Assessing the Potential Effects of Climate Change on Sumter National Forest

ASSESSING THE POTENTIAL EFFECTS OF CLIMATE CHANGE ON SUMTER NATIONAL FOREST

Forestlands across the region are experiencing increased threats from fire, insect and plant invasions, disease, extreme weather, and drought. Scientists project increases in temperature and changes in rainfall patterns that can make these threats occur more often, with more intensity, and/or for longer durations. Although many of the effects of future changes are negative, natural resource management strategies can help mitigate these impacts. Responses informed by the best current science enable natural resource professionals within the Forest Service to better protect the land and resources and protect the region’s forestlands into the future.

Forest Health - Invasive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes historically limit the range of forest pests but higher temperature will likely allow increases in their number and spread. Drought and other factors will increase the susceptibility of forests to destructive insects such as the southern pine beetle. Certain invasive plant species found in this forest, including kudzu and Japanese honeysuckle are expected to increase dramatically as they can tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

Response: Manage tree densities through practices such as thinning and prescribed fire to maximize carbon sequestration and reduce the vulnerability of forest stands to water stress, insect and disease outbreaks, and wildfire.

Response: Continually monitor for new invasive species moving into areas where they were not traditionally found, especially following events such as hurricanes and fire. Japanese Honeysuckle Plant Communities - Heat stress may limit the growth of some southern pine and hardwood species. Stress from drought and wide-scale pest outbreaks have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as hurricanes, ice storms, and fire, are also expected to cause changes in plant community composition. Some species of rare or endemic plants may be disproportionately impacted. Species more resistant to these disturbances will be more resilient to a changing climate. Changes in temperature and precipitation can affect the flowering dates of certain species, which in turn can impact the animals and insects that depend on them for food.

Longleaf pine Response: Focus restoration efforts in hurricane-resistant forests, such as longleaf pine as well as sweetgum or red oak hardwood.

Response: Manage for a range of ages and species in forests to lessen potential loss from drought or infestation.

Animal Communities - Wildlife species will be affected in different ways. Amphibians may be most at risk, as suitable habitat decreases due to warmer, dryer conditions. Bird species, such as the recently reintroduced Red-cockaded Woodpecker may see a decrease in population as vegetation types change and heat stress makes food more difficult to come by. Alternatively, deer populations may increase due to higher survival rates during warmer winters. Red-Cockaded Woodpecker

Response: Maintain piles of natural woody debris in areas of high amphibian diversity to supplement habitats that retain cool, moist conditions. Response: Create habitat corridors, assist in species movement, and identify high-value conservation lands adjacent to National Forests.

May 2020 Coastal Ecosystems - Coastal areas in the Southeast have already experienced an average of one inch of sea level rise per decade over the 20th century, a rate that will continue to increase in the future. Rising sea levels, in combination with more intense hurricanes, will alter the composition of coastal marshes. As saltwater flooding expands, low-lying coastal wet forests could become marshland or turn into ghost forests where land use barriers do not exist. Sea level rise can also increase the potential for saltwater intrusion into coastal freshwater tables. Increasing salinity of coastal aquifers may affect groundwater resources within three miles of the coast.

Response: Identify and preserve landward migration corridors next to coastal wetlands that can allow these ecosystems to shift landward as sea levels rise. Boardwalk in the Enoree Ranger District Extreme Weather - The potential for severe storm events is expected to increase in the future, including more intense hurricanes making landfall in the southern US. Extended periods of extreme high temperature and drought may lead to drier forest fuels which will burn more easily and contribute to larger and more frequent wildfires. More cloud-to-ground lightning due to warming may also increase wildfire ignitions.

Response: Identify areas that provide particularly valuable ecosystem services, like timber harvest or carbon sequestration, and are also vulnerable to extreme weather, like hurricanes or fires. Then plan Mark Oleg conservation strategies (e.g. thinning, selective species planting) Yellow Branch Waterfall accordingly to mitigate for extreme weather impacts.

Response: Reduce increased wildfire potential by conducting prescribed burns.

Water Resources - Shifts in rainfall patterns will lead to periods of flooding and drought that can significantly impact water resources. Increases in heavy downpours and more intense hurricanes can lead to greater erosion and more sedimentation in waterways. Increased periods of drought may lead to poor water quality. Geographically isolated wetlands are critical wildlife habitat and can be impacted by changes in surrounding landcover. Mountain biking Response: Reduce the amount of water taken in by surrounding trees and plants, using management strategies such as thinning and prescribed burns, in order relieve stress on isolated wetlands and streams. Response: Restore and reinforce vegetation in headwater and marsh areas to help alleviate runoff of sediment during heavy rain, reduce climate-induced warming of water, and decrease water sensitivity to changes in air temperature.

Recreation - Environmental changes may negatively impact recreational experiences due to changes in the plant and animal communities that make those experiences unique. More days above freezing, especially in the northern part of the state, could increase tick and mosquito populations throughout the year, leading to an increase in vector-borne illness. With more days of extreme heat, recreation areas could see decreased use in the summer if temperatures impact visitor comfort.

Response: Examine the goals for a water system or area of land when considering changing dynamics. For example, a stream managed mostly for recreation must balance the demand for rainbow trout from anglers with other aquatic and terrestrial impacts.

Response: Communicate early warnings for extreme weather to protect vulnerable groups from health impacts, such as heat illnesses, and monitor for early outbreaks of disease. CLIMATE CHANGE AND YOUR NATIONAL FOREST: CITATIONS Information in this factsheet is summarized from 52 peer-reviewed science papers found in the USDA Forest Service’s TACCIMO tool. TACCIMO (the Template for Assessing Climate Change Impacts and Management Options) is a web-based application integrating climate change science with management and planning options through search and reporting tools that connect land managers with peer-reviewed information they can trust. For more information and the latest science about managing healthy forests for the future visit the TACCIMO tool online: www.forestthreats.org/taccimotool

Forest Health longleaf pine ecosystems in the Southeast United States. Journal of the Coyle, D.R., Klepzig, K., Koch, F., Morris, L.A. Nowak, J.T., Oak, S.W., Southeast Association of Fish and Wildlife Agencies, 5, 160-168. Otrosina, W.J., Smith, W.D., and Gandhi, K.J.K. (2015). A review of Conrad, A. O., Crocker, E. V., Li, X., Thomas, W. R., Ochuodho, T. O., southern pine decline in North America. Forest Ecology and Holmes, T. P., & Nelson, C. D. (2020). Threats to Oaks in the Eastern Management. United States: Perceptions and Expectations of Experts. Journal of Formby, J. P., Rodgers, J. C., Koch, F. H., Krishnan, N., Duerr, D. A., & Forestry, 118(1), 14-27. Riggins, J. J. (2018). Cold tolerance and invasive potential of the Cope, M. P., Mikhailova, E. A., Post, C. J., Schlautman, M. A., McMillan, P. redbay ambrosia beetle (Xyleborus glabratus) in the eastern United D., Sharp, J. L., & Gerard, P. D. (2017). Impact of extreme spring States. Biological invasions, 20(4), 995-1007. temperature and summer precipitation events on flowering phenology Greenberg, C. H., Perry, R. W., Franzreb, K. E., Loeb, S. C., Saenz, D., in a three-year study of the shores of Lake Issaqueena, South Rudolph, D. C., & Tanner, G. W. (2013). Climate Change and Wildlife Carolina. Ecoscience, 24(1-2), 13-19. in the Southern United States. In: Vose, J. M., Klepzig, K. D., eds. Guldin, J. M. (2019). Silvicultural options in forests of the southern United Climate change adaptation and mitigation management options: A States under changing climatic conditions. New forests, 50(1), 71-87. guide for natural resource managers in southern forest ecosystems. Hellmann, J. J., Byers, J. E., Bierwagen, B. G., & Dukes, J. S. (2008). Five Boca Raton, FL: CRC Press. 379-420 potential consequences of climate change for invasive species. Iverson, L. R., Prasad, A. M., Peters, M. P., & Matthews, S. N. (2019). Conservation Biology, 22(3), 534-543. Facilitating Adaptive Forest Management under Climate Change: A Potter, K. M., Crane, B. S., & Hargrove, W. W. (2017). A United States Spatially Specific Synthesis of 125 Species for Habitat Changes and national prioritization framework for tree species vulnerability to Assisted Migration over the Eastern United States. Forests, 10(11), climate change. New forests, 48(2), 275-300. 989. Walter, J. A., Neblett, J. C., Atkins, J. W., & Epstein, H. E. (2017). Regional- Just, M. G., & Frank, S. D. (2020). Thermal Tolerance of Gloomy Scale and watershed-scale analysis of red spruce habitat in the southeastern (Hemiptera: Diaspididae) in the Eastern United States. Environmental United States: implications for future restoration efforts. Plant ecology, Entomology. 218(3), 305-316. Kolb, T. E., Fettig, C. J., Ayres, M. P., Bentz, B. J., Hicke, J. A., Stewart, J.E. & Weed, A. S. (2016). Observed and anticipated impacts of drought on forest insects and diseases in the United States. Forest Animal Communities Ecology and Management, 380, 321 – 344. http://dx.doi.org/10.1016/j.foreco.2016.04.051 Blaustein, A. R., Walls, S. C., Bancroft, B. A., Lawler, J. J., Searle, C. L., & Gervasi, S. S. (2010). Direct and indirect effects of climate change on McNulty, S., Baca, A., Bowker, M., Brantley, S., Dreaden, T., Golladay, S. amphibian populations. Diversity, 2(2), 281- 313. W., Holmes, T., James, N., Liu, S., Lucardi, R. & Mayfeld, A. (2019). doi:10.3390/d2020281 Managing Effects of Drought in the Southeast United States. In: Vose, DeMay, S. M., & Walters, J. R. (2019). Variable effects of a changing James M.; Peterson, David L.; Luce, Charles H.; Patel-Weynand, climate on lay dates and productivity across the range of the Red- Toral, eds. Effects of drought on forests and rangelands in the United cockaded Woodpecker. The Condor. States: translating science into management responses. Gen. Tech. Grant, E. H. C., Brand, A. B., De Wekker, S. F., Lee, T. R., & Wofford, J. E. Rep. WO-98. Washington, DC: US Department of Agriculture, Forest Service, Washington Office. 191-220. Chapter 9., 191-220. (2018). Evidence that climate sets the lower elevation range limit in a high‐elevation endemic salamander. Ecology and evolution, 8(15), Miller, J. H., Lemke, D., Couston, J. The Invasion of Southern For- ests by 7553-7562.Emanuel, K. (2005). Increasing destructiveness of tropical Nonnative Plants: Current and Future Occupation, with Impacts, cyclones over the past 30 years. Nature, 436, 686-688. doi: Management Strategies, and Mitigation Approaches (2013) In, Wear, 10.1038/nature03906 D. N., Greis, J. G., eds. The Southern Forest Futures Project. General Technical Report SRS-GTR-178. Ashe- ville, NC: U.S. Department of Jacobsen, C. D., Brown, D. J., Flint, W. D., Pauley, T. K., Buhlmann, K. A., & Mitchell, J. C. (2020). Vulnerability of high-elevation endemic Agriculture, Forest Service, Southern Research Station. salamanders to climate change: A case study with the Cow Knob Minucci, J. M., Miniat, C. F., & Wurzburger, N. (2019). Drought sensitivity Salamander (Plethodon punctatus). Global Ecology and Conservation, of an N2‐fixing tree may slow temperate deciduous forest recovery 21, e00883. from disturbance. Ecology. Joyce, L. A., Blate, G. M., Littell, J. S., McNulty, S. G., Millar, C. I., Moser, S. Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., C., Peterson, D. L. (2008). National forests. in: Preliminary review of Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J. & Lexer, adaptation options for climate-sensitive ecosystems and resources. a M. J. (2017). Forest disturbances under climate change. Nature report by the U.S. climate change science program and the climate change, 7(6), 395. subcommittee on global change research. U.S. Environmental Protection Agency, 1-127. Plant Communities Lawler, J. J. & Olden, J. D. (2011). Reframing the debate over assisted colonization. Frontiers in Ecology and the Environment, Bernazzani, P., Bradley, B., and Opperman, J. (2012). Integrating climate doi:10.1890/100106 change into habitat conservation plans under the U.S. Endangered Matthews, S. N., O'Connor, R. J., Iverson, L. R., & Prasad, A. M. (2004). Species Act. Environmental Management, 49(6), 1103-1114. Atlas of climate change effects in 150 bird species of the Eastern doi:10.1007/s00267-012-9853-2. United States (General Technical Report NE-318). Newtown Square, Clark, K. E., Chin, E., Peterson, M. N., Lackstrom, K., Dow, K., Foster, M., PA: U.S. Department of Agriculture, Forest Service, Northeastern & Cubbage, F. (2018). Evaluating climate change planning for Research Station: 1-46. Mainwaring, M. C., Barber, I., Deeming, D. C., Pike, D. A., Roznik, E. A., & W., Holmes, T., James, N., Liu, S., Lucardi, R. & Mayfeld, A. (2019). Hartley, I. R. (2017). Climate change and nesting behaviour in Managing Effects of Drought in the Southeast United States. In: Vose, vertebrates: a review of the ecological threats and potential for James M.; Peterson, David L.; Luce, Charles H.; Patel-Weynand, Toral, adaptive responses. Biological Reviews, 92(4), 1991-2002. eds. Effects of drought on forests and rangelands in the United States: O'Keefe, J. M., & Loeb, S. C. (2017). Indiana bats roost in ephemeral, fire- translating science into management responses. Gen. Tech. Rep. WO- dependent pine snags in the southern Appalachian Mountains, USA. Forest Ecology and Management, 391, 264-274. 98. Washington, DC: US Department of Agriculture, Forest Service, Shoo, L. P., Olson, D. H., McMenamin, S. K. Murray, K. A. Van Sluys, M., Washington Office. 191-220. Chapter 9., 191-220. Herbert, S. M., Bishopm, P. J., … & Hero, J. –M. (2011). Engineering a Rieman, B. E., Hessburg, P. F., Luce, C., & Dare, M. R. (2010). future for amphibians under climate change. Journal of Applied Wildfire and management of forests and native fishes: Conflict or Ecology, 48, 487-492. doi: 10.1111/ j.1365-2664.2010.01942.x opportunity for convergent solutions? BioScience, 60 (6), Taillie, P. J., Moorman, C. E., Smart, L. S., & Pacifici, K. (2019). Bird 460-468. community shifts associated with saltwater exposure in coastal forests Susaeta, A., Adams, D. C., Gonzalez-Benecke, C., & Soto, J. R. (2017). at the leading edge of rising sea level. PloS one, 14(5), e0216540. Economic Feasibility of Managing Loblolly Pine Forests for Water Production under Climate Change in the Southeastern United States. Extreme Weather Forests, 8(3), 83. Delphin, S., Escobedo, F. J., Abd-Elrahman, A., & Cropper Jr, W. (2013). Wisser, D., Frolking, S., Hagen, S. & Bierkens, M. F. P. (2013). Mapping potential carbon and timber losses from hurricanes using a Beyond peak reservoir storage? A global estimate of declining water decision tree and ecosystem services driver model. Journal of storage capacity in large reservoirs. Water Resources Research, 49, Environmental Management, 129, 599-607. 5732 – 5739. doi:10.1002/wrcr.20452. Flanagan, S. A., Bhotika, S., Hawley, C., Starr, G., Wiesner, S., Hiers, J. Zhu, J., Sun, G., Li, W., Zhang, Y., Miao, G., Noormets, A., McNulty, S.G., K., O'Brien, J.J., Goodrick, S., Callaham Jr, M.A., Scheller, R.M. & King, J.S., Kumar, M. & Wang, X. (2017). Modeling the potential Klepzig, K. D. (2019). Quantifying carbon and species dynamics under impacts of climate change on the water table level of selected forested different fire regimes in a southeastern US pineland. Ecosphere, 10(6), wetlands in the southeastern United States. Hydrology and Earth e02772. System Sciences, 21(12), 6289-6305 Fill, J. M., Davis, C. N., & Crandall, R. M. (2019). Climate change Recreation lengthens southeastern USA lightning‐ignited fire seasons. Global Irland, L. C., Adams, D., Alig, R., Betz, C. J., Chen, C., Hutchins, M., & change biology. Sohngen, B.L. (2001). Assessing Socioeconomic Impacts of Climate Hu, H., Wang, G. G., Bauerle, W. L., & Klos, R. J. (2017). Drought impact Change on US Forests, Wood-Product Markets, and Forest Recreation. on forest regeneration in the Southeast USA. Ecosphere, 8(4), e01772. BioScience, 51(9), 753-764. doi: Jensen, A. M., Scanlon, T. M., & Riscassi, A. L. (2017). Emerging 10.1641/0006-3568(2001)051[0753:ASIOCC]2.0.CO;2 investigator series: the effect of wildfire on streamwater mercury and organic carbon in a forested watershed in the southeastern United Joyce, L. A., Blate, G. M., Littell, J. S., McNulty, S. G., Millar, C. I., Moser, States. Environmental Science: Processes & Impacts, 19(12), 1505- S. C., Peterson, D. L. (2008). National forests. in: Pre- 1517. liminary review of adaptation options for climate-sensitive eco- Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., systems and resources. a report by the U.S. climate change science Landsea, C., Held, I., Kossin, J. P., Srivastava, A. K., & Sugi, M. program and the subcommittee on global change research. (2010). Tropical cyclones and climate change. Nature Geosci- ence, U.S.Environmental Protection Agency, 1-127. 3(3), 157-163. doi:10.1038/ngeo779 Jurjonas, M., & Seekamp, E. (2018). Rural coastal community resilience: Liu, Y., Prestemon, J. P., Goodrick, S. L., Holmes, T. P., Stanturf, J. A., Assessing a framework in eastern North Carolina. Ocean & coastal Vose, J. M., Sun, G. (2014) Future wildfire trends, impacts, and management, 162, 137-150. mitigation options in the Southern United States. In: Vose, J. M., Luber, G., K. Knowlton, J. Balbus, H. Frumkin, M. Hayden, J. Hess, Klepzig, K. D., eds. Climate change adaptation and mitigation M. McGeehin, N. Sheats, L. Backer, C. B. Beard, K. L. Ebi, E. management options: A guide for natural resource managers in Maibach, R. S. Ostfeld, C. Wiedinmyer, E. Zielinski-Gutiérrez, & southern forest ecosystems. Boca Raton, FL: CRC Press. 85-126. L. Ziska, (2014). Ch. 9: Human Health. Climate Change Im- pacts in Mitchell, R. J., Liu, Y., O’Brien, J. J., Elliott, K. J., Starr, G., Miniat, C. F., & the United States: The Third National Climate Assessment, J. M. Hiers, J. K. (2014). Future climate and fire interactions in the Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global southeastern region of the United States. Forest Ecology and Change Research Program, 220-256. Management, 327, 316-326. Richardson, R. B., Loomis, J. B. (2004). Adaptive recreation planning and Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., climate change: a contingent visitation approach. Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J. & Lexer, M. Ecological Economics, 50, 83-99. doi:10.1016/ j.ecolecon.2004.02.010 J. (2017). Forest disturbances under climate change. Nature climate Scott, D., McBoyle, G., & Schwartzentruber, M. (2004). Climate change, 7(6), 395. change and the distribution of climatic resources for tourism in North Trenberth, K. E., Cheng, L., Jacobs, P., Zhang, Y., & Fasullo, J. (2018). America. Climate Research, 105-117. Hurricane Harvey links to ocean heat content and climate change Tully, K., Gedan, K., Epanchin-Niell, R., Strong, A., Bernhardt, E. S., adaptation. Earth's Future, 6(5), 730-744. BenDor, T., Mitchell, M., Kominoski, J., Jordan, T.E., Neubauer, S.C. & Weston, N. B. (2019). The Invisible Flood: The Chemistry, Ecology, Water Resources and Social Implications of Coastal Saltwater Intrusion. BioScience, 69(5), 368-378. Boyer, T. A., Melstrom, R. T., & Sanders, L. D. (2017). Effects of climate variation and water levels on reservoir recreation. Lake and reservoir management, 33(3), 223-233. Erwin, K. L. (2009). Wetlands and global climate change: the role of wetland restoration in a changing world. Wetlands Ecology and Management, 17(1), 71-84. doi:10.1007/s11273-008-9119-1 McDonnell, T. C., Sloat, M. R., Sullivan, T. J., Dolloff, C. A., Hess- burg, P. F., Povak, N. A., ... & Sams, C. (2015). Downstream Warming and Headwater Acidity May Diminish Coldwater Habitat in Southern Appalachian Mountain Streams. PloS one, 10 (8), e0134757. McNulty, S., Baca, A., Bowker, M., Brantley, S., Dreaden, T., Golladay, S. ASSESSING THE POTENTIAL EFFECTS OF CLIMATE CHANGE ON THE SOUTHEAST UNITED STATES

Forest Health - Southeast forests will be affected by many factors including extreme weather, shifts in plant hardiness zones, sea level rise and saltwater intrusion, and increased pressure from invasive plants and pests, drought, and wildfire frequency. Increasing temperatures will worsen disturbance due to invasive plants and insects. Warmer temperatures due to climate change are converting saltwater marsh to mangroves and shifting where the marsh to mangrove ecotone exists. Sea level rise will increase soil salinity levels in coastal communities. Coastal forest retreat due to saltwater intrusion and the formation of “ghost forests” has been documented along the Southeast U.S. coastline. In addition, coastal wetlands have seen plant community shifts due to higher levels of salinity.

Response: Develop a coordinated system of monitored and Neuse River, NC controlled entrance points that control the majority of water flow inland from the shoreline and high-value water and land restoration areas in order to reduce salt-intrusion as well as to preserve marshes and swamps.

Response: Efforts to restore ecological integrity to impacted ecosystems (ex. by managing for longleaf and shortleaf pine) can have positive effects on disease and pest resistance, as well as wildfire and drought resilience.

bugwood.org Plant Communities - Suitability conditions are projected to change for different tree species, with certain species having more adaptive capacity Southern Pine Beetle impacts (southern pines, oaks, and hickories) than others (balsam fir, red spruce, eastern hemlock, and sugar maple) due to pests and climate competition. Changes in growing season and flowering dates are also possible with increasing minimum temperatures. Projected increase in temperatures can allow invasive pests and plants to increase their spread.

Response: Manage for tree species with high adaptive capacity.

Response: Early detection and rapid response (EDDR) is the most effective way to respond to invasive species and should be implemented where possible. Diana Fritillary

Animal Communities - Some bird species along the coast have been negatively affected by the development of ghost forests and consequent habitat loss. Certain amphibian and insect species such as the red legged salamander or the Diana Fritillary that are highly dependent on elevation are becoming more and more isolated due to habitat fragmentation and loss. Response: Conserve buffer areas along riparian habitats to provide habitat for amphibian species.

Response: High elevation areas are crucial refugia for many species. Preventing the addition of new roads and heavy equipment in these areas can maintain habitat connectivity. Response: Create habitat corridors, assist in species movement, increase National Forest management unit sizes, and identify high- value conservation lands adjacent to National Forests.

Extreme Weather - Extreme precipitation events are becoming more likely; however, there are longer dry periods between storms. Increasing Wildfire in the Southeast US drought frequency and a projected increase in dry season, as much as 156 days in some areas, will increase the risk of wildfires. Not only are extreme precipitation events becoming more likely, hurricanes are becoming more severe and are able to sustain damaging conditions for longer periods of time. These events have large impacts on nearby estuaries and coastal waters, including negatively impacting carbon sources and sinks.

Response: Manage tree densities through practices such as thinning and prescribed fire to maximize carbon sequestration and Prescribed Fire reduce the vulnerability of forest stands to water stress, insect and Region 2, USFS, Bugwood.org disease outbreaks, and fire.

Response: Communicate early warnings to visitors of extreme weather events.

Water Resources - With climate change projected to cause warmer temperatures and variable precipitation in the future, water resources will likely be even more affected by drought and extreme weather events. Severe drought impacts could lower streamflow in forested watersheds. bugwood.org Increased water temperature due to warming climate can potentially lead to an increase in toxic algal blooms in lakes. Longleaf pine Forest

Response: Reduce impact on aquatic ecosystems affected by drought by favoring tree species that are fire tolerant and have relatively low water use (e.g. longleaf pine).

Response: Remove invasive species that use more water to reduce stress on the aquatic ecosystems.

Recreation - Changes in precipitation due to drought could negatively impact water based outdoor recreation like canoeing, kayaking, and motorized activities. Increase in temperature can impact visitors comfort. Climate change can also have impacts on culturally significant natural resources.

Response: Enact monitoring to determine when it is safe for recreational activities to take place in water recreation areas and communicate effectively to visitors the potential risks of higher temperature or high water levels.

Response: Work with local indigenous populations and cultural groups to provide resources for them to adapt to the climate driven changes on their cultural sites. CLIMATE CHANGE AND YOUR NATIONAL FOREST: CITATIONS Information in this factsheet is summarized from 52 peer-reviewed science papers found in the USDA Forest Service’s TACCIMO tool. TACCIMO (the Template for Assessing Climate Change Impacts and Management Options) is a web-based application integrating climate change science with management and planning options through search and reporting tools that connect land managers with peer-reviewed information they can trust. For more information and the latest science about managing healthy forests for the future visit the TACCIMO tool online: www.forestthreats.org/taccimotool

