due to historical disturbances, aging, and climate.

The effects of future climate conditions are complex and remain uncertain. However, under changing climate and environmental conditions, forests of the FMNF may be increasingly vulnerable to a variety of stressors, namely sea-level rise and more intense storms due to the Forests coastal geography. These potentially negative effects might be balanced somewhat by the positive effects of longer growing season, elevated atmospheric CO2 concentrations, and nitrogen deposition. However, it is difficult to judge how these factors and their interactions will affect future carbon dynamics on the Forest.

Forested area on the FMNF will be maintained as forest in the foreseeable future, which will allow for a continuation of carbon uptake and storage over the long term. The population in the region is growing, and some conversion of forested lands to non-forest uses is likely on private lands adjacent to and near the Forest. The FMNF will continue to have an important role in maintaining the carbon sink, regionally and nationally, for decades to come.

23

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time

Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 10 0 0 0 0 0 0 0 0 0 0 0 1593 0 100 0 0 0 0 0 0 0 0 0 0 0 538 0 100/101 0 0 0 0 1601 0 0 0 0 1571 0 0 0 101 0 0 0 0 0 0 0 0 0 0 0 1033 0 101A 0 0 0 0 0 0 0 0 448 0 0 0 0 103 0 0 0 0 0 0 0 0 0 0 0 0 393 104 0 0 0 0 0 0 0 0 0 0 0 260 0 104KV 0 0 0 0 0 0 0 0 0 0 280 0 0 107 0 0 0 0 0 0 0 0 0 0 539 0 106 107/15 0 0 0 0 0 0 0 0 0 0 82 0 0 107/18 0 0 0 0 0 0 0 0 0 0 40 0 0 107/23 0 0 0 0 0 0 0 0 0 0 14 0 0 108 0 0 0 0 0 0 0 0 0 0 0 371 214 110 0 421 0 0 0 0 0 0 0 0 0 418 0 111 0 0 0 0 0 0 0 0 0 0 683 0 335 111 Plot 0 0 0 11 0 0 0 0 0 0 0 0 0 111A 0 0 0 0 0 0 0 0 0 104 0 0 0 111WL 0 0 0 0 0 11 0 0 0 0 0 0 0 112 0 0 0 0 0 0 0 0 0 0 912 0 863 113 0 0 0 0 0 0 0 0 0 0 550 0 0 113/12 0 0 0 0 0 0 0 0 0 0 30 0 0 113/8 0 0 0 0 0 0 0 0 0 0 34 0 0 114 0 0 0 0 0 0 0 0 0 0 1078 0 0 115 0 0 0 0 0 0 0 0 0 0 238 0 267 115/116 0 0 1061 0 1061 0 0 0 1152 0 0 0 0 116 0 0 0 0 0 0 0 0 0 0 318 0 825 120 0 0 346 0 0 0 334 0 0 334 0 0 0 121 0 0 916 0 0 0 893 0 1185 0 1180 0 1131 122 0 0 0 0 0 0 0 0 0 0 1461 1049 0 122A 0 0 638 0 0 0 0 0 514 0 0 0 0 122B 0 1430 0 0 0 0 0 0 0 0 0 0 0 D-1 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 122BC 0 0 0 1899 0 0 0 0 1583 0 0 0 0 122BC_KV 0 0 0 0 0 0 0 0 371 0 0 0 0 123 0 750 0 0 0 0 758 0 0 0 766 0 0 123/139 0 0 0 1505 0 0 0 0 0 0 0 0 0 124 0 0 0 667 0 676 0 676 0 0 642 0 0 125 0 813 0 808 0 813 0 763 0 804 0 804 0 126 0 0 0 0 1571 0 1047 0 0 0 0 1068 0 126/128 0 0 2024 0 0 0 0 0 0 1875 0 0 0 128 0 0 0 0 453 0 0 0 0 206 0 2739 0 128A 0 0 0 0 0 486 0 0 0 0 0 0 0 129 0 0 0 0 0 0 0 0 0 0 0 0 0 130 0 898 0 0 0 0 0 917 0 1288 0 0 1289 130A 0 0 0 0 127 0 0 0 0 0 0 0 0 131 0 712 0 0 621 0 0 655 0 0 0 0 0 131/150 0 0 0 1402 0 0 0 0 0 1402 0 0 0 132 0 1047 0 1254 0 1241 597 0 0 1252 0 0 0 133 0 0 1286 0 1286 0 0 1294 0 1379 0 0 0 134 0 0 0 0 0 1104 0 1250 0 1108 0 1122 0 134AB 0 0 0 1110 0 0 0 0 0 0 0 0 0 134B 135 0 0 0 0 0 0 0 0 0 0 0 0 135 0 0 0 588 0 0 0 0 0 0 0 570 0 136 0 258 0 567 0 0 0 0 0 0 0 0 0 137 0 0 0 0 821 0 0 0 0 0 818 0 421 137A 0 0 360 0 0 0 0 372 0 0 0 0 0 138 0 0 818 0 933 0 938 0 938 0 934 0 0 139 0 765 0 0 0 657 0 741 0 777 0 806 0 140 897 0 0 0 892 0 0 0 1002 0 0 415 544 141 0 0 0 0 0 1451 0 0 0 0 0 0 1620 142 0 0 0 0 0 1572 0 0 0 1737 0 0 0 143 0 0 0 0 0 0 0 0 0 1401 0 0 0 147 0 0 0 948 0 0 0 997 0 0 0 0 607 148 0 0 0 0 860 0 0 0 1107 0 0 1125 0 D-2 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 148/159 0 0 1277 0 0 0 0 0 0 0 0 0 0 149 0 0 1896 0 1353 0 1296 0 0 0 1100 0 1110 149A 0 0 0 0 0 0 0 0 985 0 0 0 0 15 0 0 0 0 0 0 0 0 0 637 0 0 637 150 0 661 0 0 661 0 0 661 0 0 0 665 0 151 0 0 1205 0 1205 0 0 1550 0 1634 0 1631 0 152 0 0 0 0 0 0 0 0 2130 0 0 0 1275 153 0 0 0 0 0 0 0 0 0 0 179 0 590 156 0 0 0 665 0 639 0 602 0 549 0 0 549 157 0 0 0 0 0 0 0 0 670 0 1037 0 0 157/158 0 0 1085 0 1043 0 1043 0 0 0 0 0 0 158 0 0 0 0 0 0 0 0 373 0 0 0 0 159 0 0 0 0 960 0 0 0 0 0 0 929 0 159A 0 0 0 0 0 0 337 0 337 0 0 0 0 160 0 0 0 952 0 0 1002 0 1002 0 0 1019 0 161 0 0 0 0 0 0 345 0 0 0 0 0 1025 161A 0 345 0 0 0 345 0 0 0 0 0 0 0 161B 0 0 0 0 0 0 429 0 0 0 0 0 0 161W 0 0 0 0 0 0 218 0 0 0 0 0 0 162 0 916 0 916 0 0 0 0 1044 0 947 0 1007 162A 0 0 0 0 0 540 0 0 0 0 0 0 0 163 0 451 0 0 451 566 0 0 758 0 0 691 0 164 0 0 577 0 509 0 667 0 0 0 211 0 0 164A 0 0 0 0 0 0 0 0 383 0 0 0 0 165 0 0 0 0 650 0 684 0 761 0 0 0 0 166 945 0 0 1477 0 0 1922 0 0 0 0 1075 0 167 0 0 600 0 0 680 0 0 0 0 0 0 0 168 0 0 1148 0 0 1234 0 0 0 0 0 0 0 169 0 0 0 0 0 833 0 0 0 0 778 0 149 17 0 0 0 0 0 0 0 0 0 0 702 0 0 170 0 0 1333 0 0 0 0 1276 0 0 0 0 1338 171 591 0 0 0 0 1528 0 1127 0 0 0 667 0 D-3 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 171/188 0 0 0 1187 0 0 0 0 0 1627 0 0 0 172 0 315 0 319 0 315 0 315 0 319 0 319 0 173 0 0 0 0 0 0 0 0 0 0 0 0 71 174 0 519 0 519 0 589 0 0 546 0 534 0 0 175 0 1945 0 0 0 1945 0 0 0 0 0 2167 0 175A 0 0 0 0 0 0 0 0 1184 0 0 0 0 175AB 0 0 0 1970 0 0 0 0 0 0 0 0 0 175B 0 0 0 0 0 0 0 0 1095 0 0 0 0 176 0 958 0 964 0 0 0 0 0 0 1097 0 0 176/177 0 0 0 0 0 1932 0 0 0 0 0 0 0 176A 0 0 0 0 0 249 0 0 0 0 0 0 0 177 0 908 0 902 0 0 0 0 0 0 887 0 0 178 0 821 0 836 0 821 0 0 0 0 0 0 0 179 0 0 0 0 0 0 0 0 0 0 383 0 0 179/181 0 0 1136 0 0 0 1468 0 0 0 0 0 0 17A 0 0 0 0 703 0 0 703 0 0 0 0 0 18 0 0 0 1318 0 0 0 1385 0 0 1422 0 1424 180 0 0 0 0 0 0 1701 0 0 0 0 0 0 180/184 0 0 1695 0 0 0 0 0 0 0 0 0 0 182 0 0 0 0 0 0 0 0 1699 0 1654 0 0 182/183 0 0 2160 0 2160 0 2542 0 0 0 0 0 0 183 0 0 0 0 0 0 664 0 0 0 0 0 0 184 0 0 0 0 0 0 1701 0 876 0 0 876 0 185 0 0 0 0 0 0 0 0 0 0 667 0 669 186 0 877 0 900 0 1382 0 0 0 1455 0 1243 0 187 0 0 456 0 0 0 0 460 0 457 0 953 0 187/188 460 0 0 566 0 558 0 564 0 551 0 0 0 188 541 0 0 0 0 0 0 0 0 0 0 833 0 19/20 0 0 0 0 0 1627 0 2019 0 0 0 0 0 191 0 0 0 0 0 0 0 0 0 0 165 0 0 192 0 0 0 0 0 0 0 0 505 0 81 330 0 192A 0 0 0 0 0 0 0 0 29 0 0 0 0 D-4 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 192S 0 0 0 81 0 0 0 0 52 0 0 0 0 193 0 0 0 0 0 0 0 0 424 0 0 424 0 194 0 0 0 0 0 0 0 635 0 0 0 0 615 194/195 0 0 1254 0 0 0 0 0 0 1289 0 0 0 195 0 0 0 0 0 1144 0 672 0 0 0 914 0 196 0 0 1106 0 1106 0 0 1460 0 0 1065 0 155 197 0 0 0 0 0 0 0 0 0 0 1175 0 0 197/198 0 0 1110 0 0 0 0 0 0 0 0 0 0 198 0 0 0 0 0 0 0 0 0 0 621 0 668 204 0 0 0 0 0 0 0 0 0 0 0 515 0 204/208 0 0 0 0 0 0 0 260 0 0 0 0 0 204A 0 0 0 355 0 0 0 0 0 0 0 0 0 204B 0 0 0 0 260 0 0 0 0 0 0 0 0 205 0 0 0 0 0 0 0 0 0 0 2277 58 154 205A - 1/2 0 0 0 244 0 0 0 0 0 0 0 0 0 205B 0 0 0 0 149 0 0 0 0 0 0 0 0 205WL 0 0 0 0 0 48 0 0 48 0 0 0 0 208 0 0 0 0 0 0 0 67 0 0 0 0 0 37 0 0 0 0 0 0 0 645 0 0 633 0 0 39 0 0 0 0 0 0 0 0 0 0 0 0 17 40 0 0 0 0 0 0 0 0 0 0 1121 0 0 41/42/44 0 0 1097 0 0 0 0 0 0 0 0 0 0 42 0 0 0 0 0 0 0 0 0 969 0 0 0 43 0 646 0 0 647 0 844 0 577 0 0 752 0 44 0 0 0 0 430 0 0 0 567 0 0 631 0 44A 0 0 0 0 0 0 0 0 66 0 0 0 0 46 0 0 0 0 0 0 0 0 0 0 0 128 0 46/63/64 0 0 1155 0 1182 0 0 0 0 0 0 0 0 47 0 0 725 0 725 0 0 728 0 728 0 728 0 48 0 0 0 0 0 0 0 0 0 0 0 780 0 53 0 998 0 0 0 0 0 0 998 0 979 0 979 53/57 0 0 0 1774 0 1774 0 0 0 0 0 0 0 D-5 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 57 0 727 0 0 0 0 0 978 0 0 727 0 0 58 0 0 0 658 0 0 0 0 0 0 791 0 0 62 0 0 0 0 503 0 0 511 0 0 0 907 0 63 0 0 0 0 0 0 0 0 342 0 0 456 337 64 0 0 0 0 0 359 0 1197 0 0 0 881 0 65 0 0 0 0 0 0 830 0 0 0 0 1040 0 65/66 0 0 0 0 0 0 1521 0 0 0 0 0 0 65/67/68 0 0 0 0 692 0 0 0 0 0 0 0 0 66 0 0 0 0 0 0 0 0 0 0 0 1720 0 67 0 0 0 0 0 0 0 0 0 0 0 557 0 67/68 0 0 673 0 673 0 0 0 0 0 0 0 0 67/68A 0 0 0 0 0 0 0 1865 0 0 0 0 0 68 0 0 0 0 0 0 0 276 0 0 0 1845 0 68/69 0 0 0 0 0 0 0 955 0 0 0 0 0 69 0 0 0 0 0 0 0 0 0 0 0 1464 0 69A 0 0 0 0 0 0 0 931 0 0 0 0 0 70 0 0 0 0 19 0 0 0 976 0 0 35 0 70/71WL 0 0 0 0 0 0 0 175 0 0 0 0 0 71 0 462 0 0 0 0 0 0 0 0 0 938 0 71/80 0 0 0 0 1198 0 0 0 0 0 0 0 0 71B 0 0 0 0 0 0 0 0 0 299 0 0 0 72 0 0 396 0 0 0 0 0 0 1229 0 0 840 73 0 346 0 0 346 0 0 399 0 427 0 394 0 73/79 0 0 0 1418 0 0 0 0 0 0 0 0 0 74 0 0 690 0 690 0 691 0 690 0 690 0 0 75 0 0 0 0 0 0 0 0 0 0 672 0 673 75/76/78 0 2269 0 2329 0 2269 0 2274 0 0 0 0 0 76/78 0 0 0 0 0 0 0 0 0 1742 0 0 0 78 0 0 0 0 0 0 0 0 0 0 0 0 641 79 0 949 0 0 0 1035 0 1132 0 1001 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 258 0 8/9/10 0 0 2592 0 0 0 0 0 2592 0 0 0 0 D-6 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 80 0 0 0 0 0 0 0 0 0 523 0 13 274 80 Plot 0 0 0 11 0 0 0 0 0 0 0 0 0 81/85C 0 0 0 0 642 0 0 0 0 0 0 0 0 82 0 0 793 0 0 0 0 967 0 0 1045 0 1044 83 0 0 1057 0 1057 0 0 0 0 0 0 1328 0 84 0 1225 0 0 1225 0 1451 0 1451 0 1454 0 0 85 0 1008 0 0 0 0 0 0 0 0 1242 0 903 85A 0 901 0 798 0 0 0 1008 0 0 0 0 0 85B 0 0 0 410 0 0 0 784 0 0 0 0 0 87 0 0 0 0 0 0 0 609 0 0 522 0 39 87 Plot A 0 0 0 19 0 0 0 0 0 0 0 0 0 87 Plot B 0 0 0 25 0 0 0 0 0 0 0 0 0 87/88 0 0 0 0 0 1195 0 0 0 0 0 0 0 88 0 591 0 0 0 0 0 674 0 0 0 682 0 89 0 0 0 0 0 0 0 0 0 0 0 1196 0 89Wyden 0 0 0 0 0 0 0 1562 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 783 0 90 Plot 0 0 0 2 0 0 0 0 0 0 0 0 0 93 0 0 0 0 0 0 0 0 0 0 1048 124 162 93A 0 0 0 0 751 0 0 0 690 0 0 0 0 93B 0 0 0 0 200 0 0 0 0 0 0 0 0 94 0 0 0 308 0 0 232 0 0 0 324 0 0 95 0 1473 0 1495 0 0 1971 0 0 0 0 1720 0 96 0 0 0 0 0 0 0 0 0 299 0 739 0 96/185 0 0 0 1912 0 0 2170 0 1221 0 0 0 0 97 0 1383 0 0 1383 0 705 0 0 0 0 0 0 97A 0 0 0 0 0 0 787 0 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 211 99/102 0 0 0 0 0 0 0 641 0 779 0 0 0 99ADMIN 0 0 0 0 0 0 0 131 0 0 0 0 0 9WL 0 0 0 0 0 464 0 0 0 0 0 0 0 C142S16 0 0 0 0 0 31 0 0 0 0 0 0 0 D-7 | Page