Forest Health Mech, A. M., Tobin, P. C., Teskey, R. O., Rhea, J. R., & Gandhi, K. J. (2018). Increases in summer temperatures decrease the Bhattachan, A., Emanuel, R. E., Ardon, M., Bernhardt, E. S., Ander- survival of an invasive forest insect. Biological invasions, 20 son, S. M., Stillwagon, M. G., Ury, E.A., Bendor, T.K. & Wright, (2), 365-374. J. P. (2018). Evaluating the effects of land-use change and Neubauer, S. C. (2013). Ecosystem responses of a tidal freshwater future climate change on vulnerability of coastal landscapes to marsh experiencing saltwater intrusion and altered hydrology. saltwater intrusion. Elem Sci Anth, 6(1) Estuaries and Coasts, 36(3), 491-507. Borchert, S. M., Osland, M. J., Enwright, N. M., & Griffith, K. T. Osland, M. J., Day, R. H., Hall, C. T., Feher, L. C., Armitage, A. R., (2018). Coastal wetland adaptation to sea level rise: Quantify- Cebrian, J., Dunton, K.H., Hughes, A.R., Kaplan, D.A., Lang- ston, A.K. & Macy, A. (2019). Temperature thresholds for ing potential for landward migration and coastal squeeze. black mangrove (Avicennia germinans) freeze damage, mor- Journal of applied ecology, 55(6), 2876-2887. tality and recovery in North America: Refining tipping points Cavanaugh, K. C., Dangremond, E. M., Doughty, C. L., Williams, A. for range expansion in a warming climate. Journal of Ecology. P., Parker, J. D., Hayes, M. A., Rodriguez, W. & Feller, I. C. Osland, M. J., Gabler, C. A., Grace, J. B., Day, R. H., McCoy, M. L., (2019). Climate-driven regime shifts in a mangrove–salt marsh McLeod, J. L., From, A.S., Enwright, N.M., Feher, L.C., Stagg, ecotone over the past 250 years. Proceedings of the National C.L. & Hartley, S. B. (2018). Climate and plant controls on soil Academy of Sciences, 116(43), 21602-21608. organic matter in coastal wetlands. Global change biology, 24 Coldren, G. A., Langley, J. A., Feller, I. C., & Chapman, S. K. (11), 5361-5379. (2019). Warming accelerates mangrove expansion and surface Osland, M. J., & Feher, L. C. (2019). Winter climate change and elevation gain in a subtropical wetland. Journal of Ecology, the poleward range expansion of a tropical invasive tree 107(1), 79-90. (Brazilian pepper‐Shinus terebinthifolius). Global change biolo- Epanchin-Niell, R., Kousky, C., Thompson, A., & Walls, M. (2017). gy. Threatened protection: Sea level rise and coastal protected Pierfelice, K. N., Graeme Lockaby, B., Krauss, K. W., Conner, W. lands of the eastern United States. Ocean & coastal manage- H., Noe, G. B., & Ricker, M. C. (2017). Salinity influences on ment, 137, 118-130. aboveground and belowground net primary productivity in Gabler, C. A., Osland, M. J., Grace, J. B., Stagg, C. L., Day, R. H., tidal wetlands. Journal of Hydrologic Engineering, 22(1), Hartley, S. B., Enwright, N.M., From, A.S., McCoy, M.L. & D5015002. McLeod, J. L. (2017). Macroclimatic change expected to trans- Schieder, N. W., & Kirwan, M. L. (2019). Sea-level driven accelera- form coastal wetland ecosystems this century. Nature Climate tion in coastal forest retreat. Geology, 47(12), 1151-1155. Change, 7(2), 142. Schwantes, A. M., Swenson, J. J., González‐Roglich, M., Johnson, Iverson, L. R., Prasad, A. M., Peters, M. P., & Matthews, S. N. D. M., Domec, J. C., & Jackson, R. B. (2017). Measuring cano- (2019). Facilitating Adaptive Forest Management under Cli- py loss and climatic thresholds from an extreme drought along mate Change: A Spatially Specific Synthesis of 125 Species for a fivefold precipitation gradient across Texas. Global change Habitat Changes and Assisted Migration over the Eastern Unit- biology, 23(12), 5120-5135. ed States. Forests, 10(11), 989. Stahl, M., Widney, S., & Craft, C. (2018). Tidal freshwater forests: Kirwan, M. L., & Gedan, K. B. (2019). Sea-level driven land conver- Sentinels for climate change. Ecological engineering, 116, 104- sion and the formation of ghost forests. Nature Climate 109. Change, 9(6), 450. Tully, K., Gedan, K., Epanchin-Niell, R., Strong, A., Bernhardt, E. Kirwan, M. L., Walters, D. C., Reay, W. G., & Carr, J. A. (2016). S., BenDor, T., Mitchell, M., Kominoski, J., Jordan, T.E., Sea level driven marsh expansion in a coupled model of marsh Neubauer, S.C. & Weston, N. B. (2019). The Invisible Flood: erosion and migration. Geophysical Research Letters, 43(9), The Chemistry, Ecology, and Social Implications of Coastal 4366-4373. Saltwater Intrusion. BioScience, 69(5), 368-378. Langston, A. K., Kaplan, D. A., & Putz, F. E. (2017). A casualty of Ury, E. A., Anderson, S. M., Peet, R. K., Bernhardt, E. S., & Wright, climate change? Loss of freshwater forest islands on Florida's J. P. (2019). Succession, regression and loss: does evidence of Gulf Coast. Global change biology, 23(12), 5383-5397. saltwater exposure explain recent changes in the tree commu- Liu, X., Conner, W. H., Song, B., & Jayakaran, A. D. (2017). Forest nities of North Carolina’s Coastal Plain?. Annals of Botany. composition and growth in a freshwater forested wetland Wigand, C., Ardito, T., Chaffee, C., Ferguson, W., Paton, S., community across a salinity gradient in South Carolina, USA. Raposa, K., Vandemoer, C. & Watson, E. (2017). A climate Forest ecology and management, 389, 211-219. change adaptation strategy for management of coastal marsh McKee, K. L., & Vervaeke, W. C. (2018). Will fluctuations in salt systems. Estuaries and Coasts, 40(3), 682-693. marsh–mangrove dominance alter vulnerability of a subtropical Yando, E. S., Osland, M. J., & Hester, M. W. (2018). Microspatial wetland to sea‐level rise?. Global change biology, 24(3), 1224- ecotone dynamics at a shifting range limit: plant–soil variation 1238. across salt marsh–mangrove interfaces. Oecologia, 187(1), 319-331. McMullin, R. T., Wiersma, Y. F., Newmaster, S. G., & Lendemer, J. C. (2019). Risk assessment and conservation strategies for Plant Communities rare lichen species and communities threatened by sea-level rise in the Mid-Atlantic Coastal Plain. Biological Conservation, 239, 108281. Cope, M. P., Mikhailova, E. A., Post, C. J., Schlautman, M. A., McTigue, N., Davis, J., Rodriguez, T., McKee, B., Atencio, A., & McMillan, P. D., Sharp, J. L., & Gerard, P. D. (2017). Impact of Currin, C. (2019). Sea‐level rise explains changing carbon ac- extreme spring temperature and summer precipitation events cumulation rates in a salt marsh over the past two millennia. on flowering phenology in a three-year study of the shores of Journal of Geophysical Research: Biogeosciences. Lake Issaqueena, South Carolina. Ecoscience, 24(1-2), 13-19. Formby, J. P., Rodgers, J. C., Koch, F. H., Krishnan, N., Duerr, D. Forest Service, Washington Office. 191-220. Chapter 9., 191- A., & Riggins, J. J. (2018). Cold tolerance and invasive poten- 220. tial of the redbay ambrosia beetle (Xyleborus glabratus) in the Osburn, C. L., Rudolph, J. C., Paerl, H. W., Hounshell, A. G., & Van eastern United States. Biological invasions, 20(4), 995-1007. Dam, B. R. (2019). Lingering carbon cycle effects of Hurricane Hwang, T., Martin, K. L., Vose, J. M., Wear, D., Miles, B., Kim, Y., Matthew in North Carolina's coastal waters. Geophysical Re- & Band, L. E. (2018). Nonstationary Hydrologic Behavior in search Letters, 46(5), 2654-2661. Forested Watersheds Is Mediated by Climate‐Induced Chang- Stambaugh, M. C., Guyette, R. P., Stroh, E. D., Struckhoff, M. A., & es in Growing Season Length and Subsequent Vegetation Whittier, J. B. (2018). Future southcentral US wildfire probabil- Growth. Water Resources Research, 54(8), 5359-5375. ity due to climate change. Climatic change, 147(3-4), 617-631. Just, M. G., & Frank, S. D. (2020). Thermal Tolerance of Gloomy Stroh, E. D., Struckhoff, M. A., Stambaugh, M. C., & Guyette, R. P. Scale (Hemiptera: Diaspididae) in the Eastern United States. (2018). Fire and Climate Suitability for Woody Vegetation Environmental Entomology. Communities in the South Central United States. Fire Ecology, Mech, A. M., Tobin, P. C., Teskey, R. O., Rhea, J. R., & Gandhi, K. 14(1), 106-124. J. (2018). Increases in summer temperatures decrease the Trenberth, K. E., Cheng, L., Jacobs, P., Zhang, Y., & Fasullo, J. survival of an invasive forest insect. Biological invasions, 20 (2018). Hurricane Harvey links to ocean heat content and cli- (2), 365-374. mate change adaptation. Earth's Future, 6(5), 730-744. Osland, M. J., & Feher, L. C. (2019). Winter climate change and Van Der Wiel, K., Kapnick, S. B., Van Oldenborgh, G. J., Whan, K., the poleward range expansion of a tropical invasive tree Philip, S., Vecchi, G. A., Singh, R.K., Arrighi, J. & Cullen, H. (Brazilian pepper‐Shinus terebinthifolius). Global change biol- (2017). Rapid attribution of the August 2016 flood-inducing ogy. extreme precipitation in south Louisiana to climate change. Pearson, K. D. (2019). Spring-and fall-flowering species show Hydrol. Earth Syst. Sci, 21(2), 897-921. diverging phenological responses to climate in the Southeast USA. International journal of biometeorology, 63(4), 481-492. Water Resources Rogers, B. M., Jantz, P., & Goetz, S. J. (2017). Vulnerability of eastern US tree species to climate change. Global change biology, 23(8), 3302-3320. Acharya, A. (2017). Quantification of modeled streamflows under climate change over the flint river watershed in Northern Ala- bama. Journal of Hydrologic Engineering, 22(9), 04017032. Animal Communities Havens, K. E., Ji, G., Beaver, J. R., Fulton, R. S., & Teacher, C. E. (2019). Dynamics of cyanobacteria blooms are linked to the DeMay, S. M., & Walters, J. R. (2019). Variable effects of a chang- hydrology of shallow Florida lakes and provide insight into ing climate on lay dates and productivity across the range of possible impacts of climate change. Hydrobiologia, 829(1), 43- the Red-cockaded Woodpecker. The Condor. 59. Gade, M. R., & Peterman, W. E. (2019). Multiple environmental McNulty, S., Baca, A., Bowker, M., Brantley, S., Dreaden, T., Gol- gradients influence the distribution and abundance of a key laday, S. W., Holmes, T., James, N., Liu, S., Lucardi, R. & forest-health indicator species in the Southern Appalachian Mayfeld, A. (2019). Managing Effects of Drought in the South- Mountains, USA. Landscape Ecology, 34(3), 569-582. east United States. In: Vose, James M.; Peterson, David L.; Jacobsen, C. D., Brown, D. J., Flint, W. D., Pauley, T. K., Luce, Charles H.; Patel-Weynand, Toral, eds. Effects of Buhlmann, K. A., & Mitchell, J. C. (2020). Vulnerability of high drought on forests and rangelands in the United States: trans- -elevation endemic salamanders to climate change: A case lating science into management responses. Gen. Tech. Rep. study with the Cow Knob Salamander (Plethodon punctatus). WO-98. Washington, DC: US Department of Agriculture, For- Global Ecology and Conservation, 21, e00883. est Service, Washington Office. 191-220. Chapter 9., 191-220. Taillie, P. J., Moorman, C. E., Smart, L. S., & Pacifici, K. (2019). Ouyang, Y., Parajuli, P. B., Li, Y., Leininger, T. D., & Feng, G. Bird community shifts associated with saltwater exposure in (2017). Identify temporal trend of air temperature and its im- coastal forests at the leading edge of rising sea level. PloS pact on forest stream flow in Lower Mississippi River Alluvial one, 14(5), e0216540. Valley using wavelet analysis. Journal of environmental man- Wells, C., & Tonkyn, D. (2018). Changes in the geographic distri- agement, 198, 21-31. bution of the Diana fritillary (Speyeria diana: Nymphalidae) under forecasted predictions of climate change. Insects, 9(3), Recreation 94. Boyer, T. A., Melstrom, R. T., & Sanders, L. D. (2017). Effects of Extreme Weather climate variation and water levels on reservoir recreation. Lake and reservoir management, 33(3), 223-233. Emanuel, K. (2017). Assessing the present and future probability Emanuel, R. E. (2018). Climate change in the Lumbee River water- of Hurricane Harvey’s rainfall. Proceedings of the National shed and potential impacts on the Lumbee tribe of North Caro- Academy of Sciences, 114(48), 12681-12684. lina. Journal of Contemporary Water Research & Education, Fill, J. M., Davis, C. N., & Crandall, R. M. (2019). Climate change 163(1), 79-93. lengthens southeastern USA lightning‐ignited fire seasons. McNulty, S., Baca, A., Bowker, M., Brantley, S., Dreaden, T., Gol- Global change biology. laday, S. W., Holmes, T., James, N., Liu, S., Lucardi, R. & Flanagan, S. A., Bhotika, S., Hawley, C., Starr, G., Wiesner, S., Mayfeld, A. (2019). Managing Effects of Drought in the South- Hiers, J. K., O'Brien, J.J., Goodrick, S., Callaham Jr, M.A., east United States. In: Vose, James M.; Peterson, David L.; Scheller, R.M. & Klepzig, K. D. (2019). Quantifying carbon Luce, Charles H.; Patel-Weynand, Toral, eds. Effects of and species dynamics under different fire regimes in a south- drought on forests and rangelands in the United States: trans- eastern US pineland. Ecosphere, 10(6), e02772. lating science into management responses. Gen. Tech. Rep. McNulty, S., Baca, A., Bowker, M., Brantley, S., Dreaden, T., Gol- WO-98. Washington, DC: US Department of Agriculture, For- laday, S. W., Holmes, T., James, N., Liu, S., Lucardi, R. & est Service, Washington Office. 191-220. Chapter 9., 191-220. Mayfeld, A. (2019). Managing Effects of Drought in the Southeast United States. In: Vose, James M.; Peterson, David Images were sourced from USFS sites and https://images.bugwood.org/ L.; Luce, Charles H.; Patel-Weynand, Toral, eds. Effects of drought on forests and rangelands in the United States: translating science into management responses. Gen. Tech. Rep. WO-98. Washington, DC: US Department of Agriculture,

Assessment of the Influence of Disturbance, Management Activities, and Environmental Factors on Carbon Stocks of U.S. National Forests

General Technical Report RMRS-GTR-402

Appendix 4: Southern Region, Individual Forests

Office of Sustainability and Climate National Forest System

November, 2019

United States Department of Agriculture Forest Service

Page 1 of 77

Citation: Birdsey, Richard A.; Dugan, Alexa J.; Healey, Sean P.; Dante-Wood, Karen; Zhang, Fangmin; Mo, Gang; Chen, Jing M.; Hernandez, Alexander J.; Raymond, Crystal L.; McCarter, James. 2019. Assessment of the influence of disturbance, management activities, and environmental factors on carbon stocks of U.S. national forests. Gen. Tech. Rep. RMRS-GTR-402. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Abstract: This report assesses how carbon stocks at regional scales and in individual national forests are affected by factors such as timber harvesting, natural disturbances, climate variability, increasing atmospheric carbon dioxide concentrations, and nitrogen deposition. Previous baseline assessments of carbon stocks (https://www.fs.fed.us/managing-land/sc/carbon) evaluated observed trends based on forest inventory data but were limited in ability to reveal detailed causes of these trends. The expanded assessments reported here are based on an extensive disturbance and climate history for each national forest, and two forest carbon models, to estimate the relative impacts of disturbance (e.g., fires, harvests, insect outbreaks, disease) and nondisturbance factors (climate, carbon dioxide concentrations, nitrogen deposition). Results are summarized for each region of the National Forest System in the main document. A set of appendixes (available online) provides more detailed information about individual national forests within each region. Results are highly variable across the United States. Generally, carbon stocks are increasing in forests of the eastern United States as these forests continue to recover and grow older after higher historical harvesting rates and periods of nonforest land use. In contrast, carbon stocks in forests of the western United States may be either increasing or decreasing, depending on recent effects of natural disturbances and climate change. The information supports national forest units in assessing carbon stocks, quantifying carbon outcomes of broad forest management strategies and planning, and meeting carbon assessment requirements of the 2012 Planning Rule and directives. Results of these expanded assessments will provide context for project-level decisions, separated from the effects of factors that are beyond land managers’ control.

Keywords: forest carbon stock, national forest, land management, natural disturbance, climate change

Acknowledgements: Individuals from the USDA Forest Service Washington Office provided valuable guidance and support for this research, particularly Duncan McKinley and Cindi West. The research could not have been done without substantial participation by technicians at Utah State University. The authors wish to acknowledge contributions from the staff of the Forest Service’s Northern Research Station and Rocky Mountain Research Station for providing technical support and data used in this report, particularly Chris Woodall, Grant Domke, and Jim Smith. Throughout development of this report, the authors received significant input and feedback from individual national forests which helped us compile the material in a useful and understandable way. We also thank technical reviewers Grant Domke, Bill Connelly, Nadia Tase, Jim Alegria, Dave L. Peterson, Barry Bollenbacher, Elizabeth Wood, Marilyn Buford, and Leslie Brandt.

Page 2 of 77

Contents

1. Disturbance trends

2. Effects of disturbance and management activities (ForCaMF)

3. Management implications of ForCaMF results

4. Effects of disturbance, management, and environmental factors (InTEC)

Page 3 of 77

1. Disturbance trends

Disturbance records are summarized by type (fire, harvest, insects, wind) and by magnitude in Figure 1.1 a-m. In the National Forests of Mississippi (Figure 1.1a), for example, significant abiotic disturbance related to Hurricane Katrina was detected in 2005, and significant fire activity was picked up around the year 2007. Harvest in those forests affected approximately three quarters of a percent of forestland per year in the early 1990s but generally much less after 2000. Mortality due to insects was picked up only in 2011. Significant insect activity was not detected in Mississippi (Figure 1.1a), although there were periodic detected outbreaks in nearby Alabama (1.1b).

Disturbance Type Disturbance Magnitude

2 2 a) National Forests in Mississippi 4 Abiotic Insects 3 Harvest 2 1 Fire 1 1

Percentage Percentage of forest disturbed 0 0

1 1 b) National Forests in Alabama 4 Abiotic 3 Insects 2 Harvest 1 Fire

0 0 Percentage of Percentage disturbed forest

Year Year

Page 4 of 77

7 7 c) National Forests in Florida 6 6 4 5 Insects 5 Harvest 3 4 4 Fire 2 3 3 1 2 2

1 1 0 Percentage Percentage of forest disturbed 0

2 2 d) Francis Marion and Sumter 4 Abiotic 3 Insects 2 Harvest 1 1 1 Fire

0 0 Percentage Percentage of forest disturbed

1 1 e) Kisatchie Abiotic 4 Insects 3 Harvest 2 Fire 1

0 0 Percentage Percentage of forest disturbed

Year Year

Page 5 of 77

1 1 f) Ouachita 4 Insects 3 Harvest 2 Fire 1

0 0 Percentage Percentage of forest disturbed

1 1 g) Ozark St. Francis 4 Abiotic 3 Insect Harvest 2 Fire 1

Percentage Percentage of forest distrubed 0 0

1 1 h) National Forests in Texas 4 3 Insects 2 Harvests 1 Fire

0 Percentage Percentage of forest disturbed 0

Year Year

Page 6 of 77

1 1 i) Chattahoochee Oconee 4 Abiotic 3 Insects 2 Harvest

Fire 1 Percentage Percentage of forest area 0 0

2 2 j) Cherokee 4 Abiotic 3 Insect 1 Harvest 1 2

Fire 1 Percentage of Percentage disturbed forest 0 0

1 1 k) Daniel Boone Insects 4 Harvest 3 Fire 2 1

Percentage Percentage of forest disturbed 0 0

Year Year

Page 7 of 77

1 1 l) National Forests in North Carolina 4 Abiotic 3 Insect 2 Harvest 1 Fire

0 0 Percentage Percentage of forest disturbed

2 2 m) George Washington & Jefferson

4 Abiotic 3 1 Insects 1 2 Harvest Fire 1

0 0 Percentage Percentage of forest disturbed

Year Year Figure 1.1. Annual rates of disturbance in the Southern Region, mapped using visual interpretation of several independent datasets and summarized as the percentage of the forested area disturbed from 1991 through 2011 by (a) disturbance types including fire, harvests, insects, and abiotic; and b) magnitude classes, characterized by percentage change in canopy cover (CC) and categorized as follows: (1) 0 to 25 percent CC, (2) 25 to 50 percent CC, (3) 50 to 75 percent CC, and (4) 75 to 100 percent CC.

Page 8 of 77

2. Effects of disturbance and management activities (ForCaMF)

ForCaMF uses C dynamics derived from the combination of FIA plot data and FVS to interpret the consequences of recorded harvests and natural disturbances. This appendix contains ForCaMF results for each national forest in the Southern Region from 1990 to 2011. The disturbance patterns depicted in Figure 1.1a-m are clearly visible in the ForCaMF representation of C impact over time (Figure 2.1). In Mississippi (Figure 2.1a), for example, the impact of wind is not different from zero until 2005, when the effects of Katrina begin to be felt. The effect of fire diverges from zero a couple of years later with the fires depicted in Figure 1.1a. It must be emphasized that there is a residual effect for almost every disturbance because impact is being compared to what would happen to C storage if the stand had remained undisturbed. For fires, ForCaMF accounts for gradual decay of fire-killed material, so net C storage will likely continue to diverge from the undisturbed scenario for several years. This explains the fact that the effects of wind events can increase even in years when there are no storms.

Units in Figure 2.1 represent reduced C storage per square meter. Error bars around the impact of each type of disturbance represent 95% confidence intervals derived from 500 simulations of all recognized constituent uncertainties, as described earlier. Figure 2.2 summarizes the ForCaMF output shown in Figure 2.1: the pie chart represents the proportional importance of each type of disturbance as measured in 2011 (the last date in Figure 2.1). In the National Forests of Mississippi, for example, harvests occurring between 1990 and 2011 had the dominant impact on C storage, although both fire and wind did have significant effects.

Page 9 of 77

2.1a. National Forests in Mississippi 2.1b. National Forests in Alabama

2.1c. National Forests in Florida 2.1d. Francis Marion and Sumter National Forests

2.1e. Kisatchie National Forest 2.1f. Ouachita National Forest

Page 10 of 77

2.1g. Ozark-St. Francis National Forest 2.1h. National Forests in Texas

2.1i. Chattahoochee National Forest 2.1j. Cherokee National Forest

2.1k. Daniel Boone National Forest 2.1l. National Forests in North Carolina

Page 11 of 77

2.1m George Washington and Jefferson National Forests

Figure 2.1 The impact of different kinds of disturbance, occurring from 1990 through 2011, on carbon (C) stores in the Southern Region. The difference in storage for each year is shown between an “undisturbed” scenario and a scenario that includes only observed amounts of the specified type of disturbance. Error bars represent a 95-percent confidence interval; 100 g/m2 equals 1 metric tonne (or Mg)/ha.

Page 12 of 77

Page 13 of 77

Page 14 of 77

Page 15 of 77

Page 16 of 77

Figure 2.2. Proportional effect of different kinds of disturbance on carbon storage in eacg national forest in the Southern Region for the period 1990 through 2011.

Page 17 of 77

3. Management implications of ForCaMF results

Earlier baseline assessments (http://www.fs.fed.us/climatechange/advisor/products.html) presented inventory-derived estimates of how much carbon is stored in the forests and in the harvested wood product pools of each national forest. The ForCaMF analyses here focused on how different types and intensities of disturbance have influenced those stocks in recent decades. Specifically, results given in Section 6.2 of the main report and previous sections of this appendix provide details about: 1) patterns of disturbance; 2) how disturbance impacts on C storage evolved in each forest from 1990 to 2011, and; 3) the level of uncertainty associated with assessments of each forest. In this section, we bring this information together to answer the simple questions of: “How much do disturbances really disrupt C storage?” and “which disturbance processes in each forest are the most important?”

In highlighting what information managers and planners can gain from these analyses, it is useful to remember that C storage is simply one ecological service, among many, that forests provide. That service mitigates the climate impacts of greenhouse gases emitted through the use of fossil fuels by removing carbon dioxide (CO2) from the atmosphere. Figure 3.1 shows how much less C (by percentage) was stored in each forest in 2011 because of different types of disturbance since 1990. Disturbance patterns continue to change, but this assessment of the recent past represents the best available insight into how sensitive National Forest C storage is to fire, harvest, insects, disease, and weather events. Residual disturbance effects (e.g., decaying dead C) of monitored events will depress C storage for many years after 2011, just as many pre-1990 disturbances continue to affect current stocks. In most cases, forests re-grow after disturbance and become C sinks for many decades or centuries after a relatively short period of reduced C stocks. In some regions where C stocks have reached elevated levels because of disturbance suppression, a lower level of C stock may be more sustainable compared with the recent past.

The period of this snapshot was somewhat arbitrary; however, every analysis needs sideboards, and the period used here coincides with our best monitoring data (satellite imagery, Agency activity records, FIA data). The percentages recorded in Figure 3.1 may seem relatively small, but they often represent very large amounts of climate mitigation benefit. For instance, if a national forest has half a million hectares of forestland that FIA tells us is storing 50 Mg of C per hectare, and ForCaMF tells us that there would be 2% more C without insect activity from 1990-2011, that is a difference of half a million metric tonnes (Mg) of C, or 1.835 million tonnes of CO2 (using a 3.67 conversion ratio for C to CO2). For perspective, this is approximately the amount of CO2 released by burning around 200 million gallons of gasoline (US Energy Information Administration), and its offset value (amount it would be worth if its continued storage were sold on an open market at a conservative price of $10/tonne) would be almost $20 million.

There are certain ways that Figure 3.1 does not tell the complete story. The FVS model, which supplies stand dynamics within ForCaMF, does not cover soil organic C, and Figure 3.1’s calculations exclude soils. Fortunately, the InTEC model presented in this assessment do provide insight into soil C dynamics. More importantly, there are some types of disturbance known to be important that were excluded. For instance, root diseases are known to be prevalent in many parts of the country, but they can be difficult to detect with satellite or aerial imagery because their effects in most years can be limited to reduced growth and suppression of regeneration. ForCaMF was used to assess the impacts of root disease in only 6 national forests, all in the Northern Region. That analysis, which was only possible because of a specialized “regional add-on” variable to core FIA measurements, showed significant root disease impacts that equaled the impacts of fire despite several large fire events in the Region (Healey et al.,

Page 18 of 77

2016). We know that we are missing similar processes across the country that are not well addressed by available monitoring data.

Disturbances due to climate variability were assessed with the InTEC model which includes precipitation and temperature as major factors affecting forest processes. The effects of climate variability may be positive or negative, and are often highly variable from year to year, depending on the region and how the climate variables interact to affect photosynthesis and respiration. The effects of climate also interact with other atmospheric changes particularly increasing atmospheric CO2 concentration and nitrogen deposition, both of which typically enhance growth rates of forests.

Lastly, this assessment does not consider storage of harvested C in product pools. Conversion of forest material to durable wood products defers emissions of the associated C until decay or combustion occurs following disposal. Earlier baseline assessments and assessments by Stockmann et al. (2012) quantified C stocks in wood products that remain in use or landfills, and work is ongoing to combine ForCaMF and product C dynamics models. In the present assessment, however, harvest effects (like the effects of all disturbances) are restricted to ecosystem stocks, a limitation that overstates the emissions of CO2 from harvest from an atmospheric point of view. The effect of substituting wood products for other materials such as concrete and aluminum are not considered in any of the assessments but are potentially significant and will be assessed in future work.

It is outside the scope of this assessment to suggest the importance of ecosystem services associated with C relative to other values such as water yield or habitat conservation. What we do provide is tangible information about how management and disturbance prevention/suppression can impact (and has impacted) the climate change mitigation a national forest generates. To the degree planners value C storage as a service, the disturbance rates published here, along with resultant C storage differences, can frame management goals moving forward.

Page 19 of 77

NFs in Texas

Francis Marion-Sumter

NFs in North Carolina

Ozark and St. Francis Wind Insects

Ouachita Harvest Fire

George Washington and Jefferson All disturbances

NFs in Mississippi

Kisatchie

NFs in Florida

Cherokee

Chattahoochee-Oconee

Daniel Boone

NFs in Alabama

REGION 8

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

Figure 3.1. Carbon stock reduction in 2011 due to disturbances occurring from 1990 through 2011, by each national forest and for all national forests combined in the Southern Region. Percent reduction represents how much nonsoil carbon was lost from the baseline forest inventory carbon stock estimates. “Wind” is a combination of ice and wind storms and was labelled “abiotic” in Figure 1.

Page 20 of 77

4. Effects of disturbance, management, and environmental factors (InTEC)

The set of figures for each of 13 National Forests units in the Southern Region were generated from both input datasets and outputs from the InTEC model. Note that the numbering sequence of figures in this section is repeated for each National Forest. The input dataset figures presented here include stand age- forest type distributions (Figure 4.1), net primary productivity and stand age relationships (Figure 4.2), total annual precipitation (Figure 4.3a), mean annual temperature (Figure 4.3b), and total annual nitrogen (N) deposition (Figure 4.3c) from 1950-2010. A single atmospheric CO2 dataset indicating an increasing trend from 280 ppm in 1901 to 390 ppm 2010 (Keeling et al. 2009) was used for all National Forest units across the U.S. The disturbance type and magnitude figures (Figure 1.1) are also referenced as they are useful for understanding model results. Summary figures of the input datasets have been included in these reports because they provide useful context for interpreting the model outputs. George Washington National Forest and Jefferson National Forest were modeled separately in InTEC to correspond with the Carbon Calculation Tool (CCT) model results.

Model outputs presented here include C stock changes and C accumulation due to disturbance and non- disturbance factors, and C emissions due to disturbances alone from 1950-2011. C stock change outputs show the change in C stocks over the course of a year, thus the value in a given year is the difference between total C stocks in that year and total C stocks in the previous year. C stock change is equivalent to Net Biome Production, which is the total photosynthetic uptake of C by the forest minus the loss of C due to autotrophic and heterotrophic respiration and disturbances. The change in C stocks have been attributed to the following effects: (1) individual non-disturbance factors (climate, N deposition, CO2 concentrations) (Figure 4.4a), (2) combined disturbances factors (fire, harvests, insects, aging and regrowth) (Figure 4.4b), (3) combined non-disturbance factors (Figure 4.4b), and (4) all factors which is the sum of all non-disturbance and disturbance effects (Figure 4.4c). A positive C stock change value in a given year signifies that the factor(s) caused the forest to absorb more C from the atmosphere than it emitted, thus acting as a C sink. A negative C stock change value indicates that the factor(s) caused the forest to release more C to the atmosphere than it absorbed, thus acting as a C source.

Consecutively summing the annual C stock changes (Figure 4.4a-4.4c) yields the total accumulated ecosystem C since 1950 (Figure 4.4d). Positive values indicate accumulated effects that enhanced the total C stock, and negative values represent accumulated effects that reduced the total C stock. The total C emissions due to disturbances alone are also included (Figure 4.4e) and when added to the C stock change (NBP), yields the Net Ecosystem Productivity. The results of the InTEC model runs are numerous and include mapped outputs, C densities, and the effects of both non-disturbance and disturbance factors on individual component pools (e.g. aboveground live C, soil C), thus only summary results are presented here.

The modeled disturbance/aging effect on C stock change for several of the forests in the Southern Region, especially those in the Appalachian Mountain region during the 1960s, show higher than average inter-annual variability. The causes and validity of this high variability in model results have not yet been determined. While there may be greater uncertainty with regards to the modeled results for these particular years, their influence on longer term trends is not significant, and during this period, the disturbance/aging effects shown in the accumulated C results (Figure 4d) effectively capture the most important drivers of change.

Page 21 of 77

National Forests in Mississippi

Between 1950 and 1998, forests in the National Forests in Mississippi fluctuated from a C sink to a C source, but then became a C sink again through 2010 (Fig. 4.4c). During the 1950s and 1960s, disturbance/aging effects promoted a C sink (Fig. 4.4b) and rapid increase in C accumulation (Fig. 4.4d). This trend is supported by the stand age distribution (Fig. 4.1), which shows a pulse of stands aged 70-89 years old that were establishing from 1921-1940. These stands were likely a result of restoration management after a period of intensive logging. According to the NPP-age relationships, these stands would have reached peak productivity at roughly 25-40 years of age (Fig. 4.2) or around the 1950s and 1960s, which was when forests were a C sink (Fig. 4.4c). As these forests aged and productivity declined, disturbance/aging effects shifted to promoting a C source from the 1970s through mid-late 1990s (Fig. 4.4b). Intense harvests in 1992 and 1993, abiotic disturbances in 2005, and fire in 2007 (Fig. 1.1a) all caused pulses of C emissions (Fig. 4.4e). However, these disturbances were quite small (<2% of the forested area) and did not affect stand age or changing C stocks dramatically (Fig. 4.4b).

Climate variability, mainly temperature variability, influenced changing C stocks throughout the past few decades. For instance, warmer temperatures in 1990 and 1998 (Fig. 3b) caused forests to switch to a C source (Figs. 4.4a, c). The heat wave of 1998, combined with all other factors, caused a loss of approximately 2.2 Tg C in that year alone (Fig. 4.4d). Slightly cooler temperatures and sustained precipitation in the 2000s have allowed forests to become a C sink again (Fig. 4.4a, c). Increasing CO2 and nitrogen (N) deposition (Fig. 4.3) also had positive effects, promoting a C sink (Fig. 4.4c) and C accumulation (Fig. 4.4d). Declines in N deposition and further increases in CO2 over the past few decades have caused the CO2 effect to become stronger than the N deposition effect (Fig. 4.4d). Despite a period of declining C stocks from the late 1970s to the late 1990s, forests have again been accumulating C, resulting in a net increase of 17.6 Tg C between 1950 and 2010 (Fig. 4.4d).

25 (1)

20 Maple/beech/birch Elm/ash/cottonwood 15 Oak/gum/cypress Oak/hickory 10 Oak/pine Loblolly/shortleaf pine Longleaf/slash pine

5 Percentage Percentage of forested area

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the National Forests in Mississippi displaying the percentage of forested area of each forest type group in 10-year age classes. Forest types are symbolized by stacked, colored bars that also correspond to the forest types in Figure 2 below.

Longleaf/slash pine Loblolly/shortleaf pine 14 Oak/pine Oak/hickory (2) Oak/gum/cypress Elm/ash/cottonwood

12 Maple/beech/birch 1)

- 10 1 yr 1 - 8

6 NPP (t ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the National Forests in Mississippi.

Page 23 of 77

240 20 (3a) (3b) 200 19

160 C) ° 18 120 17 80 Total Precip 16 Avg Temp

40 Temperature ( Precipitation(cm/yr) 0 15 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

70 ) 2 (3c) 60 50 40 30 20 Total N dep

10 Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the National Forests in Mississippi. Linear trend lines shown in black.

Page 24 of 77

3 3

1) (4a)

1) (4b) - 2 - 2

1 1

0 0

-1 -1 Climate effects

-2 CO2 effect -2 Disturbance\aging effects

C stock stock C change (Tg yr C C stock stock C change (Tg yr C N deposition effects Non-disturbance effects -3 -3 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 3 20

(4c) (4d) 1)

- 2 10 1

0 0 All effects -1 Climate

Accumulated (Tg) C -10 CO2 -2 All effects N deposition C stock stock C change (Tg yr C Disturbance\aging -3 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.3

(4e) 1) - C emissions due 0.2 to disturbance

0.1 C emissions emissions C Tg ( yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the National Forests in Mississippi due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 25 of 77

National Forests in Alabama

Between 1950 and 1970 forests in the National Forests of Alabama were a C sink (Fig. 4.4c) and rapidly accumulating C (Fig. 4.4d). After 1970s, forests fluctuated between a C sink and C source, but overall experienced a slow decline in C stocks through 2010 (Fig. 4.4c). Fluctuations in C stocks were mostly a result of climate effects (Fig. 4.4a), but disturbance/aging also had significant effects on forest C trends (Fig. 4.4b). The stand age distribution shows that a pulse of stands established from 1921-1940 (70-89 years old) (Fig. 4.1) which was around the time the National Forests were created from mostly submarginal lands and extensively cleared forests. Forest rehabilitation and restoration became a priority in the 1930s, thus these heavily disturbed forests were given the opportunity to re-establish, resulting in this pulse of stand establishment. Depending on the forest type, these stands would have been most productive between roughly 30-45 years old (Fig. 4.2) or from the 1950s to the1970s, which is when disturbance/aging effects were positive (Fig. 4.4c, d). Aside from a few recent, moderate-severity disturbances, such as the harvests in 1992 and 1993 (Fig. 1.1b) which caused a slight increase in C emissions (Fig. 4.4e), recent disturbance effects have been minimal, especially in the 2000s (Fig. 4.4b) and have not altered age-structures (Fig. 4.1).

During the 1950s-1970s, climate effects were also mostly positive, further enhancing the C sink (Fig. 4.4a, c) and C accumulation (Fig. 4.4d). However, climate effects on changing C stocks fluctuated following the variability in temperature and precipitation (Figs. 4.3a-b). Generally, consecutive warmer years cause a C source, as warmer temperatures favor greater decomposition, while consecutive wetter years enhance growth and thus contribute to a C sink. Given the trend of warming temperatures (Fig. 4b), these positive climate effects have subtly declined since 2000 (Fig. 4.4d). Since the 1950s nitrogen (N) deposition and CO2 fertilization had slight positive effects on changing C stocks (Fig. 4a) and aided in the accumulation of C (Fig. 4.4d) but were generally overshadowed by the stronger climate and disturbance/aging effects. However, without N deposition and CO2 fertilization, by 2010 forests would have experienced a net loss of C, but instead saw a net gain of roughly 5.8 Tg C since 1950 (Fig. 4.4d).

20 (1) 18 Maple/beech/birch Elm/ash/cottonwood 16 Oak/gum/cypress 14 Oak/hickory 12 Oak/pine Loblolly/shortleaf 10 Longleaf/slash pine 8 White/red/jack pine 6

4 Percentage Percentage of forested area 2 0

Stand age (years) Figure 4.1. Age class distribution in 2010 in the National Forests of Alabama displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

White/red/jack pine 14 Longleaf/slash pine (2) Loblolly/shortleaf pine 12 Oak/pine Oak/hickory

10 Oak/gum/cypress 1) - Elm/ash/cottonwood

Maple/beech/birch 1 yr 1

- 8

NPP (t ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the National Forests of Alabama. Due to the small number of Spruce/fir stands and similar growth and yields as White/red/jack pine stands, these forest type groups were combined for NPP-age modeling.

Page 27 of 77

200 19 (3a) (3b)

160 18 C) ° 17 120 16 80 15

Total Precip Temperature ( Precipitation(cm) 40 14 Avg Temp 0 13 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30 20 Total N dep 10

Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the National Forests of Alabama. Linear trend lines shown in black.

Page 28 of 77

2 2

(4a) (4b)

- 1) - 1 1

0 0

-1 Climate effects -1

CO2 effects Disturbance /agingeffects C stock stock C change (Tg yr C

C stock stock C change (Tg yr C N deposition effects Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 15

1) (4c)

- (4d) All effects 10 1 5

0 0 All effects -5 Climate

-1 CO2 Accumulated (Tg) C C stock stock C change (Tg yr C -10 N deposition Disturbance /aging -2 -15 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e)

1) - C emissions due to disturbances

0.1 C emissions emissions C (Tg yr C

0.0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.4. Changes in carbon stocks in the National Forests of Alabama due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 29 of 77

National Forests in Florida

Forests in the National Forests of Florida maintained a C sink from 1950-1971 (Fig. 4.4c) mostly following disturbance and aging effects (Fig. 4.4b), but then fluctuated between a C sink and C source through 2010 as a result of climate effects and variability (Fig. 4.4a). The stand age distribution shows a pulse of stands 70-89 years old that were establishing in 1921-1940 (Fig. 4.1), likely due to substantial regrowth following the acquisition of these heavily cut-over, waste lands from private owners in 1911. This pulse of mostly longleaf-pine/slash pine and oak/pine stands (Fig. 4.1), would have reached peak productivity at 30-55 years old, or 1950s-80s (Fig. 4.2). As a result, during this period, forests were a C sink, accumulating C stocks due to disturbance/aging effects (Figs. 4.4b-d). Despite some larger fires in 1998 and 2007, these were mostly low-moderate severity (Fig. 1.1b), causing initial increases in C emissions (Fig. 4e), but did not result in enough mortality to alter age structures (Fig. 4.1) or strongly impact disturbance/aging effects on C stocks (Fig. 4.4b).

Changing C stocks after the early 1970s closely follow climate variability, such that coolers years, like 1976, 1992, and 1996 caused a C sink, while warmer years like 1990 and 1998 promoted a C source (Figs 4.3a-b, 4.4a). Warmer temperatures increase decomposition rates and subsequently soil respiration rates. By the late 1980s, climate effects were more often promoting a C source (Fig. 4.4a). For the most part, nitrogen deposition and CO2 concentrations had slight positive effects on changing C stocks (Fig. 4.4a) and promoted C accumulation (Fig. 4.4d). Overall, between 1950-2010, forests in the National Forests of Florida saw a net gain of approximately 14.5 Tg C (Fig. 4.4d).

18 (1) 16 Elm/ash/cottonwood 14 Oak/gum/cypress 12 Oak/hickory 10 Oak/pine Loblolly/shortleaf pine 8 Longleaf/slash pine 6

4 Percentage Percentage of forested area 2 0

Stand age (years) Figure 4.1. Age class distribution in 2010 in the National Forests of Florida displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

14 Longleaf/slash pine (2) Loblolly/shortleaf pine 12 Oak/pine Oak/hickory

10 Oak/gum/cypress 1)

- Elm/ash/cottonwood 1 yr 1

- 8

NPP (t ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the National Forests of Florida.

Page 31 of 77

240 22 (3a) (3b)

200 21 C) 160 ° 20

120 19

80 18

Total Precip Temperature ( Precipitation(cm) 40 17 Mean Temp 0 16 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

50 )

2 (3c) 40

20 Total N dep 10

Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. Average annual (a) precipitation (cm), (b) temperature (°C), and (c) nitrogen deposition (g/m2) from 1950-2010 in the National Forests of Florida. Linear trend lines shown in black.

Page 32 of 77

2 2

(4a) (4b)

- - 1 1

0 0

-1 Climate effects -1 CO2 effects

Disturbance/aging effects C stock stock C change (Tg yr C

C stock stock C change (Tg yr C N deposition effects Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20

(4c) (4d)

1) - 1 10

0 0 All effects Climate -1 -10 CO2 Accumulated (Tg) C N deposition

C stock stock C change (Tg yr C All effects Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.5 (4e) 0.4

1) C emissions due to - 0.3 disturbance

0.2

0.1

C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the National Forests of Florida due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 33 of 77

Francis Marion & Sumter National Forests

Between 1950 and 1970 forests in the Francis Marion and Sumter National Forests maintained a C sink (Fig. 4.4c) and were rapidly accumulating C (Fig. 4.4d), but fluctuated between a C sink and source thereafter. Due to the variable nature of climate, changing C stocks were generally driven by fluctuations in climate variables (Figs. 4.4a-c), although disturbance/aging effects also had a significant impact on longer term forest C trends (Fig. 4.4b). The stand age distribution shows a pulse of stands 70- 89 years old (Fig. 4.1) which were establishing from 1921-1940. By the 1920s, these forests were depleted of timber and restoration became a management goal, which explains the large peak of establishment ~80 year ago. The mostly Loblolly/shortleaf pine stands in this pulse would have been most productive when they were around 25 years old or from 1950-1970 (Fig. 4.2), which corresponds with when disturbance/aging effects were positive.

Another pulse of stands 20-29 years old (Fig. 4.1) is likely a result of regeneration after Hurricane Hugo, a category 4 storm, which devastated about 4.45 million acres of forests in the Francis Marion NF in 1989. In addition to widespread mortality prompting the regeneration pulse, the hurricane also caused a significant increase in C emissions (Fig. 4.4e) and a slight decline in changing C stocks (Fig. 4.4d). However, as the forests began to recover in 1990s and 2000s, disturbance/aging affects again promoted a C sink and C accumulation (Figs. 4.4c-d). Aside from larger fires in 2006 and 2008, disturbances have been relatively small and low-magnitude (Fig. 1.1d), thus there has been lower levels of stand establishment in recent years (Fig. 4.1).

Climate effects on changing stocks were also positive between the mid-1950s and 1970 further enhancing the C sink and amplifying C accumulation (Figs. 4.4a-d). However years with notably warmer average temperatures, such as 1990 and 1998 (Fig. 4.3b), correspond with declines in climate effects on C flux (Fig. 4.4a) with forests switching to C sources during those years (Fig. 4.4c). Warmer temperatures both increase soil respiration as well as constrain productivity given increased evapotranspiration and subsequent water stress. Consistently positive CO2 fertilization and nitrogen deposition effects helped forests to achieve a net gain of approximately 5.2 Tg C between 1950 and 2010 (Fig. 4.4d).

20 (1) 18 Maple/beech/birch 16 Elm/ash/cottonwood Oak/gum/cypress 14 Oak/hickory 12 Oak/pine 10 Loblolly/shortleaf Longleaf/slash pine 8 White/red/jack pine

6 Percentage Percentage of foreste 4 2 0

Stand age (years)

Figure 4.1. Age-class distribution in 2010 in the Francis Marion and Sumter National Forests displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

14 White/red/jack pine Longleaf/slash pine (2) Loblolly/shortleaf Oak/pine 12 Oak/hickory Oak/gum/cypress Elm/ash/cottonwood Maple/beech/birch

- 1 yr 1

- 8

NPP (t ha C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Francis Marion and Sumter National Forests.

Page 35 of 77

200 20 (3a) (3b) 175 19

150 C) ° 18 125 100 17 75 16

50 Temperature ( Total Precip 15 Mean Temp

Totalprecipitation (cm/yr) 25 0 14 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 60

(3c) ) 2 50 40 30 20 Total N dep 10

Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Francis Marion and Sumter National Forests. Trend lines shown in black.

Page 36 of 77

2 2

(4b) 1)

1) (4a)

- - 1 1

0 0

-1 Climate effects -1

CO2 effects Disturbance/aging effects

C stock stock C change (Tg yr C C stock stock C change (Tg yr C N deposition effects Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 10

(4c) (4d)

1) - 1 5

0 0 All effects Climate

-1 -5 CO2 Accumulated (Tg C C) All effects N deposition C stock stock C change (Tg yr C Disturbance/aging -2 -10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.3

(4e)

1) -

0.2 C emission due to disturbance

0.1 C emission emission C (Tg yr C

0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Francis Marion and Sumter National Forests due to: (a) individual non-disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre-1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 37 of 77

Kisatchie National Forest

After maintaining a C sink for roughly two decades, forests in the Kisatchie National Forest switched from a C sink to a C source in the late 1970s (Fig. 4.4c). Changing C stocks were influenced primarily by climate effects (Fig. 4.4a), although disturbance/aging effects played an important role enhancing the C sink in the 1950s-1960s (Fig. 4.4b). The stand age distribution shows a period of elevated stand establishment 70-79 years old (1930s) when the National Forest was first designated. Though the Kisatchie NF was created from submarginal lands and extensively logged forests, restoration and rehabilitation were encouraged in the 1930s, likely fostering this pulse in stand establishment (Fig. 1.1e). The predominately loblolly/shortleaf pine stands (Fig. 4.1) would have been most productive around age 25 or in the 1950s and 60s (Fig. 4.2), corresponding to the positive disturbance/aging effects during this time period. There is a second, though more minor, pulse of stands which established 20-29 years ago or in the 1980s (Fig. 1.1e). This period of elevated establishment rates, the decline in C stock change due to disturbance/aging effects in the late 1980s (Fig. 4b), and the pulse of in C emissions in 1987 (Fig. 4.4e) were likely the result of the 1987 Kisatchie Hills Wilderness Fire that destroyed some 7000 acres.

Climate effects on changing C stocks fluctuated annually, though have become more negative over the past few decades. Warmer than average temperatures in 1990 and 1998 (Fig. 4.4b) caused forests to switch to a C source (Figs. 4.4a, c), as warmer temperatures favor increased decomposition and soil respiration. The notably warmer temperatures of the 1998 “heat wave” caused a loss of approximately 1.2 Tg C from the previous year (Fig. 4.4d). Increasing nitrogen (N) deposition (Fig. 4.3c) and atmospheric CO2 concentrations had a small positive effect on C stocks, helping forests to accumulate additional C. However, due to declines in the amount of N deposition (Fig. 4.3c), the positive N deposition effect has also been declining in recent years (Fig. 4.4a, d). Overall, Kisatchie National Forest saw a net increase of approximately 8 Tg C between 1950 and 2010 (Figs. 4.4d).

25 (1) Maple/beech/birch Elm/ash/cottonwood 20 Oak/gum/cypress Oak/hickory 15 Oak/pine Loblolly/shortleaf pine Longleaf/slash pine 10

Percentage Percentage of forested area 5

Stand age (years) Figure 4.1. Age class distribution in 2010 in the Kisatchie National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

Longleaf/slash pine 14 (2) Loblolly/shortleaf pine Oak/pine 12 Oak/hickory Oak/gum/cypress

1) 10

- Elm/ash/cottonwood

Maple/beech/birch 1 yr 1 - 8

NPP (Tg NPPha (Tg C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Kisatchie National Forest.

Page 39 of 77

240 21 (3a) (3b)

200 20 C) 160 ° 19 120 18 80 Total Precip Temperature Temperature ( 17 40

Precipitation(cm/yr) Avg Temp 0 16 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30

20 Total N dep 10 Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Kisatchie National Forest. Linear trend lines shown in black.

Page 40 of 77

2 2 1)

- (4a) (4b)

1) - 1 1

0 0

-1 Climate effects -1

CO2 effects Disturbance/aging effects C stock stock C change (Tg yr C

N deposition effects stock C change (Tg yr C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20

(4c) (4d)

1) - 1 10

0 0 All effects Climate -1 All effects -10 CO2

N deposition Accumulated (Tg C C)

C stock stock C change (Tg yr C Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e) 1) - C emissions due to disturbance

0.1 C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.4. Changes in carbon stocks in the Kisatchie National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 41 of 77

Ouachita National Forest

Forests in the Ouachita National Forest maintained a C sink from 1950 through the mid-1970s (Fig. 4.4c) due to positive disturbance/aging and climate effects (Figs. 4.4a-b). Forests then switched to mostly a C source through 2010 due to negative disturbance/aging effects (Fig. 4.4b). The stand age distribution indicates a period of elevated stand establishment 70-99 years ago (1911 to 1940) (Fig. 4.1f). This pulse of stand establishment is likely the result of restoration and recovery after extensive logging operations in the region. The predominately loblolly/shortleaf pine stands making up this pulse would have reached peak productivity around 35 years old (Fig. 4.2) or from the 1940s to 1970s, which is generally when disturbance/aging effects were positive (Fig. 4.4b) and forests were accumulating C (Fig. 4.4d). The stand age distribution shows a smaller pulse of stands 20-29 years old (Fig. 4.1), establishing during the 1980s. This pulse likely reflects regrowth after a disturbance event in 1984, as it also corresponds to a large decline in changing C stocks due to disturbance/aging and a pulse of C emissions (Figs. 4.4b, e). Despite maintaining a C source through 2010, forests have been able to slowly recover as more recent disturbances have been smaller (< 1% annually) and mostly low-moderate in severity (Fig. 1.1f).

Climate effects on changing C stocks have fluctuated with variability in temperature and precipitation. Changing C stocks generally decline when temperatures are warmer than usual such as in 1954 and 1998 (Figs. 4.3b, 4.4a), as warmer temperatures can increase soil respiration and decomposition. Specifically, the prominent heat wave of 1998 (Fig. 3b) caused the most significant C source over these few decades (Fig. 4.4a) and the forest lost approximately 2 Tg C during that year alone (Fig. 4.4d). Increasing nitrogen deposition and atmospheric CO2 concentrations consistently promoted a C sink and helped forest to accumulate C despite losses due to disturbance/aging and climate effects (Fig. 4.4d). Between 1950 and 2010, forests in Ouachita National Forest experienced a net C gain of approximately 10.4 Tg C (Fig. 4.4d).

25 (1) Maple/beech/birch 20 Elm/ash/cottonwood Oak/gum/cypress 15 Oak/hickory Oak/pine 10 Loblolly/shortleaf

5 Percentage Percentage of forested area

Stand age (years) Figure 4.1. Age class distribution in 2010 in the Ouachita National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

Loblolly/shortleaf pine 14 Oak/pine (2) Oak/hickory 12 Oak/gum/cypress Elm/ash/cottonwood & Maple/beech/birch

1) 10

- 1 yr 1

- 8

NPP (t ha C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Ouachita National Forest. Due to a small sample size and similar growth and yields, Maple/beech/birch stands were combined with Elm/ash/cottonwood stands for NPP-stand age modeling.

Page 43 of 77

240 19 (3a) (3b) 200 18

Mean Temp C)

160 ° 17 120 16 80 15

Precipitation(cm) 40 Total precip 14 Temperature Temperature ( 0 13 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 80

) (3c) 2 70 60 50 40 30 20 Total N dep 10

Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Ouachita National Forest. Linear trend lines shown in black.

Page 44 of 77

3 3

(4a) Climate effects (4b)

1) -

1) 2 CO2 effects 2 - N deposition effects 1 1

0 0

-1 -1

-2 -2 Disturbance/aging effects C stock stock C change (Tg yr C Non-disturbance effects C stock stock C change (Tg yr C -3 -3 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 3 40

(4c) (4c) 1)

- All effects 30 2 20 1 10 0 0 All effects -10 -1 Climate -20 CO2 -2 Accumulated (Tg C C) -30 N deposition C stock stock C change (Tg yr C Disturbance/aging -3 -40 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e)

1) - C emissions due to disturbance

0.1 C emissions emissions C (Tg yr C

0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Ouachita National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 45 of 77

Ozark-St. Francis National Forests

Forests in the Ozark-St. Francis National Forests fluctuated between a C sink and a C source, though were mostly a C source, especially since the 1970s (Fig. 4.4c). From the 1950-1960 disturbances/aging had positive effects on C flux, enhancing the forest C sink, but after 1960 effects were consistently negative, adding to the C source (Fig. 4.4b). This decline is due to a strong aging effect as the significant pulse of mostly Oak/hickory stands establishing 80-99 years ago (Fig. 4.1) during the 1910s-1930 have aged beyond their maximum productivity (Fig. 4.2). Ozark St. Francis National Forest experienced mostly small (< 0.5% of forest annually), low-moderate severity harvests from 1991-2010 (Fig. 1.1g), which had a minimal effect on C emissions (Fig. 4.4e). Though disturbance/aging effects remained negative through 2010, the lack of large and severe disturbances in recent decades may have allowed the forest to recover and C stock change due to disturbance/aging effects to approach zero (Fig. 4.4b).

Climate effects on changing C stocks fluctuated annually between positive and negative. Very warm years such as 1954 and 1998 had negative effects on C stocks, causing forests to be a source, whereas cooler years, with average to above average precipitation (ex. 1958, 1979, 2008) had positive effects, enhancing the C sink (Figs. 4.3a-b, 4.4a). Warmer temperatures can increase decomposition rates and subsequently soil respiration. Increases in nitrogen (N) deposition and atmospheric CO2 concentrations had a positive effect on changing C stocks (Fig. 4.4a) and C accumulation (Fig. 4.4d). However due to C losses associated with an aging forest, by 2010 the forest experienced a net loss of 3.2 Tg C as compared to 1950 levels. C losses would have been much more significant if it was not for the C gains due to N deposition, CO2 fertilization, and to a lesser extent climate (Fig. 4.4d).

30 (1) Maple/beech/birch 25 Elm/ash/cottonwood 20 Oak/gum/cypress Oak/hickory 15 Oak/pine Loblolly/shortleaf 10

Percentage Percentage of forested area 5

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the Ozark-St. Francis National Forests displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

14 Loblolly/shortleaf Oak/pine (2) Oak/hickory Oak/gum/cypress Elm/ash/cottonwood Maple/beech/birch 12

1) 10

- 1 yr 1 - 8

NPP (Tg ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type in the Ozark-St. Francis National Forests.

Page 47 of 77

200 18 (3a) (3b) 17

160 Mean Temp C)

° 16 120 15 80 14 Total Precip 40 Temperature (

Precipitation(cm) 13

0 12 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

70 ) 2 (3c) 60 50 40 30 20 Total N dep 10 Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Ozark-St. Francis National Forests. Linear trend lines shown in black.

Page 48 of 77

2 2

(4a) Climate effects (4b)

1) -

- CO2 effects 1 N deposition effects 1

0 0

-1 -1

Disturbance/aging effects

C stock stock C change (Tg yr C C stock stock C change (Tg yr C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20

(4c) (4d)

1) - 1 All effects 10

0 0 All effects Climate -1 -10 CO2

Accumulated (Tg C C) N deposition C stock stock C change (Tg yr C Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e) 1) 1)

- C emissions due to disturbance

0.1 C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Ozark-St. Francis National Forests due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1951-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects

Page 49 of 77

National Forests in Texas

Forests in the National Forests of Texas generally maintained a C sink in the 1950s and 1960s (Fig. 4.4c), but thereafter C accumulation slowly declined (Fig. 4.4d) and the forests fluctuated between a C sink and source through 2010 (Fig. 4.4c). Disturbance/aging effects promoted the C sink during the 1950s, as stands that regenerated after intensive harvesting in the 1920s were at their most productive ages (Fig. 4.2). The stand age distribution shows a pulse of predominantly loblolly/shortleaf pine stands aged 70- 99, which were establishing between 1911 and 1940 (Fig. 4.1). These stands would have reached peak productivity at age 25 (Fig. 4.2) or between the mid-1930s-1960s, when forests were accumulating C due to disturbance/aging (Fig. 4.4d). By the mid-to-late 1960s, forests had aged beyond this maximum productivity and disturbance/aging effect declined and became negative for several decades. A smaller pulse of stands aged 20-39 years old were establishing between 1971 and 1990 (Fig. 4.1) and reached peak productivity in the mid-1990s, when aging/disturbance effects started to increase C again (Fig. 4.4b, d). Recent disturbances were mostly moderate-high intensity harvests, but were very small (Fig. 1.1h) thus did not greatly influence C emissions (Fig. 4.4e), C flux, or the forest age structure.

Climate effects on changing C stocks fluctuated with inter-annual variability in temperatures and precipitation (Fig. 4.4a). For instance, cooler temperatures in the late-1950s to early-1960s promoted a C sink as cooler temperatures can reduce soil respiration and decomposition rates. On the other hand, warmer than usual temperatures such as the heat wave in 1998, and the generally warmer temperatures of the past decade (Fig. 4.3b) increased the rates of respiration and decomposition, thus climate has more recently promoted a C source (Fig. 4.4c) and declines in C accumulation (Fig. 4.4d). Sustained positive effects of both nitrogen (N) deposition and atmospheric CO2 promoted a C sink and helped forest to accumulate more C, but their effects were overshadowed by the stronger, slightly negative effects of climate and disturbance/aging since the late 1998s. Between 1950 and 2010 the forest experienced a small net loss of approximately 1.8 Tg C (Fig. 4.4d).

20 (1) 18 Maple/beech/birch 16 Elm/ash/cottonwood 14 Oak/gum/cypress 12 Oak/hickory Oak/pine 10 Loblolly/shortleaf pine 8 Longleaf/slash pine 6

4 Percentage Percentage of forested area 2 0

Stand age (year)

Figure 4.1. Age class distribution in 2010 in the National Forests of Texas displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

Longleaf/slash pine 14 (2) Loblolly/shortleaf pine Oak/pine 12 Oak/hickory Oak/gum/cypress

1) 10 - Elm/ash/cottonwood

Maple/beech/birch 1 yr 1 - 8

NPP (Tg ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the National Forests of Texas.

Page 51 of 77

200 22 (3a) (3b)

160 21 C)

° 20 120 19 80 18

Precipitation(cm) 40 Total Precip Mean Temp Temperature Temperature ( 17

0 16 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

70 )

2 (3c) 60 50 40 30

20 Total N dep

10 Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the National Forests of Texas. Linear trend lines shown in black.

Page 52 of 77

2 2

(4a) (4b)

- 1) 1 - 1

0 0

-1 Climate effects -1 CO2 effects Disturbance/aging effects C stock stock C change (Tg yr C N deposition effects Non-disturbance effects -2 stock C change (Tg yr C -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20

(4c) (4d)

1) - 1 10

0 0 All effects Climate -1 Accumulated (Tg) C -10 CO2 N deposition

C stock stock C change (Tg yr C All effects Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e) 1) - C emissions due to disturbances

0.1 C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.4. Changes in carbon stocks in the National Forests of Texas due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 53 of 77

Chattahoochee-Oconee National Forest

Forests in the Chattahoochee National Forest switched from a C sink to a C source in the early 1960s and have remained a C source through 2010 (Fig. 4.4c). Disturbance and aging effects have been largely responsible for the declining C stocks, while non-disturbance effects have had lesser but generally positive effects (Fig. 4.4b). This decline in C is primarily due to a strong aging effect as ~73% of the forest is > 70 years old (Fig. 4.1), thus their rates of productivity have declined (Fig. 4.2). The stand age distribution shows a pulse of stands that established from 1900-1940 (70-109 years old) (Fig. 4.1), which likely represents regeneration of the forests which were originally denuded for hydraulic mining and lumber in the late 1800s and early 1900s, then replanted by the Civilian Conservation Corp in the 1930s. These stands which consist of predominantly Oak/Hickory forests would have been most productive from ages 25-30 (Fig. 4.2) or between roughly 1930 and 1970. Although recent disturbances generally been small harvests (Fig. 1.1i), there may have been several large and/or more severe disturbances such as in 1978, 1983, and 1992, that caused increases in C emissions (Fig. 4.4e), and further negative disturbance/aging effects (Fig. 4.4b).

Climate effects were initially negative, promoting a C source in the early 1950s (Fig. 4.4c) due to warmer temperatures (Fig. 4.4b) which increase soil respiration and decomposition rates. While climate effects have been quite variable, following variability in temperature and precipitation, when combined with nitrogen deposition and CO2, the combined non-disturbance effect has generally been positive. Although these non-disturbance effects promoted a C sink (Fig. 4.4b) and caused positive C accumulation (Fig. 4.4d), non-disturbance effects were greatly overshadowed by the stronger, negative disturbance and aging effects, resulting in a net loss of roughly 12.1 Tg C between 1950 and 2010 (Fig. 4.4d).

20 (1) Aspen/birch 18 Maple/beech/birch 16 Elm/ash/cottonwood 14 Oak/gum/cypress Oak/hickory 12 Oak/pine 10 Loblolly/shortleaf 8 Longleaf/slash Spruce/fir 6 White/red/jack pine

4 Percentage Percentage of forested area 2 0

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the Chattahoochee National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

White/red/jack pine & Spruce/fir Longleaf/slash pine 14 Loblolly/shortleaf (2) Oak/pine 12 Oak/hickory

Oak/gum/cypress 1) - 10 Elm/ash/cottonwood

1 yr 1 Maple/beech/birch - 8

4 NPP (Tg ha C

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Chattahoochee National Forest. Due to a small sample size Spruce/fir stands were combined with White/red/jack pine stands for modeling NPP-age relationships, given their similar growth and yield trends.

Page 55 of 77

240 17 (3a) (3b)

200 16 C) 160 ° 15 14 120 13 80

12 Temperature Temperature ( Precipitation(cm) 40 11 Total Precip MeanTemp 0 10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

(3c) ) 2 60 50 40 30 20 Total N dep 10

0 Nitrogendeposition (g/m 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Chattahoochee National Forest. Linear trend lines shown in black.

Page 56 of 77

2 2

(4a) Climate effects (4b)

1) - CO2 effects - 1 N deposition effects 1

0 0

-1 -1 Disturbance/aging effects C stock stock C change (Tg yr C C stock stock C (Tg yr change C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 10

(4c) (4d)

1) - 1 All effects 0

0 -10 All effects Climate -1 -20 CO2

Accumulated (Tg C C) N deposition C stock stock C change (Tg yr C Disturbance/aging -2 -30 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e) 1) - C emissions due to disturbances

0.1 C emissions emissions C (Tg yr C

0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Chattahoochee National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 57 of 77

Cherokee National Forest

Forests in the Cherokee National Forest maintained a small C sink in the 1950s, then switched to a C source in the early-mid 1960s and remained a source through 2010 (Fig. 4.4c). Disturbance and aging effects have been largely responsible for the declining C stocks, while non-disturbance effects have had lesser but generally positive effects (Fig. 4.4b). This negative disturbance/aging effect is primarily due to the forest aging and declining in productivity, as roughly 77% of the stands are > 70 years old (Fig. 1.1j), thus have aged past their maximum productivity (Fig. 2). The stand age distribution shows a pulse of stands that were establishing from 1910-1930 (80-100 years old) (Fig. 4.1j), which likely represents regeneration after forests were heavily harvested for timber or cleared for agriculture and development in the late 1800s and early 1900s. These stands which consist of predominantly Oak/Hickory forests would have been at most productive at age 35 (Fig. 4.2) or between roughly 1945 and 1965, which is when forests were still a C sink (Fig. 4.4c). As stands continued to age, productivity declined to the point where C losses due to decomposition and decay were greater than gains, causing the forest to remain a C source. Recent disturbances have generally been small and low-severity (Fig. 1.1j), thus did not increase C emissions, alter stand age, or affect C stocks greatly.

While climate effects have been quite variable (Fig. 4.4a), following variability in temperature and precipitation (Figs. 4.3a-b), cumulatively climate did not significantly affect C accumulation from 1950- 2010 (Fig. 4.4d). However, in the late 1990s and 2000s, climate effects were mostly negative due to warming temperatures which increase soil respiration and C loss. Nitrogen deposition and atmospheric CO2 effects mostly promoted a C sink and C accumulation (Fig. 4.4a, d). However, these positive non- disturbance effects were greatly overshadowed by the stronger, negative disturbance/aging effects, resulting in a net loss of roughly 13.6 Tg C between 1950 and 2010 (Fig. 4.4d).