Table D-1. Prescribed Fire Program (Acres) for Management Area 1 and Management Area 2 over time Burn Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 C67S2 0 0 0 0 0 0 0 15 0 0 0 0 0 C70S23 0 0 0 0 0 0 0 0 50 0 0 0 0 Total 3569 28788 34665 36089 33799 32113 31788 39878 33421 31752 38829 49319 26171

D-8 | Page

Table D-2. Prescribed Fire (acres) burned by year in Management Area 2.

Burn 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Units 10 0 0 0 0 0 0 0 0 0 0 0 1591 0 103 0 0 0 0 0 0 0 0 0 0 0 0 391 104 0 0 0 0 0 0 0 0 0 0 0 258 0 104KV 0 0 0 0 0 0 0 0 0 0 278 0 0 110 0 3 0 0 0 0 0 0 0 0 0 20 0 115 0 0 0 0 0 0 0 0 0 0 1 0 1 115/116 0 0 0 0 0 0 0 0 71 0 0 0 0 116 0 0 0 0 0 0 0 0 0 0 0 0 0 121 0 0 0 0 0 0 0 0 67 0 67 0 67 136 0 0 0 0 0 0 0 0 0 0 0 0 0 137 0 0 0 0 4 0 0 0 0 0 0 0 4 137A 0 0 4 0 0 0 0 0 0 0 0 0 0 140 21 0 0 0 0 0 0 0 21 0 0 0 0 141 0 0 0 0 0 0 0 0 0 0 0 0 3 143 0 0 0 0 0 0 0 0 0 1400 0 0 0 15 0 0 0 0 0 0 0 0 0 627 0 0 627 152 0 0 0 0 0 0 0 0 61 0 0 0 82 157 0 0 0 0 0 0 0 0 0 0 0 0 0 157/158 0 0 41 0 0 0 0 0 0 0 0 0 0 160 0 0 0 0 0 0 0 0 0 0 0 0 0 162 0 0 0 0 0 0 0 0 0 0 0 0 53 163 0 0 0 0 0 43 0 0 171 0 0 172 0 164 0 0 68 0 0 0 149 0 0 0 81 0 0 165 0 0 0 0 0 0 0 0 36 0 0 0 0 166 184 0 0 0 0 0 184 0 0 0 0 211 0 169 0 0 0 0 0 0 0 0 0 0 0 0 7 17 0 0 0 0 0 0 0 0 0 0 0 0 0 170 0 0 0 0 0 0 0 0 0 0 0 0 0

D-9 | Page

Table D-2. Prescribed Fire (acres) burned by year in Management Area 2.

Burn 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Units 17A 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 182 0 0 0 0 0 0 0 0 0 0 8 0 0 186 0 0 0 0 0 0 0 0 0 0 0 0 0 187 0 0 4 0 0 0 0 4 0 4 0 170 0 187/188 4 0 0 0 0 0 0 0 0 0 0 0 0 191 0 0 0 0 0 0 0 0 0 0 164 0 0 192 0 0 0 0 0 0 0 0 494 0 0 328 0 193 0 0 0 0 0 0 0 0 424 0 0 424 0 198 0 0 0 0 0 0 0 0 0 0 375 0 378 204 0 0 0 0 0 0 0 0 0 0 0 0 0 204/208 0 0 0 0 0 0 0 0 0 0 0 0 0 204A 0 0 0 0 0 0 0 0 0 0 0 0 0 204B 0 0 0 0 0 0 0 0 0 0 0 0 0 205A - 0 0 0 0 0 0 0 0 0 0 0 0 0 1/2 208 0 0 0 0 0 0 0 67 0 0 0 0 0 39 0 0 0 0 0 0 0 0 0 0 0 0 16 40 0 0 0 0 0 0 0 0 0 0 1120 0 0 43 0 7 0 0 7 0 205 0 0 0 0 114 0 47 0 0 0 0 0 0 0 0 0 0 0 0 0 53 0 1 0 0 0 0 0 0 1 0 1 0 1 53/57 0 0 0 1 0 1 0 0 0 0 0 0 0 57 0 0 0 0 0 0 0 0 0 0 0 0 0 62 0 0 0 0 0 0 0 0 0 0 0 392 0 63 0 0 0 0 0 0 0 0 338 0 0 281 334 65 0 0 0 0 0 0 74 0 0 0 0 74 0 68 0 0 0 0 0 0 0 274 0 0 0 0 0 69 0 0 0 0 0 0 0 0 0 0 0 302 0

D-10 | Page

Table D-2. Prescribed Fire (acres) burned by year in Management Area 2.

Burn 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Units 69A 0 0 0 0 0 0 0 0 0 0 0 0 0 70 0 0 0 0 0 0 0 0 935 0 0 2 0 70/71WL 0 0 0 0 0 0 0 139 0 0 0 0 0 71 0 0 0 0 0 0 0 0 0 0 0 139 0 71/80 0 0 0 0 0 0 0 0 0 0 0 0 0 72 0 0 1 0 0 0 0 0 0 687 0 0 240 8 0 0 0 0 0 0 0 0 0 0 0 259 0 8/9/10 0 0 2589 0 0 0 0 0 2589 0 0 0 0 81/85C 0 0 0 0 1 0 0 0 0 0 0 0 0 85 0 0 0 0 0 0 0 0 0 0 2 0 0 85A 0 0 0 0 0 0 0 0 0 0 0 0 0 85B 0 0 0 1 0 0 0 2 0 0 0 0 0 87 0 0 0 0 0 0 0 93 0 0 39 0 0 87/88 0 0 0 0 0 1 0 0 0 0 0 0 0 88 0 1 0 0 0 0 0 1 0 0 0 1 0 9 0 0 0 0 0 0 0 0 0 0 0 782 0 93 0 0 0 0 0 0 0 0 0 0 176 0 0 93A 0 0 0 0 0 0 0 0 140 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 204 99/102 0 0 0 0 0 0 0 640 0 772 0 0 0 99ADMIN 0 0 0 0 0 0 0 131 0 0 0 0 0 9WL 0 0 0 0 0 169 0 0 0 0 0 0 0 C70S23 0 0 0 0 0 0 0 0 50 0 0 0 0 Total 209 12 2707 2 12 214 612 1351 5398 3490 2312 5520 2408

D-11 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 MA1 (total) 3351 28750 31951 36046 33749 31891 31110 37788 26386 27515 35138 43088 21643 100 0 0 0 0 0 0 0 0 0 0 0 538 0 100/101 0 0 0 0 1601 0 0 0 0 1571 0 0 0 101 0 0 0 0 0 0 0 0 0 0 0 1033 0 101A 0 0 0 0 0 0 0 0 448 0 0 0 0 107 0 0 0 0 0 0 0 0 0 0 539 0 106 107/15 0 0 0 0 0 0 0 0 0 0 82 0 0 107/18 0 0 0 0 0 0 0 0 0 0 40 0 0 107/23 0 0 0 0 0 0 0 0 0 0 14 0 0 108 0 0 0 0 0 0 0 0 0 0 0 371 214 110 0 418 0 0 0 0 0 0 0 0 0 398 0 111 0 0 0 0 0 0 0 0 0 0 683 0 335 111 Plot 0 0 0 11 0 0 0 0 0 0 0 0 0 111A 0 0 0 0 0 0 0 0 0 104 0 0 0 111WL 0 0 0 0 0 11 0 0 0 0 0 0 0 112 0 0 0 0 0 0 0 0 0 0 912 0 863 113 0 0 0 0 0 0 0 0 0 0 518 0 0 113/12 0 0 0 0 0 0 0 0 0 0 30 0 0 113/8 0 0 0 0 0 0 0 0 0 0 32 0 0 114 0 0 0 0 0 0 0 0 0 0 1078 0 0 115 0 0 0 0 0 0 0 0 0 0 238 0 267 115/116 0 0 1060 0 1060 0 0 0 1080 0 0 0 0 116 0 0 0 0 0 0 0 0 0 0 317 0 824 120 0 0 346 0 0 0 334 0 0 334 0 0 0 121 0 0 916 0 0 0 893 0 1119 0 1114 0 1065 122 0 0 0 0 0 0 0 0 0 0 1461 1049 0 122A 0 0 638 0 0 0 0 0 514 0 0 0 0 122B 0 1430 0 0 0 0 0 0 0 0 0 0 0 122BC 0 0 0 1895 0 0 0 0 1583 0 0 0 0 122BC_KV 0 0 0 0 0 0 0 0 371 0 0 0 0 123 0 750 0 0 0 0 758 0 0 0 766 0 0 123/139 0 0 0 1505 0 0 0 0 0 0 0 0 0 124 0 0 0 667 0 676 0 676 0 0 638 0 0

D-12 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 125 0 813 0 808 0 813 0 763 0 804 0 804 0 126 0 0 0 0 1571 0 1047 0 0 0 0 1068 0 126/128 0 0 2024 0 0 0 0 0 0 1875 0 0 0 128 0 0 0 0 453 0 0 0 0 206 0 2738 0 128A 0 0 0 0 0 486 0 0 0 0 0 0 0 130 0 898 0 0 0 0 0 917 0 911 0 0 913 130A 0 0 0 0 127 0 0 0 0 0 0 0 0 131 0 712 0 0 621 0 0 655 0 0 0 0 0 131/150 0 0 0 1402 0 0 0 0 0 1402 0 0 0 132 0 1047 0 1254 0 1241 597 0 0 1252 0 0 0 133 0 0 1286 0 1286 0 0 1294 0 1379 0 0 0 134 0 0 0 0 0 1104 0 1250 0 1108 0 1122 0 134AB 0 0 0 1110 0 0 0 0 0 0 0 0 0 134B 135 0 0 0 0 0 0 0 0 0 0 0 0 135 0 0 0 574 0 0 0 0 0 0 0 570 0 136 0 258 0 555 0 0 0 0 0 0 0 0 0 137 0 0 0 0 817 0 0 0 0 0 818 0 417 137A 0 0 356 0 0 0 0 372 0 0 0 0 0 138 0 0 818 0 933 0 938 0 938 0 934 0 0 139 0 765 0 0 0 657 0 741 0 777 0 806 0 140 876 0 0 0 891 0 0 0 980 0 0 415 544 141 0 0 0 0 0 1451 0 0 0 0 0 0 1444 142 0 0 0 0 0 1572 0 0 0 1737 0 0 0 143 0 0 0 0 0 0 0 0 0 0 0 0 0 147 0 0 0 948 0 0 0 997 0 0 0 0 607 148 0 0 0 0 857 0 0 0 861 0 0 867 0 148/159 0 0 1274 0 0 0 0 0 0 0 0 0 0 149 0 0 1896 0 1353 0 1296 0 0 0 1100 0 1110 149A 0 0 0 0 0 0 0 0 985 0 0 0 0 150 0 661 0 0 661 0 0 661 0 0 0 665 0 151 0 0 1205 0 1205 0 0 1205 0 1288 0 1288 0 152 0 0 0 0 0 0 0 0 707 0 0 0 416 153 0 0 0 0 0 0 0 0 0 0 179 0 0 156 0 0 0 661 0 639 0 602 0 549 0 0 549

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Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 157 0 0 0 0 0 0 0 0 670 0 1036 0 0 157/158 0 0 1043 0 1043 0 1043 0 0 0 0 0 0 158 0 0 0 0 0 0 0 0 373 0 0 0 0 159 0 0 0 0 960 0 0 0 0 0 0 929 0 159A 0 0 0 0 0 0 337 0 337 0 0 0 0 160 0 0 0 952 0 0 1002 0 1002 0 0 1019 0 161 0 0 0 0 0 0 345 0 0 0 0 0 1025 161A 0 345 0 0 0 345 0 0 0 0 0 0 0 161B 0 0 0 0 0 0 429 0 0 0 0 0 0 161W 0 0 0 0 0 0 218 0 0 0 0 0 0 162 0 916 0 916 0 0 0 0 1044 0 947 0 954 162A 0 0 0 0 0 540 0 0 0 0 0 0 0 163 0 451 0 0 451 520 0 0 574 0 0 507 0 164 0 0 509 0 509 0 517 0 0 0 129 0 0 164A 0 0 0 0 0 0 0 0 383 0 0 0 0 165 0 0 0 0 650 0 684 0 724 0 0 0 0 166 752 0 0 1477 0 0 1728 0 0 0 0 762 0 167 0 0 600 0 0 680 0 0 0 0 0 0 0 168 0 0 1148 0 0 1234 0 0 0 0 0 0 0 169 0 0 0 0 0 833 0 0 0 0 774 0 141 17 0 0 0 0 0 0 0 0 0 0 702 0 0 170 0 0 1333 0 0 0 0 1276 0 0 0 0 1338 171 591 0 0 0 0 1528 0 1127 0 0 0 667 0 171/188 0 0 0 1187 0 0 0 0 0 1627 0 0 0 172 0 315 0 319 0 315 0 315 0 319 0 319 0 173 0 0 0 0 0 0 0 0 0 0 0 0 71 174 0 519 0 519 0 589 0 0 546 0 534 0 0 175 0 1945 0 0 0 1945 0 0 0 0 0 2167 0 175A 0 0 0 0 0 0 0 0 1184 0 0 0 0 175AB 0 0 0 1970 0 0 0 0 0 0 0 0 0 175B 0 0 0 0 0 0 0 0 1095 0 0 0 0 176 0 958 0 964 0 0 0 0 0 0 1097 0 0 176/177 0 0 0 0 0 1932 0 0 0 0 0 0 0 176A 0 0 0 0 0 249 0 0 0 0 0 0 0

D-14 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 177 0 908 0 902 0 0 0 0 0 0 887 0 0 178 0 821 0 836 0 821 0 0 0 0 0 0 0 179 0 0 0 0 0 0 0 0 0 0 383 0 0 179/181 0 0 1136 0 0 0 1416 0 0 0 0 0 0 17A 0 0 0 0 703 0 0 703 0 0 0 0 0 18 0 0 0 1313 0 0 0 1381 0 0 1422 0 1424 180 0 0 0 0 0 0 1701 0 0 0 0 0 0 180/184 0 0 1695 0 0 0 0 0 0 0 0 0 0 182 0 0 0 0 0 0 0 0 1699 0 1646 0 0 182/183 0 0 2160 0 2160 0 2542 0 0 0 0 0 0 183 0 0 0 0 0 0 664 0 0 0 0 0 0 184 0 0 0 0 0 0 1701 0 876 0 0 875 0 185 0 0 0 0 0 0 0 0 0 0 667 0 669 186 0 877 0 900 0 1382 0 0 0 1455 0 1242 0 187 0 0 451 0 0 0 0 456 0 452 0 783 0 187/188 456 0 0 566 0 558 0 564 0 551 0 0 0 188 541 0 0 0 0 0 0 0 0 0 0 833 0 19/20 0 0 0 0 0 1627 0 2019 0 0 0 0 0 192 0 0 0 0 0 0 0 0 9 0 80 0 0 192A 0 0 0 0 0 0 0 0 29 0 0 0 0 192S 0 0 0 80 0 0 0 0 52 0 0 0 0 193 0 0 0 0 0 0 0 0 0 0 0 0 0 194 0 0 0 0 0 0 0 635 0 0 0 0 615 194/195 0 0 1254 0 0 0 0 0 0 1289 0 0 0 195 0 0 0 0 0 1144 0 672 0 0 0 914 0 196 0 0 1106 0 1106 0 0 1460 0 0 1065 0 150 197 0 0 0 0 0 0 0 0 0 0 1175 0 0 197/198 0 0 1110 0 0 0 0 0 0 0 0 0 0 198 0 0 0 0 0 0 0 0 0 0 244 0 288 204 0 0 0 0 0 0 0 0 0 0 0 515 0 204/208 0 0 0 0 0 0 0 260 0 0 0 0 0 204A 0 0 0 355 0 0 0 0 0 0 0 0 0 204B 0 0 0 0 260 0 0 0 0 0 0 0 0 205 0 0 0 0 0 0 0 0 0 0 946 58 154

D-15 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 205A - 1/2 0 0 0 244 0 0 0 0 0 0 0 0 0 205B 0 0 0 0 149 0 0 0 0 0 0 0 0 205WL 0 0 0 0 0 48 0 0 48 0 0 0 0 208 0 0 0 0 0 0 0 0 0 0 0 0 0 37 0 0 0 0 0 0 0 645 0 0 633 0 0 40 0 0 0 0 0 0 0 0 0 0 0 0 0 41/42/44 0 0 1097 0 0 0 0 0 0 0 0 0 0 42 0 0 0 0 0 0 0 0 0 969 0 0 0 43 0 638 0 0 639 0 638 0 577 0 0 638 0 44 0 0 0 0 430 0 0 0 567 0 0 631 0 44A 0 0 0 0 0 0 0 0 66 0 0 0 0 46 0 0 0 0 0 0 0 0 0 0 0 128 0 46/63/64 0 0 1155 0 1178 0 0 0 0 0 0 0 0 47 0 0 725 0 725 0 0 728 0 728 0 728 0 48 0 0 0 0 0 0 0 0 0 0 0 780 0 53 0 997 0 0 0 0 0 0 997 0 978 0 978 53/57 0 0 0 1773 0 1773 0 0 0 0 0 0 0 57 0 727 0 0 0 0 0 978 0 0 727 0 0 58 0 0 0 658 0 0 0 0 0 0 791 0 0 62 0 0 0 0 502 0 0 510 0 0 0 513 0 63 0 0 0 0 0 0 0 0 0 0 0 205 0 64 0 0 0 0 0 359 0 1197 0 0 0 881 0 65 0 0 0 0 0 0 756 0 0 0 0 966 0 65/66 0 0 0 0 0 0 1521 0 0 0 0 0 0 65/67/68 0 0 0 0 692 0 0 0 0 0 0 0 0 66 0 0 0 0 0 0 0 0 0 0 0 1720 0 67 0 0 0 0 0 0 0 0 0 0 0 557 0 67/68 0 0 673 0 673 0 0 0 0 0 0 0 0 67/68A 0 0 0 0 0 0 0 1865 0 0 0 0 0 68 0 0 0 0 0 0 0 2 0 0 0 1845 0 68/69 0 0 0 0 0 0 0 955 0 0 0 0 0 69 0 0 0 0 0 0 0 0 0 0 0 1163 0 69A 0 0 0 0 0 0 0 931 0 0 0 0 0 70 0 0 0 0 19 0 0 0 39 0 0 33 0

D-16 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 70/71WL 0 0 0 0 0 0 0 35 0 0 0 0 0 71 0 438 0 0 0 0 0 0 0 0 0 781 0 71/80 0 0 0 0 1170 0 0 0 0 0 0 0 0 71B 0 0 0 0 0 0 0 0 0 293 0 0 0 72 0 0 396 0 0 0 0 0 0 542 0 0 427 73 0 346 0 0 346 0 0 399 0 427 0 394 0 73/79 0 0 0 1418 0 0 0 0 0 0 0 0 0 74 0 0 690 0 690 0 691 0 690 0 690 0 0 75 0 0 0 0 0 0 0 0 0 0 672 0 673 75/76/78 0 2268 0 2329 0 2268 0 2274 0 0 0 0 0 76/78 0 0 0 0 0 0 0 0 0 1742 0 0 0 78 0 0 0 0 0 0 0 0 0 0 0 0 641 79 0 949 0 0 0 1035 0 1132 0 1001 0 0 0 80 0 0 0 0 0 0 0 0 0 523 0 13 274 80 Plot 0 0 0 11 0 0 0 0 0 0 0 0 0 81/85C 0 0 0 0 640 0 0 0 0 0 0 0 0 82 0 0 793 0 0 0 0 967 0 0 1045 0 1044 83 0 0 1057 0 1057 0 0 0 0 0 0 1328 0 84 0 1225 0 0 1225 0 1451 0 1451 0 1454 0 0 85 0 1007 0 0 0 0 0 0 0 0 1240 0 902 85A 0 900 0 798 0 0 0 1007 0 0 0 0 0 85B 0 0 0 409 0 0 0 781 0 0 0 0 0 87 0 0 0 0 0 0 0 517 0 0 483 0 39 87 Plot A 0 0 0 19 0 0 0 0 0 0 0 0 0 87 Plot B 0 0 0 25 0 0 0 0 0 0 0 0 0 87/88 0 0 0 0 0 1194 0 0 0 0 0 0 0 88 0 590 0 0 0 0 0 673 0 0 0 681 0 89 0 0 0 0 0 0 0 0 0 0 0 1196 0 89Wyden 0 0 0 0 0 0 0 1177 0 0 0 0 0 90 Plot 0 0 0 2 0 0 0 0 0 0 0 0 0 93 0 0 0 0 0 0 0 0 0 0 872 124 162 93A 0 0 0 0 751 0 0 0 549 0 0 0 0 93B 0 0 0 0 200 0 0 0 0 0 0 0 0 94 0 0 0 308 0 0 232 0 0 0 325 0 0

D-17 | Page

Table D-3. Prescribed Fire (Acres) by Burn Unit in Management Area 1 over time.

Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 95 0 1473 0 1495 0 0 1971 0 0 0 0 1720 0 96 0 0 0 0 0 0 0 0 0 299 0 739 0 96/185 0 0 0 1912 0 0 2170 0 1221 0 0 0 0 97 0 1383 0 0 1383 0 705 0 0 0 0 0 0 97A 0 0 0 0 0 0 787 0 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 0 99/102 0 0 0 0 0 0 0 1 0 1 0 0 0 9WL 0 0 0 0 0 294 0 0 0 0 0 0 0 C142S16 0 0 0 0 0 31 0 0 0 0 0 0 0 C67S2 0 0 0 0 0 0 0 15 0 0 0 0 0 C70S23 0 0 0 0 0 0 0 0 0 0 0 0 0

D-18 | Page

Table D-4. Area by Fire District at Risk and Area of MA 2 in Fire District.

Fire District Area by Area of Fire Percent Area of Percent of Fire District District at of Fire Fire MA 2 in (acres) Risk (acres) District District in Fire at Risk MA 2 District (acres) ALVIN 31479 8937 28 16868 54 AWENDAW 205191 49116 24 34224 17 BONNEAU 6912 4773 69 2930 42 C & B 3242 2942 91 0 0 CAINHOY 51175 15232 30 2403 5 CAROMI 2330 2274 98 0 0 CITY OF CHARLESTON 26393 9893 37 1870 7 CITY OF GOOSE CREEK 26506 15970 60 0 0 CITY OF HANAHAN 7382 5550 75 0 0 CORDESVILLE 51673 11645 23 1647 3 CROSS 51241 26929 53 0 0 EADYTOWN 26158 6666 25 0 0 FORTY ONE 6921 4637 67 0 0 GOOSE CREEK RURAL 19393 5885 30 0 0 HUGER 146500 13252 9 14482 10 JAMESTOWN 56612 8810 16 9468 17 LAKE MOULTRIE 1635 1553 95 0 0 LEBANON 17305 8642 50 0 0 LONGRIDGE 7212 3906 54 0 0 MACEDONIA 65302 18411 28 20947 32 MONCKS CORNER RURAL 20994 13435 64 0 0 MT PLEASANT 35744 27903 78 0 0 PIMLICO 12015 4486 37 0 0 PINE RIDGE 22455 17098 76 0 0 PRINGLETOWN 16545 5840 35 0 0 RUSSELLVILLE-PINEVILLE 29961 8624 29 0 0 SANDRIDGE 24299 13446 55 0 0 SANTEE CIRCLE 9579 5820 61 860 9 SHULLERVILLE-HONEY HILL 152495 6942 5 7400 5 ST. STEPHEN 24795 12134 49 1621 7 TOWN OF BONNEAU 1922 1667 87 253 13 TOWN OF JAMESTOWN 400 381 95 26 7 TOWN OF MONCKS CORNER 4732 4330 92 0 0 TOWN OF ST. STEPHEN 1512 1479 98 0 0 TOWN OF SUMMERVILLE 1542 1450 94 0 0 WHITESVILLE 27984 21508 77 0 0 Total 1197677 371566 31 115083 10

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Table D-5. Fire District summary with highest risk stands in Management Area 2 (MA 2).

Area of Area of Highest 2015 2016 2017 2018 Fire District MA 2 (acres) Risk (acres) (acres) (acres) (acres) (acres) ALVIN 16868 10621 509 0 2632 510 AWENDAW 34224 9785 4 620 1133 467 BONNEAU 2930 2341 0 0 0 0 CAINHOY 2403 998 0 0 20 0 CITY OF CHARLESTON 1870 614 0 1 0 1 CORDESVILLE 1647 250 337 40 1 1 HUGER 14482 6216 772 463 560 929 JAMESTOWN 9468 4185 0 1107 201 19 MACEDONIA 20947 10873 468 1 801 357 SHULLERVILLE-HONEY HILL 7400 2892 1400 67 172 124 ST STEPHEN 1621 987 0 0 0 0 TOWN OF BONNEAU 253 233 0 0 00 0 TOWN OF JAMESTOWN 26 26 0 13 0 TOTAL 114139 50021 3490 2312 5520 2408

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Table D-6. Acreage burned in MA 2 by Burn Unit by Fire District by Year.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 FireDistrict Burn Unit ALVIN 0 0 2589 0 0 169 0 0 2589 509 0 2632 510 10 0 0 0 0 0 0 0 0 0 0 0 1591 0 15 0 0 0 0 0 0 0 0 0 509 0 0 510 18 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 259 0 8/9/10 0 0 2589 0 0 0 0 0 2589 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 782 0 9WL 0 0 0 0 0 169 0 0 0 0 0 0 0 AWENDAW 188 0 113 0 0 0 333 71 1015 4 620 1133 467 110 0 0 0 0 0 0 0 0 0 0 0 0 0 152 0 0 0 0 0 0 0 0 61 0 0 0 82 157 0 0 0 0 0 0 0 0 0 0 0 0 0 157/158 0 0 41 0 0 0 0 0 0 0 0 0 0 164 0 0 68 0 0 0 149 0 0 0 81 0 0 165 0 0 0 0 0 0 0 0 36 0 0 0 0 166 184 0 0 0 0 0 184 0 0 0 0 211 0 169 0 0 0 0 0 0 0 0 0 0 0 0 7 170 0 0 0 0 0 0 0 0 0 0 0 0 0 186 0 0 0 0 0 0 0 0 0 0 0 0 0 187 0 0 4 0 0 0 0 4 0 4 0 170 0 187/188 4 0 0 0 0 0 0 0 0 0 0 0 0 191 0 0 0 0 0 0 0 0 0 0 164 0 0 192 0 0 0 0 0 0 0 0 494 0 0 328 0 193 0 0 0 0 0 0 0 0 424 0 0 424 0 198 0 0 0 0 0 0 0 0 0 0 375 0 378