30 (1) Aspen/birch 25 Maple/beech/birch Elm/ash/cottonwood 20 Oak/gum/cypress Oak/hickory 15 Oak/pine Loblolly/shortleaf 10 Spruce/fir

Percentage Percentage of forest White/red/jack pine 5

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the Cherokee National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

White/red/jack pine & Spruce/fir Loblolly/shortleaf 14 (2) Oak/pine Oak/hickory 12 Oak/gum/cypress

Elm/ash/cottonwood 1) - 10 Maple/beech/birch

1 yr 1 Aspen/birch - 8

4 NPP (Tg ha C

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Cherokee National Forest. Due to the low number of Spruce/fir stands and similar growth and yields, they were combined with White/red/jack pine stands for modeling NPP-age relationships.

Page 59 of 77

200 16 (3a) (3b)

160 15 C)

° 14 Mean Temp 120 13 80 12 40 Total Precip

Temperature Temperature ( 11 Precipitation(cm) 0 10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year

70 )

2 (3c) 60 50 40 30

20 Total N dep 10

Nitrogen deposition (g/m Nitrogen 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Cherokee National Forest. Linear trend lines shown in black.

Page 60 of 77

2 2 (4b)

(4a) 1)

- - 1 1

0 0

Climate effects -1 -1 CO2 effects Disturbance/aging effects

N deposition effects stock C change (Tg yr C C stock stock C change (Tg yr C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 (4c) 20

1) (4d) - 1 10 0 0 -10 All effects Climate -1 CO2 All effects -20 Accumulated (Tg C C) N deposition C stock stock C change (Tg yr C Disturbance\aging -2 -30 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e)

1) - C emissions due to disturbances

0.1 C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Cherokee National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 61 of 77

Daniel Boone National Forest

Forests in the Daniel Boone National Forest fluctuated between a small C sink and source, but were mostly a C sink from 1950 into the mid-1970s and more often a C source through 2010 (Fig. 4.4c). A combination of negative disturbance and aging effects and mostly positive non-disturbance factors were responsible for fluctuating C stocks (Fig. 4.4b). The stand age distribution shows that a pulse of stands, largely consisting of oak/hickory, were establishing roughly 80-109 years ago (1901-1930). These stands would have been most productive around 35 years old (Fig. 4.2) or 1935-1965. This period corresponds to when disturbance/aging effects were promoting a C sink (Fig. 4.4b) and the forests were accumulating C (Fig. 4.4d). Small disturbances in the 1960s, likely timber harvests, again caused a greater release of C (Fig. 4.4e) and enhanced the C source, but were often counteracted by the positive climate effects, such that the forest remained a C sink. Recent disturbances have been relatively small, less severe, and have declined since the 1990s (Fig. 1.1k) resulting in low rates of stand establishment in recent years (Fig. 4.1).

From the 1950s through the 1970s, climate effects fluctuated between positive and negative following variability in temperature and precipitation. Generally cooler years like 1976 had a positive effect on C stocks while warmer years like 1998 had a negative effect (Fig. 4.3b, 4.4a), as warmer temperatures increase decomposition rates and soil respiration. Nitrogen deposition and atmospheric CO2 concentrations consistently promoted a small C sink and C accumulation (Fig. 4.4a, d). After the short period of C accumulation due the positive disturbance and non-disturbance effects, C accumulation declined slightly in the late 1970s-early 1980s due to negative climate and disturbance/aging effects. From the 1980s through 2010, C accumulation remained relatively stable, around zero, due to the balance of the positive non-disturbance effects and more negative disturbance/aging effects (Fig. 4.4d).

16 (1) 14 Maple/beech/birch Elm/ash/cottonwood 12 Oak/gum/cypress Oak/hickory 10 Oak/pine 8 Loblolly/shortleaf Spruce/fir 6 White/red/jack pine

Percentage Percentage of forested area 2

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the Daniel Boone National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

White/red/jack pine & Spruce/Fir 14 Loblolly/shortleaf (2) Oak/pine 12 Oak/hickory Oak/gum/cypress

10 Elm/ash/cottonwood 1)

- Maple/beech/birch 1 yr 1

- 8

NPP (t ha C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Daniel Boone National Forest. Due to the low number of Spruce/fir stands and similar growth and yields, they were combined with White/red/jack pine stands for modeling NPP-age relationships.

Page 63 of 77

200 16 (3a) (3b) 15

160 C) ° 14 120 13 80

12 Temperature Temperature (

Precipitation(cm) Total Precip 40 11 Mean Temp

0 10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30 20 Total N dep

10 Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Daniel Boone National Forest. Linear trend lines shown in black.

Page 64 of 77

2 2

(4a) 1) (4b)

- 1) - 1 1

0 0

Climate effects -1 -1 CO2 effects Disturbance/aging effects

N deposition effects stock C change (Tg yr C Non-disturbance effects C stock stock C change (Tg yr c -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20

(4e) (4d)

1) - All effects 1 10

0 0 All effects Climate -1 -10 CO2 Accumulated (Tg C C) N deposition C stock stock C change (Tg yr C Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2 (4e)

1) C emissions due to - disturbances

0.1 C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the Daniel Boone National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 65 of 77

National Forests in North Carolina

Between 1951 and the 1960s, forests in the National Forests in North Carolina maintained a C sink then shifted to mostly a C source through 2010 (Fig. 4.4b). During the 1950s and 1960s, disturbance/aging effects promoted a C sink (Fig. 4.4b) and rapid increase in C accumulation (Fig. 4.4d). This trend is supported by the stand age distribution (Fig. 4.1), which shows a pulse of stands aged 70-99 years old that were establishing from 1911-1940. These stands were likely a result of restoration management after a period of intensive logging and land use in the late 1800s and early 1900s. According to the NPP- age relationships, these stands mostly consisting of Oak/hickory, Loblolly/shortleaf pine, and Oak/pine would have been most productive at roughly 30-45 years of age (Fig. 4.2) or through much of the mid- 1900s when forests were a C sink (Fig. 4.4c). As these forests further aged and productivity declined, C losses due to decomposition and decay exceeded C gains causing the C source (Fig. 4.4b). Despite relatively large disturbances in the early 1990s (fires and harvests) (Fig. 1.1l), recent disturbances have been quite small (<1% of the forested area) and less severe, thus did not affect stand age, C emissions (Fig. 4.4e) or changing C stocks dramatically (Fig. 4.4b).

Climate variability, mainly temperature variability, influenced changing C stocks throughout the past few decades. For instance, warmer temperatures in 1990 and 1998 (Fig. 4.3b) caused a greater C source (Figs. 4.4a, c). Warmer temperatures increase evaporative demands and soil respiration causing a loss of C. Though highly variable, overall climate did not affect C accumulation greatly (Fig. 4.4d). Increasing CO2 and nitrogen (N) deposition (Fig. 4.3c) had mostly positive effects, promoting a C sink (Fig. 4.4a) and C accumulation (Fig. 4.4d). Declines in N deposition and further increases in CO2 over the past few decades have caused the CO2 effect to become stronger than that of N deposition (Fig. 4.4d). The C gains due to the CO2 and N deposition effects were not significant enough to offset the C losses due to disturbance/aging, therefore, the forest experienced a net C loss of 6.6 Tg C from 1950-2010 (Fig. 4.4d).

25 (1) Aspen/birch Maple/beech/birch 20 Elm/ash/cottonwood Oak/gum/cypress Oak/hickory 15 Oak/pine Loblolly/shortleaf Longleaf/slash pine 10 Spruce/fir White/red/jack pine

5 Percentage Percentage of forested area

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the National Forests in North Carolina displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

Longleaf/slash pine Loblolly/shortleaf 14 Oak/hickory (2) Oak/gum/cypress 12 Oak/pine White/red/jack pine & Spruce/Fir Elm/ash/cottonwood 10

1) Maple/beech/birch -

Aspen/birch 1 yr 1 - 8

NPP (Tg ha C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the National Forests in North Carolina. Due to the small number of Spruce/fir stands and similar growth and yields as White/red/jack pine stands, these forest type groups were combined for modeling NPP-age relationships.

Page 67 of 77

240 16 (3a) (3b)

200 15 C)

160 ° 14 120 13 80 12 Total Precip

40 Temperature ( 11 Precipitation(cm) Mean Temp 0 10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30 20 Total N dep 10

Page 68 of 77

2 2

(4a) Climate effects (4b)

1) - CO2 effects - 1 N deposition effects 1

0 0

-1 -1

Disturbance effects C stock stock C change (Tg yr C C stock stock C change (Tg yr C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 3 20

(4c) (4d) 1)

- 2 1) All factors - 10 1 0 0 All factors -10 -1 Climate CO2 -2 -20 N deposition C stock stock C change (Tg yr C Disturbance -3 Accumulated (Tg C yr C -30 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2 (4e)

1) C emissions due to - disturbance

0.1 C emissions emissions C (Tg yr C

0.0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.4. Changes in carbon stocks in the National Forests in North Carolina due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 69 of 77

George Washington National Forest

Between 1950 and 2010 forests in the George Washington National Forest have mostly been a C source due to negative disturbance/aging effects (Figs. 4.4b, c). The stand age distribution shows a period of elevated stand establishment from 90 to109 years ago (1901-1920) (Fig. 4.1). These stands were likely a result of restoration management after a period of intensive logging and land use in the late 1800s and early 1900s. Consisting of mostly Oak/hickory forests, these stands would have been most productive in the mid-20th century (Fig. 4.2). As these forests further aged and productivity declined, C losses due to decomposition and decay exceeded C gains causing the C source over the past few decades (Fig 4.4b-c). Aside from an increase in insect disturbances in 2008, recent disturbances have been very small and low-severity (Fig. 1.1m), resulting in low-levels of stand establishment and the stand age structure to remain relatively older (Fig 4.1).

Climate effects on changing C stocks have been quite variable following interannual variability in temperature and precipitation. Warmer temperatures, such as those in 1990 and 1998 (Fig. 4.3b), can cause an increase in decomposition and soil respiration rates thus promoting a C source (Fig. 4.4a). On the other hand, very cool and dry conditions such as those in 1963 (Fig. 4.3a-b) can impede growth and productivity (Fig. 4.4a). Overall the cumulative climate effects have been small but mostly negative since the 1950s (Fig. 4.4d). In contrast, increases in CO2 and nitrogen (N) deposition had mostly positive effects, increasing forest productivity and causing a C sink (Fig. 4.4a). However, the gains from CO2 fertilization and N deposition were greatly overshadowed by negative disturbance/aging effects causing the forest to experience a net loss of approximately 14 Tg C from 1950 to 2010 (Fig. 4.4d).

25 (1) Maple/beech/birch 20 Elm/ash/cottonwood Oak/hickory Oak/pine 15 Loblolly/shortleaf Longleaf/slash pine 10 Spruce/fir White/red/jack pine

5 Percentage Percentage of forested area

Stand age (years) Figure 4.1. Age class distribution in 2010 in the George Washington National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 2 below.

White/red/jack pine & Spruce/fir Loblolly/shortleaf, Longleaf/slash pine, other eastern softwoods Oak/pine 14 (2) Oak/hickory Elm/ash/cottonwood 12 Maple/beech/birch

1) 10

- 1 yr 1 - 8

NPP (Tg ha C 4

Stand age (years)

Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the George Washington National Forest. Due to the small number of Spruce/fir stands and similar growth and yields as White/red/jack pine stands, these forest type groups were combined for modeling NPP-age relationships. Loblolly/shortleaf pine, longleaf/slash pine, and other eastern pines were also combined.

Page 71 of 77

200 13 (3a) (3b) 12

160 C) ° 11 120 10 80 9

Mean Temp Temperature Temperature ( Precipitation(cm) 40 Total Precip 8

0 7 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30 20 Total N dep 10

0 Nitrogendeposition (g/m 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the George Washington National Forest. Linear trend lines shown in black.

Page 72 of 77

2 2

(4a) 1) (4b)

- 1) - 1 1

0 0

-1 Climate effects -1 CO2 effects Disturbance/aging effects

N deposition effects stock C change (Tg yr C Non-disturbance effects

C stock stock C change (Tg yr C -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 10

1) (4c) (4d) - 1 0

0 -10 All effects Climate CO2 -1 -20 N deposition All effects Accumulated (Tg C C) Disturbance/aging C stock stock C change (Tg yr C -2 -30 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e)

1) -

0.1 C emssions due to disturbance

C emissions emissions C (Tg yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.4. Changes in carbon stocks in the George Washington National Forest due to: (a) individual non-disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effects.

Page 73 of 77

C.14 Jefferson National Forest

From the 1950s through the 1970s, forests in the Jefferson National Forest were mostly a C sink, but later transitioned to a C source due to disturbance/aging effects (Fig. 4.4b-c). The stand age distribution indicates a period of elevated establishment of mostly Oak/hickory and Oak/pine stands from 80 to 99 years ago (1910-1930) (Fig. 4.1). This coincided with the promotion of forest restoration and recovery after a period of intensive logging and land use in the late 1800s and early 1900s. These stands would have reached maximum productivity at around 50 years old or during the mid-20th century (Fig. 4.2). As these forests further aged and productivity declined, C losses due to decomposition and decay exceeded C gains, causing the forest to shift to a C source over the past few decades (Fig 4b-c). Aside from an increase in insect disturbances in 2008, recent disturbances have been very small and low-severity (Fig. 1.1m), resulting in low-levels of stand establishment and the prevalence of this predominately older forest structure (Fig 4.1).

Climate effects on changing C stocks have been quite variable following interannual variability in temperature and precipitation. Warmer temperatures, such as those in 1990 and 1998 (Fig. 3b), can cause an increase in soil respiration and decomposition rates thus promoting a C source (Fig. 4a). On the other hand, wetter and cooler conditions such as those in 1996 (Fig. 3a-b) can decrease forest productivity causing a C sink (Fig. 4a). Overall the cumulative climate effects have been negligible but have resulted in a small C loss over more recent decades (Fig. 4d). In contrast, increases in CO2 and nitrogen (N) deposition had mostly positive effects, helping to offset C losses due to disturbance/aging and climate (Fig. 4a). Despite the C sink and C accumulation during the mid-20th century, between 1950 and 2010 the forest saw a net loss of roughly 2.8 Tg C (Fig. 4d).

30 (1) Maple/beech/birch 25 Elm/ash/cottonwood Oak/hickory 20 Oak/pine Loblolly/shortleaf Longleaf/slash pine 15 Spruce/fir White/red/jack pine

10 Percentage Percentage of forest

Stand age (years)

Figure 4.1. Age class distribution in 2010 in the Jefferson National Forest displaying the percentage of forested area of each forest type group in 10-year age classes. Forest type groups are symbolized by stacked, colored bars that also correspond to the forest type groups in Fig. 3 below.

White/red/jack pine & Spruce/fir 14 (2) Loblolly/shortleaf & Longleaf/slash pine Oak/pine 12 Oak/hickory Elm/ash/cottonwood

10 Maple/beech/birch

- 1 yr 1 - 8

NPP (Tg NPPha (Tg C 4

Stand age (years) Figure 4.2. Relationship between net primary productivity (NPP) and stand age for each forest type group in the Jefferson National Forest. Due to small sample sizes and similar growth and yield trends, Spruce/fir stands were combined with White/red/jack pine stands and Loblolly/shortleaf pine and Longleaf/slash pine stands were combined.

Page 75 of 77

200 13 (3a) (3b) 12

160 C)

° 11 120 10 80 9

Precipitation(cm) 40 Total precip 8 Temperature Temperature ( Mean Temp 0 7 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 70

) (3c) 2 60 50 40 30 20 Total N dep 10

Nitrogendeposition (g/m 0 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.3. (a) Total annual precipitation (cm), (b) average annual temperature (°C), and (c) total annual nitrogen deposition from 1950-2010 in the Jefferson National Forest. Linear trend lines shown in black.

Page 76 of 77

2 2

(4a) (4b)

- - 1 1

0 0

-1 Climate effects -1 Disturbance/aging effects

CO2 effects C stock stock C change (Tg yr C N deposition effects stock C change (Tg yr C Non-disturbance effects -2 -2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 2 20 (4c) (4d)

1 10

0 0 All effects Climate -1 -10 CO2 N deposition All effects Accumulated (Tg C C)

C stock stock C change (Tg yr_1) C Disturbance/aging -2 -20 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year 0.2

(4e) 1) - C emissions due to disturbance

0.1 C emissions emissions C Tg ( yr C 0.0 1950 1960 1970 1980 1990 2000 2010 Year

Figure 4.4. Changes in carbon stocks in the Jefferson National Forest due to: (a) individual non- disturbance factors alone including climate variability, atmospheric CO2 concentration, and nitrogen deposition; (b) all disturbance factors such as fire, harvest, and insects, as well as regrowth and aging and the sum of all non-disturbance factors combined; and (c) all factors combined which is the sum of disturbance\aging and non-disturbance effects; (d) Accumulated C due to individual disturbance\aging and non-disturbance factors and all factors combined from 1950-2010 excluding C accumulated pre- 1950; and (e) C emissions due to disturbance events only. Positive values in Figure 4.4 a-c represent C sinks from the atmosphere or enhancement effects, whereas negative values represent C sources to the atmosphere, or detraction effect.

Page 77 of 77

Forest Carbon Assessment for the Sumter National Forest in the Forest Service’s Southern Region

Duncan McKinley ([email protected]) Alexa Dugan ([email protected]), Patrick Yamnik ([email protected])

November 8, 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, and 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 (Pan et al., 2011a). Forests in the U.S. remove the equivalent of about 12 percent of annual U.S. fossil fuel emissions or about 206 teragrams of carbon after accounting for natural emissions, such as wildfire and decomposition (US EPA, 2015; Hayes et al., 2018).

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. When trees and other vegetation die, either through natural aging and competition processes or disturbance events (e.g., fires, insects), carbon is transferred 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 commodities (e.g., paper, lumber) for a variable duration ranging from days to many decades or even centuries. In the absence of commercial thinnings, harvests, and fuel reduction treatments, forests will thin naturally from mortality-inducing disturbances or aging, resulting in dead trees decaying and emitting carbon to the atmosphere.

Following natural disturbances or harvests, forests regrow, resulting in the uptake and storage of carbon from the atmosphere. Over the long term, forests regrow and often accumulate the same amount of carbon that was emitted from disturbance or mortality (McKinley et al., 2011). 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 (Caspersen et al., 2000; Pan et al., 2009).

The Intergovernmental Panel on Climate Change (IPCC) has summarized the contributions of global human activity sectors to climate change in its Fifth Assessment Report (IPCC, 2014).

From 2000 to 2009, forestry and other land uses contributed just 12 percent of human-caused 1 global CO2 emissions. The forestry sector contribution to GHG emissions has declined over the last decade (FAOSTAT, 2013; IPCC, 2014; Smith et al., 2014). Globally, the largest source of GHG emissions in the forestry sector is deforestation,(Pan et al., 2011a; Houghton et al., 2012; IPCC, 2014) 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 (IPCC, 2000). 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 (Birdsey et al., 2006), a trend expected to continue for at least another decade (Wear et al., 2013; USDA Forest Service, 2016).

In this section, we provide an assessment of the amount of carbon stored on the Sumter National Forest (NF) and how disturbances, management, and environmental factors have influenced carbon storage overtime. This assessment primarily Box 1. Description of the primary forest carbon models used to used two recent U.S. conduct this carbon assessment Forest Service reports: the Carbon Calculation Tool (CCT) Baseline Report (USDA Forest Service, 2015) and Estimates annual carbon stocks and stock change from 1990 to Disturbance Report 2013 by summarizing data from two or more Forest Inventory (Birdsey et al., 2019). and Analysis (FIA) survey years. CCT relies on allometric Both reports relied on models to convert tree measurements to biomass and carbon. Forest Inventory and Analysis (FIA) and Forest Carbon Management Framework (ForCaMF) several validated, data- Integrates FIA data, Landsat-derived maps of disturbance type driven modeling tools to and severity, and an empirical forest dynamics model, the provide nationally Forest Vegetation Simulator, to assess the relative impacts of consistent evaluations of disturbances (harvests, insects, fire, abiotic, disease). forest carbon trends ForCaMF estimates how much more carbon (non-soil) would across the National Forest be on each national forest if disturbances from 1990 to 2011 System (NFS). The had not occurred. Baseline Report applies the Carbon Calculation Integrated Terrestrial Ecosystem Carbon (InTEC) model Tool (CCT) (Smith et al., A process-based model that integrates FIA data, Landsat- 2007), which summarizes derived disturbance maps, as well as measurements of climate available FIA data across variables, nitrogen deposition, and atmospheric CO2. InTEC multiple survey years to estimates the relative effects of aging, disturbance, regrowth, estimate forest carbon and other factors including climate, CO2 fertilization, and stocks and changes in nitrogen deposition on carbon accumulation from 1950 to stocks at the scale of the 2011. Carbon stock and stock change estimates reported by national forest from 1990 InTEC are likely to differ from those reported by CCT to 2013. The Baseline because of the different data inputs and modeling processes. Report also provides information on carbon

1 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.

2 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) (Healey et al., 2014; Raymond et al., 2015; Healey et al., 2016). 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 (Chen et al., 2000; Zhang et al., 2012). See Box 1 for descriptions of the carbon models used for these analyses. Additional reports, including the most recent Resource Planning Act (RPA) assessment (USDA Forest Service, 2016) and regional climate vulnerability assessments (Carter et al., 2014) are used to help infer future forest carbon dynamics. Collectively, these reports incorporate advances in data and analytical methods, representing the best available science to provide comprehensive assessments of NFS carbon trends.

The Sumter National Forest (SNF) was administratively combined with the Francis-Marion National Forest to form the administrative unit of the Francis Marion & Sumter National Forests (FMS). According to recent estimates from the latest FIA inventory, the Sumter NF accounts for about 59 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 disturbances of the Sumter relative to the entire FMS to provide a reasonable interpretation of how these model results may apply more specifically to the Sumter.

1.1 Background The Francis Marion and Sumter National Forests (FMS NFs), located in South Carolina, cover approximately 239,922 ha of forestland, and the Sumter NF constitutes approximately 58 percent of this area. Loblolly-shortleaf and oak-hickory forest types are the most abundant across the Sumter NF, Box 2. Carbon Units. The following table provides a crosswalk according to FIA among various metric measurements units used in the assessment of data. The carbon carbon stocks and emissions. legacy of the Tonnes Grams Sumter NF and Multiple Name Symbol Multiple Name Symbol other national 100 Gram G forests in the 103 kilogram Kg region is tied to 100 tonne t 106 Megagram Mg the history of 103 kilotonne Kt 109 Gigagram Gg Euro-American 106 Megatonne Mt 1012 Teragram Tg settlement, land 109 Gigatonne Gt 1015 Petagram Pg management, and 1012 Teratonne Tt 1018 Exagrame Eg disturbances. After 1015 Petatonne Pt 1021 Zettagram Zg the Civil War in 1018 Exatonne Et 1024 yottagram Yg the 1860s, 1 hectare (ha) = 0.01 km2 = 2.471 acres = 0.00386 mi2 agricultural 1 Mg carbon = 1 tonne carbon = 1.1023 short tons (U.S.) carbon expansion and 1 General Sherman Sequoia tree = 1,200 Mg (tonnes) carbon large-scale timber 1 Mg carbon mass = 1 tonne carbon mass = 3.67 tonnes CO2 mass extraction became A typical passenger vehicle emits about 4.6 tonnes CO2 a year the dominant

3 driving forces of forest change in the South. The timber industry soon became centered in the South, and by 1919 the region was producing 37 percent of U.S. lumber (Williams, 1989). During this period, most of the remaining primary forests of the South were harvested, and much of the previous forestland was replaced with agriculture and grazing.

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 (Shands, 1992). The Sumter NF 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 (Shands, 1992 and Johnson and Govatski, 2013). This legacy of timber harvesting and early efforts to restore the forest is visible today, influencing forest age structures, tree composition, and carbon dynamics (Birdsey et al., 2006). 2.0 Baseline Carbon Stocks and Flux 2.1 Forest Carbon Stocks and Stock Change According to results of the Baseline Report (USDA Forest Service, 2015), carbon stocks in the FMS NFs increased from 36.7±5.6 teragrams of carbon (Tg C) in 1990 to 44.7±8.0 Tg C in 2013, a 22 percent increase in carbon stocks over this period (Fig. 1). For context, 44.7 Tg C is equivalent to the emissions from approximately 36 million passenger vehicles in a year. Despite some uncertainty in annual carbon stock estimates, reflected by the 95 percent confidence intervals, there is a high degree of certainty that carbon stocks on the FMS NFs have been stable or increased from 1990 to 2013 (Fig. 1). About 48 percent of forest carbon stocks in the FMS NFs are stored in the soil carbon contained in organic material to a depth of one meter (excluding roots). The aboveground portion of live trees, which includes all live woody vegetation at least one inch in diameter (Fig. 2) is the second largest carbon pool, storing another 35 percent of the forest carbon stocks. Recently, new methods for measuring soil carbon Figure 1. Total forest carbon stocks (Tg) from 1990 to 2013 for have found that the Francis Marion and Sumter National Forests, bounded by 95 amount of carbon stored percent confidence intervals. Estimated using the CCT model. in soils generally exceeds the estimates derived

4 from using the methods of the CCT model by roughly 12 percent across forests in the United States (Domke et al., 2017).

The annual carbon stock change can be used to evaluate whether a forest is a carbon sink or source in a given year. Carbon stock change is typically reported from the perspective of the atmosphere. A negative value indicates a carbon sink: the forest is absorbing more carbon from the atmosphere (through growth) than it emits (via decomposition, removal, and combustion). A positive value indicates a source: the forest is emitting more carbon than it takes up.

Annual carbon stock changes in the FMS NFs were 0.43 ± 0.94 Tg C per year (loss) in 1990 and -0.27 ± 1.4 Tg C per year in 2012 (gain) (Fig. 3). The initial loss in 1990 is likely due to decomposition and heightened respiration after Hurricane Hugo, which only affected the Francis Marion NF. Thus

Figure 2. Percentage of carbon stocks in 2013 in each of the the loss of carbon in the forest carbon pools, for the Francis Marion and Sumter National early 1990s may not Forests. Estimated using the CCT model. have occurred on the Sumter NF. 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 from 1990 to 2013 (Fig. 1) over the

Figure 3. Carbon stock change (Tg/yr) from 1990 to 2012 for the 23-year period suggests Francis Marion and Sumter National Forests, bounded by 95 that the FMS NFs are a percent confidence intervals. A positive value indicates a carbon modest carbon sink. source, and a negative value indicates a carbon sink. Estimated using the CCT model.

Changes in forested area may 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 the FMS NFs has increased from 230,241 ha in 1990 to 239,922 ha in 2013, a net change of 9,681 ha.2 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 (Woodall et al., 2011).

Carbon density, which is an estimate of forest carbon stocks per unit area, can help identify the effects of changing forested area. In the FMS NFs, carbon density increased from about 159 Megagrams of carbon (Mg C) per ha in 1990 to 186 Mg C per ha in 2013 (Fig. 4). This increase in carbon density suggests that total carbon stocks may have indeed increased.

Carbon density is also useful for comparing trends among units or ownerships with different forest areas. Similar to the FMS NFs, most national forests in the Southern Region have experienced increasing carbon densities from 1990 to 2013. Carbon density in the FMS NFs has been similar to but somewhat higher than the average for all national forest units in the Southern Region (Fig.4). Differences in carbon Figure 4. Carbon stock density (Megagrams per hectare) in density between units may be the Francis Marion and Sumter National Forests and the related to inherent differences average carbon stock density for all forests in the Southern in biophysical factors that Region from 1990 to 2013. Estimated using CCT. influence growth and productivity, such as climatic conditions, elevation, and forest types. These differences may also be affected by disturbance and management regimes (see Section 3.0).

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 simulations3 and

2 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. 3 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.

6 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 error bounds. 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.

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 NFs have experienced minimal changes in land use or adjustments to the boundaries of the national forests in recent years, the change in forested area incorporated in CCT is more likely a data artefact of altered inventory design and protocols (Woodall et al., 2013).

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 (Woodall et al., 2011). 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 (Woodall et al., 2013). 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 (Dugan et al., 2017).

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. Wood products can be used in place of other more emission intensive materials, like steel or concrete, and wood-based energy can displace fossil fuel energy, resulting in a substitution effect (Gustavsson et al., 2006; Lippke et al., 2011). Much of the harvested carbon that is initially transferred out of the forest can also be recovered with time as the affected area regrows.

Carbon accounting for harvested wood products (HWP) contained in the Baseline Report was conducted by incorporating data on harvests on national forests documented in cut-and-sold reports within a production accounting system (Smith et al., 2006; Loeffler et al., 2014). This approach tracks the entire cycle of carbon, from harvest to timber products to primary wood products to disposal. As more commodities are produced and remain in use, the amount of carbon stored in products increases. As more products are discarded, the carbon stored in solid waste disposal sites (landfills, dumps) increases. Products in solid waste disposal sites may continue to store carbon for many decades.

In national forests in the Southern Region, harvest levels remained low until after the start of World War II in the late 1930s, when they began to increase, which caused an increase in carbon storage in HWP (Fig. 5). Timber harvesting and subsequent carbon storage increased rapidly in the 1980s. Storage in products and landfills

Figure 5. Cumulative total carbon (Tg) stored in harvested wood reached about 25 Tg C products (HWP) sourced from national forests in the Southern in 2001. However, Region. Carbon in HWP includes products that are still in use because of a significant and carbon stored at solid waste disposal sites (SWDS). decline in timber Estimated using the IPCC production accounting approach. harvesting in the late 1990s and early 2000s (to 1950s levels), carbon accumulation in the product sector has slowed, and carbon storage in products in use has declined since it peaked in the late 1990s. Despite this decline, the contribution of national forest timber harvests to the HWP carbon pool exceeds the decay of retired products, causing a net increase in product-sector carbon stocks in the Southern Region.

In 2013, the carbon stored in HWP was equivalent to approximately 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, which can be significant (Gustavsson et al., 2006). The collective effect of 4 uncertainty was assessed using a Monte Carlo Abiotic approach. Results 3 Insects indicated a ±0.05 Harvest percent difference from Fire 2 the mean at the 90 percent confidence level for 2013, suggesting that

Percentage of Forest of Percentage 1 uncertainty is relatively small at this regional scale (Loeffler et al., 0 2014).

4 Magnitude 0-25% 26-50% 51-75% 76-100%

1 Percentage of Forest of Percentage

Year Figure 6. Percentage of forest disturbed from 1990 to 2011 in Sumter National Forest by (a) disturbance type including fire, harvests, insects, and abiotic (wind), and (b) magnitude of disturbance (change in canopy cover). Estimated using annual disturbance maps derived from Landsat satellite imagery.

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 (Healey et al., 2018). 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 disturbance maps indicate that timber harvest has been the dominant disturbance type detected on the Sumter NF from 1990 to 2011, in terms of the total percentage of forested area disturbed over the period (Fig. 6a). However, according to the satellite imagery, timber harvests affected a relatively small area of the forest during this time. In most years, timber harvests affected less than 0.5 percent of the total forested area of the Sumter NF in any single year from 1990 to 2011, and in total about 10.5 percent (approximately 14,240 ha) of the average forested area during this period (138,405 ha). Harvests varied widely in the proportion of trees removed in terms of the percent of the canopy cover removed (Fig. 6b). Disturbances from sources other than timber harvest have generally been limited. However, fire has become more frequent during the last few years of the study period, disturbing nearly 3 percent of forested acres in the Sumter NF between 2008 and 2011. Additionally, insects have generally had an extremely limited impact, but there was a notable uptick in 2011 when nearly 1.5 percent of the Sumter NF was subject to disturbance from insects.