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Table D-6. Acreage burned in MA 2 by Burn Unit by Fire District by Year.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 FireDistrict Burn Unit 204 0 0 0 0 0 0 0 0 0 0 0 0 0 204/208 0 0 0 0 0 0 0 0 0 0 0 0 0 204A 0 0 0 0 0 0 0 0 0 0 0 0 0 204B 0 0 0 0 0 0 0 0 0 0 0 0 0 205A - 1/2 0 0 0 0 0 0 0 0 0 0 0 0 0 208 0 0 0 0 0 0 0 67 0 0 0 0 0 BONNEAU 0 0 0 0 0 0 0 0 0 0 0 0 0 CAINHOY 0 3 0 0 0 0 0 0 71 0 0 20 0 110 0 3 0 0 0 0 0 0 0 0 0 20 0 115/116 0 0 0 0 0 0 0 0 71 0 0 0 0 116 0 0 0 0 0 0 0 0 0 0 0 0 0 CITY OF 0 0 0 0 0 0 0 0 0 0 1 0 1 CHARLESTON 115 0 0 0 0 0 0 0 0 0 0 1 0 1 115/116 0 0 0 0 0 0 0 0 0 0 0 0 0 116 0 0 0 0 0 0 0 0 0 0 0 0 0 CORDESVILLE 0 1 1 0 0 1 0 95 0 337 40 1 1 72 0 0 1 0 0 0 0 0 0 337 0 0 1 85 0 0 0 0 0 0 0 0 0 0 1 0 0 85B 0 0 0 0 0 0 0 1 0 0 0 0 0 87 0 0 0 0 0 0 0 93 0 0 39 0 0 87/88 0 0 0 0 0 1 0 0 0 0 0 0 0 88 0 1 0 0 0 0 0 1 0 0 0 1 0 HUGER 0 0 0 1 1 0 0 1046 1339 772 463 560 929 103 0 0 0 0 0 0 0 0 0 0 0 0 391

D - 22 | Page

Table D-6. Acreage burned in MA 2 by Burn Unit by Fire District by Year.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 FireDistrict Burn Unit 104 0 0 0 0 0 0 0 0 0 0 0 258 0 104KV 0 0 0 0 0 0 0 0 0 0 278 0 0 182 0 0 0 0 0 0 0 0 0 0 8 0 0 186 0 0 0 0 0 0 0 0 0 0 0 0 0 63 0 0 0 0 0 0 0 0 338 0 0 0 334 68 0 0 0 0 0 0 0 274 0 0 0 0 0 69 0 0 0 0 0 0 0 0 0 0 0 302 0 69A 0 0 0 0 0 0 0 0 0 0 0 0 0 70 0 0 0 0 0 0 0 0 811 0 0 0 0 81/85C 0 0 0 0 1 0 0 0 0 0 0 0 0 85 0 0 0 0 0 0 0 0 0 0 1 0 0 85A 0 0 0 0 0 0 0 0 0 0 0 0 0 85B 0 0 0 1 0 0 0 1 0 0 0 0 0 93 0 0 0 0 0 0 0 0 0 0 176 0 0 93A 0 0 0 0 0 0 0 0 140 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 204 99/102 0 0 0 0 0 0 0 640 0 772 0 0 0 99ADMIN 0 0 0 0 0 0 0 131 0 0 0 0 0 C70S23 0 0 0 0 0 0 0 0 50 0 0 0 0 JAMESTOWN 0 7 0 0 7 0 279 0 0 0 1107 201 19 141 0 0 0 0 0 0 0 0 0 0 0 0 3 39 0 0 0 0 0 0 0 0 0 0 0 0 16 40 0 0 0 0 0 0 0 0 0 0 1107 0 0 43 0 7 0 0 7 0 205 0 0 0 0 114 0 47 0 0 0 0 0 0 0 0 0 0 0 0 0

D - 23 | Page

Table D-6. Acreage burned in MA 2 by Burn Unit by Fire District by Year.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 FireDistrict Burn Unit 62 0 0 0 0 0 0 0 0 0 0 0 13 0 65 0 0 0 0 0 0 74 0 0 0 0 74 0 MACEDONIA 0 1 0 1 0 1 0 139 125 468 1 801 357 15 0 0 0 0 0 0 0 0 0 118 0 0 117 17 0 0 0 0 0 0 0 0 0 0 0 0 0 17A 0 0 0 0 0 0 0 0 0 0 0 0 0 53 0 1 0 0 0 0 0 0 1 0 1 0 1 53/57 0 0 0 1 0 1 0 0 0 0 0 0 0 57 0 0 0 0 0 0 0 0 0 0 0 0 0 62 0 0 0 0 0 0 0 0 0 0 0 379 0 63 0 0 0 0 0 0 0 0 0 0 0 281 0 70 0 0 0 0 0 0 0 0 124 0 0 2 0 70/71WL 0 0 0 0 0 0 0 139 0 0 0 0 0 71 0 0 0 0 0 0 0 0 0 0 0 139 0 71/80 0 0 0 0 0 0 0 0 0 0 0 0 0 72 0 0 0 0 0 0 0 0 0 350 0 0 239 85 0 0 0 0 0 0 0 0 0 0 0 0 0 SANTEE 0 0 0 0 0 0 0 0 0 0 0 0 0 CIRCLE SHULLERVILLE- 21 0 4 0 4 43 0 0 259 1400 67 172 124 HONEY HILL 121 0 0 0 0 0 0 0 0 67 0 67 0 67 136 0 0 0 0 0 0 0 0 0 0 0 0 0 137 0 0 0 0 4 0 0 0 0 0 0 0 4 137A 0 0 4 0 0 0 0 0 0 0 0 0 0

D - 24 | Page

Table D-6. Acreage burned in MA 2 by Burn Unit by Fire District by Year.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 FireDistrict Burn Unit 140 21 0 0 0 0 0 0 0 21 0 0 0 0 141 0 0 0 0 0 0 0 0 0 0 0 0 0 143 0 0 0 0 0 0 0 0 0 1400 0 0 0 160 0 0 0 0 0 0 0 0 0 0 0 0 0 162 0 0 0 0 0 0 0 0 0 0 0 0 53 163 0 0 0 0 0 43 0 0 171 0 0 172 0 ST. STEPHEN 0 0 0 0 0 0 0 0 0 0 0 0 0 TOWN OF 0 0 0 0 0 0 0 0 0 0 0 0 0 BONNEAU TOWN OF 0 0 0 0 0 0 0 0 0 0 13 0 0 JAMESTOWN 40 0 0 0 0 0 0 0 0 0 0 13 0 0 Total 209 12 2707 2 12 214 612 1351 5398 3490 2312 5520 2408

D - 25 | Page

Figure D-1. Map of Fire District Boundaries with other land features.

D-26 | Page

Figure D-2. Map of Fire Districts (names only)

D-27 | Page

Region 8 Broad Scale Monitoring

Socioeconomic Indicators

Prepared by: Allison Borchers Economist

for: US Forest Service, Region 8

August 30, 2018 UPDATED April, 2020 Economic Indicators Region 8

Contents

Economic Monitoring Indicators ...... 1 Introduction ...... 1 Data Sources and Methods ...... 1 Scale of Analysis (Area of Influence) ...... 2 Economic Conditions in Region 8 Areas of Influence ...... 4 Demographics ...... 4 Economy ...... 8 Forest Operations...... 13 Economic Contribution Analysis ...... 14 Employment by Program Area ...... 14 Labor Income by Program Area ...... 15 Summary / Conclusion ...... 16 Areas for Future Consideration ...... 17 References Cited ...... 1 Appendix A. Counties by Planning Unit ...... 1 Appendix B. Total Number of Jobs Contributed, by Resource Program, 2015 ...... 1 Appendix C. Total Labor Income Contributed, by Resource Program, 2015 ...... 1 Appendix D. Unemployment rate...... 1 Appendix E. 2013 Rural Urban Continuum Codes ...... 2

Tables

Table 1. National Forest Planning Units in Region 8 ...... 3 Table 2. Population and population change...... 4 Table 3. Averaged County Rural Urban Continuum Codes, by Planning Unit ...... 6 Table 4. Percent of Total Population by Race ...... 7 Table 5. Percent of Total Population Hispanic or Latino ...... 8 Table 6. Unemployment rate, 2016 ...... 9 Table 7. Per capita income and population poverty levels, 2016 ...... 10 Table 8. Shannon-Weaver Economic Diversity Index, by Planning Area, 2016 ...... 11 Table 9. Secure Rural Schools (SRS) Act Payments and 1908 Act 25 Percent Payments, 2017 .. 12 Table 10. Expenditure by Forest Planning Unit, FY2016 ...... 13 Table 11. Total number of jobs contributed by program area, Region 8, 2015 ...... 15 Table 12. Total labor income, by program area ...... 15 Table 13. Description of 2013 Rural-Urban Continuum Codes ...... 2 Table 14. 2013 Rural-Urban Continuum Codes, NF Alabama ...... 2 Table 15. 2013 Rural-Urban Continuum Codes, Chattahoochee-Oconee NF ...... 3 Table 16. 2013 Rural-Urban Continuum Codes, Cherokee NF...... 3 Table 17. 2013 Rural-Urban Continuum Codes, Kisatchie NF ...... 4 Table 18. 2013 Rural-Urban Continuum Codes, Daniel Boone NF ...... 4 Table 19. 2013 Rural-Urban Continuum Codes, Land Between the Lakes ...... 5 Table 20. 2013 Rural-Urban Continuum Codes, El Yunque NF ...... 5 Table 21. 2013 Rural-Urban Continuum Codes, NF Florida ...... 5 Table 22. 2013 Rural-Urban Continuum Codes, Francis Marion NF ...... 6 Table 23. 2013 Rural-Urban Continuum Codes, George Washington NF ...... 6 Table 24. 2013 Rural-Urban Continuum Codes, Jefferson NF ...... 7 Table 25. 2013 Rural-Urban Continuum Codes, NF of Mississippi ...... 7 Table 26. 2013 Rural-Urban Continuum Codes, Croatan NF ...... 8

i Economic Indicators Region 8

Table 27. 2013 Rural-Urban Continuum Codes, Nantahala-Pisgah NF ...... 8 Table 28. 2013 Rural-Urban Continuum Codes, Uwharrie NF ...... 9 Table 29. 2013 Rural-Urban Continuum Codes, Ozark-St. Francis NF ...... 9 Table 30. 2013 Rural-Urban Continuum Codes, Ouachita NF ...... 10 Table 31. 2013 Rural-Urban Continuum Codes, Sumter NF ...... 10 Table 32. 2013 Rural-Urban Continuum Codes, NF of Texas ...... 11

ii Economic Indicators Region 8

Economic Monitoring Indicators Introduction The U.S. Forest Service manages approximately 13.3 million acres of public land across thirteen southeastern states. Currently, these forests are covered in 19 Land and Resource Management Plans. These management plans are considered the unit of analysis for this report and are aggregated to the regional level for presentation. These public lands are administered under multiple-use management to protect and obtain the greatest benefit from all forest resources: recreation, timber, range, fish and wildlife, soil, water and minerals. These resources provide a variety of benefits and services that are valued by local communities, regional economies and visitors from across the nation.

After developing a forest plan, the Forest Service planning rule requires monitoring of the national forests and grasslands. This broad-scale approach is intended to provide a regional overview of changes in social and economic conditions and offer needed information and analysis for National Forest System units undertaking similar monitoring efforts. The indicators are intended to cover the forests economic contribution and economic and demographic conditions of the area influenced by the plans.

The focus of the indicators for economic conditions is to help identify and evaluate the available economic information regarding the monitoring question:

1. What changes are occurring in the social, cultural, and economic conditions in the areas influenced by management units in the region? Data Sources and Methods Measuring the human relationship with the ecological environment requires two types of indicators: those that help to understand social and economic conditions in communities near the national forests and grasslands and those that measure human uses of national forest and grassland lands and resources. Relevant indicators to understand economic conditions include population size and growth, employment, income and poverty. In addition, relevant indicators of the contribution of the management of the national forests to local economies include jobs and income, payment to states and counties, and forest expenditures and employment.

Baseline demographic and economic data are drawn from federal sources, such as the U.S. Census Bureau and the Bureau of Economic Analysis. The El Yunque National Forest, located in Puerto Rico, at times required a different source to obtain similar indicators. Therefore, the indicators for the El Yunque National Forest may not be strictly comparable for all indicators, but every effort was made to maintain consistency. Due to the different economic and social conditions of Puerto Rico, the indicators were not always combined or averaged with the remainder of Region 8 national forests and grasslands. This is noted in the relevant tables. The scale for the monitoring indicators in Table 1 is the national forest plan social and economic areas of influence.

The economic contribution analysis combines baseline economic data with Forest Service resource data (such as recreation visits and grazing forage consumed) to estimate employment and labor income associated with Forest Service programs, resources, and uses.

1 Economic Indicators Region 8

Table 1. Monitoring indicators Time Period(s) Suggested Covered in Collection Current Indicator Source Frequency Subregion(s) Report Population Change Economic Profile 5-years Region-wide 2000, 2016 System (EPS), U.S. Department of Commerce Rural-Urban Continuum USDA Economic 5-years Region-wide 2013 Code Research Service Population by Race Economic Profile 5-years Region-wide 2016 System (EPS), U.S. Department of Commerce Population Hispanic Economic Profile 5-years Region-wide 2016 System (EPS), U.S. Department of Commerce Unemployment Economic Profile 5-years Region-wide (El 2016 System (EPS), Yunque not included) Bureau of Labor Statistics Personal Income U.S. Bureau of 5-years Region-wide 2016 Economic Analysis, U.S. Census Bureau Shannon-Weaver IMPLAN 5-years Region-wide 2016 Economic Diversity Indicator Forest Expenditures and Forest Economic 5-years Region-wide 2016 Employment Analysis Spreadsheet Tool (FEAST) Payments to States and USFS 5-years Region-wide 2017 Counties Jobs and Income USFS EMC, 5-years Region-wide 2015 IMPLAN, Forest Economic Analysis Spreadsheet Tool (FEAST) IMPLAN = Impact Analysis for Planning; USFS = Unites States Forest Service; EMC = Ecosystem Management Coordination Scale of Analysis (Area of Influence) The national forests and grasslands in Region 8 are made up of approximately 13.3 million acres of public land. This land is divided into 19 land and resource management plans (table 2). When possible indicators are reported at the plans’ levels of aggregation, before aggregating to the regional level. However, at times due to data sources and secondary reports relied upon, different levels of aggregation may be used.

2 Economic Indicators Region 8

Table 2. National Forest Planning Units in Region 8 Planning Unit Grouped National Forests (when applicable) National Forests in Alabama Bankhead, Talladega, Tuskegee, Conecuh Chattahoochee-Oconee National Forests NA Cherokee National Forest NA Kisatchie National Forest NA Daniel Boone National Forest NA Land Between the Lakes Research Natural Area NA El Yunque National Forest NA National Forests in Florida Apalachicola, Oseola, Ocala Francis Marion National Forest NA George Washington National Forest NA Jefferson National Forest NA National Forests in Mississippi Bienville, Chickasawhay Delta, Do Soto, Holly Springs, Homochito, Tombigbee Croatan National Forest NA Nantahala and Pisgah National Forests NA Uwharrie National Forest NA Ozark-St. Francis National Forests NA Ouachita National Forest NA Sumter National Forest NA National Forests and Grasslands in Texas Angelina, Davy Crockett, Sabine, Sam Houston, Caddo/LBJ National Grasslands

Political and administrative designations (for example, county or forest boundaries) do not necessarily correspond with economically-meaningful units. Therefore, the appropriate scale for addressing the social and economic environment in each forest plan will differ from the scales used to address other resources and topics in the monitoring report. Functional economic areas are the primary scale for the social and economic analysis. Typically, these areas are a group of counties. Reliable demographic and economic data are available at the county-level. Sub-county (for example, towns and cities) data are limited and have large margins of error, particularly in rural areas. State or national level data would mask characteristics unique to the areas surrounding the national forests and grasslands. For most of the indicators, the area of influence for each forest unit (table 2) follows that used in the forest plan. The regional indicators are considered to be the grouping of all 279 counties within each of the 19 planning unit’s areas of influence. See Appendix A. Counties by Planning Unit for a listing of counties by forest planning unit.

Because this report is relying on readily available data and other reports which compile primary data or summarize analysis the forest or county groupings in those reports will take precedence. For example, the Forest Service’s Ecosystem Management Coordination makes available an economic contribution analysis for each National forest and grassland. Their economic contribution analysis, which estimates employment and labor income in the regional economies which result from the management and resource uses of the national forests and grasslands (see the Economic Contribution Analysis section below), uses a different set of counties, compared to the counties aggregated for the population and income tables, to define their economic area of influences, as determined by the modeling needs.

3 Economic Indicators Region 8

Economic Conditions in Region 8 Areas of Influence The following sections will examine current conditions related to the economic environment within the region’s forest planning units, including: population and growth and employment and income conditions. In addition, resource outputs, not addressed by other specialists, and the resulting contributions to the area of influence are reported. Where relevant, state or national conditions are presented to give context to forest and region-level data.

Demographics

Population Dynamics Population is an important consideration in managing natural resources. In particular, population structure (size, composition, density) and population dynamics (how the structure changes over time) are essential to describing the consequences of changes to the forest on the social environment (Seesholtz et al. 2006). Population growth can be an indicator of a regions desirability to live and work.

Many of the areas of influence surrounding Region 8’s national forests and grasslands have seen significant population growth. Ozark-St. Francis, Chattahoochee-Oconee, National Forests in Florida, and Francis-Marion have all experienced population growth in the area of influence far greater than the national (metro and non-metro areas) average. With the exception of four national forests (Sumter, Kiskatchie, Daniel Boone and El Yunque) all areas of influence experienced growth greater than the non- metro national average.

Growing populations and development will place greater demand on forest resources and may affect the perceived aesthetics and uses associated with Forest Service lands. Forest management can expect to be tasked with maintaining the quality of visitors’ experiences while providing forest products and cultural and recreational experiences to a greater number of people. Growing populations, specifically homes, near public lands also contributes to the costs of fighting wildland fires.

As populations grow, conflicts between local residents and forest visitors may increase. While living close to public lands may provide residents with amenities such as convenient access to recreation and wildlife viewing, increased forest congestion causes disamenities such as crowds, litter, and noise (Garber-Yonts 2004; Bolitzer and Netusil 2000; Moore et al. 1992). Increased population of residential areas surrounding the forest also increases the region’s need for infrastructure and may place greater pressure on the forest to provide utility right-of-ways, for example, to meet the region’s growing infrastructure needs. These pressures may threaten the forest’s role in contributing to sense of place and the quality of life in surrounding communities (Stedman 2003).

On average, Region 8 saw population growth match that of the Nation as a whole. However, many of the areas of influence surrounding Region 8’s national forests and grasslands have seen population growth above this average. And most had growth greater than the U.S. non-metro average. This likely reflects the more urban nature of many communities surrounding Region 8 lands. This serves to highlight the pressures population growth will likely have on Forest-Service-managed lands and the need for management to address the challenges population growth can pose.

Table 3. Population and population change Percent Change Planning Unit 2000 2016 2000-2016 National Forests in Alabama 736,578 768,901 4%

4 Economic Indicators Region 8

Percent Change Planning Unit 2000 2016 2000-2016 Chattahoochee-Oconee National Forests 879,200 1,081,647 23% Cherokee National Forest 578,052 622,432 8% Croatan National Forest 161,649 182,180 13% Daniel Boone National Forest 445,397 447,315 0% El Yunque National Forest 336,795 326,091 -3% National Forests in Florida 1,232,584 1,631,303 32% Francis Marion National Forest 966,212 1,298,705 34% George Washington National Forest 587,309 673,028 15% Jefferson National Forest 779,875 810,922 4% Kiskatchie National Forest 395,644 400,706 1% Land Between the Lakes Research Natural Area 138,430 146,896 6% National Forests in Mississippi 1,079,419 1,136,356 5% Nantahala and Pisgah National Forests 820,564 943,759 15% Ouachita National Forest 545,030 597,548 10% Ozark-St. Francis National Forests 678,374 867,283 28% Sumter National Forest 405,695 414,921 2% National Forests and Grasslands in Texas 1,083,846 1,429,561 32% Uwharrie National Forest 305,600 335,760 10% Region 8 (excluding El Yunque) 11,711,122 13,676,759 17% United States 282,162,411 323,127,513 15% United States (Non metro) 45,201,471 46,494,722 3% Data Sources: U.S. Department of Commerce. 2017. Bureau of Economic Analysis, Regional Economic Accounts, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps; U.S. Census Bureau, 2012- 2016 American Community Survey 5-Year Estimates; U.S. Census Bureau, 2000 Decennial Census.

Rural Urban Continuum Codes There are a variety of ways to gain a better understanding of the unique strengths and challenges that exist in communities. The U.S. Department of Agriculture’s Economic Research Service classifies all counties along a rural-urban continuum, which describes the degree of urbanization in a county. This is one measure of the degree to which human populations may act as a stressor on forest lands and resources. Terms such as metropolitan/nonmetropolitan status or urban/rural designation are two commonly used approaches for distinguishing counties on the basis of their geographic characteristics. However, the 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. This scheme breaks county data into finer residential groups, beyond metro and nonmetro, particularly useful for understanding trends in nonmetro areas that are related to population density and metro influence. The codes span a scale from 1 through 9. Smaller numbers are more urban, larger numbers are more rural.

Table 4, below, reports an average of the county codes by planning unit. On average, Region 8 rural-urabn continuum code nearly matches that of the Nation as a whole. The areas of influence surrounding Region 8’s forests and grasslands vary. El Yunque National Forest is entirely urban (See Appendix E. 2013 Rural Urban Continuum Codes). Daniel Boone National Forest surrounding counties averaged 7.5 on the scale

5 Economic Indicators Region 8 ranging to 9, and is the most rural area of influence, on average. And most had growth greater than the U.S. non-metro average. This likely reflects the more urban nature of many communities surrounding Region 8 lands. This serves to highlight the pressures population growth will likely have on Forest- Service-managed lands and the need for management to address the challenges population growth can pose.

Each planning area is made up of a grouping of counties and most planning areas have counties spanning the rural-urban continuum codes – with exception of El Yunque which is entirely urban. Appendix E contains tables reporting the code for each county within each planning unit. The appendix tables show the distribution of the codes within a planning unit.

Table 4. Averaged County Rural-Urban Continuum Codes, by Planning Unit Average County Rural-Urban Location Continuum Code El Yunque National Forest 1.0 National Forests in Florida 3.2 Croatan National Forest 3.3 Uwharrie National Forest 3.3 Francis Marion National Forest 3.5 Sumter National Forest 3.5 Cherokee National Forest 4.3 Chattahoochee-Oconee National Forests 4.7 George Washington National Forest 4.8 National Forests in Alabama 4.9 Jefferson National Forest 5.1 Ouachita National Forest 5.3 Kiskatchie National Forest 5.4 National Forests and Grasslands in Texas 5.5 Nantahala and Pisgah National Forests 5.6 National Forests in Mississippi 5.9 Ozark-St. Francis National Forests 6.0 Land Between the Lakes Research 6.8 Natural Area Daniel Boone National Forest 7.5 Region 8 5.2 United States 5.0 Source: USDA Economic Research Service. 2013. Rural-Urban Continuum Codes. Available at https://www.ers.usda.gov/data- products/rural-urban-continuum-codes Downloaded April 6, 2020.

Race and Ethnicity In 1994, President Clinton issued Executive Order 12898. This order directs federal agencies to focus attention on the human health and environmental conditions in minority and low-income communities. The purpose of Executive Order 12898 is to identify and address, as appropriate, disproportionately high and adverse human health or environmental effects on minority and low-income populations.

According to U.S. Census data reported in table 5 and table 6, area of influences differ substantially in their racial and ethnic composition. Many national forests in Region 8 are surrounded by significantly

6 Economic Indicators Region 8 higher than average concentrations of Black or African American residents and Hispanic or Latino populations. This suggests that many areas surrounding national forests in Region 8 are at risk for environmental justice issues. However, even in counties with relatively small minority populations, disproportionate impacts to vulnerable groups may occur. Forest Service management actions should consider the potential for adverse effects to all area residents, with a particular attention to any potential disproportionate impacts on minority residents, low-income residents, or both. Income and poverty is addressed in a later section of this report.

Table 5. Percent of total population by race Native Black or Hawaiian Some Two African American & Other other or White American Indian Asian Pacific race more Location alone alone alone alone Is. alone alone races National Forests in Alabama 69% 27% 1% 1% 0% 1% 2% Chattahoochee-Oconee 86% 8% 0% 1% 0% 3% 2% National Forests Cherokee National Forest 94% 2% 0% 1% 0% 1% 2% Croatan National Forest 77% 16% 1% 2% 0% 2% 3% Daniel Boone National Forest 96% 1% 0% 0% 0% 0% 1% El Yunque National Forest * 58% 13% 0% 1% 0% 27% 2% National Forests in Florida 79% 15% 0% 2% 0% 2% 3% Francis Marion National Forest 67% 28% 0% 1% 0% 1% 2% George Washington National 92% 4% 0% 1% 0% 1% 2% Forest Jefferson National Forest 92% 4% 0% 2% 0% 0% 1% Kiskatchie National Forest 63% 32% 1% 1% 0% 1% 2% Land Between the Lakes 93% 4% 0% 1% 0% 0% 2% Research Natural Area National Forests in Mississippi 66% 30% 0% 1% 0% 1% 1% Nantahala and Pisgah National 90% 4% 1% 1% 0% 2% 2% Forests Ouachita National Forest 83% 7% 2% 2% 0% 3% 3% Ozark-St. Francis National 86% 4% 1% 2% 1% 4% 3% Forests Sumter National Forest 67% 28% 0% 0% 0% 2% 2% National Forests and 83% 10% 0% 1% 0% 3% 2% Grasslands in Texas Uwharrie National Forest 86% 9% 0% 1% 0% 2% 2% Region 8 (excluding El Yunque) 80% 14% 1% 1% 0% 2% 2% United States (Non Metro) 88% 6% 2% 1% 0% 1% 2% Note: The American Community Survey is based on a survey and subject to error. Some data points in this table have lower accuracy due to small sample sizes, particularly in rural areas. Therefore, some estimates should be interpreted with caution. Data Sources: U.S. Department of Commerce. 2017. Census Bureau, American Community Survey Office, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps; U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates.

7 Economic Indicators Region 8

Table 6. Percent of Total Population Hispanic or Latino Location Hispanic or Latino (of any race) National Forests in Alabama 3% Chattahoochee-Oconee National Forests 13% Cherokee National Forest 3% Croatan National Forest 6% Daniel Boone National Forest 1% El Yunque National Forest * 99% National Forests in Florida 9% Francis Marion National Forest 5% George Washington National Forest 4% Jefferson National Forest 2% Kiskatchie National Forest 3% Land Between the Lakes Research Natural 2% Area National Forests in Mississippi 4% Nantahala and Pisgah National Forests 6% Ouachita National Forest 7% Ozark-St. Francis National Forests 11% Sumter National Forest 5% National Forests and Grasslands in Texas 18% Uwharrie National Forest 9% Region 8 (excuding El Yunque) 7% United States (Non Metro) 6% Data Sources: U.S. Department of Commerce. 2017. Census Bureau, American Community Survey Office, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps; U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates.

Economy This section highlights economic trends in the areas of influence for Region 8 forest planning units. Income and unemployment are two important considerations to understanding local economic conditions and therefore how Federal land management impacts local economies.

Unemployment The unemployment rate is a commonly cited and watched figure helping people to understand local and national economic conditions. It provides insight into the correspondence between residents’ skills and employment opportunities. The unemployment rate is the percentage of the labor force that is unemployed. Though it may seem full-employment is often the goal, structural unemployment (mismatch between labor skills and available jobs within a region) and frictional unemployment (people moving or transitioning employment) cause rates to remain above zero even in times of economic prosperity. The existence of structural and frictional unemployment implies that there is an inherent “natural” rate of unemployment. The natural rate of unemployment is believed to fall somewhere between 5 and 6 percent and allows workers to move between jobs and industries without signaling broad economic distress.

Region 8 planning area average falls between the national average and national non-metro average. This does not indicate any particularly special circumstances within the region relative to the nation. Generally, the unemployment rate for the national forests and grasslands’ area of influence in Region 8 is also within

8 Economic Indicators Region 8 an acceptable range. Three planning areas—Kiskatchie National Forest, National Forests in Alabama, and Daniel Boone National Forest —have unemployment rates above six percent. These are regions which maybe more sensitive to changes in Forest management which impact the local economy.