The Forest Carbon Management Framework (ForCaMF) incorporates Landsat disturbance maps summarized in Figure 6, along with FIA data in the Forest Vegetation Simulator (FVS) (Crookston & Dixon, 2005). 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) (Raymond et al., 2015). 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 Figure 7. Lost potential storage of carbon (Megagrams) as a national forest if the result of disturbance for the period 1990-2011 in the FMS disturbances and harvests National Forest. The zero line represents a hypothetical during 1990-2011 had not undisturbed scenario. Gray lines indicate 95% confidence occurred. ForCaMF simulates intervals. Estimated using the ForCaMF model. 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 (Healey et al., 2014). ForCaMF was run across the entire FMS NFs.

Timber harvesting on the FMS NFs was the primary disturbance influencing carbon stocks from 1990 to 2011 (Fig. 7). Harvesting accounted for approximately 75 percent of the total non-soil carbon lost from the forest due to disturbances (USDA Forest Service, 2015). The ForCaMF model indicates that, by 2011, the FMS NFs contained 6.4 Mg C per ha less non-soil carbon (i.e., vegetation and associated pools) due to harvests since 1990, as compared to a hypothetical

11 undisturbed scenario (Fig. 7). As a result, non-soil carbon stocks in the FMS NFs would have been approximately 5.3 percent higher in 2011 if harvests had not occurred since 1990 (Fig. 8).

Figure 8. The degrees to which 2011 carbon storage on each national forest in the Southern Region was reduced by disturbance from 1990 to 2011 relative to a hypothetical baseline with no disturbance. The black line indicates the effect of all disturbances types combined. Estimated using disturbance effects from ForCaMF and non-soil carbon stock estimates from CCT.

The ForCaMF analysis was conducted over a relatively short time. After a forest is harvested, 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 type of the harvest (e.g., clear-cut versus partial cut), as well as the conditions prior the harvest (e.g., forest type and amount of carbon) (Raymond et al., 2015). 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 (Lippke et al., 2011; McKinley et al., 2011; Skog et al., 2014; Dugan et al., 2018). Therefore, the IPCC recognizes wood as a renewable resource that can provide a mitigation benefit to climate change (IPCC, 2000).

ForCaMF helps to identify the biggest local influences on continued carbon storage and puts the recent effects of those influences into perspective. Factors such as stand age, drought, and climate may affect overall carbon change in ways that are independent of disturbance trends. The purpose of the InTEC model was to reconcile recent disturbance impacts with these other factors.

3.2 Effects of Forest Aging InTEC models the collective effects of forest disturbances and management, aging, mortality, and subsequent regrowth on carbon stocks from 1950 to 2011. The model uses inventory-derived maps of stand age, Landsat-derived disturbance maps (Fig. 6), and equations describing the relationship between net primary productivity (NPP) and stand age. Stand age serves as a proxy for past disturbances and management activities (Pan et al., 2011a). In the model, when a forested stand is disturbed by a severe, stand-replacing event, the age of the stand resets to zero and the forest begins to regrow. Thus, peaks of stand establishment can indicate stand-replacing disturbance events that subsequently promoted regeneration.

Stand-age distribution for the Sumter NFs derived from 2011 forest inventory data indicates elevated stand establishment around 1910–1940 (Fig. 9a). This period likely reflects regeneration following decades of intensive logging and agricultural use in the late 1800s and early 1900s (Foster, 2006). There was a second pulse in the period of roughly 1970-1990 which likely reflects regrowth following a large increase in timber removals across national forests in the Southern Region. 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 (Pregitzer & Euskirchen, 2004; He et al., 2012), as indicated by the in NPP-age curves (Fig. 9b), derived in part from FIA data.

InTEC model, which was run for all the FMS NF, indicates show that forests were accumulating carbon steadily at the start of the analysis in the 1950s through the 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 the 1970s and 1980s, along with mortality associated with Hurricane Hugo in 1989 in the Francis Marion NF and increased harvesting across the FMS, the rate of carbon accumulation declined (negative slope). While forest regrowth and aging following historical

13 disturbances (early 1900s harvesting and land-use change), have collectively played an important role in carbon accumulation trends since 1950 in the FMS NFs (Fig. 10), the effects of non- disturbance factors have become more important in influencing carbon trends on the forest.

18% 16% Elm Ash cottonwood Oak/Gum/Cypress 14% Oak/Hickory 12% Oak/pine 10% Loblolly/shortleaf pine 8% Longleaf/slash pine White/Red/Jack pine 6% 4% 2%

0-9

10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

110-119 120-129 130-139 140-149 150-159 160-169 180-189 100-109 Figure 9. (a) Stand age distribution in 2011 by forest type group in Sumter National Forest. Derived from forest inventory data.

Figure 9. (b) Net primary productivity-stand age curves by forest type group in Francis Marion and Sumter National Forests. Derived from forest inventory data and He et al. 2012.

3.3 Effects of Climate and Environment The InTEC model also isolates the effects of climate (temperature and precipitation), atmospheric CO2 concentrations, and nitrogen deposition on forest carbon stock change and accumulation. Generally annual precipitation and temperature conditions fluctuate considerably. The modeled effects of variability in temperature and precipitation on carbon stocks has varied from year-to-year, but overall, climate since 1950 has had a small positive effect on carbon stocks in the FMS NFs (Fig. 10). Warmer temperatures can increase forest carbon emissions through enhanced soil microbial activity and higher respiration (Ju et al., 2007; Melillo et al.,

2017), but warming temperatures can also reduce soil moisture through increased evapotranspiration, causing lower forest growth (Xu et al., 2013).

In addition to climate, the availability of CO2 and nitrogen can alter forest growth rates and subsequent carbon uptake and accumulation (Caspersen et al., 2000; Pan et al., 2009). Increased fossil fuel combustion, expansion of agriculture, and urbanization have caused a significant increase in both CO2 and nitrogen emissions (Chen et al., 2000; Keeling et al., 2009; Zhang et al., 2012). According to the InTEC model, higher CO2 has consistently had a positive effect on carbon stocks in the FMS NFs, 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 (Jones et al., 2014; Zhang et al., 2015). Long-term studies examining increased atmospheric CO2 show that forests initially respond with higher productivity and growth, but the effect is greatly diminished or lost within 5 years in most forests (Zhu et al., 2016). There has been considerable debate regarding the effects of elevated CO2 on forest growth and biomass accumulation, thus warranting additional study (Körner et al., 2005; Norby et al., 2010; Zhu et al., 2016).

Figure 10. Accumulated carbon in FMS National Forests due to disturbance/aging, climate, nitrogen deposition, CO2 fertilization, and all factors combined (shown in black line) for1950–2011, excluding carbon accumulated pre-1950 Estimated using the InTEC model.

Modeled estimates suggest that overall nitrogen deposition had a positive effect on carbon accumulation in the FMS NFs (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 (Thomas et al., 2010). 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 (Pardo et al., 2011). Some regional studies have documented negative effects on forest productivity associated with chronically high levels of nitrogen deposition in the eastern United States (Aber et al., 1998; Boggs et al., 2005; Pardo et al., 2011). The InTEC model simulated that rates of carbon accumulation associated with nitrogen deposition decreased as deposition

15 rates declined. Overall, the InTEC model suggests that CO2 and nitrogen fertilization partially 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. ForCaMF results may also incorporate errors from the inventory data and the FVS-derived carbon accumulation functions (Raymond et al., 2015). To quantify uncertainties, the ForCaMF model employed a Monte Carlo-based approach to supply 95 percent confidence intervals around estimates (Healey et al., 2014).

Uncertainty analyses such as the Monte Carlo 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 (Zaehle et al., 2005). 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 assumptions (Schimel et al., 2015), as well as calibration with observational datasets (Zhang et al., 2012) suggest that model results produce a reasonable range of estimates of the total effect (e.g., Fig. 10, “All effects”). 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.

Results from the ForCaMF and InTEC models may differ substantially from baseline estimates (CCT), given the application of different datasets, modeling approaches, and parameters (Zhang et al., 2012; Dugan et al., 2017). The baseline estimates are almost entirely rooted in empirical forest inventory data, whereas ForCaMF and InTEC involve additional data inputs and modeling complexity beyond summarizing ground data.

16 soil carbon stocks continue to accumulate (Luyssaert et al., 2008). Furthermore, while past and present aging trends can inform future conditions, the applicability may be limited, because potential changes in management activities or disturbances could affect future stand age and forest growth rates (Davis et al., 2009; Keyser & Zarnoch, 2012). The RPA assessment provides regional projections of forest carbon trends across forestland ownerships in the United States based on a new approach that uses the annual inventory to estimate carbon stocks retrospectively to 1990 and forward to 2060 (Woodall et al., 2015; USDA Forest Service, 2016). The RPA reference scenario assumes forest area in the U.S. will continue to expand at current rates until 2022, when it will begin to decline due to land use change. However, 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 began to decline Figure 11. Projections of forest carbon stock changes in the Southern since Region (equivalent to the boundaries of Southern Region, but includes all approximately land tenures) for the RPA reference scenario. Net sequestration of forests is 2010 and will the total carbon stock change minus losses associated with land-use continue to change. decline through 2060, but at a slower rate in the middle of the century. This decline in the carbon sink is mostly due to the loss of forestland (land-use transfer), and to a lesser extent through forest aging and increased disturbances (net sequestration) (Fig. 11). At the global and national scales, changes in land use—especially the conversion of forests to non-forest land (deforestation)—have a substantial effect on carbon stocks (Pan et al., 2011a; Houghton et al., 2012). Converting forest land to a non-forest use removes a large amount of carbon from the forest and inhibits future carbon sequestration. National forests tend to experience low rates of land-use change, and thus, forest land area is not expected to change substantially within the FMS NF in the future. Therefore, on national forest lands, the projected carbon trends may closely resemble the “net sequestration” trend in Fig. 11, which isolates the effects of forest aging, disturbance, mortality, and growth from land-use transfers and indicates a 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 the role of natural forest development and succession as the major driver of historic and current forest carbon sequestration that is occurring at the FMS NFs and elsewhere across the region.

Climate change introduces additional uncertainty about how forests—and forest carbon sequestration and storage—may change in the future. Climate change causes many direct alterations of the local environment, such as changes in temperature and precipitation, and it has indirect effects on a wide range of ecosystem processes (Vose et al., 2012). Further, disturbance rates are projected to increase with climate change (Vose et al., 2018) making it challenging to use past trends to project the effects of disturbance and aging on forest carbon dynamics.

According to regional projections from the National Climate Assessment, 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 (Carter et al., 2014 and McNulty et al. 2015). Forests in the Sumter NF may be at increased risk to damage from extreme weather events such as more frequent and intense hurricanes that are projected under a warming climate. 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.Error! Bookmark not defined. Error! Bookmark not defined.

Elevated temperatures may increase soil respiration and reduce soil moisture through increased evapotranspiration, which would negatively affect growth rates and carbon accumulation (Ju et al., 2007; Melillo et al., 2017). 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 NFs (Fig. 10). Heat stress is also projected to limit growth and carbon uptake of some Southern pines and hardwood species (McNulty et al. 2015).Error! Bookmark not defined.Error! 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 pathogens (Ayres and Lombardero, 2000), which can significantly reduce carbon uptake (D’Amato et al., 2011; Healey et al., 2016; Kurz et al., 2008).

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 (McKenney et al., 2007).

Carbon dioxide emissions are projected to increase through 2100 under even the most conservative emission scenarios (IPCC, 2014). Several models, including the InTEC model

(Figure 10), project greater increases in forest productivity when the CO2 fertilization effect is included in modeling (Aber et al., 1995; Ollinger et al., 2008; Pan et al., 2009; Zhang et al., 2012). However, the effect of increasing levels of atmospheric CO2 on forest productivity is transient and can be limited by the availability of nitrogen and other nutrients (Norby et al., 2010). 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, and nutrients, it is difficult to project how forests and carbon trends will respond to novel future conditions. 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 some variables like increasing 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. Thus, it will be important for forest managers to continue to monitor forest responses to these changes and potentially alter management activities to better enable forests to better adapt to future conditions. 5.0 Summary Forests in the Sumter NF are maintaining a carbon sink. Forest carbon stocks 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 has been the most prevalent disturbance detected on the Forest since 1990. However, harvests during this period have been relatively small. Forest carbon losses associated with harvests on the FMS NFs have been small compared to the total amount of carbon stored in the Forests, resulting in a loss of about 5.3 percent of non- soil carbon from 1990 to 2011. 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 sourced from national forests increased since the early 1900s. Recent declines in timber harvesting have slowed the rate of carbon accumulation in the product sector.

The biggest influence on current carbon dynamics on the Sumter NF is the legacy of intensive timber harvesting and land clearing for agriculture during the 19th century, followed by a period of forest recovery and more sustainable forest management beginning in the early to mid-20th century, which continues to promote a carbon sink today (Birdsey et al., 2006). However, stands on the Sumter NF are now mostly young to middle aged. The rates of carbon uptake and sequestration generally decline as forests age. Thus, the age structure of the Sumter NF suggests that the Forest may continue to act as a carbon sink for several decades.

Climate and environmental factors, including elevated atmospheric CO2 and nitrogen deposition, have also influenced carbon accumulation on the Sumter NF. 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 Sumter NF may be increasingly vulnerable to a variety of stressors. These potentially negative effects might be balanced somewhat by the positive effects of longer growing season, greater precipitation, and elevated atmospheric CO2 concentrations. However, it is difficult to judge how these factors and their interactions will affect future carbon dynamics on the Sumter NF.

Forested area on the Sumter NF will be maintained as forest in the foreseeable future, which will allow for a continuation of carbon uptake and storage over the long term. Across the broader region, the population is growing, and some land conversion for development on private ownerships is likely. The Sumter NF will continue to have an important role in maintaining the carbon sink, regionally and nationally, for decades to come. 6.0 References Aber, J., McDowell, W., Nadelhoffer, K., Magill, A., Berntson, G., Kamakea, M., McNulty, S., Currie, W., Rustad, L. & Fernandez, I. (1998) Nitrogen saturation in temperate forest ecosystems. BioScience, 921-934. Aber, J.D., Ollinger, S.V., Fédérer, C.A., Reich, P.B., Goulden, M.L., Kicklighter, D.W., Melillo, J. & Lathrop, R. (1995) Predicting the effects of climate change on water yield and forest production in the northeastern United States. 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. Berg, A., Sheffield, J. & Milly, P.C.D. (2017) Divergent surface and total soil moisture projections under global warming. Geophysical Research Letters, 44, 236-244. 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. Birdsey, R., Pregitzer, K. & Lucier, A. (2006) Forest carbon management in the United States: 1600-2100. Journal of Environmental Quality, 35, 1461-1469. Boggs, J.L., McNulty, S.G., Gavazzi, M.J. & Myers, J.M. (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. 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. Caspersen, J.P., Pacala, S.W., Jenkins, J.C., Hurtt, G.C., Moorcroft, P.R. & Birdsey, R.A. (2000) Contributions of land-use history to carbon accumulation in U.S. Forests. Science, 290, 1148-1151. Chen, W., Chen, J. & Cihlar, J. (2000) An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry. Ecological Modelling, 135, 55-79.

Crookston, N.L. & Dixon, G.E. (2005) The forest vegetation simulator: A review of its structure, content, and applications. Computers and Electronics in Agriculture, 49, 60-80. D’Amato, A.W., Bradford, J.B., Fraver, S. & Palik, B.J. (2011) Forest management for mitigation and adaptation to climate change: Insights from long-term silviculture experiments. Forest Ecology and Management, 262, 803-816. Davis, S.C., Hessl, A.E., Scott, C.J., Adams, M.B. & Thomas, R.B. (2009) Forest carbon sequestration changes in response to timber harvest. Forest Ecology and Management, 258, 2101-2109. Domke, G., Perry, C., Walters, B., Nave, L., Woodall, C. & Swanston, C. (2017) Toward inventory‐based estimates of soil organic carbon in forests of the United States. Ecological Applications, 27, 1223-1235. Dugan, A.J., Birdsey, R., Mascorro, V.S., Magnan, M., Smyth, C.E., Olguin, M. & Kurz, W.A. (2018) A systems approach to assess climate change mitigation options in landscapes of the United States forest sector. Carbon Balance and Management, 13, doi.org/10.1186/s13021-018-0100-x. Dugan, A.J., Birdsey, R., Healey, S.P., Pan, Y., Zhang, F., Mo, G., Chen, J., Woodall, C.W., Hernandez, A.J. & McCullough, K. (2017) Forest sector carbon analyses support land management planning and projects: assessing the influence of anthropogenic and natural factors. Climatic Change, 144, 207-220. FAOSTAT (2013) Food and agriculture organization of the United Nations. Statistical database. Gustavsson, L., Madlener, R., Hoen, H. F., Jungmeier, G., Karjalainen, T., Klöhn, S., . . . Spelter, H. (2006) The role of wood material for greenhouse gas mitigation. Mitigation and Adaptation Strategies for Global Change, 11, 1097-1127. Foster, D.R. (2006) Forests in time: the environmental consequences of 1,000 years of change in New England. Yale University Press. Hayes, D.J., Vargas, R., Alin, S.R., Conant, R.T., Hutyra, L.R., Jacobson, A.R., Kurz, W.A., Liu, S., McGuire, A.D., Poulter, B. & Woodall, C.W. (2018) Chapter 2: The North American carbon budget. Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report (ed. by N. Cavallaro, G. Shrestha, Birdsey, R. M. A. Mayes, R.G. Najjar, S.C. Reed, P. Romero-Lankao and Z. Zhu), pp. 71-108. U.S. Global Change Research Program, Washington, DC. He, L., Chen, J.M., Pan, Y., Birdsey, R. & Kattge, J. (2012) Relationships between net primary productivity and forest stand age in U.S. forests. Global Biogeochemical Cycles, 26 (GB3009). doi:10.1029/2010GB003942. Healey, S.P., Urbanski, S.P., Patterson, P.L. & Garrard, C. (2014) A framework for simulating map error in ecosystem models. Remote Sensing of Environment, 150, 207-217. Healey, S.P., Raymond, C.L., Lockman, I.B., Hernandez, A.J., Garrard, C. & Huang, C. (2016) Root disease can rival fire and harvest in reducing forest carbon storage. Ecosphere, 7, Article e01569. Healey, S.P., Cohen, W.B., Yang, Z., Kenneth Brewer, C., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Joseph Hughes, M., Kennedy, R.E., Loveland, T.R., Moisen, G.G., Schroeder, T.A., Stehman, S.V., Vogelmann, J.E., Woodcock, C.E., Yang, L. & Zhu, Z. (2018) Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment, 204, 717-728.

Houghton, R.A., House, J.I., Pongratz, J., Van Der Werf, G.R., Defries, R.S., Hansen, M.C., Le Quéré, C. & Ramankutty, N. (2012) Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125-5142. IPCC (2000) Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change, Summary for Policy Makers, Geneva, Switzerland. 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. (ed. by R.K. Pachauri and L.A. Meyer), p. 151. Intergovernmental Panel on Climate Change, Geneva, Switzerland. Johnson, C. and D. Govatski. 2013. Forests for the people: the story of America’s eastern national forests. Island Press, Washington, D.C. Jones, A.G., Scullion, J., Ostle, N., Levy, P.E. & Gwynn-Jones, D. (2014) Completing the FACE of elevated CO2 research. Environment International, 73, 252-258. Ju, W.M., Chen, J.M., Harvey, D. & Wang, S. (2007) Future carbon balance of China's forests under climate change and increasing CO2. Journal of Environmental Management, 85, 538-562. Keeling, R., Piper, S., Bollenbacher, A. & Walker, S. (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. In: Carbon Dioxide Research Group Scripps Institution of Oceanography (SIO), University of California, La Jolla. California USA. URL: http://cdiac. ornl. gov/ftp/trends/co2/maunaloa. co2. Oak Ridge Natl. Lab., U.S. Dep. of Energy, Oak Ridge, TN. Keyser, T.L. & Zarnoch, S.J. (2012) Thinning, age, and site quality influence live tree carbon stocks in upland hardwood forests of the southern appalachians. Forest Science, 58, 407- 418. Körner, C., Asshoff, R., Bignucolo, O., Hättenschwiler, S., Keel, S.G., Peláez-Riedl, S., Pepin, S., Siegwolf, R.T.W. & Zotz, G. (2005) Ecology: Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2. Science, 309, 1360-1362. Kurz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L., Ebata, T. & Safranyik, L. (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature, 452, 987-990. Lippke, B., Oneil, E., Harrison, R., Skog, K., Gustavsson, L. & Sathre, R. (2011) Life cycle impacts of forest management and wood utilization on carbon mitigation: knowns and unknowns. Carbon Management, 2, 303-333. Loeffler, D., Anderson, N., Stockmann, K., Skog, K., Healey, S., Jones, J.G., Morrison, J. & Young, J. (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. Luyssaert, S., Schulze, E.D., Börner, A., Knohl, A., Hessenmöller, D., Law, B.E., Ciais, P. & Grace, J. (2008) Old-growth forests as global carbon sinks. Nature, 455, 213-215. 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. McKinley, D.C., Ryan, M.G., Birdsey, R.A., Giardina, C.P., Harmon, M.E., Heath, L.S., Houghton, R.A., Jackson, R.B., Morrison, J.F., Murray, B.C., Pataki, D.E. & Skog, K.E.

(2011) A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 21, 1902-1924. McMahon, S.M., Parker, G.G. & Miller, D.R. (2010) Evidence for a recent increase in forest growth. Proceedings of the National Academy of Sciences, 107, 3611-3615. 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. Melillo, J.M., Frey, S.D., DeAngelis, K.M., Werner, W.J., Bernard, M.J., Bowles, F.P., Pold, G., Knorr, M.A. & Grandy, A.S. (2017) Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358, 101-105. Norby, R.J., Warren, J.M., Iversen, C.M., Medlyn, B.E. & McMurtrie, R.E. (2010) CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences, 107, 19368-19373. Ollinger, S., Goodale, C., Hayhoe, K. & Jenkins, J. (2008) Potential effects of climate change and rising CO2 on ecosystem processes in northeastern U.S. forests. Mitigation and Adaptation Strategies for Global Change, 13, 467-485. Pan, Y., Birdsey, R., Hom, J. & McCullough, K. (2009) Separating effects of changes in atmospheric composition, climate and land-use on carbon sequestration of US Mid- Atlantic temperate forests. Forest Ecology and Management, 259, 151-164. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., Ciais, P., Jackson, R.B., Pacala, S.W., McGuire, A.D., Piao, S., Rautiainen, A., Sitch, S. & Hayes, D. (2011b) A large and persistent carbon sink in the world's forests. Science, 333, 988-993. Pan, Y., Chen, J.M., Birdsey, R., McCullough, K., He, L. & Deng, F. (2011a) Age structure and disturbance legacy of North American forests. Biogeosciences, 8, 715-732. Pardo, L.H., Fenn, M.E., Goodale, C.L., Geiser, L.H., Driscoll, C. T., Allen, E.B. …Dennis, R.L. (2011) Effects of nitrogen deposition and empirical nitrogen critical loads for ecoregions of the United States. Ecological Applications, 21, 3049-3082. Pregitzer, K.S. & Euskirchen, E.S. (2004) Carbon cycling and storage in world forests: biome patterns related to forest age. Global change biology, 10, 2052-2077. Raymond, C.L., Healey, S., Peduzzi, A. & Patterson, P. (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. Schimel, D., Stephens, B.B. & Fisher, J.B. (2015) Effect of increasing CO2 on the terrestrial carbon cycle. Proceedings of the National Academy of Sciences of the United States of America, 112, 436-441. Shands, W. (1992) The Lands Nobody Wanted: The Legacy of the Eastern National Forests. The origins of the National Forests. In. Pinchot Institute for Conservation Studies. Shifley, S.R. & Moser, W.K. (2016) Future forests of the northern United States. Gen. Tech. Rep. NRS-151. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 388 p. Skog, K.E., McKinley, D.C., Birdsey, R.A., Hines, S.J., Woodall, C.W., Reinhardt, E.D. & Vose, J.M. (2014) Chapter 7: Managing Carbon. In: Climate Change and United States Forests, Advances in Global Change Research, 57, 151-182.

Smith, J.E., Heath, L.S. & Nichols, M.C. (2007) US forest carbon calculation tool: forest-land carbon stocks and net annual stock change. Revised. Gen. Tech. Rep. NRS-13. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 34 p. Smith, J.E., Heath, L.S., Skog, K.E. & Birdsey, R.A. (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. Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E.A., Haberl, H. & et al. (2014) Agriculture, Forestry and Other Land Use (AFOLU). Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (ed. by O. Edenhofer, R. Pichs-Madruga, Y. Sokona and et al.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Thomas, R.Q., Canham, C.D., Weathers, K.C. & Goodale, C.L. (2010) Increased tree carbon storage in response to nitrogen deposition in the US. Nature Geoscience, 3, 13-17. Thompson, J.R., Foster, D.R., Scheller, R. & Kittredge, D. (2011) The influence of land use and climate change on forest biomass and composition in Massachusetts, USA. Ecological Applications, 21, 2425-2444. US EPA (2015) Executive Summary. US inventory of greenhouse gas emissions and sinks: 1990 – 2013. U.S. Environmental Protection Agency, Washington, DC. 27 pp. USDA Forest Service (2015) Baseline estimates of carbon stocks in forests and harvested wood products for National Forest System Units, Southern Region. 44 pp. USDA Forest Service (2016) Future of America's Forests and Rangelands: Update to the 2010 Resources Planning Act Assessment. Gen. Tech. Report WO-GTR-94. Washington, DC. 250 p. Vose, J.M., Peterson, D.L. & Patel-Weynand, T. (2012) Effects of climatic variability and change on forest ecosystems: a comprehensive science synthesis for the U.S. forest sector. Gen. Tech. Rep. PNW-GTR-870. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 265 p. Vose, J.M., Peterson, D.L., Domke, G.M., Fettig, C.J., Joyce, L.A., Keane, R.E., Luce, C.H., Prestemon, J.P., Band, L.E., Clark, J.S., Cooley, N.E., D’Amato, A. & Halofsky, J.E. (2018) Chapter 6: Forests. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II (ed. by D.R. Reidmiller, C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock and B.C. Stewart), US Global Change Research Program, Washington, DC, USA, 232-267. Wear, D. N., Huggett, R., Li, R., Perryman, B., Liu, S. (2013) Forecasts of forest conditions in regions of the United States under future scenarios: a technical document supporting the Forest Service 2012 RPA Assessment. Gen. Tech. Rep. SRS-GTR-170. Asheville, NC: USDA-Forest Service, Southern Research Station. 101 p. Williams, M. 1989. Americans and Their Forests: An Historical Geography. New York: Cambridge University Press. Woodall, C.W., Smith, J.E. & Nichols, M.C. (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. U.S. Department of Agriculture, Forest Service, Northern Research Station. 23 p.

Woodall, C.W., Heath, L.S., Domke, G.M. & Nichols, M.C. (2011) Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p. Woodall, C.W., Coulston, J.W., Domke, G.M., Walters, B.F., Wear, D.N., Smith, J.E., Andersen, H.-E., Clough, B.J., Cohen, W.B., Griffith, D.M., Hagen, S.C., Hanou, I.S., Nichols, M.C., Perry, C.H.H., Russell, M.B., Westfall, J. & Wilson, B.T.T. (2015) The U.S. forest carbon accounting framework: stocks and stock change, 1990-2016. Gen. Tech. Rep. NRS-154. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 49 p. Xu, W., Yuan, W., Dong, W., Xia, J., Liu, D. & Chen, Y. (2013) A meta-analysis of the response of soil moisture to experimental warming. Environmental Research Letters, 8, 044027 (8pp) Zaehle, S., Sitch, S., Smith, B. & Hatterman, F. (2005) Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochemical Cycles, 19, 1-16. Zhang, F., Chen, J.M., Pan, Y., Birdsey, R.A., Shen, S., Ju, W. & He, L. (2012) Attributing carbon changes in conterminous U.S. forests to disturbance and non-disturbance factors from 1901 to 2010. Journal of Geophysical Research: Biogeosciences, 117, 18 p. Zhang, F., Chen, J.M., Pan, Y., Birdsey, R.A., Shen, S., Ju, W. & Dugan, A.J. (2015) Impacts of inadequate historical disturbance data in the early twentieth century on modeling recent carbon dynamics (1951-2010) in conterminous U.S. forests. Journal of Geophysical Research: Biogeosciences, 120, 549-569. Zhao, T. & Dai, A. (2017) Uncertainties in historical changes and future projections of drought. Part II: model-simulated historical and future drought changes. Climatic Change, 144, 535-548. Zhu, Z., Piao, S., Myneni, R.B., Huang, M., Zeng, Z., Canadell, J.G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peuelas, J., Poulter, B., Pugh, T.A.M., Stocker, B.D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S. & Zeng, N. (2016) Greening of the Earth and its drivers. Nature Climate Change, 6, 791-795.

Forest Carbon Assessment for the Southern Region.

Alexa Dugan ([email protected]), Duncan McKinley ([email protected]) Patti Turpin ([email protected])

March 2020

Contents 1.0 Introduction ...... 2 Box 1. Description of the primary forest carbon models used to conduct this carbon assessment ...... 3 1.1 Background ...... 4 Box 2. Carbon Units...... 4 2.0 Baseline Carbon Stocks and Flux ...... 5 2.1 Forest Carbon Stocks and Stock Change ...... 5 2.2 Uncertainty associated with baseline forest carbon estimates ...... 7 2.3 Carbon in Harvested Wood Products ...... 8 2.4 Uncertainty associated with estimates of carbon in harvested wood products ...... 9 3.0 Factors Influencing Forest Carbon ...... 10 3.1 Effects of Disturbance ...... 10 3.2 Effects of Forest Aging ...... 13 3.3 Effects of Climate and Environment ...... 15 3.4 Uncertainty associated with disturbance effects and environmental factors ...... 16 4.0 Future Carbon Conditions ...... 17 4.1 Prospective Forest Aging Effects ...... 17 4.2 Prospective Climate and Environmental Effects ...... 18 5.0 Summary ...... 19 6.0 References ...... 20

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 (Pan et al., 2011a). Forests in the U.S. remove the equivalent of about 12 percent of annual U.S. fossil fuel emissions or about 206 teragrams of carbon after accounting for natural emissions, such as wildfire and decomposition (US EPA, 2015; Hayes et al., 2018).