Table 7. Unemployment rate, 2016 Location 2016 National Forests in Alabama 6.5% Chattahoochee-Oconee National Forests 5.3% Cherokee National Forest 5.4% Croatan National Forest 5.2% Daniel Boone National Forest 7.2% El Yunque National Forest 23.1% National Forests in Florida 4.9% Francis Marion National Forest 5.0% George Washington National Forest 3.9% Jefferson National Forest 4.8% Kiskatchie National Forest 6.8% Land Between the Lakes Research Natural 5.6% Area National Forests in Mississippi 6.0% Nantahala and Pisgah National Forests 4.6% Ouachita National Forest 4.4% Ozark-St. Francis National Forests 3.5% Sumter National Forest 5.3% National Forests and Grasslands in Texas 5.4% Uwharrie National Forest 4.9% Region 8** (excl. El Yunque) 5.1% United States 4.9% US (Non Metro) 5.4% **Reported as a population weighted average of Forest-level unemployment rate. Some counties are double counted if they are included in more than one Forest impact area. Note: Unemployment Trends, by forest planning unit included in an appendix. Data Source: U.S. Department of Labor. 2018. Bureau of Labor Statistics, Local Area Unemployment Statistics, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps; U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates.

Income and Poverty Per capita income is an indicator of economic well-being. For management, income is an important consideration because low income populations may be more vulnerable to any adverse effects that result from changes to forest management. For example, if people must travel farther to access recreation sites this increases the cost to use these recreation sites and this may have a disproportionate effect on low income households. Table 8 provides per capita income and the percent of the population below poverty levels for each forest unit and the aggregate region. For reference, non-metro U.S. data are also listed.

Region 8’s per capita income, $38,200, is similar to the non-metro national average, $39,000, with less than $1,000 difference between the two estimates. The planning units range from a minimum of $34,200

9 Economic Indicators Region 8 per capita in the National Forests of Alabama area of influence to $47,200 per capita in the Ozark-St. Francis area of influence. The region has many forest planning units with per capita incomes below the region-wide average. In fact, less than half—eight of the 18 planning areas—have per capita incomes greater than the region average.

Similarly, the percent of population below poverty level is slightly higher for Region 8 than the national non-metro average—18 percent compared to 15 percent. The communities adjacent to some Forests experience higher poverty levels (table 8). Poverty is an important indicator of both economic and social well-being. Individuals with low incomes are more vulnerable to a number of hardships which may negatively affect their health, cognitive development, emotional well-being, and school achievement. In general, low income individuals tend to rely more heavily on natural resources and depend more directly on National Forest System lands for sustenance and home heating. Communities or households with low incomes will be more sensitive to management actions which impact costs to use or access Forest resources, for example. Since these individuals will be more vulnerable to changes in the management of local resources, it is important for forest management to understand how these forest users may be affected by changes or restrictions to forest uses.

Table 8. Per capita income and population poverty levels, 2016 Percent of population below Planning Unit Per capita income poverty level National Forests in Alabama $ 34,211 21% Chattahoochee-Oconee National Forests $ 36,464 18% Cherokee National Forest $ 37,054 19% Croatan National Forest $ 44,153 15% Daniel Boone National Forest $ 30,908 29% El Yunque National Forest a $ 9,968 47% National Forests in Florida $ 39,664 17% Francis Marion National Forest $ 41,187 17% George Washington National Forest $ 40,378 11% Jefferson National Forest $ 39,750 15% Kiskatchie National Forest $ 38,961 23% Land Between the Lakes Research Natural Area $ 36,928 18% National Forests in Mississippi $ 34,252 23% Nantahala and Pisgah National Forests $ 37,623 18% Ouachita National Forest $ 36,141 19% Ozark-St. Francis National Forests $ 47,198 17% Sumter National Forest $ 34,872 21% National Forests and Grasslands in Texas $ 44,573 16% Uwharrie National Forest $ 35,575 17% Region 8 $ 38,237 18% United States (Non Metro) $ 39,024 13% a These estimates use different data sources and therefore are not strictly comparable, although every effort was used to make comparable. Sources: U.S. Department of Commerce. 2017. Bureau of Economic Analysis, Regional Economic Accounts, Washington, D.C., and U.S. Department of Commerce. 2017. Census Bureau, American Community Survey Office, Washington, D.C. reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps. Downloaded May 24, 2018; U.S. Census

10 Economic Indicators Region 8

Bureau, 2012-2016 American Community Survey 5-Year Estimates, available at https://factfinder.census.gov. Downloaded June 7, 2018.

Economic Diversity Diversified economies – those with employment in a variety of industries – are more resilient to changes in a single sector. While some individuals will still experience periods of unemployment, economic diversification helps to lessen the potential of economic collapse due to the decline of one industry. One measure of economic diversity is the Shannon-Weaver index, which is based on the number of sectors present in an economy and the size of those sectors. In the 13-state Southern Region, the diversity index is 0.77 out of 1, which is equivalent to the national-level diversity index (table 9). Therefore, the Southern Region is approximately as economically diverse as the nation overall. There is minimal variation in this index at the planning unit level. For comparison, Utah’s economic diversity index is 0.77 (IMPLAN 2014). The county-level diversity indices likely reveal a more substantial amount of variation within the planning area counties. However, that level of detail is not reported here.

Determining the degree of specialization in an economy is important for decisionmakers, particularly when the dominant industry can be affected by changes in policy. For Forest Service decisionmakers, this is likely to be the case where the forest products industry or the tourism and recreation industries, for instance, are reliant on the local national forests. In many areas surrounding Southern National Forests Local employment will reflect government presence due to public land management, a retiree population that consumes health and social services, and amenities that attract tourists who support the retail trade and accommodation and food services sectors.

Table 9. Shannon-Weaver economic diversity index, by planning area, 2016 Shannon-Weaver Location Diversity Index National Forests in Alabama 0.760 Chattahoochee-Oconee National Forests 0.763 Cherokee National Forest 0.750 Croatan National Forest 0.686 Daniel Boone National Forest 0.737 El Yunque National Forest NA National Forests in Florida 0.729 Francis Marion National Forest 0.740 George Washington National Forest 0.762 Jefferson National Forest 0.761 Kiskatchie National Forest 0.744 Land Between the Lakes Research Natural Area 0.731 National Forests in Mississippi 0.746 Nantahala and Pisgah National Forests 0.755 Ouachita National Forest 0.751 Ozark-St. Francis National Forests 0.742 Sumter National Forest 0.770 National Forests and Grasslands in Texas 0.739 Uwharrie National Forest 0.758 Region 8 (Entire States excl. El Yunque) 0.772

11 Economic Indicators Region 8

Shannon-Weaver Location Diversity Index United States 0.776 Source: IMPLAN 2016

Payments to States and Counties The national forests and grasslands make payments to states and local governments through three programs. These are Federal payments in-lieu of taxes (PILT) and Forest Service county payments—the Secure Rural Schools Act (SRS) or the Federal 25 Percent Fund and Payments to Grassland counties via the Bankhead-Jones Farm tenant Act. Payments in-lieu of taxes are not reported here. While local governments receive these payments, they are largely outside the control of national forest management. Generally larger payments reflect larger acres under Federal management.

Forest Service County Payments Counties receive revenue sharing payments from commercial activities on Federal lands, such as oil and gas leasing, livestock grazing, and timber harvesting. For national forests, beginning in 1908 the payment was 25-percent of the moneys received annually. Since 2008, the payments are based on 25-percent of the 7-year rolling average annual receipts. These payments are commonly called 25-percent payments. However, in 2000, the Secure Rural Schools and Community Self-determination Act was passed which offered a guaranteed source of payments that was not tied to annual commercial revenue on National Forests. The vast majority of counties in the planning areas of influence in Region 8 elected to receive the Secure Rural Schools Act State Payment share in fiscal year 2017 and not the 25-percent payments. Table 10 shows the forest unit and per-acre revenue from Secure Rural School and 25-percent Forest Service payments in fiscal year 2017. Payments to counties with national grasslands are made through the Bankhead-Jones Farm Tenant Act. These payments are similar to 25-percent payments but are not reflected in the below table.

The Secure Rural Schools Act has periodically lapsed due to not being reauthorized by Congress. Without reauthorization these payments revert to 25 percent payments. The 25-percent payments are, in many cases, are significantly smaller than the Secure Rural Schools Act payments.

Table 10. Secure Rural Schools (SRS) Act Payments and 1908 Act 25 Percent Payments, 2017 Average payment per National Forest Acres Total payment Acre National Forests in Alabama 670,804 1,572,325 $2.34 Chattahoochee-Oconee National Forests 867,841 1,358,396 $1.57 Cherokee National Forest 657,324 908,446 $1.38 Kisatchie National Forest 608,565 1,556,216 $2.56 Daniel Boone National Forest 711,230 1,377,585 $1.94 Land Between the Lakes Research Natural 171,251 167,416 $0.98 Area El Yunque National Forest 28,709 128,632 $4.48 National Forests in Florida 1,203,413 2,303,596 $1.91 Francis Marion National Forest 260,495 411,491 $1.58 George Washington National Forest 1,067,079 776,193 $0.73 Jefferson N National Forest F 726,778 831,087 $1.14 National Forests in Mississippi * 1,191,094 4,764,452 $4.00

12 Economic Indicators Region 8

Average payment per National Forest Acres Total payment Acre Croatan National Forest 161,325 148,190 $0.92 Nantahala and Pisgah National Forests 1,043,297 1,354,315 $1.30 Uwharrie National Forest 51,398 75,615 $1.47 Ozark-St. Francis National Forests 22,827 71,717 $3.14 Ouachita National Forest 1,785,583 4,480,368 $2.51 Sumter National Forest 372,972 1,149,341 $3.08 National Forests and Grasslands in Texas ** 639,959 2,051,063 $3.20 Region 8 Total 12,241,944 25,486,446 $2.08 *Chichasaway is not included. **Payments to counties with National Grasslands (e.g. LBJ/Caddo) are made through the Bankhead-Jones Farm Tenant Act, which is not included in this table. Source: USDA Forest Service ASR: Final Payment Detail Report PNF (ASR-10-02) Available at: https://www.fs.usda.gov/main/pts/securepayments/projectedpayments Downloaded May 23, 2018.

Payments to states and local government support public services in communities near the national forests and grasslands and contribute to employment and labor income in the counties that surround the national forests and grasslands. Some of the least affluent areas—for example, the National Forests in Mississippi area of influence—receive the largest payments from the national forests. Forest Service payments to local governments in sparsely populated and low-income areas are likely to be particularly meaningful, since these areas typically get less revenue from property, sales, and income taxes to fund local government operations.

The employment and labor income contributions of Secure Rural Schools Act and other county payments, such as payments in lieu of taxes, are incorporated into the Economic Contribution Analysis section of this report.

Forest Operations National forests and grasslands operations and infrastructure include personnel, program activities, roads, and facilities that contribute to the use and enjoyment of the forest.

The national forests and grasslands in Region 8 combined annual budget (including expenditures and salaries and fire expenditures) was $310.9 million in fiscal year 2016.

Table 11. Expenditure by forest planning unit, FY2016 Planning Unit Salary Nonsalary National Forests in Alabama $1,742,580.92 $7,186,656.96 Chattahoochee-Oconee National Forests $10,005,183.81 $5,269,440.15 Cherokee National Forest $11,739,961.80 $7,158,868.86 Kisatchie National Forest $12,722,862.62 $5,903,195.28 Daniel Boone National Forest $10,582,339.08 $4,599,586.71 El Yunque National Forest NA NA Land Between the Lakes Research Natural Area $4,401,967.73 $8,205,400.21 National Forests in Florida $15,507,263.20 $14,314,412.37 Francis Marion and Sumter National Forests $11,767,310.36 $7,121,841.05

13 Economic Indicators Region 8

Planning Unit Salary Nonsalary George Washington & Jefferson National Forests $16,339,132.90 $9,825,710.84 National Forests in Mississippi $17,640,918.59 $10,722,997.83 National Forests in North Carolina $16,726,864.58 $13,162,510.27 Ozark-St. Francis National Forests $15,617,260.62 $10,055,547.41 Ouachita National Forest $19,441,214.58 $14,177,367.85 National Forests and Grasslands in Texas $12,123,554.61 $6,881,347.32 Region 8 $186,358,415.40 $124,584,883.09 Source: USFS, Forest Economic Analysis Spreadsheet Tool (FEAST), version Aphelia 10/24/2017.

An average of 60 percent of budgets was spent on salaries in fiscal year 2016. The remaining 40 percent was spent on non-salary expenditures. These expenditures support programs that contribute to recreation opportunities, providing and maintaining wildlife habitat, and ecosystem restoration projects, to name a few.

The national forests and grasslands’ operational expenditures contribute to economic activity in the communities that surround the national forests and grasslands. Forest Service employees live in these communities and spend their income on housing, food, and a variety of other local goods and services. The forest’s non-salary expenditures generate economic activity in businesses that supply goods and services to support Forest Service programs. The economic contributions to the local economies of the national forests and grasslands expenditures are captured in the Economic Contribution Analysis section of this report. Economic Contribution Analysis The economic contribution analysis estimates the role of Forest Service resources, uses, and management activities on employment and income in the communities that surround national forests and grasslands.

The role of the national forests and grasslands in their respective regional economies was modeled with IMPLAN Professional 3.1 software using 2015 data. IMPLAN is an input-output model, which estimates the economic consequences of activities, projects, and policies on a region. Input-output analysis represents linkages between sectors in an economy. For example, forest visitors spend money on accommodations and food. Accommodation and food service businesses buy supplies from other businesses. The employees of these firms spend their earnings on a variety of goods and services. These transactions result in direct, indirect, and induced effects in the regional economy, respectively. IMPLAN uses Forest Service data on expenditures and resource uses to estimate the economic consequences of national forests and grasslands management.

The national forests and grasslands area of influence for these economic contribution analysis are not the same as those considered for the indicators above. For these analyses an economic area of encompasses a contiguous set of counties where direct expenditures are made by the following groups: recreationists, range permittees, timber harvesters, timber processors, minerals and energy producers and local government (from revenue sharing and payments in lieu of taxes). These economic areas of influence include a larger collection of counties than those considered above.

Employment by Program Area The extraction and consumption of forest products (for example, timber, minerals, forage), recreation visitors, and forest expenditures (for example, equipment and salaries) all contribute to the economic

14 Economic Indicators Region 8 activity in the region. Based on IMPLAN analysis, table 12 shows the number of jobs attributable to various Forest Service program areas. Local and non-local recreation visitors account for nearly 50 percent of all jobs, contributing a total approximate 14,229 of the 24,268 jobs on an average annual basis. The Forest Service expenditures category captures both salary and non-salary expenditures. Therefore, this category includes national forests and grasslands employees, forest contractors and suppliers, as well as employees of businesses where forest employees spend their household income. The jobs contributed by Forest Service expenditures make up 19 percent of the total contribution.

Table 12. Total number of jobs contributed by program area, Region 8, 2015 Program area Jobs Recreation 14,229 Grazing 80 Timber 4,208 Minerals 174 Payments to States/Counties 1,038 Forest Service Expenditures 4,536

Total Region 8 Forest Management 24,268 Note: The reported figures are a summation of the analysis for each planning unit. The region is not modeled as a whole. Forest- planning unit level detail is included in the appendix. The job estimates serve as an annual average, but they do not differentiate between the provision of full-time, part-time, or seasonal work. Due to changes in the methods used to define the areas of influence 2015 estimates are not strictly comparable to earlier year estimates. Source: Economic Contributions at a Glance, 2015 via personal communications with Susan Winter, WO EMC, May 13, 2018; 2014 reports available at https://www.fs.fed.us/emc/economics/contributions/at-a-glance.shtml

Labor Income by Program Area

Table 13 displays labor income attributable to various Forest Service programs. The jobs estimates, presented above, offer an incomplete picture of the National Forests and Grasslands’ contributions to the regional economies. Labor income estimates help to clarify the role of forest management in supporting livelihoods in communities near the National Forests and Grasslands. Not all jobs are equivalent. Whereas Table 12 indicated program area contributions to regional employment, Table 13 demonstrates the contribution in terms of labor income. Combined these reveal that jobs associated with mining on forests or grasslands pay more, on average, than jobs associated with livestock grazing or Forest Service expenditures.

Table 13. Total labor income, by program area Total Labor Income (thousands of 2015 Program area dollars) Recreation $454,544 Grazing $1,162 Timber $216,010 Minerals $17,657 Payments to States/Counties $50,441 Forest Service Expenditures $271,820

15 Economic Indicators Region 8

Total Labor Income (thousands of 2015 Program area dollars) Total Region 8 Forest Management $1,011,632 Note: The reported figures are a summation of the analysis for each planning unit. The region is not modeled as a whole. Source: Economic Contributions at a Glance, 2015 via personal communications with Susan Winter, WO EMC, May 13, 2018; 2014 reports available at https://www.fs.fed.us/emc/economics/contributions/at-a-glance.shtml Note: Due to changes in the methods used to define the areas of influence 2015 estimates are not strictly comparable to earlier year estimates Summary / Conclusion Based on this review of indicators population and poverty are indicators worth noting at this time. Many of the areas of influence surrounding Region 8’s forests and grasslands have seen significant population growth (table 3). Managing the demands population growth places on public lands will be a challenge that Region 8 will likely continually address into the future.

The percent of population below poverty level is slightly higher for Region 8 than the national non-metro average—18 percent compared to 15 percent. The communities adjacent to some national forests and grasslands experienced even higher poverty levels (table 8). Poverty is an important indicator of both economic and social well-being. Since these individuals will be more vulnerable to changes in the management of local resources, it is important for forest management to understand how these forest users may be affected by changes or restrictions to forest uses.

Finally, recreation-related employment is substantial relative to other resource areas in Region 8. Recreation visitor spending is the largest single source of economic activity associated with Region 8’s national forests and grasslands. Managing sustainable outdoor recreation opportunities with decreasing budgets and increasing population is a challenge the Region is already confronting through their sustainable recreation effort. This collaboration with communities, tourism providers, recreation enthusiasts, and other stakeholders is intended to maintain recreation experiences that are economically beneficial, as well as, socially and ecologically sustainable in the long term.

Table 14. Socioeconomic summary of findings Based on the evaluation If a change of may be monitoring warranted, Do monitoring results demonstrate results, may where may the Monitoring Year intended progress or trend toward changes be change be Question Updated Southern Region targets? warranted? needed? What changes are 2018 (First Yes: Forest management considers No Plan Monitoring occurring in the Evaluation) impact of population and population Program social, cultural, and 2020 growth Forest Plans economic Yes: Forest management addressing Management conditions in the sustainable recreation needs Activities areas influenced by Yes: Forest management contributes management units to local economies in the region?

16 Economic Indicators Region 8

Areas for Future Consideration The data gathered in this document was guided both by relevant, interesting, and important indicators, but also ease of data availability.

Payments in-lieu of taxes are not reported above but are easily available. As a single data point, it is questionable whether this is interesting or not from a forest management standpoint—forest management does not have direct control over these payments. However, as a longer term time trend it may be informative to see if these payment amounts are trending in any direction, or are highly variable. These payments may be significant to some communities and seeing the changes could help understand communities’ sensitivities to changes in forest management.

Similarly, expanding other indicators above to show time trends should be informative. Similar to the current comparisons to national averages, comparing regional and local trends to state and national trends helps understand how an area is responding to economic and social changes.

Employment by sector, and relative size of the sector, is an area which may be of interest in future iterations. This report choose to report the Shannon-Weaver Diversity Index as a single measure of economic diversity. A broader overview of employment by sectors could illustrate the size and importance of the timber sector, for example, and therefore help understand the relative importance of forest product removal and changing relationship to public lands. However, some linkages are harder to make. The recreation program, as mentioned above, make a significant contribution to the local economies, but sectors related to tourism and recreation are more dispersed throughout the economy and National Forest lands only one component providing these services. Regardless, a thoughtful assessment of sector employment trends is an area which is considered in forest specific analysis.

17 Economic Indicators Region 8

References Cited Bolitzer, B. and N.R. Netusil. 2000. “The Impact of Open Space on Property Values in Portland, Oregon.” Journal of Environmental Management. 59:185-193.

Garber-Yonts, B.E. 2004. “The Economics of Amenities and Migration in the Pacific Northwest: Review of Selected Literature with Implications for National Forest Management.” General Technical. Report PNW-GTR-617. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 48 pages.

Moore R., A. Graefe, R. Gitelson and E. Porter. 1992. “The Impact of Rail-Trails: a Study of the Users and Property Owners from Three Trails.” Washington, DC: Rivers, Trails, and Conservation Assistance Program, National Park Service.

Seesholtz, D.; Wickwar, D.; Russell, J. 2006. Social economic profile technical guide. USDA Forest Service, Inventory Monitoring Institute.

Stedman, R. 2003. Sense of place and forest science: toward a program of quantitative research. Forest Science. 49(6): 822–829.

U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates.

U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates, available at https://factfinder.census.gov. Downloaded June 7, 2018.

USDA Economic Research Service. 2013. Rural-Urban Continuum Codes. Available at https://www.ers.usda.gov/data-products/rural-urban-continuum-codes Downloaded April 6, 2020.

USDA Forest Service ASR: Final Payment Detail Report PNF (ASR-10-02) Available at: https://www.fs.usda.gov/main/pts/securepayments/projectedpayments Downloaded May 23, 2018.

USDA Forest Service. Economic Contributions at a Glance, 2015 via personal communications with Susan Winter, WO EMC, May 13, 2018; 2014 reports available at https://www.fs.fed.us/emc/economics/contributions/at-a-glance.shtml

U.S. Department of Commerce. 2017. Census Bureau, American Community Survey Office, Washington, D.C. reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps. Downloaded May 24, 2018

U.S. Department of Commerce. 2017. Bureau of Economic Analysis, Regional Economic Accounts, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps

U.S. Department of Labor. 2018. Bureau of Labor Statistics, Local Area Unemployment Statistics, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps.

1 Economic Indicators Region 8

Appendix A. Counties by Planning Unit These are the counties considered for all the indicators except the economic contribution analysis. The economic contribution analysis uses a larger more comprehensive set of counties for each planning unit as determined by the modeling needs.

Planning Unit State Counties National Forests in Alabama: Bibb, Calhoun, Cherokee, Chilton, Clay, Cleburne, Covington, Dallas, Escambia, Franklin, Hale, Alabama Lawrence, Macon, Perry, Talladega, Tuscaloosa, Winston

Chattahoochee-Oconee Georgia: Banks, Catoosa, Chattooga, Dawson, Fannin, Floyd, Gilmer, Gordon, Greene, Habersham, Hall, National Forests Jasper, Jones, Lumpkin, Morgan, Murray, Oconee, Oglethorpe, Putnam, Rabun, Stephens, Towns, Union, Walker, White, Whitfield Cherokee National Tennessee: Carter, Cocke, Greene, Johnson, McMinn, Monroe, Polk, Sullivan, Unicol, Washington Forest Cherokee National Fores North Carolina: Ashe

Kisatchie National Forest Louisiana: Claiborne, Lincoln, Jackson, Winn, Grent, Rapdes, Vernon, Natchitoches, Red River, Bienville, Webster

Kisatchie National Forest Tennessee: Stewart, Henry

Daniel Boone National Kentucky: Bath, Clay, Estill, Harlan, Jackson, Knox, laurel, Lee, Leslie, McCreary, Menifee, Morgan, Owsley, Forest Perry, Powell, Pulaski, Rockcastle, Rowan, Wayne, Whitley, Wolfe

Land Between the Lakes Kentucky: Lyon, Trigg, Calloway, Livingston, Marshall Research Natural Area El Yunque National Puerto Rico Canovanas, Ceiba, Fajardo, Humacao, Juncos, Las Piedras, Luquillo, Rio Grande, Naguabo Forest (municipalities): National Forests in Florida: Franklin, Leon, Liberty, Wakulla, Okaloosa, Santa Rosa, Walton, Lake, Marion, Putnam, Baker, Florida Columbia Francis Marion National South Carolina: Berkeley, Charleston, Clarendon, Dorchester, Georgetown, Horry, Orangeburg, Williamsburg Forest George Washington Virginia: Alleghany, Amherst, Augusta, bath, Botetourt, Fredrick, highland, Nelson, Page, Rockbridge, National Forest Rockingham, Shenandoah, Warren George Washington West Virginia: Hampshire, Hardy, Monroe, Pendleton National Forest Jefferson National Forest Virginia: Bedford, Bland, Botetourt, Carroll, Dickenson, Giles, Grayson, Lee, Montgomery, Pulaski, Roanoke, Rockbridge, Scott, Smyth, Tazewell, Washington, Wise , Wythe

1 Economic Indicators Region 8

Planning Unit State Counties National Forests in Mississippi: Jasper, Newton, Scott, Smith, Forrest, George, Greene, Harrison, Jackson, Pearl River, Perry, Stone, Mississippi Jones, Wayne, Issaquena, Sharkey, Benton, Lafayette, Marshall, Tippah, Union, Yalobusha, Adams, Amite, Copiah, Franklin, Jefferson, Lincoln, Wilkinson, Chickasaw, Choctaw, Oktibbeha, Pontotoc, Winston

Croatan National Forest North Carolina: Carteret, Craven, Jones

Nantahala and Pisgah North Carolina: Cherokee, Clay, Graham, Swain, Macon, Jackson, Haywood, Transylvania, Henderson, Buncombe, National Forests Madison, Yancey, McDowell, Burke, Caldwell, Watauga, Avery, Mitchell

Uwharrie National Forest North Carolina: Montgomery, Randolph, Davidson

Ozark-St. Francis Arkansas: Baxter , Benton , Conway , Crawford, Franklin, Johnson, Logan, Madison, Marion, Newton, Pope, National Forests Searcy, Stone, Van Buren, Washington, Yell, Lee, Philips

Ouachita National Forest Arkansas: Ashley, Garland, Hot Spring, Howard, Logan, Montgomery, Perry, Pike, Polk, Saline, Scott, Sebastian, Yell Ouachita National Forest Oklahoma: LeFlore, McCurtain

Sumter National Forest South Carolina: Abbeville, Chester, Edgefield, Fairfield, Greenwood, Laurens, McCormick, Newberry, Oconee, Saluda, Union National Forests and Texas: Angelina, Fannin, Houston, Jasper, Montague, Montgomery, Nacogdoches, Newton, Sabine, San Grasslands in Texas Augustine, San Jacinto, Shelby, Trinity, Tyler, Walker, Wise

2 Economic Indicators Region 8

Appendix B. Total Number of Jobs Contributed, by Resource Program, 2015

Payments to Forest Service Total Forest Planning Unit Recreation Grazing Timber Minerals States/Counties Expenditures Management Chattahoochee-Oconee 1,364 6 67 0 66 253 1,756 National Forests Cherokee National Forest 566 0 49 0 35 268 918 Daniel Boone National 597 0 62 3 50 239 952 Forest El Yunque National Forest 661 0 281 0 1 55 997 Francis Marion and 197 0 334 0 38 281 850 Sumter National Forests George Washington and 776 33 197 1 102 378 1,487 Jefferson National Forests Kisatchie National Forest 80 1 480 4 47 291 903 Land Between the Lakes 544 0 45 0 12 120 722 Research Natural Area National Forests in 166 0 224 0 49 264 702 Alabama National Forests in Florida 485 2 148 0 88 463 1,187 National Forests in 467 0 997 0 108 421 1,993 Mississippi National Forests in North 6,064 0 95 2 116 402 6,679 Carolina National Forests and 446 15 270 125 62 248 1,167 Grasslands in Texas Ouschita National Forest 727 11 548 0 157 462 1,905 Ozark St Francis National 1,089 12 411 39 107 391 2,050 Forests Region 8 16,808 80 4,208 174 1,038 4,536 24,268 Source: Economic Contributions at a Glance, 2015 via personal communications with Susan Winter, WO EMC, May 13, 2018; 2014 reports available at https://www.fs.fed.us/emc/economics/contributions/at-a-glance.shtml

1 Economic Indicators Region 8

Appendix C. Total Labor Income Contributed, by Resource Program, 2015

Forest Payments to Service Total Forest Planning Unit Recreation Grazing Timber Minerals States/Counties Expenditures Management Chattahoochee-Oconee National $46,869 $88 $3,359 $0 $3,395 $15,800 $69,510 Forests Cherokee National Forest $16,608 $0 $2,117 $0 $1,591 $13,552 $33,868 Daniel Boone National Forest $17,831 $0 $2,544 $187 $2,254 $13,438 $36,254 El Yunque National Forest $19,245 $0 $16,476 $0 $42 $3,784 $39,547 Francis Marion and Sumter $6,516 $0 $16,246 $0 $1,894 $17,519 $42,175 National Forests George Washington and Jefferson $26,504 $434 $8,111 $96 $5,428 $22,810 $63,382 National Forests Kisatchie National Forest $2,510 $10 $24,051 $248 $2,204 $18,159 $47,181 Land Between the Lakes $14,863 $0 $1,751 $0 $540 $6,195 $23,348 Research Natural Area National Forests in Alabama $5,658 $0 $11,960 $0 $2,515 $16,441 $36,574 National Forests in Florida $15,975 $33 $7,660 $0 $4,287 $27,265 $55,219 National Forests in Mississippi $15,463 $1 $48,787 $0 $5,107 $25,671 $95,029 National Forests in North Carolina $192,534 $0 $4,519 $104 $5,869 $24,174 $227,200 National Forests and Grasslands $19,355 $237 $17,136 $15,428 $3,662 $17,386 $73,206 in Texas Ouschita National Forest $20,067 $175 $28,205 $8 $6,706 $27,552 $82,714 Ozark St Francis National Forests $34,546 $184 $23,088 $1,586 $4,947 $22,074 $86,425 Region 8 $909,088 $1,162 $216,010 $17,657 $50,441 $271,820 $1,011,632 Source: Economic Contributions at a Glance, 2015 via personal communications with Susan Winter, WO EMC, May 13, 2018; 2014 reports available at https://www.fs.fed.us/emc/economics/contributions/at-a-glance.shtml