The Intergovernmental Panel on Climate Change (IPCC) has summarized the contributions of global human activity sectors to climate change in its Fifth Assessment Report (IPCC, 2014). From 2000 to 2009, forestry and other land uses contributed just 12 percent of human-caused 1 global CO2 emissions. The forestry sector contribution to GHG emissions has declined over the last decade (FAOSTAT, 2013; IPCC, 2014; Smith et al., 2014). Globally, the largest source of GHG emissions in the forestry sector is deforestation (Pan et al., 2011a; Houghton et al., 2012; IPCC, 2014), 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 (IPCC, 2000). However, the U.S. 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 (Birdsey et al., 2006), a

trend expected to Box 1. Description of the primary forest carbon models used to continue for at least conduct this carbon assessment another decade (Wear et al., 2013; Birdsey et al, Carbon Calculation Tool (CCT) 2016).

Estimates annual carbon stocks and stock change from 1990 to In this section, we 2013 by summarizing data from two or more Forest Inventory provide an assessment of and Analysis (FIA) survey years. CCT relies on allometric the amount of carbon models to convert tree measurements to biomass and carbon. stored on the Southern Forest Carbon Management Framework (ForCaMF) Region and how disturbances, Integrates FIA data, Landsat-derived maps of disturbance type management, and and severity, and an empirical forest dynamics model, the environmental factors Forest Vegetation Simulator (FVS), to assess the relative have influenced carbon impacts of disturbances (harvests, insects, fire, abiotic, storage overtime. This disease). ForCaMF estimates how much more carbon (non- assessment primarily used soil) would be on each national forest if disturbances from two recent U.S. Forest 1990 to 2011 had not occurred. Service reports: the Integrated Terrestrial Ecosystem Carbon (InTEC) model Baseline Report (USDA Forest Service, 2015) and A process-based model that integrates FIA data, Landsat- Disturbance Report derived disturbance maps, as well as measurements of climate (Birdsey, et al., 2019). variables, nitrogen deposition, and atmospheric CO2. InTEC Both reports relied on estimates the relative effects of aging, disturbance, regrowth, Forest Inventory and and other factors including climate, CO2 fertilization, and Analysis (FIA) and nitrogen deposition on carbon accumulation from 1950 to several validated, data- 2011. Carbon stock and stock change estimates reported by driven modeling tools to InTEC are likely to differ from those reported by CCT provide nationally because of the different data inputs and modeling processes. consistent evaluations of forest carbon trends across the National Forest System (NFS). The Baseline Report applies the CCT (Smith et al., 2007), 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 ForCaMF (Healey et al., 2014; Raymond et al., 2015; Healey et al., 2016). 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 InTEC model (Chen et al., 2000; Zhang et al., 2012). See Box 1 for descriptions of the carbon models used for these analyses. Additional reports, including the most recent Resource Planning Act (RPA) assessment (Birdsey et al., 2016) and regional climate vulnerability assessments (Wear and Greis, 2012; McNulty et al., 2015; USDA Forest Service, 2016; Vose and Klepzig, 2013) are used to help infer future forest carbon dynamics. Collectively, these reports

incorporate advances in data and analytical methods, representing the best available science to provide comprehensive assessments of NFS carbon trends.

1.1 Background The Southern Region (also referred to as Region 8) in the NFS contains 14 national forests or groups of national forests (5.4 million hectares or 13.3 million acres) which have been administratively combined, spread across 13 States in the southeastern United States from Virginia to Florida and as far west as Texas and Oklahoma, as well as a national forest in Puerto Rico. The history of development, land use, and policies provides useful context for understanding the regional forest C trends that are indicated by the inventory and assessment results. 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. Oak-Hickory, Oak-Pine, and Loblolly Pine-Shortleaf Pine forest types are the most abundant across the Southern Region according to FIA data. The timber industry soon became centered in the South, and by 1919 the region was producing 37 percent of U.S. lumber (Williams, 1989). During this period, most of the remaining primary forests of the South were harvested, and much of the previous forest land was replaced with Box 2. Carbon Units. The following table provides a crosswalk agriculture and among various metric measurements units used in the assessment of grazing. carbon stocks and emissions. After passage of Tonnes Grams the Weeks Act in Multiple Name Symbol Multiple Name Symbol 1911, the Forest 100 Gram G Service began 103 kilogram Kg buying large areas 100 tonne t 106 Megagram Mg of these heavily 103 kilotonne Kt 109 Gigagram Gg cut-over and sub 106 Megatonne Mt 1012 Teragram Tg marginal lands, 109 Gigatonne Gt 1015 Petagram Pg referred to as “the 1012 Teratonne Tt 1018 Exagrame Eg lands nobody 1015 Petatonne Pt 1021 Zettagram Zg wanted” (Shands, 1018 Exatonne Et 1024 yottagram Yg 1992), throughout 1 hectare (ha) = 0.01 km2 = 2.471 acres = 0.00386 mi2 the eastern United 1 Mg carbon = 1 tonne carbon = 1.1023 short tons (U.S.) carbon States. By 1920 1 General Sherman Sequoia tree = 1,200 Mg (tonnes) carbon approximately 2 million acres 1 Mg carbon mass = 1 tonne carbon mass = 3.67 tonnes CO2 mass (800,000 hectares) A typical passenger vehicle emits about 4.6 tonnes CO2 a year of excessively logged and degraded land was purchased as national forest land. Soon forest restoration and recovery became the new goal. With help from the Civilian Conservation Corps (CCC), millions of trees were planted, erosion was controlled, and forest fires were fought (Williams, 2003). The increased effort to suppress fires allowed forests to regrow, which increased stocking of trees. As these forests were restored, the timber industry was also revived. From 1936 to the mid-1950s, timber harvests increased steadily and peaked in the mid-1980s before declining steeply in the 1990s into the 2000s (Loeffler et al., 2014). The restoration of southern national forests not only is a conservation success story but also shaped the legacy of forest C dynamics in Region 8.

In addition to land use and policies, natural disturbances have played an important role in the forest C trends in the Southern Region. Except for timber harvesting, fire is the most common disturbance affecting southern forests. Major wildfires occurred during the droughts of the 1930s and 1950s. As research and experience increased understanding of the important role of fire in forest ecosystems, prescribed fire was again used in the South in recent decades to reduce hazardous fuels. Despite prescribed burning to keep hazardous fuels in check, wildfires—most of them human caused (Stanturf et al., 2002)—are still common in the South. The Southern Region also experiences tropical storms and hurricanes that can significantly affect forests. For instance, in 1989 Hurricane Hugo devastated some 3.5 million acres (1.4 million hectares) of the Francis Marion National Forest (Sheffield and Thompson, 1992). Outbreaks of insects such as the southern pine beetle and gypsy moth have also impacted forest productivity, C cycling, and overall ecosystem health.

2.0 Baseline Carbon Stocks and Flux 2.1 Forest Carbon Stocks and Stock Change According to results of the Baseline Report (USDA Forest Service, 2015), carbon stocks in the Southern Region increased from 703.76 teragrams of carbon (Tg C) in 1990 to 912.4 Tg C in 2013, a 29.7 percent increase in carbon stocks over this period (Fig. 1). For context, 912.4 Tg C is equivalent to the emissions from approximately 727.9 million passenger vehicles in a year. Despite some uncertainty in annual carbon stock estimates, there is a high degree of certainty that carbon stocks in the Southern Region have increased from 1990 to 2013 (Fig. 1). The aboveground portion of live trees, which includes all live woody vegetation at least one inch in diameter (Fig. 2) is the largest carbon pool, storing another 43 percent of the forest carbon stocks. About 35 percent of forest carbon stocks in the Southern Region are stored in the soil carbon contained in organic material to a depth of one meter (excluding roots). Figure 1. Total forest carbon stocks (Tg) from 1990 to 2013 for Recently, new methods Southern Region. Confidence intervals were not calculate for for measuring soil carbon regional summaries or combined forests. Estimated using the have found that the CCT model. amount of carbon stored in soils generally exceeds the estimates derived from using the methods of the CCT model by roughly 12 percent across forests in the U.S. (Domke et al., 2017).

The annual carbon stock change can be used to evaluate whether a forest and/or region is a carbon sink or source in a given year. Carbon stock change is typically reported from the perspective of the atmosphere. A negative value indicates a carbon sink: the forest is absorbing more carbon from the atmosphere (through growth) than it emits (via decomposition, removal, and combustion). A positive value indicates a source: the forest is emitting more carbon than it takes up.

Annual carbon stock changes in the Southern Region were -5.5 Tg C per year (gain) in 1990 and -9.5 Tg C per year in 2012 (gain) (Fig. 3). The uncertainty between annual estimates can make it difficult to determine whether the forest and/or region is a

sink or a source in a Figure 2. Percentage of carbon stocks in 2013 in each of the specific year (i.e., forest carbon pools for Southern Region. Estimated using the CCT model. uncertainty bounds overlap zero) (Fig. 3). However, the trend of increasing carbon stocks from 1990 to 2013 (Fig. 1) over the 23-year period suggests that the Southern Region is a modest carbon sink.

Changes in forested area may affect whether forest carbon stocks are increasing or decreasing. The CCT estimates from

Figure 3. Carbon stock change (Tg/yr) from 1990 to 2012 for the Baseline Report are Southern Region. A positive value indicates a carbon source and a based on FIA data, which negative value indicates a carbon sink. Estimated using the CCT may indicate changes in model. 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 Southern Region has increased from 4,991,771 ha in 1990 to 5,666,317 ha in

2013, a net change of 674,546 ha.2 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 and/or region is a carbon source or sink (Woodall et al., 2011).

Carbon density, which is an estimate of forest carbon stocks per unit area, can help identify the effects of changing forested area. In the Southern Region, carbon density increased from about 141 Megagrams of carbon (Mg C) per ha in 1990 to 161 Mg C per ha in 2013 (Fig. 4). This increase in carbon density suggests that total carbon stocks may have indeed increased.

Carbon density is also useful for comparing trends among units or ownerships with different forest areas. 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, and forest types. These differences may also be affected by Figure 4. Average Carbon stock density (Megagrams per disturbance and management hectare) in Southern Region. Estimated using Carbon CCT regimes (see Section 3.0). model. 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. 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 Southern Region has experienced minimal changes in land use or adjustments to the boundaries of the national forests in recent years, the change in forested area incorporated in CCT is more likely a data artefact of altered inventory design and protocols (Woodall et al., 2013).

2 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.

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 five (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 (Woodall et al., 2013). 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.

Carbon accounting for HWP contained in the Baseline Report was conducted by incorporating data on harvests on national forests documented in cut-and-sold reports within a production accounting system (Smith et al., 2006; Loeffler et al., 2014). This approach tracks the entire cycle of carbon, from harvest to timber products to primary wood products to disposal. As more commodities are produced and remain in use, the amount of carbon stored in products increases. As more products are discarded, the carbon stored in solid waste disposal sites (SWDS) (e.g., landfills, dumps) increases. Products in SWDS may continue to store carbon for many decades.

In national forests in the Southern Region, harvest levels remained low until after the start of World War II in the late 1930s, when they began to increase, which caused an increase in carbon storage in HWP (Fig. 5). Timber harvesting Figure 5. Cumulative total carbon (Tg) stored in harvested wood and subsequent products (HWP) sourced from national forests in the Southern Region carbon storage from 1912 to 2013. Carbon in HWP includes products that are still in increased rapidly in use and carbon stored at SWDS. Estimated using the the 1980s. Storage in Intergovernmental Panel on Climate Change production accounting products and approach. landfills reached about 25 Tg C in 2001. However, because of a significant decline in timber harvesting in the late 1990s and early 2000s (to 1950s levels), carbon accumulation in the product sector has slowed, and carbon storage in products in use has declined since it peaked in the late 1990s. Despite this decline, the contribution of national forest timber harvests to the HWP carbon pool exceeds the decay of retired products, causing a net increase in product-sector carbon stocks in the Southern Region. In 2013, the carbon stored in HWP was equivalent to approximately three (3) 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 CH4 (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, which can be significant (Gustavsson et al., 2006). The collective effect of uncertainty was assessed using a Monte Carlo approach. 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 (Loeffler et al., 2014).

1990 to 2011, in terms of the total percentage of Figure 6. Percentage of forest disturbed, by (a) types of forested area disturbed disturbance and (b) by year and magnitude of disturbance (change over the period (Fig. 6a). in canopy cover), for all forests in the Southern Region. Estimated However, according to using annual disturbance maps derived from Landsat satellite the satellite imagery, imagery. timber harvests affected a relatively small area of the Southern Region during this time. In most years, timber harvests affected less than five (5) percent (approximately 247,000 ha) of the total forested area (5,281,370 ha) of the Southern Region in any single year from 1990 to 2011 during this period. The percentage of the forest harvested annually has also stabilized over this 21-year period. Although harvests varied in proportion of trees removed, they generally removed less than 25 percent of canopy cover

(magnitude) (Fig. 6b). The satellite imagery indicates that fire (prescribed and wildfire) was the second most common disturbance in the Southern Region, affecting an average of three (3) percent (135,751 ha) of the forested area per year. Fires affected the greatest area of forestland in 1998 and 2007, although disturbing less than one (1) percent of the forested area in the Southern Region during each of those years. Although fires varied in proportion of trees removed, they generally removed less than one (1) percent of canopy cover (magnitude) (Fig. 6b). The ForCaMF incorporates Landsat disturbance maps summarized in Figure 6, along with FIA data in the FVS (Crookston & Dixon, 2005). 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) (Raymond et al., 2015). The ForCaMF model then compares the undisturbed scenario with the carbon dynamics associated with the historical disturbances to estimate how much more

Figure 7. Lost potential storage of carbon (in megagrams carbon would be on each per hectare) as a result of disturbance for the period 1990- national forest and/or region if 2011 in Southern Region. The zero line represents a the disturbances and harvests hypothetical undisturbed scenario. Gray lines indicate 95% during 1990-2011 had not confidence intervals. Estimated using the Forest Carbon occurred. ForCaMF simulates Management Framework (ForCaMF) model. 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 (Healey et al., 2014).

Timber harvesting in the Southern Region was the primary disturbance influencing carbon stocks from 1990 to 2011 (Fig. 7). The ForCaMF model indicates that, by 2011, Southern Region contained 2.6 Mg C per ha less non-soil carbon (i.e., vegetation and associated pools) due to harvests since 1990, as compared to a hypothetical undisturbed scenario (Fig. 7). As a result, non-soil carbon stocks in the Southern Region would have been approximately 2.4 percent higher in 2011 if harvests had not occurred since 1990 (Fig. 8).

Figure 8. The percent by which 2011 carbon storage on each national forest in the Southern Region was reduced by disturbance from 1990 to 2011 relative to a hypothetical baseline with no disturbance. Estimated using disturbance effects from the ForCaMF and non-soil carbon stock estimates from the CCT.

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, fire accounted for the loss of 0.9 percent of non-soil carbon stocks, insects only 0.2 percent, and abiotic factors (wind, ice storms) only 0.1 percent. The ForCaMF analysis was conducted over a relatively short time. After a forest is harvested, 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 type of the harvest (e.g., clear-cut versus partial cut), as well as the conditions prior the harvest (e.g., forest type and amount of carbon) (Raymond et al., 2015). 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 (Lippke et al., 2011; McKinley et al., 2011; 12

Skog et al., 2014; Dugan et al., 2018). Therefore, the IPCC recognizes wood as a renewable resource that can provide a mitigation benefit to climate change (IPCC, 2000).

3.2 Effects of Forest Aging InTEC models the collective effects of forest disturbances and management, aging, mortality, and subsequent regrowth on carbon stocks from 1950 to 2011. The model uses inventory-derived maps of stand age, Landsat-derived disturbance maps (Fig. 6), and equations describing the relationship between net primary productivity (NPP) and stand age. Stand age serves as a proxy for past disturbances and management activities (Pan et al., 2011b). In the model, when a forested stand is disturbed by a severe, stand-replacing event, the age of the stand resets to zero and the forest begins to regrow. Thus, peaks of stand establishment can indicate stand-replacing disturbance events that subsequently promoted regeneration.

Stand-age distribution for the Southern Region derived from 2011 forest inventory data indicates elevated stand establishment around 1902-1941 (Fig. 9a). This period of elevated stand regeneration came after decades of intensive logging and large wildfires in the late 1800s and early 1900s (Foster, 2006). 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 have been widely observed in eastern U.S. forests (Birdsey et al., 2006). 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 (Pregitzer & Euskirchen, 2004; He et al., 2012), as indicated by the in NPP-age curves (Fig. 9b), derived in part from FIA data.

InTEC model results show that Southern Region was accumulating carbon steadily at the start of the analysis in the 1950s through the mid-1970s (Fig. 10) (orange line; positive slope) as a result of regrowth following disturbances and heightened productivity of the young to middle-aged forests (25-50 years old) (Fig. 9b). As stand establishment declined and more stands reached slower growth stages around the late 1970s, the rate of carbon accumulation declined (negative slope).

Figure 9. (a) Stand age distribution in 2011 by forest type group in Southern Region. Derived from forest inventory data. Y axis is percent of forest type.

Figure 9. (b) Net primary productivity-stand age curves (in megagrams of carbon per hectare per year) by forest type group in the Southern Region. The same curve was used for White/Red/Jack Pine and Spruce/Fir stands. The same curve was used for longleaf/Slash Pine and Other Eastern Softwood stands. Derived from forest inventory data. Y axis is percent of forest type.

fluctuate considerably.

The modeled effects of Figure 10. Accumulated carbon (in teragrams) in the Southern variability in Region due to disturbance/aging, climate, nitrogen deposition, CO2 temperature and fertilization, and all factors combined (shown in black line) for precipitation on carbon 1950–2010, excluding carbon accumulated pre-1950. Estimated stocks has varied from using the InTEC model. year-to-year, but

overall, climate since 1950 has had a small positive effect on carbon stocks in the Southern Region (Fig. 10). That positive climate effect started to decline in the mid-2000s most likely due to warmer temperatures and heat waves. Warmer temperatures can increase forest carbon emissions through enhanced soil microbial activity and higher respiration (Ju et al., 2007; Melillo et al., 2017), but warming temperatures can also reduce soil moisture through increased evapotranspiration, causing lower forest growth (Xu et al., 2013).

In addition to climate, the availability of CO2 and nitrogen can alter forest growth rates and subsequent carbon uptake and accumulation (Caspersen et al., 2000; Pan et al., 2009). Increased fossil fuel combustion, expansion of agriculture, and urbanization have caused a significant increase in both CO2 and nitrogen emissions (Chen et al., 2000; Keeling et al., 2009; Zhang et al., 2012). According to the InTEC model, higher CO2 has consistently had a positive effect on carbon stocks in the Southern Region, 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 (Jones et al., 2014; Zhang et al., 2015). Long-term studies examining increased atmospheric CO2 show that forests initially respond with higher productivity and growth, but the effect is greatly diminished or lost within five (5) years in most forests (Zhu et al., 2016). There has been considerable debate regarding the effects of elevated CO2 on forest growth and biomass accumulation, thus warranting additional study (Körner et al., 2005; Norby et al., 2010; Zhu et al., 2016).

Modeled estimates suggest that overall nitrogen deposition had a positive effect on carbon accumulation in the Southern Region through 1990 and leveled off, staying positive, through to 2013 (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 (Thomas et al., 2010). 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 (Pardo et al., 2011). Some regional studies have documented negative effects on forest productivity associated with chronically high levels of nitrogen deposition in the eastern United States (Aber et al., 1998; Boggs et al., 2005; Pardo et al., 2011). 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 partially 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 C 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. ForCaMF results may also incorporate errors from the inventory data and the FVS-derived carbon accumulation functions (Raymond et al., 2015). To quantify uncertainties, the ForCaMF model employed a Monte Carlo-based approach to supply 95 percent confidence intervals around estimates (Healey et al., 2014).

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 forests of the Southern Region are evenly split between middle-aged and older (greater than and equal to or less than 80 years) (Fig. 9a). If the Southern Region continues on an aging trajectory, more stands will reach a slower growth stage in coming years and decades (Fig. 9b), potentially causing the rate of carbon accumulation to decline and the Southern Region may eventually transition to a steady state in the future. 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 (Luyssaert et al., 2008). Furthermore, while past and present aging trends can inform future conditions, the applicability may be limited, because potential changes in management activities or disturbances could affect future stand age and forest growth rates (Davis et al., 2009; Keyser & Zarnoch, 2012). The RPA assessment provides regional projections of forest carbon trends across forestland ownerships in the U.S. based on a new approach that uses the annual inventory to estimate carbon stocks retrospectively to 1990 and forward to 2060 (Woodall et al., 2015; Birdsey et al., 2016). The RPA reference scenario assumes forest area in the U.S. will continue to expand at current rates until 2022, when it will begin to decline due to land use change. However, 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 the Southern Region boundary, but includes all land ownerships), projections indicate that the rate of carbon sequestration began to decline Figure 11. Projections of forest carbon stock changes in the South Region since (equivalent to the boundaries of Southern Region, but includes all land approximately tenures) for the RPA reference scenario. Net sequestration of forests is the 2010 and will total carbon stock change minus losses associated with land-use change. continue to decline through 2060, but at a slower rate in the middle of the century. This decline in the carbon sink is mostly due to the loss of forestland (land-use transfer), and to a lesser extent through forest aging and increased disturbances (net sequestration) (Fig. 11). At the global and national scales, changes in land use—especially the conversion of forests to non-forest land (deforestation)—have a

substantial effect on carbon stocks (Pan et al., 2011a; Houghton et al., 2012). Converting forest land to a non-forest use removes a large amount of carbon from the forest and inhibits future carbon sequestration. National forests tend to experience low rates of land-use change, and thus, forest land area is not expected to change substantially within the Southern Region in the future. Therefore, on national forest lands, specifically the Southern Region, the projected carbon trends may closely resemble the “net sequestration” trend in Fig. 11, which 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.

A climate change fact sheet for the Southern Region and several of its national forests (Treasure et al., 2014; USDA Forest Service, 2020) indicate forestlands are experiencing increased threats from fire, insect and plant invasions, disease, extreme weather, and drought. Scientists project increases in temperature and changes in rainfall patterns that can make these threats occur more often, with more intensity, and/or for longer durations, which may affect forest carbon dynamics. For instance, elevated temperatures may also increase soil respiration and reduce soil moisture through increased evapotranspiration, which would negatively affect growth rates and carbon accumulation (Ju et al., 2007; Melillo et al., 2017).

Modeled results of recent climate effects using the InTEC model indicate that years with elevated temperatures, such as in the 2000s, have generally had a negative effect on carbon uptake in the Southern Region (Fig. 10). Heat stress is also projected to limit growth and carbon uptake of some Southern pines and hardwood species (McNulty et al., 2015). Mean annual precipitation projections across the Southern Region vary, with projected decreases in the western part and increases in the Southern Appalachian Mountains, although uncertainty remains relatively high. More intense precipitation and extreme storm events are expected to continue increasing in the Southern Region. The potential for reduced soil moisture and drought is also predicted to increase, especially later in the growing season as increased temperatures drive evapotranspiration (Wear and Greis, 2012; Vose and Klepzig, 2013). Although a longer growing season may increase annual biomass accumulation, droughts could offset these potential growth enhancements and increase the potential for other forest stressors. Drought-stressed trees may also be more susceptible to insects and pathogens (Dukes et al., 2009), which can significantly reduce carbon uptake (Kurz et al., 2008; D’Amato et al., 2011).

Changes in climate are expected to drive many other changes in forests through the next century, including changes in forest establishment and composition (Wear and Greis, 2012; Vose and Klepzig, 2013). Some Southern Region tree species are expected to be particularly vulnerable in the future as climate conditions drive declines or failures in species establishment or habitat suitability (Iverson et al., 2017). Model projections suggest that many northern conifer species, including balsam fir, red spruce, and black spruce, are the most vulnerable to climate change— particularly at lower elevations or more southerly locations and at the end of this century. The potential for future declines of northern species increase the risk of carbon losses in forest communities dominated by these species, particularly under scenarios of greater warming (Wear and Greis, 2012; Vose and Klepzig, 2013). Climate-driven failures in species establishment further reduce the ability of forests to recover carbon lost after mortality-inducing events or harvests. Although future climate conditions also allow for other future-adapted species to increase, there is greater uncertainty about how well these species will be able to take advantage of new niches that may become available (Iverson et al., 2017).

Carbon dioxide emissions are projected to increase through 2100 under even the most conservative emission scenarios (IPCC, 2014). Several models, including the InTEC model (Figure 10), project greater increases in forest productivity when the CO2 fertilization effect is included in modeling (Aber et al., 1995; Ollinger et al., 2008; Pan et al., 2009; Zhang et al., 2012). However, the effect of increasing levels of atmospheric CO2 on forest productivity is transient and can be limited by the availability of nitrogen and other nutrients (Norby et al., 2010). Productivity increases under elevated CO2 could be offset by losses from climate-related stress or disturbance.

5.0 Summary Forests in the Southern Region are maintaining a modest carbon sink. Carbon stocks increased by about 29.7 percent on average across the Southern Region between 1990 and 2013, and negative impacts on carbon stocks caused by disturbances and environmental conditions have been modest, not exceeding forest growth. According to satellite imagery, timber harvesting has been the most prevalent disturbance detected within the Southern Region since 1990. However, harvests during this period have been relatively small and low intensity. Forest carbon losses associated with harvests have been small compared to the total amount of carbon stored in the Southern Region, resulting in a loss of about 2.4 percent of non-soil carbon from 1990 to 2011. 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 sourced from national forests within the Southern Region increased since the early 1900s. Recent declines in timber harvesting have slowed the rate of carbon accumulation in the product sector.

The biggest influence on current carbon dynamics in the Southern Region is the legacy of intensive timber harvesting and land clearing for agriculture during the 19th century, followed by a period of forest recovery and more sustainable forest management beginning in the early to mid-20th century, which continues to promote a carbon sink today (Birdsey et al., 2006). However, forests within the Southern Region are becoming older (half are > 80 years of age). The rate of carbon uptake and sequestration generally decline as forests age. Accordingly, projections from the RPA assessment indicate a potential age-related decline in forest carbon stocks in the Southern Region (all land ownerships) beginning in the 2020s.

Climate and environmental factors, including elevated atmospheric CO2 and nitrogen deposition, have also influenced carbon accumulation within the Southern Region. 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 Southern Region may be increasingly vulnerable to a variety of stressors. These potentially negative effects might be balanced somewhat by the positive effects of longer growing season, greater precipitation, and elevated atmospheric CO2 concentrations. However, it is difficult to judge how these factors and their interactions will affect future carbon dynamics within the Southern Region.

Forested area within the Southern Region will be maintained as forest in the foreseeable future, which will allow for a continuation of carbon uptake and storage over the long term. Across the broader region, land conversion for development on private ownerships is a concern and this activity can cause substantial carbon losses (USDA Forest Service, 2016). The Southern Region will continue to have an important role in maintaining the carbon sink, regionally and nationally, for decades to come.

6.0 References Aber, J., McDowell, W., Nadelhoffer, K., Magill, A., Berntson, G., Kamakea, M., McNulty, S., Currie, W., Rustad, L. & Fernandez, I. (1998) Nitrogen saturation in temperate forest ecosystems. BioScience, 921-934. Aber, J.D., Ollinger, S.V., Fédérer, C.A., Reich, P.B., Goulden, M.L., Kicklighter, D.W., Melillo, J. & Lathrop, R. (1995) Predicting the effects of climate change on water yield and forest production in the northeastern United States. Birdsey, R., Pregitzer, K. & Lucier, A. (2006) Forest carbon management in the United States: 1600-2100. Journal of Environmental Quality, 35, 1461-1469.

Birdsey, R., Dugan, A.J., Healey, S., Dante-Wood, K., Zhang, F., Mo, G., Chen, J., Hernandez, A., Raymond, C., McCarter, J. (2016) Future of America's Forests and Rangelands: Update to the 2010 Resources Planning Act Assessment. Gen. Tech. Report WO-GTR-94. Washington, DC. 250 p. Birdsey, Richard A.; Dugan, Alexa J.; Healey, Sean P.; Dante-Wood, Karen; Zhang, Fangmin; Mo, Gang; Chen, Jing M.; Hernandez, Alexander J.; Raymond, Crystal L.; McCarter, James. 2019. Assessment of the influence of disturbance, management activities, and environmental factors on carbon stocks of U.S. national forests. Gen. Tech. Rep. RMRS- GTR-402. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 116 pages plus appendices. Boggs, J.L., McNulty, S.G., Gavazzi, M.J. & Myers, J.M. (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. Caspersen, J.P., Pacala, S.W., Jenkins, J.C., Hurtt, G.C., Moorcroft, P.R. & Birdsey, R.A. (2000) Contributions of land-use history to carbon accumulation in U.S. Forests. Science, 290, 1148-1151. Chen, W., Chen, J. & Cihlar, J. (2000) An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry. Ecological Modelling, 135, 55-79. Crookston, N.L. & Dixon, G.E. (2005) The forest vegetation simulator: A review of its structure, content, and applications. Computers and Electronics in Agriculture, 49, 60-80. D’Amato, A.W., Bradford, J.B., Fraver, S. & Palik, B.J. (2011) Forest management for mitigation and adaptation to climate change: Insights from long-term silviculture experiments. Forest Ecology and Management, 262, 803-816. Davis, S.C., Hessl, A.E., Scott, C.J., Adams, M.B. & Thomas, R.B. (2009) Forest carbon sequestration changes in response to timber harvest. Forest Ecology and Management, 258, 2101-2109. Domke, G., Perry, C., Walters, B., Nave, L., Woodall, C. & Swanston, C. (2017) Toward inventory‐based estimates of soil organic carbon in forests of the United States. Ecological Applications, 27, 1223-1235. Dugan, A.J., Birdsey, R., Mascorro, V.S., Magnan, M., Smyth, C.E., Olguin, M. & Kurz, W.A. (2018) A systems approach to assess climate change mitigation options in landscapes of the United States forest sector. Carbon Balance and Management, 13, doi.org/10.1186/s13021-018-0100-x. Dugan, A.J., Birdsey, R., Healey, S.P., Pan, Y., Zhang, F., Mo, G., Chen, J., Woodall, C.W., Hernandez, A.J. & McCullough, K. (2017) Forest sector carbon analyses support land management planning and projects: assessing the influence of anthropogenic and natural factors. Climatic Change, 144, 207-220. Dukes, J.S., Pontius, J., Orwig, D., Garnas, J.R., Rodgers, V.L., Brazee, N., Cooke, B., Theoharides, K.A., Stange, E.E., Harrington, R., Ehrenfeld, J., Gurevitch, J., Lerdau, M., Stinson, K., Wick, R. & Ayres, M. (2009) Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: What can we predict? Canadian Journal of Forest Research, 39, 231-248. Gustavsson, L., Madlener, R., Hoen, H. F., Jungmeier, G., Karjalainen, T., Klöhn, S., . . . Spelter, H. (2006) The role of wood material for greenhouse gas mitigation. Mitigation and Adaptation Strategies for Global Change, 11, 1097-1127.

Foster, D.R. (2006) Forests in time: the environmental consequences of 1,000 years of change in New England. Yale University Press. Hayes, D.J., Vargas, R., Alin, S.R., Conant, R.T., Hutyra, L.R., Jacobson, A.R., Kurz, W.A., Liu, S., McGuire, A.D., Poulter, B. & Woodall, C.W. (2018) Chapter 2: The North American carbon budget. Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report (ed. by N. Cavallaro, G. Shrestha, Birdsey, R. M. A. Mayes, R.G. Najjar, S.C. Reed, P. Romero-Lankao and Z. Zhu), pp. 71-108. U.S. Global Change Research Program, Washington, DC. He, L., Chen, J.M., Pan, Y., Birdsey, R. & Kattge, J. (2012) Relationships between net primary productivity and forest stand age in U.S. forests. Global Biogeochemical Cycles, 26 (GB3009). doi:10.1029/2010GB003942. Healey, S.P., Urbanski, S.P., Patterson, P.L. & Garrard, C. (2014) A framework for simulating map error in ecosystem models. Remote Sensing of Environment, 150, 207-217. Healey, S.P., Raymond, C.L., Lockman, I.B., Hernandez, A.J., Garrard, C. & Huang, C. (2016) Root disease can rival fire and harvest in reducing forest carbon storage. Ecosphere, 7, Article e01569. Healey, S.P., Cohen, W.B., Yang, Z., Kenneth Brewer, C., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Joseph Hughes, M., Kennedy, R.E., Loveland, T.R., Moisen, G.G., Schroeder, T.A., Stehman, S.V., Vogelmann, J.E., Woodcock, C.E., Yang, L. & Zhu, Z. (2018) Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment, 204, 717-728. Houghton, R.A., House, J.I., Pongratz, J., Van Der Werf, G.R., Defries, R.S., Hansen, M.C., Le Quéré, C. & Ramankutty, N. (2012) Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125-5142. IPCC (2000) Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change, Summary for Policy Makers, Geneva, Switzerland. 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. (ed. by R.K. Pachauri and L.A. Meyer), p. 151. Intergovernmental Panel on Climate Change, Geneva, Switzerland. Iverson, L.R., Thompson, F.R., Matthews, S., Peters, M., Prasad, A., Dijak, W.D., Fraser, J., Wang, W.J., Hanberry, B. & He, H. (2017) Multi-model comparison on the effects of climate change on tree species in the eastern US: results from an enhanced niche model and process-based ecosystem and landscape models. Landscape Ecology, 32, 1327-1346. Jones, A.G., Scullion, J., Ostle, N., Levy, P.E. & Gwynn-Jones, D. (2014) Completing the FACE of elevated CO2 research. Environment International, 73, 252-258. Ju, W.M., Chen, J.M., Harvey, D. & Wang, S. (2007) Future carbon balance of China's forests under climate change and increasing CO2. Journal of Environmental Management, 85, 538-562. Keeling, R., Piper, S., Bollenbacher, A. & Walker, S. (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. In: Carbon Dioxide Research Group Scripps Institution of Oceanography (SIO), University of California, La Jolla. California USA. URL: http://cdiac. ornl. gov/ftp/trends/co2/maunaloa. co2. Oak Ridge Natl. Lab., U.S. Dep. of Energy, Oak Ridge, TN.

Keyser, T.L. & Zarnoch, S.J. (2012) Thinning, age, and site quality influence live tree carbon stocks in upland hardwood forests of the southern appalachians. Forest Science, 58, 407- 418. Körner, C., Asshoff, R., Bignucolo, O., Hättenschwiler, S., Keel, S.G., Peláez-Riedl, S., Pepin, S., Siegwolf, R.T.W. & Zotz, G. (2005) Ecology: Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2. Science, 309, 1360-1362. Kurz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L., Ebata, T. & Safranyik, L. (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature, 452, 987-990. Lippke, B., Oneil, E., Harrison, R., Skog, K., Gustavsson, L. & Sathre, R. (2011) Life cycle impacts of forest management and wood utilization on carbon mitigation: knowns and unknowns. Carbon Management, 2, 303-333. Loeffler, D., Anderson, N., Stockmann, K., Skog, K., Healey, S., Jones, J.G., Morrison, J. & Young, J. 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. Luyssaert, S., Schulze, E.D., Börner, A., Knohl, A., Hessenmöller, D., Law, B.E., Ciais, P. & Grace, J. (2008) Old-growth forests as global carbon sinks. Nature, 455, 213-215. McKinley, D.C., Ryan, M.G., Birdsey, R.A., Giardina, C.P., Harmon, M.E., Heath, L.S., Houghton, R.A., Jackson, R.B., Morrison, J.F., Murray, B.C., Pataki, D.E. & Skog, K.E. (2011) A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 21, 1902-1924. 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. Melillo, J.M., Frey, S.D., DeAngelis, K.M., Werner, W.J., Bernard, M.J., Bowles, F.P., Pold, G., Knorr, M.A. & Grandy, A.S. (2017) Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358, 101-105. Norby, R.J., Warren, J.M., Iversen, C.M., Medlyn, B.E. & McMurtrie, R.E. (2010) CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences, 107, 19368-19373. Ollinger, S., Goodale, C., Hayhoe, K. & Jenkins, J. (2008) Potential effects of climate change and rising CO2 on ecosystem processes in northeastern U.S. forests. Mitigation and Adaptation Strategies for Global Change, 13, 467-485. Pan, Y., Birdsey, R., Hom, J. & McCullough, K. (2009) Separating effects of changes in atmospheric composition, climate and land-use on carbon sequestration of US Mid- Atlantic temperate forests. Forest Ecology and Management, 259, 151-164. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., Ciais, P., Jackson, R.B., Pacala, S.W., McGuire, A.D., Piao, S., Rautiainen, A., Sitch, S. & Hayes, D. (2011a) A large and persistent carbon sink in the world's forests. Science, 333, 988-993. Pan, Y., Chen, J.M., Birdsey, R., McCullough, K., He, L. & Deng, F. (2011b) Age structure and disturbance legacy of North American forests. Biogeosciences, 8, 715-732.

Pardo, L.H., Fenn, M.E., Goodale, C.L., Geiser, L.H., Driscoll, C. T., Allen, E.B. …Dennis, R.L. (2011) Effects of nitrogen deposition and empirical nitrogen critical loads for ecoregions of the United States. Ecological Applications, 21, 3049-3082. Pregitzer, K.S. & Euskirchen, E.S. (2004) Carbon cycling and storage in world forests: biome patterns related to forest age. Global change biology, 10, 2052-2077. Raymond, C.L., Healey, S., Peduzzi, A. & Patterson, P. (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. Schimel, D., Stephens, B.B. & Fisher, J.B. (2015) Effect of increasing CO2 on the terrestrial carbon cycle. Proceedings of the National Academy of Sciences of the United States of America, 112, 436-441. Shands, W. (1992) The Lands Nobody Wanted: The Legacy of the Eastern National Forests. The origins of the National Forests. In. Pinchot Institute for Conservation Studies. Sheffield, R.M.; Thompson, M.T. 1992. Hurricane Hugo: Effects on South Carolina’s forest resource. Res. Pap. SE-284. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. 60 p. Skog, K.E., McKinley, D.C., Birdsey, R.A., Hines, S.J., Woodall, C.W., Reinhardt, E.D. & Vose, J.M. (2014) Chapter 7: Managing Carbon. In: Climate Change and United States Forests, Advances in Global Change Research, 57, 151-182. Smith, J.E., Heath, L.S. & Nichols, M.C. (2007) US forest carbon calculation tool: forest-land carbon stocks and net annual stock change. Revised. Gen. Tech. Rep. NRS-13. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 34 p. Smith, J.E., Heath, L.S., Skog, K.E. & Birdsey, R.A. (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. Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E.A., Haberl, H. & et al. (2014) Agriculture, Forestry and Other Land Use (AFOLU). Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (ed. by O. Edenhofer, R. Pichs-Madruga, Y. Sokona and et al.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Stanturf, J.A.; Wade, D.D.; Waldrop, T.A.; [et al.]. (Background Paper FIRE): Fire in southern forest landscapes. In: Wear, D.N.; Greis, J.G., eds. 2002. Southern forest resource assessment. Gen. Tech. Rep. SRS-53. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 607–630. Chapter 25. Thomas, R.Q., Canham, C.D., Weathers, K.C. & Goodale, C.L. (2010) Increased tree carbon storage in response to nitrogen deposition in the US. Nature Geoscience, 3, 13-17. Treasure, E.; McNulty, S; Moore Myers, J.; Jennings, L. N. 2014. Template for assessing climate change impacts and management options: TACCIMO user guide version 2.2. Gen. Tech. Rep. SRS-GTR-186. Asheville, NC: USDA-Forest Service, Southern Research Station. 33 p. US EPA (2015) Executive Summary. US inventory of greenhouse gas emissions and sinks: 1990 – 2013. U.S. Environmental Protection Agency, Washington, DC. 27 p. USDA Forest Service (2015) Baseline estimates of carbon stocks in forests and harvested wood products for National Forest System Units, Southern Region. 44 p.

USDA Forest Service (2020). Climate Change and Your National Forest: Assessing the potential effects of climate change for the Southern Region. 1-4. Retrieved from: https://forestthreats.org/research/tools/taccimo/nfs-climate-change-fact-sheets Vose, J.M., Peterson, D.L. & Patel-Weynand, T. (2012) Effects of climatic variability and change on forest ecosystems: a comprehensive science synthesis for the U.S. forest sector. Gen. Tech. Rep. PNW-GTR-870. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 265 p. Vose, J.M.; Klepzig, K.D. (2013). Climate Change Adaptation and Mitigation Management Options: A Guide for Natural Resource Managers in Southern Forest Ecosystems. CRC Press. Vose, J.M., Peterson, D.L., Domke, G.M., Fettig, C.J., Joyce, L.A., Keane, R.E., Luce, C.H., Prestemon, J.P., Band, L.E., Clark, J.S., Cooley, N.E., D’Amato, A. & Halofsky, J.E. (2018) Chapter 6: Forests. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II (ed. by D.R. Reidmiller, C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock and B.C. Stewart), US Global Change Research Program, Washington, DC, USA, 232-267. Wear, D.N.; Greis, J.G. 2012. The Southern Forest Futures Project: summary report. Gen. Tech. Rep. SRS-GTR-168. Asheville, NC: USDA-Forest Service, Southern Research Station. 54 p. Wear, D. N., Huggett, R., Li, R., Perryman, B., Liu, S. (2013) Forecasts of forest conditions in regions of the United States under future scenarios: a technical document supporting the Forest Service 2012 RPA Assessment. Gen. Tech. Rep. SRS-GTR-170. Asheville, NC: USDA-Forest Service, Southern Research Station. 101 p. Williams, G.W. 2003. The beginnings of the national forests in the South: Protection of watersheds. Paper presented at the14th Annual Environment Virginia Conference; April 29–May 1, 2003; Lexington, VA. Revised version available at https://foresthistory.org/ wp- content/uploads/2017/01/ProtectionofWatersheds_Willimas.pdf [Accessed August 8, 2019]. Williams, M. 1989. Americans and their forests: A historical geography. New York, NY: Cambridge University Press. 599 p. Woodall, C.W., Smith, J.E. & Nichols, M.C. (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. U.S. Department of Agriculture, Forest Service, Northern Research Station. 23 p. Woodall, C.W., Heath, L.S., Domke, G.M. & Nichols, M.C. (2011) Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p. Woodall, C.W., Coulston, J.W., Domke, G.M., Walters, B.F., Wear, D.N., Smith, J.E., Andersen, H.-E., Clough, B.J., Cohen, W.B., Griffith, D.M., Hagen, S.C., Hanou, I.S., Nichols, M.C., Perry, C.H.H., Russell, M.B., Westfall, J. & Wilson, B.T.T. (2015) The U.S. forest carbon accounting framework: stocks and stock change, 1990-2016. Gen. Tech. Rep. NRS-154. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 49 p. Xu, W., Yuan, W., Dong, W., Xia, J., Liu, D. & Chen, Y. (2013) A meta-analysis of the response of soil moisture to experimental warming. Environmental Research Letters, 8, 044027 (8pp)

Zaehle, S., Sitch, S., Smith, B. & Hatterman, F. (2005) Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochemical Cycles, 19, 1-16. Zhang, F., Chen, J.M., Pan, Y., Birdsey, R.A., Shen, S., Ju, W. & He, L. (2012) Attributing carbon changes in conterminous U.S. forests to disturbance and non-disturbance factors from 1901 to 2010. Journal of Geophysical Research: Biogeosciences, 117, 18 p. Zhang, F., Chen, J.M., Pan, Y., Birdsey, R.A., Shen, S., Ju, W. & Dugan, A.J. (2015) Impacts of inadequate historical disturbance data in the early twentieth century on modeling recent carbon dynamics (1951-2010) in conterminous U.S. forests. Journal of Geophysical Research: Biogeosciences, 120, 549-569. Zhu, Z., Piao, S., Myneni, R.B., Huang, M., Zeng, Z., Canadell, J.G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peuelas, J., Poulter, B., Pugh, T.A.M., Stocker, B.D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S. & Zeng, N. (2016) Greening of the Earth and its drivers. Nature Climate Change, 6, 791-795.

United States Department of Agriculture

Broad-Scale Climate Change Monitoring Evaluation Report for the Southern Region

Forest Service Sothern Region April, 2020

For More Information Contact:

Chelsea Leitz 1720 Peachtree St NW Atlanta, GA 30309 404-347-7193

Scott Williams [email protected] 760-382-7371

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, gender identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program information may be made available in languages other than English. To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632- 9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: [email protected]. USDA is an equal opportunity provider, employer and lender. Southern Region Broad-scale Monitoring Evaluation Report

Contents Summary of Findings and Results ...... 5 Introduction ...... 5 Purpose ...... 5 Monitoring Objectives ...... 6 How to Use this Report ...... 7 How Our Southern Region Broad-scale Monitoring Strategy Works ...... 7 Monitoring Evaluation ...... 8 Monitoring Activities ...... 8 Monitoring Question 1: How has climate variability changed and how is it projected to change across the region? ...... 8 Information Sources ...... 9 Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? ...... 11 Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? ...... 11 Monitoring Results ...... 11 Status and trend of the monitoring indicator in relation to the target ...... 14 What level of confidence is there in the accuracy and precision? ...... 14 Adaptive Management Considerations ...... 15 Monitoring Question 2: How is climate variability and change influencing the ecological, social, cultural, and economic conditions and contributions provided by plan areas in the region? ...... 16 Information Sources ...... 17 Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? ...... 17 Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? ...... 17 Monitoring Results ...... 17 What level of confidence is there in the accuracy and precision? ...... 19 Adaptive Management Considerations ...... 19 Monitoring Question 3: What effect do management units in the region have on a changing climate? ...... 21 Information Sources ...... 21 Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? ...... 22 Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? ...... 22 Monitoring Results ...... 22 Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? ...... 24 Status and trend of the monitoring indicator in relation to the target ...... 25 What level of confidence is there in the accuracy and precision? ...... 25

Southern Region

Monitoring Discussion and Findings ...... 25 Adaptive Management Considerations ...... 26 Appendix A: Monitoring Items Not Evaluated in Detail ...... 27 Appendix B: Listing of Supporting Plan Monitoring Program Documents ...... 28 Supporting USFS Documents ...... 28 Other Supporting Documents ...... 28 Appendix C: Summary of Monitoring Question 2 Ecological Sub-Sections ...... 29 Appendix D: Summary of Southern Region National Forest Potential Effects of Climate Change Assessments ...... 42

Southern Region Broad-scale Monitoring Evaluation Report

Summary of Findings and Results

Table 1. Summary of findings Based on the If a change evaluation may be Do monitoring results demonstrate Monitoring Year of warranted, intended progress or trend toward Question Updated monitoring where may the Southern Region targets? results, may change be changes be needed? warranted? 1. How has A, B, C (below1): Precipitation Plan Monitoring climate Program 2018 (First Climate Extremes variability Forest Plans changed and Evaluation) Land Cover Changes Yes Management how is it 2020 Activities projected to No: Increasing Temperatures change across Forest Sea Level Rise the region? Assessment 2. How is climate Yes: Prescribed Fire variability and change Plan Monitoring No: Non-native Invasive Species Program influencing the 2018 (First Forest Health ecological, Evaluation) Forest Plans social, cultural, Animal and Plant Communities Yes 2020 Management and economic Water Resources Activities

conditions and Wildfire Forest contributions Assessment provided by Recreation Use and Satisfaction plan areas in Extreme Weather the region? 3. What effect Plan Monitoring Program do 2018 (First Yes: Overall Carbon Sequestration management Forest Plans Evaluation) units in the Yes 2020 Management region have on No: Aging Forests Resulting in a Activities Reduction in Carbon Sequestration a changing Forest climate? Assessment 1Interval of data collection is beyond this reporting cycle (A); or more time/data are needed to understand status or progress of the plan component (B); or methods/results are inadequate to answer monitoring question (C).

Introduction

Purpose This report is a 2020 update to a 2018 draft copy that was not completed. The purpose of the regional broad-scale monitoring strategy (BSMS) 5-year evaluation report is to help responsible officials determine whether a change is needed in forest plan direction, such as plan components or other plan content that guide management of resources in the plan area.

Southern Region

The regional BSMS evaluation report represents one part of the Forest Service’s overall monitoring program, designed to provide efficiency for biennial monitoring evaluation conducted on each national forest unit (36 CFR 219.12(d)). The monitoring evaluation report is not a decision document—it evaluates monitoring questions and indicators presented in the 2016 Southern Region Broad Scale Monitoring Strategy. The Southern Region Broad Scale Monitoring Strategy, though initiated in 2016 for the Southern Region, is built over time based on the ongoing implementation of the 2012 Planning Rule and related complementary efforts. Monitoring and evaluation are continuous learning tools that form the backbone of informed decision making. Broader-scale monitoring is required to disclose results at least every 5- years, but the regional office will produce an evaluation more often for monitoring questions for which more frequent evaluation and reporting is justified. This is our first written report of this evaluation since the BSMS was finalized. This report indicates whether a change to forest plans, management activities, monitoring program, or forest assessment may be needed based on the new information. The Southern Region’s BSMS addressed one (1) of eight (8) topics required under Forest Service Handbook (FSH) 1909.12 (36 CFR 219.12(a)(5)), plus the additional topic of social, economic, and cultural sustainability.

• (vi) Measurable changes on the plan area related to climate change and other stressors that may be affecting the plan area.

Monitoring Objectives There are several objectives for this report, including:

• Deliver broader scale monitoring information from the broad-scale monitoring strategy for consideration at the plan level. • Assess the current condition (status) and trend, where applicable, of selected forest resources. • Document implementation of the broad-scale monitoring strategy, including changed conditions or status of key characteristics used to assess accomplishments and progress toward achievement of the selected plan components. • Evaluate relevant assumptions, changed conditions, management effectiveness, and progress towards achieving the selected desired conditions, objectives, and goals described in land management plans. • Address needed changes to current attribute details associated with each monitoring question based on new best available science. Present recommended change opportunities to the responsible officials.

Southern Region Broad-scale Monitoring Evaluation Report

How to Use this Report This report is a tool and a resource for the Forest Service, specifically the Southern Region, to assess the condition of forest resources in relation to Forest Plan direction and management actions. It is also a tool and a resource for the public to learn more about how the Forest Service is managing forest resources. The BSMS identified Alerts and Adaptive Management Strategies associated with each monitoring question, which will be used to guide both evaluation of indicators and a regional response. The BSMS evaluation report is designed to help the public, as well as Federal, State, local government, and Tribal entities, especially national forests within the Southern Region anticipate key steps in the overall monitoring programs. These steps include upcoming opportunities for public participation and how the public will be informed of those opportunities, and how public input will be used as the monitoring programs progress. The BSMS evaluation report is also intended to help people better understand reported results in relation to past and future monitoring reports on national forests within the Southern Region and the BSMS that is issued at the regional level.

How Our Southern Region Broad-scale Monitoring Strategy Works Monitoring and evaluation requirements are established through the National Forest Management Act (NFMA) at 36 CFR 219. Additional direction is provided by the Forest Service in Chapter 30 – Monitoring – of the Forest Service Handbook (FSH 1909.12, Chapter 30, and Section 33). In the context of the broader-scale the three (3) main monitoring goals are to establish monitoring questions that: • Can best be answered at a geographic scale broader than one forest plan area; • Can be coordinated with the relevant responsible officials, State and Private Forestry, Research and Development, partners, and the public; and

• Are within the financial and technical capabilities of the region and complements other ongoing monitoring efforts.

Providing timely, accurate monitoring information to responsible officials, national forests within the Southern Region, and the public is a key intent of the BSMS. The BSMS evaluation report for the Southern Region is the vehicle for disseminating this information.

Southern Region

Monitoring Evaluation

Monitoring Activities

The following sections present the most current information (data and evaluations) for all monitoring questions contained within the 2016 version of the BSMS for monitoring element: Measurable changes on the plan area related to climate change and other stressors that may be affecting the plan area (aka Climate Change and Other Stressors) (36 CFR 219.12(a)(5)(vi)). All three monitoring questions identified to address this monitoring element during the current evaluation period are addressed in the next section of this report:

Monitoring Question 1 – How has climate variability changed and how is it projected to change across the region?

Monitoring Question 2 – How is climate variability and change influencing the ecological, social, cultural, and economic conditions and contributions provided by plan areas in the region?

Monitoring Question 3 – What effect do management units in the region have on a changing climate?

Monitoring Question 1: How has climate variability changed and how is it projected to change across the region?

Table 2. Monitoring Collection Summary For monitoring question 1: Year Data was last collected or compiled 2018 Next scheduled data collection/compilation 2020 Results were last evaluated in 2018 Next scheduled year for evaluation of data in an evaluation report 2020

Southern Region Broad-scale Monitoring Evaluation Report

Table 3. Detailed inventory of the source, frequency, scale, and subregions for each monitoring question indicator.1 Monitoring Question 1: How has climate variability changed and how is it projected to change across the region? Sea Level Rise and Land Cover Changes Source Frequency Scale Subregion(s)

Historic sea level trend data: National Oceanographic and Atmospheric Administration (NOAA), Sea Level Trends. National Forest Plan 6-years Coastal Plain Areas Forecasted future coastal high tide flooding data: U.S. Climate Resilience Toolkit

Climate Extremes Source Frequency Scale Subregion(s) USDA - Southeast Regional Climate Hub – Climate by Forest Tool (NOAA Climate 2-years Ecological Subsection Region-wide Explorer

Temperature Source Frequency Scale Subregion(s) USDA - Southeast Regional Climate Hub – Climate by Forest Tool (NOAA Climate 6-years NF Plan Areas Region-wide Explorer Precipitation Source Frequency Scale Subregion(s) USDA - Southeast Regional Climate Hub – Climate by Forest Tool (NOAA Climate 6-years NF Plan Areas Region-wide Explorer Water Balance Source Frequency Scale Subregion(s) See Also Monitoring Question 2 6-years NF Plan Areas Region-wide

Information Sources The monitoring elements or indicators are defined as sea level rise and land cover changes, climate extremes, temperature, precipitation, and water balance.

Sea Level Rise: Historic sea level trend data: National Oceanographic and Atmospheric Administration, Sea Level Trends. https://tidesandcurrents.noaa.gov/sltrends/sltrends.html Forecasted future coastal high tide flooding data: U.S. Climate Resilience Toolkit (Climate Explorer). https://toolkit.climate.gov/

1 Source: Table A-1, Appendix A of the Southern Region Broad-scale monitoring strategy, Feb 2016.

Southern Region

Temperature and Precipitation: This climate summary is based on global climate models developed for the United Nations Intergovernmental Panel on Climate Change and available from NOAA’s Climate Explorer tool (U.S. Federal Government, 2016). The Climate Explorer produces graphs and maps showing observed historic and modeled future conditions for two possible greenhouse gas emissions scenarios.

Observed Historic Climate Data - For all observed data, the gray bars are plotted with respect to the 1961-1990 mean using Livneh, et, al. dataset.2 The black line shows actual historical observations.

Data Application Programming Interface (API) and Requests - The values that are displayed are based on the bounding box of the selected ecoregion. See also: DeGaetano, A.T., W. Noon, and K.L. Eggleston (2014): Efficient Access to Climate Products in Support of Climate Services using the Applied Climate Information System (ACIS) Web Services, Bulletin of the American Meteorological Society, 96, 173–180.

Modeled Data - The modeled data are LOCA downscaled CMIP5 model realizations. This includes the hindcast gray bands and the projected bands for the representative concentration pathway (RCP) 4.5 and RCP8.5 emission scenarios. Each year, the band is defined by the highest model value for that year across all 32 models.34

For more detailed information on the specific changes in each ecological subsection and the time scales over which they are expected to occur, see Appendix C for static time series figures of the four ecoregions represented in the table below. For other ecoregions, visit the Climate by Forests Tool (http://climate-by-forest.nemac.org/).

2 https://www.esrl.noaa.gov/psd/data/gridded/data.livneh.metvars.html 3 Taylor K. E., Stouffer R. J., Meehl G. A. (2012): An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93, 485-498, doi:10.1175/bams-d-11-00094.1. 4 The line in each is a weighted mean that applies this strategy: Sanderson,B.M. and M.F.Wehner (2017):Weighting strategy for the Fourth National Climate Assessment.In: Climate Science Special Report: A Sustained Assessment Activity of the U.S. Global Change Research Program [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 644-653. 10

Southern Region Broad-scale Monitoring Evaluation Report

Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? At the regional level, it is difficult to prescribe a target and evaluate an accomplishment, but the region has capitalized on existing Agency work such as the baseline carbon assessments conducted under the Climate Change Scorecard, the assessment and monitoring conducted under the Watershed Condition Framework, and the monitoring of climate change indicators occurring in the Forest Inventory and Analysis program, by ensuring integration of these activities into the land management planning process. Best available science information is also used to assess current climate change conditions, trends, and stressors and provide responses via management actions in order to restore ecological integrity. At the national forest level, Forest Plans will include plan components to maintain or restore ecological integrity, so that ecosystems can resist change, are resilient under changing conditions, and are able to recover from disturbance.

Please refer back to table 1 on page 5 for an assessment of each monitoring question indicator for this 5-year report. Climate Change Monitoring Question 1 was created to address a portion of this monitoring element. Individual national forest units are directed by the 2012 Planning Rule to address climate change effects when the national forests update their Monitoring Programs, Forest Assessments, Forest Plans, and Management Activities.

Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? In the short-term, there is no need for change in individual national forests’ plan direction, management activities, or monitoring arising from this evaluation. Periodic evaluation (~5- years) of the climate monitoring should continue to detect any changes not currently projected as models improve. The significant changes in temperature should be considered in future long-term planning efforts, including those that apply to ecological systems and recreation uses on the national forests, especially within the Southern Region.

Monitoring Results

Indicator – Sea Level Rise

The NOAA provided the following historic sea level trend information. The U.S. Climate Resilience Toolkit (Climate Explorer) provided coastal high tide flooding forecast information for four (4) Southern Region national forests located near the Atlantic Ocean and Gulf of Mexico and may be at risk of adverse effects from sea level rise. The 2016 BSMS shows the Francis Marion National Forest as at risk of being impacted by sea level rise. Apalachicola National Forest - Apalachicola, Florida: The relative sea level trend is 2.56 mm/year with a 95 percent confidence interval of +/- 0.62 mm/year based on monthly mean sea level data from 1967 to 2019 which is equivalent to a change of 0.84 feet in 100 years. Forecasted 2036 coastal high tide flooding is expected to increase to 14 days per year under the RCP 4.5 (low greenhouse gas emissions scenario) and 43 days per year under the RCP 8.5

Southern Region

(high greenhouse gas emissions scenario), and increasing in 2065 to 86 days (low) and 305 days (high). Croatan National Forest - Beaufort, North Carolina: The relative sea level trend is 3.22 mm/year with a 95 percent confidence interval of +/- 0.35 mm/year based on monthly mean sea level data from 1953 to 2019 which is equivalent to a change of 1.06 feet in 100 years. Forecasted 2036 coastal high tide flooding is expected to increase to seven (7) days per year under the RCP 4.5 (low greenhouse gas emissions scenario) and 17 days per year under the RCP 8.5 (high greenhouse gas emissions scenario), and increasing in 2065 to 34 days (low) and 265 days (high). Desoto National Forest - Bay Waveland, Mississippi: The relative sea level trend is 4.81 mm/year with a 95 percent confidence interval of +/- 0.79 mm/year based on monthly mean sea level data from 1978 to 2019 which is equivalent to a change of 1.58 feet in 100 years. Forecasted 2036 coastal high tide flooding is expected to increase to 58 days per year under the RCP 4.5 (low greenhouse gas emissions scenario) and 107 days per year under the RCP 8.5 (high greenhouse gas emissions scenario), and increasing in 2065 to 253 days (low) and 351 days (high). Francis Marion National Forest - Charleston, South Carolina: The relative sea level trend is 3.32 mm/year with a 95 percent confidence interval of +/- 0.19 mm/year based on monthly mean sea level data from 1901 to 2019 which is equivalent to a change of 1.09 feet in 100 years. Forecasted 2036 coastal high tide flooding is expected to increase to 23 days per year under the RCP 4.5 (low greenhouse gas emissions scenario) and 49 days per year under the RCP 8.5 (high greenhouse gas emissions scenario), and increasing in 2065 to 85 days (low) and 245 days (high). National Forest Ecological Sub-Sections The following results reflect updates from observational climate data beginning in the 1950s through present day, as well as climate projections from the RCP 4.5 (low emissions) and 8.5 (high emissions) climate model scenarios. Both scenarios project an increase in daily average maximum and minimum temperature, an increase in the days per year in which the temperature reaches above 90°F, and a decrease in the days per year in which the temperature falls below 32°F. By mid-century (2036-2065), mean projected changes in temperature variables are statistically significant over the baseline (1961-1990).

Four national forest Ecological Sub-Sections were chosen as representative to the Southern Region due to their geographic location and landscape characteristics in the Region. Those used for assessing indicators include: • National Forests in Mississippi: Southern Loessial Plains • Ouachita National Forest: Athens Piedmont Plateau • National Forests in Florida: Florida Central Highlands 12

Southern Region Broad-scale Monitoring Evaluation Report

• National Forests in North Carolina: Southern Blue Ridge Mountains Element or Indicator - Climate Extremes and Temperature

Table 4 provides the trend and range of median changes for various temperature variables forecast under RCP 4.5 and 8.5, for the time period of 2036-2065. Each variable in each representative region indicates an overall warming trend. The magnitude of change varies by location.

More information for the Southern Region will provide a greater understanding of how this variability will affect forest management and planning. In later sections of this report, the implications of this for Southern Region national forests will become clearer. Plant and animal communities throughout the region will be affected similarly, with minor differences between some species.