1 Economic Indicators Region 8

Appendix D. Unemployment rate

Location 1990 2000 2010 2013 2014 2015 2016 2017 NF Alabama 8.0% 5.2% 11.6% 8.0% 7.4% 6.6% 6.5% 5.0% Chattahoochee-Oconee 6.0% 3.4% 10.8% 8.2% 7.0% 5.8% 5.3% 4.6% Cherokee NF 6.2% 4.6% 10.7% 8.6% 7.2% 6.1% 5.4% 4.4% Croatan 4.4% 4.1% 10.3% 8.3% 6.6% 6.0% 5.2% 4.6% Daniel Boone NF 9.2% 5.4% 12.4% 11.4% 9.0% 7.4% 7.2% 6.9% NF Florida 5.7% 3.6% 10.4% 7.0% 6.1% 5.4% 4.9% 4.1% Francis Marion 4.7% 4.0% 11.0% 7.7% 6.6% 6.3% 5.0% 4.2% George Washington 6.0% 2.3% 7.8% 5.8% 5.2% 4.5% 3.9% 3.7% Jefferson 7.4% 3.2% 8.2% 6.7% 6.0% 5.1% 4.8% 4.4% Kiskatchie 6.4% 5.8% 8.5% 7.8% 7.4% 7.3% 6.8% 6.0% Land Between the Lakes 6.7% 4.7% 10.7% 8.6% 7.0% 5.8% 5.6% 5.4% NF of Mississippi 7.7% 5.3% 10.3% 8.5% 7.5% 6.5% 6.0% 5.2% Nantahala-Pisgah NF 4.4% 3.5% 10.7% 7.6% 5.8% 5.3% 4.6% 4.1% Ouachita 7.1% 4.0% 8.3% 7.5% 6.1% 5.3% 4.4% 3.9% Ozark-St. Francis 5.9% 3.6% 7.5% 6.6% 5.4% 4.4% 3.5% 3.2% Sumter 6.4% 4.2% 12.8% 8.8% 7.1% 6.5% 5.3% 4.5% NF in TX 6.0% 4.6% 8.6% 6.6% 5.4% 5.0% 5.4% 4.9% Uwharrie 3.7% 3.3% 12.2% 8.4% 6.4% 5.7% 4.9% 4.3% US (Non Metro) 6.7% 4.6% 9.9% 7.7% 6.4% 5.7% 5.4% 4.7% Data Sources: U.S. Department of Labor. 2018. Bureau of Labor Statistics, Local Area Unemployment Statistics, Washington, D.C., reported by Headwaters Economics’ Economic Profile System, headwaterseconomics.org/eps.

1 Economic Indicators Region 8

Appendix E. 2013 Rural Urban Continuum Codes The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. This scheme allows county data to be broken into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence.

Table 15. Description of 2013 rural-urban continuum codes County code Description Metro County Code 1 Counties in metro areas of 1 million population or more Metro County Code 2 Counties in metro areas of 250,000 to 1 million population Metro County Code 3 Counties in metro areas of fewer than 250,000 population Nonmetro County Code 4 Urban population of 20,000 or more, adjacent to a metro area Nonmetro County Code 5 Urban population of 20,000 or more, not adjacent to a metro area Nonmetro County Code 6 Urban population of 2,500 to 19,999, adjacent to a metro area Nonmetro County Code 7 Urban population of 2,500 to 19,999, not adjacent to a metro area Nonmetro County Code 8 Completely rural or less than 2,500 urban population, adjacent to a metro area Nonmetro County Code 9 Completely rural or less than 2,500 urban population, not adjacent to a metro area Data Sources: U.S.D.A. Economic Research Service. Available https://www.ers.usda.gov/data-products/rural-urban-continuum- codes

Table 16. 2013 Rural-urban continuum codes, National Forests in Alabama Rural-Urban County Continuum Code Bibb County, AL 1

Chilton County, AL 1 Calhoun County, AL 3 Hale County, AL 3 Lawrence County, AL 3 Tuscaloosa County, AL 3 Dallas County, AL 4 Talladega County, AL 4 Cherokee County, AL 6 Covington County, AL 6 Escambia County, AL 6 Franklin County, AL 6 Macon County, AL 6 Winston County, AL 6 Cleburne County, AL 8 Perry County, AL 8 Clay County, AL 9 Average 5

2 Economic Indicators Region 8

Table 17. 2013 Rural-urban continuum codes, Chattahoochee-Oconee National Forests Rural-Urban County Continuum Code Dawson County, GA 1

Jasper County, GA 1 Morgan County, GA 1 Catoosa County, GA 2 Walker County, GA 2 Floyd County, GA 3 Hall County, GA 3 Jones County, GA 3 Murray County, GA 3 Oconee County, GA 3 Oglethorpe County, GA 3 Whitfield County, GA 3 Gordon County, GA 4 Chattooga County, GA 6 Gilmer County, GA 6 Greene County, GA 6 Habersham County, GA 6 Lumpkin County, GA 6 Putnam County, GA 6 White County, GA 6 Rabun County, GA 7 Stephens County, GA 7 Banks County, GA 8 Fannin County, GA 8 Towns County, GA 9 Union County, GA 9 Average 5

Table 18. 2013 Rural-urban continuum codes, Cherokee National Forest Rural-Urban County Continuum Code Sullivan County, TN 2

Carter County, TN 3 Polk County, TN 3 Unicoi County, TN 3 Washington County, TN 3 Greene County, TN 4 McMinn County, TN 4

3 Economic Indicators Region 8

Rural-Urban County Continuum Code Cocke County, TN 6 Johnson County, TN 6 Monroe County, TN 6 Ashe County, NC 7 Average 4

Table 19. 2013 Rural-urban continuum codes, Kisatchie National Forest Rural-Urban Parish Continuum Code Webster Parish, LA 2

Grant Parish, LA 3 Rapides Parish, LA 3 Lincoln Parish, LA 4 Vernon Parish, LA 5 Claiborne Parish, LA 6 Jackson Parish, LA 6 Winn Parish, LA 6 Natchitoches Parish, LA 6 Bienville Parish, LA 6 Henry County, TN 7 Red River Parish, LA 8 Stewart County, TN 8 Average 5

Table 20. 2013 Rural-urban continuum codes, Daniel Boone National Forest Rural-Urban County Continuum Code Laurel County, KY 5

Pulaski County, KY 5 Estill County, KY 6 Powell County, KY 6 Clay County, KY 7 Harlan County, KY 7 Knox County, KY 7 Perry County, KY 7 Rockcastle County, KY 7 Rowan County, KY 7 Wayne County, KY 7 Whitley County, KY 7 Bath County, KY 8

4 Economic Indicators Region 8

Rural-Urban County Continuum Code Jackson County, KY 9 Lee County, KY 9 Leslie County, KY 9 McCreary County, KY 9 Menifee County, KY 9 Morgan County, KY 9 Owsley County, KY 9 Wolfe County, KY 9 Average 8

Table 21. 2013 Rural-urban continuum codes, Land Between the Lakes Research Natural Area Rural-Urban County Continuum Code Trigg County, KY 2

Calloway County, KY 7 Marshall County, KY 7 Lyon County, KY 9 Livingston County, KY 9 Average 7

Table 22. 2013 Rural-urban continuum codes, El Yunque National Forest Rural-Urban Municipio Continuum Code Canovanas Municipio, PR 1

Ceiba Municipio, PR 1 Fajardo Municipio, PR 1 Humacao Municipio, PR 1 Juncos Municipio, PR 1 Las Piedras Municipio, PR 1 Luquillo Municipio, PR 1 Rio Grande Municipio, PR 1 Naguabo Municipio, PR 1 Average 1

Table 23. 2013 Rural-Urban continuum codes, National Forests in Florida Rural-Urban County Continuum Code Lake County, FL 1

Baker County, FL 1 Leon County, FL 2

5 Economic Indicators Region 8

Rural-Urban County Continuum Code Wakulla County, FL 2 Santa County, Rosa County, FL 2 Marion County, FL 2 Okaloosa County, FL 3 Walton County, FL 3 Putnam County, FL 4 Columbia County, FL 4 Franklin County, FL 6 Liberty County, FL 8 Average 3

Table 24. 2013 Rural-urban continuum codes, Francis Marion National Forest Rural-Urban County Continuum Code Berkeley County, SC 2

Charleston County, SC 2 Dorchester County, SC 2 Horry County, SC 2 Georgetown County, SC 4 Orangeburg County, SC 4 Clarendon County, SC 6 Williamsburg County, SC 6 Average 4

Table 25. 2013 Rural-urban continuum codes, George Washington National Forest Rural-Urban County Continuum Code Warren County, VA 1

Amherst County, VA 2 Botetourt County, VA 2 Augusta County, VA 3 Frederick County, VA 3 Nelson County, VA 3 Rockingham County, VA 3 Hampshire County, WV 3 Alleghany County, VA 6 Page County, VA 6 Rockbridge County, VA 6 Shenandoah County, VA 6 Hardy County, WV 6

6 Economic Indicators Region 8

Rural-Urban County Continuum Code Bath County, VA 8 Highland County, VA 8 Monroe County, WV 8 Pendleton County, WV 8 Average 5

Table 26. 2013 Rural-urban continuum codes, Jefferson National Forest Rural-Urban County Continuum Code Bedford County, VA 2

Botetourt County, VA 2 Roanoke County, VA 2 Scott County, VA 2 Washington County, VA 2 Giles County, VA 3 Montgomery County, VA 3 Pulaski County, VA 3 Tazewell County, VA 5 Rockbridge County, VA 6 Wythe County, VA 6 Carroll County, VA 7 Smyth County, VA 7 Wise County, VA 7 Bland County, VA 8 Lee County, VA 8 Dickenson County, VA 9 Grayson County, VA 9 Average 5

Table 27. 2013 Rural-urban continuum codes, National Forests in Mississippi Rural-Urban County Continuum Code Benton County, MS 1 Marshall County, MS 1 Harrison County, MS 2 Jackson County, MS 2 Copiah County, MS 2 Forrest County, MS 3 Perry County, MS 3 Jones County, MS 4

7 Economic Indicators Region 8

Rural-Urban County Continuum Code Lafayette County, MS 4 Adams County, MS 5 Oktibbeha County, MS 5 Scott County, MS 6 George County, MS 6 Pearl River MS 6 Stone County, MS 6 Tippah County, MS 6 Union County, MS 6 Lincoln County, MS 6 Newton County, MS 7 Wayne County, MS 7 Yalobusha County, MS 7 Chickasaw County, MS 7 Pontotoc County, MS 7 Smith County, MS 8 Greene County, MS 8 Issaquena County, MS 8 Sharkey County, MS 8 Amite County, MS 8 Jefferson County, MS 8 Wilkinson County, MS 8 Jasper County, MS 9 Franklin County, MS 9 Choctaw County, MS 9 Average 6

Table 28. 2013 Rural-urban continuum codes, Croatan National Forest Rural-Urban County Continuum Code Craven County, NC 3

Jones County, NC 3 Carteret County, NC 4 Average 3

Table 29. 2013 Rural-urban continuum codes, Nantahala and Pisgah National Forests Rural-Urban County Continuum Code Haywood County, NC 2

Henderson County, NC 2

8 Economic Indicators Region 8

Rural-Urban County Continuum Code Buncombe County, NC 2 Madison County, NC 2 Burke County, NC 2 Caldwell County, NC 2 Watauga County, NC 5 Jackson County, NC 6 Transylvania County, NC 6 McDowell County, NC 6 Macon County, NC 7 Mitchell County, NC 7 Swain County, NC 8 Yancey County, NC 8 Avery County, NC 8 Cherokee County, NC 9 Clay County, NC 9 Graham County, NC 9 Average 6

Table 30. 2013 Rural-urban continuum codes, Uwharrie National Forest Rural-Urban County Continuum Code Randolph County, NC 2

Davidson County, NC 2 Montgomery County, NC 6 Average 3

Table 31. 2013 Rural-urban continuum codes, Ozark-St. Francis National Forests Rural-Urban County Continuum Code Benton County, AR 2

Crawford County, AR 2 Madison County, AR 2 Washington County, AR 2 Pope County, AR 5 Conway County, AR 6 Franklin County, AR 6 Logan County, AR 6 Yell County, AR 6 Phillips County, AR 6 Baxter County, AR 7

9 Economic Indicators Region 8

Rural-Urban County Continuum Code Johnson County, AR 7 Lee County, AR 7 Van County, Buren County, AR 8 Marion County, AR 9 Newton County, AR 9 Searcy County, AR 9 Stone County, AR 9 Average 6

Table 32. 2013 Rural-urban continuum codes, Ouachita National Forest Rural-Urban County Continuum Code Perry County, AR 2

Saline County, AR 2 Sebastian County, AR 2 Le Flore County, OK 2 Garland County, AR 3 Hot Spring County, AR 6 Howard County, AR 6 Logan County, AR 6 Scott County, AR 6 Yell County, AR 6 Ashley County, AR 7 Polk County, AR 7 McCurtain County, OK 7 Montgomery County, AR 8 Pike County, AR 9 Average 5

Table 33. 2013 Rural-urban continuum codes, Sumter National Forest Rural-Urban County Continuum Code Chester County, SC 1

Edgefield County, SC 2 Fairfield County, SC 2 laurens County, SC 2 Saluda County, SC 2 Union County, SC 2 Greenwood County, SC 4 Oconee County, SC 4

10 Economic Indicators Region 8

Rural-Urban County Continuum Code Abbeville County, SC 6 Newberry County, SC 6 McCormick County, SC 8 Average 4

Table 34. 2013 Rural-urban continuum codes, National Forests and Grasslands in Texas Rural-Urban County Continuum Code Montgomery County, TX 1

Wise County, TX 1 Newton County, TX 2 Walker County, TX 4 Angelina County, TX 5 Nacogdoches County, TX 5 Fannin County, TX 6 Jasper County, TX 6 Montague County, TX 6 Tyler County, TX 6 Houston County, TX 7 Shelby County, TX 7 Trinity County, TX 7 Sabine County, TX 8 San Jacinto County, TX 8 San Augustine County, TX 9 Average 6

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