Table 4: Projected range of mean change in temperature variables by the period 2036-2065, using RCP 4.5 and RCP 8.5 Southern Blue Boston Piney Ridge Upper Clay Hills Hills Woods Mountains Trend + + + + Avg. Daily Max Temp Range of 3.2 - 6.7⁰F 3.4 - 6.5⁰F 3.3 - 6.6⁰F 3.1 - 6.2⁰F change Trend + + + + Avg. Daily Min Temp Range of 3.1 - 6.5⁰F 3 - 6.2⁰F 3.1 - 6.2⁰F 3.2 - 6.1⁰F change Trend + + + + Days/Year Max Temp Range of 29.6 - 64.7 35.3 - 65.7 above 90F 25.2 – 60 days 35.4 - 69.8 days change days days Trend - - - - Days/Year Min Temp Range of 14.6 - 31.4 10.5 - 23.2 below 32F 15.3 - 31.8 days 11.7 - 24.4 days change days days

Projections suggest that future warming is expected, resulting in 25-70 more days above 90°F and 11-32 fewer freezing days per year in the four national forest Ecological Sub-Sections above. Element or Indicator - Precipitation, Water Balance Trends in precipitation and water balance are less clear than those of temperature (table 5). While the range of change in forecasted dry days per year includes the possibility of a decrease, and increase, or no change, the projection lean towards an increase in dry days in three of the four representative ecoregions, with Southern Blue Ridge Mountains being the exception.

Southern Region

Table 5: Projected range of mean change in precipitation and drought variables by the period 2036-2065, using RCP 4.5 and RCP 8.5 Southern Blue Upper Clay Boston Hills Piney Woods Ridge Mountains Hills Trend +/- +/- +/- +/- Number of dry Range of days per year -2.7 - 10.1 days -1.1 - 15.8 days -8.6 - 4.6 days -3.8 - 9.7 days change Trend +/- +/- +/- +/- Total Range of precipitation -3 - 5.5 in -4.3 - 5.9 in -1 – 6 in -4.2 - 4.4 in change Days/year over 2 Trend +/- +/- +/- +/- inches Range of -0.4 - 0.7 days -0.3 - 0.8 days -0.5 - 0.2 days -0.4 - 0.4 days precipitation change

Precipitation was historically variable and will likely continue to be variable from one year to the next. There does appear to be a trend toward a modest increase in total precipitation, with little change in the number of dry days per year. Changes in total precipitation and in days per year with over two (2) inches of precipitation include a considerable amount of uncertainty when accounting for both RCP 4.5 and 8.5 scenarios.

Status and trend of the monitoring indicator in relation to the target In the short-term, there is no need for change in Forest Plan direction, management activities, or monitoring arising from this evaluation. Periodic evaluation (~5-years) of the climate monitoring should continue to detect any changes not currently projected as models improve. The significant changes in sea level rise and temperature should be considered in future long- term planning efforts, including those that apply to ecological systems and recreation uses on the national forests. Table 6 shows a monitoring indicator status summary for climate changes and other stressors. What level of confidence is there in the accuracy and precision? Uncertainty exists with the range of scenarios provided in any climate projection. The strength of the trend in temperature towards warmer temperature indicated relatively good certainty in the direction, though the magnitude will depend on other variables such as population growth and greenhouse gas emissions. Less certainty exists with precipitation and water balance. Most variables do not provide a clear trend, though increased variability in the timing and magnitude of precipitation and drought events is possible. Accuracy - Accuracy of a variety of sources of error, some of which are addressed through the consideration of uncertainty, and other that are unaddressed and beyond the technical scope of this analysis. Accuracy is primarily assessed through the consideration of historical model performance as compared with historical observations (i.e., model performance), the considering of results from multiple models (i.e., model agreement/uncertainty), and emissions scenario uncertainty. Each interpretation section in this report addresses both

Southern Region Broad-scale Monitoring Evaluation Report

characteristics of accuracy. There are other limitations of this data that are inherent to the systems, models, and assumptions used to develop them that are not readily assessed, but should be considered contextually as the results in this report are considered alongside other source of information, including findings from peer reviewed literature and local expertise. Reliability - The results presented in this section are based on peer reviewed science. Relevance - Relevance is assessable through geographic and attribute level considerations. The Climate Explorer tool summarizes results at the county scale, which is not perfectly coincident with the boundaries of our area of interest, but given the coarseness of the climate data and other sources of uncertainty, the selected county provides a representative sample that can be reasonably applied to the area of interest as a whole. While there are a multitude of potential climate variables that are relevant to the mission and operations Sothern Region national forests, the standard attributes provided by the Climate Explorer cover the major physical variables of temperature and precipitation sufficiently to give their influences on resources and management activities due consideration.

Adaptive Management Considerations Adaptive management considerations related to impacts of the climate on national forests within the Southern Region are discussed in the next section under Monitoring Question 2: Climate Variability Influence on Ecological, Social, Cultural, and Economic Conditions. The 2012 Planning Rule requires monitoring of climate change and other stressors that may be affecting the forest plan area (36 CFR Part 219 – PLANNING - Subpart A - National Forest System Land Management Planning (§§ 219.1 - 219.19). 219.12(a)(5)(vi) - Measurable changes on the plan area related to climate change and other stressors that may be affecting the plan area). The status of the monitoring indicators for Monitoring Question 1 shows a need for national forests in the Southern Region to address short and long term climate change effects and stressors to forest ecosystems and the human environment (table 6). Increasing temperatures are currently the most significant effect impacting national forest ecosystems and the human environment. National forests will need to also address effects of changes to precipitation, climate extremes, sea level rise. Land cover is not covered in the body of this report, see appendix A.

Table 6. Summary of where change may be warranted based on Monitoring Item 1 Results Changes may be Yes Unsure No warranted for the: Plan Monitoring Program X Forest Plans X Management Activities X Forest Assessment X

Southern Region

Monitoring Question 2: How is climate variability and change influencing the ecological, social, cultural, and economic conditions and contributions provided by plan areas in the region?

Table 7. Monitoring Collection Summary For monitoring question 2: Year Data was last collected or compiled in 2018 Next scheduled data collection/compilation 2020 Results were last evaluated in 2018 Next scheduled year for evaluation of data in an evaluation report 2020

Table 8. Detailed inventory of the source, frequency, scale, and subregions for each monitoring question indicator.5 Monitoring Question 2: How is climate variability and change influencing the ecological, social, cultural, and economic conditions and contributions provided by plan areas in the region? Non-native Invasive Species Source Frequency Scale Subregion(s) TACCIMO 4-years NF Plan Areas Region-wide Forest Health Source Frequency Scale Subregion(s) TACCIMO 2-years NF Plan Areas Region-wide Prescribed Fire Source Frequency Scale Subregion(s) TACCIMO 2-years NF Plan Areas Region-wide Recreation Use and Satisfaction Source Frequency Scale Subregion(s) TACCIMO 2-years NF Plan Areas Region-wide Wildfire Source Frequency Scale Subregion(s) TACCIMO 2-years NF Plan Areas Region-wide Jobs and Income Source Frequency Scale Subregion(s) See Economics 10-years Unit Plan Areas of Influence Region-wide Phenology Source Frequency Scale Subregion(s)

5 From Table A-1, Appendix A of the Southern Region Broad Scale Monitoring Strategy, Feb 2016. 16

Southern Region Broad-scale Monitoring Evaluation Report

See Appendix B 10-years NF Plan Areas Region-wide Forest Status and Trends Source Frequency Scale Subregion(s) See Appendix B 6-years NF Plan Areas Region-wide

Information Sources The monitoring elements or indicators are defined as non-native invasive species, forest health, prescribed fire, recreation use and satisfaction, and wildfire. Jobs and income, phenology and forest trends and status are not covered in the body of this report, see appendix A. Information used in this section is summarized from Assessments of the Potential Effects of Climate Change on the Southeast United States for the Southern Region National Forests. The assessments are known as Template for Assessing Climate Change Impacts and Management Options (TACCIMO) summaries. TACCIMO is a web-based application that connects federal, state, and private land managers and planners with climate change science they can trust. TACCIMO is a collaborative effort among the USDA Forest Service Eastern Forest and Western Wildland Environmental Threat Assessment Centers and the National Forest System (https://www.fs.usda.gov/ccrc/tools/taccimo).

Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? See response for Monitoring Question 1.

Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? No changes in Southern Region national forest plan direction, management activities, or the monitoring are needed during the short-term. The significant changes in temperature and drought need to be considered in future long-term planning efforts, including those that apply to ecological systems and recreation uses on the National Forests.

Monitoring Results A recent 2020 research summary: Assessing the Potential Effects of Climate Change on the Southeast United States, addressed climate change effects and response mitigations for monitoring elements that are defined as forest health, animal and plant communities, water resources, recreation and extreme weather. Consolidated summaries for each Southern Region National Forest unit are presented in appendix D. Forestlands are experiencing increased threats from fire, insect and plant invasions, disease, extreme weather, and drought. Scientists project increases in temperature and changes in rainfall patterns that can make these threats occur more often, with more intensity, and/or for longer durations. Although many of the effects of future changes are negative, natural resource management can help mitigate these impacts.

Southern Region

The Southeast United States faces continued increasing temperatures and variation in precipitation due to climate change. Extreme weather events like drought, hurricanes, and wildfires are projected to increase or worsen due to climate related effects. Saltwater intrusion is affecting wetlands and coastal forests along the coast, changing vegetation and affecting wildlife. The threat from invasive insects and diseases is projected to increase with warmer temperatures. Although many of the effects of future changes are negative, natural resource management can help mitigate these impacts. Responses informed by the best available science information enable natural resource professionals within the Forest Service to better protect the land and resources and conserve the Southern Region’s forestlands into the future. Forest Health - Southeast forests will be affected by many factors including extreme weather, shifts in plant hardiness zones, sea level rise and saltwater intrusion, and increased pressure from invasive plants and pests, drought, and wildfire frequency. Increasing temperatures will worsen disturbance due to invasive plants and insects. Warmer temperatures due to climate change are converting saltwater marsh to mangroves and shifting where the marsh to mangrove ecotone exists. Sea level rise will increase soil salinity levels in coastal communities. Coastal forest retreat due to saltwater intrusion and the formation of “ghost forests” has been documented along the Southeast U.S. coastline. In addition, coastal wetlands have seen plant community shifts due to higher levels of salinity. Animal Communities - Some bird species along the coast have been negatively affected by development of ghost forests and consequent habitat loss. Certain amphibian and insect species such as the red legged salamander or the Diana Fritillary that are highly dependent on elevation are becoming more and more isolated due to habitat fragmentation and loss. Plant Communities - Suitability conditions are projected to change for different tree species, with certain species having more adaptive capacity (southern pines, oaks, and hickories) than others (balsam fir, red spruce, eastern hemlock, and sugar maple) due to pests and climate competition. Changes in growing season and flowering dates are also possible with increasing minimum temperatures. Projected increase in temperatures can allow invasive pests and plants to increase their spread. Water Resources - With climate change projected to cause warmer temperatures and variable precipitation in the future, water resources will likely be even more affected by drought and extreme weather events. Severe drought impacts could lower streamflow in forested watersheds. Increased water temperature due to warming climate can potentially lead to an increase in toxic algal blooms in lakes. Recreation - Changes in precipitation due to drought could negatively impact water based outdoor recreation like canoeing, kayaking, and motorized activities. Increase in temperature

Southern Region Broad-scale Monitoring Evaluation Report

can impact visitors comfort. Climate change can also have impacts on culturally significant natural resources. Extreme Weather - Extreme precipitation events are becoming more likely; however, there are longer dry periods between storms. Increasing drought frequency and a projected increase in dry season, as much as 156 days in some areas, will increase the risk of wildfires. Not only are extreme precipitation events becoming more likely, hurricanes are becoming more severe and are able to sustain damaging conditions for longer periods of time. These events have large impacts on nearby estuaries and coastal waters, including negatively impacting carbon sources and sinks.

What level of confidence is there in the accuracy and precision? The information used in this section is based on summaries of peer reviewed research provided by the USDA Forest Service Eastern Forest and Western Wildland Environmental Threat Assessment Centers (https://www.fs.usda.gov/ccrc/tools/taccimo).

Adaptive Management Considerations The following Monitoring Question 2 adaptive management considerations are based on the Assessments of the Potential Effects of Climate Change on the Southeast United States for the Southern Region National Forests discussed above. Forest Health Response: Develop a coordinated system of monitored and controlled entrance points that control the majority of water flow inland from the shoreline and high-value water and land restoration areas in order to reduce salt-intrusion as well as to preserve marshes and swamps. Response: Efforts to restore ecological integrity to impacted ecosystems (e.g., managing for longleaf and shortleaf pine) can have positive effects on disease and pest resistance, as well as wildfire and drought resilience. Animal Communities Response: Conserve buffer areas along riparian habitats to provide habitat for amphibian species. Response: High elevation areas are crucial refugia for many species. Preventing the addition of new roads and heavy equipment in these areas can maintain habitat connectivity. Response: Create habitat corridors, assist in species movement, increase national forest management unit sizes, and identify high- value conservation lands adjacent to national forests. Plant Communities Response: Manage for tree species with high adaptive capacity.

Southern Region

Response: Early detection and rapid response (EDDR) is the most effective way to respond to invasive species and should be implemented where possible. Water Resources Response: Reduce impact on aquatic ecosystems affected by drought by favoring tree species that are fire tolerant and have relatively low water use (e.g. longleaf pine). Response: Remove invasive species that use more water to reduce stress on the aquatic ecosystems. Recreation Response: Enact monitoring to determine when it is safe for recreational activities to take place in water recreation areas and communicate effectively to visitors the potential risks of higher temperature or high water levels.

Response: Work with local indigenous populations and cultural groups to provide resources for them to adapt to the climate driven changes on their cultural sites. Extreme Weather Response: Manage tree densities through practices such as thinning and prescribed fire to maximize carbon sequestration and reduce the vulnerability of forest stands to water stress, insect and disease outbreaks, and fire. Response: Communicate early warnings to visitors of extreme weather events. The 2012 Planning Rule requires monitoring of climate change and other stressors that may be affecting the plan area (36 CFR Part 219 – PLANNING - Subpart A - National Forest System Land Management Planning (§§ 219.1 - 219.19). 219.12(a)(5)(vi) - Measurable changes on the plan area related to climate change and other stressors that may be affecting the plan area). The status of the monitoring indicators for Monitoring Question 2 shows a need for national forests in the Southern Region to address short and long term climate change effects and stressors to forest ecosystems and the human environment (table 9). National forests will need to also address effects of changes to non-native invasive species, forest health, animal and plant communities, water resources, wildfire, recreation use and satisfaction and extreme weather.

Southern Region Broad-scale Monitoring Evaluation Report

Table 9. Summary of where change may be warranted based on Monitoring Question 2 Results Changes may be Yes Unsure No warranted for the: Plan Monitoring Program X Forest Plans X Management Activities X Forest Assessment X

Monitoring Question 3: What effect do management units in the region have on a changing climate?

Table 10. Monitoring Collection Summary For monitoring question 3: Year Data was last collected or compiled in: 2013 Next scheduled data collection/compilation: 2019 Results were last evaluated in: 2018 Next scheduled year for evaluation of data in an evaluation report: 2020

Table 11. Detailed inventory of the source, frequency, scale, and subregions for each monitoring question indicator.6 Monitoring Question 3: What effect do management units in the region have on a changing climate? Carbon Stocks and Fluxes Source Frequency Scale Subregion(s) Forest Inventory and Analysis (FIA) 6-years NF Plan Areas Region-wide

Information Sources The monitoring elements or indicators are defined as carbon stocks and fluxes. The BSMS lists greenhouse gas emissions as an attribute detail and it has been deleted from analysis in this report. The information source used in this section is a 2020 research summary white paper by Alexa Dugan, Duncan McKinley, and Patti Turpin (Dugan et al., 20207) provides the most current research information about carbon sequestration in the Southern Region and is summarized below. Dugan et al. addressed three main carbon sequestration topics: • Baseline Carbon Stocks and Flux • Factors Influencing Forest Carbon • Future Carbon Conditions

6 Table A-1, Appendix A of Southern Region Broad-scale Monitoring Strategy, Feb 2016. 7 Dugan, A., McKinley, D., Turpin, Patti. (2020) Forest Carbon Assessment for the Southern Region. USDA Forest Service.

Southern Region

Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? See response for Monitoring Question 1.

Based on monitoring results are changes needed to forest plan direction, management activities, or the monitoring program? Forest carbon stocks have been stable and changes in carbon stocks arising from disturbances and management activities have been small relative to the total quantity of carbon stored within the Southern Region’s national forests. However, Southern Region carbon sequestration rate is slowing overall, based on the dataset used, because forests within the Southern Region are becoming older (half are > 80 years of age). The rate of carbon uptake and sequestration generally decline as forests age. Accordingly, projections from the Resource Planning Act (RPA) assessment indicate a potential age-related decline in forest carbon stocks in the Southern Region (all land ownerships) beginning in the 2020s. Future forest plan assessments and revisions need to address short and long term climate change effects to forest ecosystems and the need to manage tree densities through practices such as thinning and prescribed fire to maximize carbon sequestration and reduce the vulnerability of forest stands to water stress, insect and disease outbreaks, and fire (shown under Adaptive Management Considerations below).

Monitoring Results Dugan et al. (2020) found that forests in the Southern Region are maintaining a modest carbon sink. Carbon stocks increased by about 29.7 percent on average across the Southern Region between 1990 and 2013, and negative impacts on carbon stocks caused by disturbances and environmental conditions have been modest, not exceeding forest growth. According to satellite imagery, timber harvesting has been the most prevalent disturbance detected within the Southern Region since 1990. However, harvests during this period have been relatively small and low intensity. Forest carbon losses associated with harvests have been small compared to the total amount of carbon stored in the Southern Region, resulting in a loss of about 2.4 percent of non-soil carbon from 1990 to 2011. 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 harvested wood products sourced from national forests within the Southern Region increased since the early 1900s. Recent declines in timber harvesting have slowed the rate of carbon accumulation in the product sector.

Southern Region Broad-scale Monitoring Evaluation Report

The biggest influence on current carbon dynamics in the Southern Region is the legacy of intensive timber harvesting and land clearing for agriculture during the 19th century, followed by a period of forest recovery and more sustainable forest management beginning in the early to mid-20th century, which continues to promote a carbon sink today. However, forests within the Southern Region are becoming older (half are > 80 years of age). The rate of carbon uptake and sequestration generally decline as forests age.

Carbon dioxide emissions are projected to increase through 2100 under even the most conservative emission scenarios. Several models project greater increases in forest productivity when the CO2 fertilization effect is included in modeling. However, the effect of increasing levels of atmospheric CO2 on forest productivity is transient and can be limited by the availability of nitrogen and other nutrients. Productivity increases under elevated CO2 could be offset by losses from climate-related stress or disturbance.

TACCIMO research summaries indicate forestlands are experiencing increased threats from fire, insect and plant invasions, disease, extreme weather, and drought. Scientists project increases in temperature and changes in rainfall patterns that can make these threats occur more often, with more intensity, and/or for longer durations, which may affect forest carbon dynamics. For instance, elevated temperatures may also increase soil respiration and reduce soil moisture through increased evapotranspiration, which would negatively affect growth rates and carbon accumulation.

Modeled results of recent climate effects indicate that years with elevated temperatures, such as in the 2000s, have generally had a negative effect on carbon uptake in the Southern Region. Heat stress is also projected to limit growth and carbon uptake of some Southern pines and hardwood species. Mean annual precipitation projections across the Southern Region vary, with projected decreases in the western part and increases in the Southern Appalachian Mountains, although uncertainty remains relatively high. More intense precipitation and extreme storm events are expected to continue increasing in the Southern Region. The potential for reduced soil moisture and drought is also predicted to increase, especially later in

Southern Region

the growing season as increased temperatures drive evapotranspiration. Although a longer growing season may increase annual biomass accumulation, droughts could offset these potential growth enhancements and increase the potential for other forest stressors. Drought- stressed trees may also be more susceptible to insects, pathogens and damage from wildfires.

Changes in climate are expected to drive many other changes in forests through the next century, including changes in forest establishment and composition. Some Southern Region tree species are expected to be particularly vulnerable in the future as climate conditions drive declines or failures in species establishment or habitat suitability. Model projections suggest that many northern conifer species, including balsam fir, red spruce, and black spruce, are the most vulnerable to climate change—particularly at lower elevations or more southerly locations and at the end of this century. The potential for future declines of northern species increase the risk of carbon losses in forest communities dominated by these species, particularly under scenarios of greater warming. Climate-driven failures in species establishment further reduce the ability of forests to recover carbon lost after mortality- inducing events or harvests. Although future climate conditions also allow for other future- adapted species to increase, there is greater uncertainty about how well these species will be able to take advantage of new niches that may become available (Dugan et al., 2020).

Do monitoring results demonstrate intended progress toward Southern Regional National Forest Plan targets? Climate Change Monitoring Question 3 was added to address a portion of this monitoring element the 2012 planning rule. Individual National Forest units are directed by the planning rule to address climate change effects when the Forests update Monitoring Programs, Forest Assessments, Forest Plans, and Management Activities.

Southern Region Broad-scale Monitoring Evaluation Report

Status and trend of the monitoring indicator in relation to the target Carbon sequestration in the Southern region, based on datasets referenced, are becoming older (half are > 80 years of age). The rate of carbon uptake and sequestration generally decline as forests age. Projections from the RPA assessment indicate a potential age-related decline in forest carbon stocks in the Southern Region (all land ownerships) beginning in the 2020s (Dugan et al., 2020).

What level of confidence is there in the accuracy and precision? The following are abbreviated summaries from Dugan et al. (2020): Despite some uncertainty in annual carbon stock estimates, there is a high degree of certainty that carbon stocks in the Southern Region have increased from 1990 to 2013.

The uncertainty between annual estimates can make it difficult to determine whether the forest and/or region is a sink or a source in a specific year. However, the trend of increasing carbon stocks from 1990 to 2013 over the 23-year period suggests that the Southern Region is a modest carbon sink.

One model error has resulted from a change in Forest Inventory Analysis 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 Southern Region has experienced minimal changes in land use or adjustments to the boundaries of the national forests in recent years, the change in forested area incorporated in the Carbon Calculation Tool is more likely a data artefact of altered inventory design and protocols.

The analysis of carbon storage in Harvested Wood Products also contains uncertainties. The collective effect of uncertainty was assessed using a Monte Carlo approach. 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.

There is uncertainty associated with estimates of the relative effects of disturbances, aging, and environmental factors on forest carbon sequestration trends.

Monitoring Discussion and Findings Given the complex interactions among forest ecosystem processes, disturbance regimes, climate, and nutrients, it is difficult to project how forests and carbon trends will respond to novel future conditions. 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 some variables like increasing 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 within the Southern Region to act as a carbon sink, potentially causing various influences to offset each other. Thus, it will be important for forest managers to continue to monitor forest responses to these changes and potentially alter

Southern Region

management activities to better enable forests to better adapt to future conditions (Dugan et al., 2020).

Forested area within the Southern Region will be maintained as forest in the foreseeable future, which will allow for a continuation of carbon uptake and storage over the long term. Across the broader region, land conversion for development on private ownerships is a concern and this activity can cause substantial carbon losses. The Southern Region will continue to have an important role in maintaining the carbon sink, regionally and nationally, for decades to come (Dugan et al., 2020).

Adaptive Management Considerations An adaptive management response to climate change effects are addresses under Monitoring Question 2 above (Assessments of the Potential Effects of Climate Change on the Southeast United States for the Southern Region National Forests). The assessments recommend management of tree densities through practices such as thinning and prescribed fire to maximize carbon sequestration and reduce the vulnerability of forest stands to water stress, insect and disease outbreaks, and fire. The 2012 Planning Rule requires monitoring of climate change and other stressors that may be affecting the plan area (36 CFR Part 219 – PLANNING - Subpart A - National Forest System Land Management Planning (§§ 219.1 - 219.19). 219.12(a)(5)(vi) - Measurable changes on the plan area related to climate change and other stressors that may be affecting the plan area). The status of the monitoring indicators for Monitoring Question 3 indicates a need for national forests in the Southern Region to address short and long term climate change effects and stressors to forest ecosystems and the human environment (table 12). National forests will need to also address the effects of changes to carbon sequestration.

Table 12. Summary of where change may be warranted based on Monitoring Question 3 Results Changes may be Yes Unsure No warranted for the: Plan Monitoring Program X Forest Plans X Management Activities X Forest Assessment X

Southern Region Broad-scale Monitoring Evaluation Report

Appendix A: Monitoring Items Not Evaluated in Detail

Land Cover Changes, Phenology, Forest Status and Trends are not evaluated in detail: Interval of data collection is beyond this reporting cycle (A); or more time/data are needed to understand status or progress of the plan component (B); or methods/results are inadequate to answer monitoring question (C). Jobs and Income – Economics Indicators is being covered in a separate report by Allison Borchers.

Southern Region

Appendix B: Listing of Supporting Plan Monitoring Program Documents

Suggested listing of documents (including their published date, location …)

Supporting USFS Documents Forest Assessment Title, date, location Forest Plan Title, date, location Plan Monitoring Program (if different version of the PMP in the Forest Plan) Title, date, location NEPA Document Title, date, location Statement of Proposed Actions Title, date, location Report of Completed Actions Title, date, location Previous Monitoring & Evaluation Report Title, date, location Broader-scale Monitoring Strategy Title, date, location

Other Supporting Documents USFWS Recovery Plan Conservation Agreements

Southern Region Broad-scale Monitoring Evaluation Report

Title, date, location

Appendix C: Summary of Monitoring Question 2 Ecological Sub-Sections

Data Sources

Data API and requests: The values being displayed are based on the bounding box of the selected ecoregion. The bounding box is used in a request for data from the Applied Climate Information System: http://www.rcc-acis.org/. See also:

DeGaetano, A.T., W. Noon, and K.L. Eggleston (2014): Efficient Access to Climate Products in Support of Climate Services using the Applied Climate Information System (ACIS) Web Services, Bulletin of the American Meteorological Society, 96, 173–180.

Modeled data: The modeled data are LOCA downscaled CMIP5 model realizations. This includes the hindcast gray bands and the projected bands for the RCP4.5 and RCP8.5 emission scenarios. Each year, the band is defined by the highest model value for that year across all 32 models. See:

Taylor K. E., Stouffer R. J., Meehl G. A. (2012): An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93, 485-498, doi:10.1175/bams-d- 11-00094.1.

The line in each is a weighted mean that applies this strategy: Sanderson,B.M. and M.F.Wehner (2017):Weighting strategy for the Fourth National Climate Assessment.In: Climate Science Special Report: A Sustained Assessment Activity of the U.S. Global Change Research Program [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC USA pp 644-653.

Observed data: For the observed data, the bars (that are now gray to be consistent with Climate Explorer) are plotted with respect to the 1961-1990 mean using the Livneh, et al. dataset: https://www.esrl.noaa.gov/psd/data/gridded/data.livneh.metvars.html

Here's a reference for the methodology: Livneh, B., E.A. Rosenberg, C.Lin, B.Nijssen, V.Mishra, K.M. Andreadis, E.P.Maurer, and D.P.Lettenmaier (2013): A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions. Journal of Climate, v26. DOI: 10.1175/JCLI-D-12-00508.1

Southern Region

Table 1. Ecological Sub-Sections National Forest Areas and Acreages

FORESTNAME Ecological Sub-Sections Total

Chattahoochee-Oconee National Forests Chert Valley 96

Lower Foot Hills 30,720

Metasedimentary Mountains 124,965

Midland Plateau Central Uplands 116,413

Piedmont Ridge 11,609

Sandstone Ridge 64,619

Schist Hills 8,397

Schist Plains 4,002

Shaley Limestone Valley 426

Southern Blue Ridge Mountains 506,514 Chattahoochee-Oconee National Forests Total 867,761

Cherokee National Forest Great Valley of Virginia 5,790

Metasedimentary Mountains 437,153

Sandstone Hills 1,744

Shaley Limestone Valley 18

Southern Blue Ridge Mountains 212,120

Cherokee National Forest Total 656,825

Daniel Boone National Forest Eastern Knobs Transition 18

Kinniconick and Licking Knobs 207,318

Miami-Scioto Plain-Tipton Till Plain 41,526

Pine and Cumberland Mountains 83

Rugged Eastern Hills 133,015

Southern Region Broad-scale Monitoring Evaluation Report

Southern Cumberland Mountains 19,138

Southwestern Escarpment 310,300

Daniel Boone National Forest Total 711,398

Francis Marion and Sumter National Forests Carolina Slate 67,894

Charlotte Belt 220,613

Charlotte Belt-North 264

Coastal Marsh and Island 39,064

Lower Foot Hills 13,527

Lower Terraces 217,174

Southern Blue Ridge Mountains 70,680

Upper Terraces 3,402 Francis Marion and Sumter National Forests Total 632,619 George Washington and Jefferson National Black Mountains Forest 6,306

Central Blue Ridge Mountains 584

Eastern Allegheny Mountain and Valley 27,104

Eastern Coal Fields 52,017

Great Valley of Virginia 118,883

Northern Blue Ridge Mountains 207,861

Northern High Allegheny Mountain 38,124

Northern Ridge and Valley 6,776

Pine and Cumberland Mountains 27,504

Ridge and Valley 1,183,903

Rolling Limestone Hills 5,355

Southern Blue Ridge Mountains 117,751

Western Coal Fields 60 George Washington and Jefferson National Forest Total 1,792,227

Kisatchie National Forest Piney Woods Transition 978

Southern Region

Red River Alluvial Plain 10,716

South Central Arkansas 59,213

South Central Arkansas Flatwoods 1,330

Southern Loam Hills 530,750

Southwest Flatwoods 5,590

Kisatchie National Forest Total 608,578 Land Between the Lakes National Recreation Western Pennyroyal Karst Plain Area 171,251 Land Between the Lakes National Recreation Area Total 171,251

National Forests in Alabama Chert Valley 664

Moulton Valley 17

Quartzite and Talladega Slate Ridge 190,638

Sandstone Mountain 161,490

Sandstone Plateau 2,457

Sandstone-Shale and Chert Ridge 28,309

Schist Plains 15,931

Shale Hills and Mountain 18,023

Southern Loam Hills 84,058

Upper Clay Hills 145,449

Upper Loam Hills 23,563

National Forests in Alabama Total 670,601

National Forests in Florida Eastern Beach and Lagoons 22,845 FL Gulf Coast Flatwoods-Bays and Barrier Islands 402,339

Florida Central Highlands 361,884

Florida Northern Highlands 2,830

Gulf Southern Loam Hills 126,889

Southern Region Broad-scale Monitoring Evaluation Report

Okeechobee Plain 13

Okefenokee Swamp 106,779

South Coastal Plains 46,823

Upper Terraces 129,906

National Forests in Florida Total 1,200,307

National Forests in Mississippi Deep Loess Hills and Bluffs 20,952

Delmarva Upland 6,153

Fragipan Loam Hills 305

Interior Flatwoods 198

Jackson Hills 99,011

Jackson Prairie 333 LA-MS Gulf Coast Flatwoods-Bays and Barrier Islands 50,530

North Mississippi River Alluvial Plain 61,473

Northern Loessial Hills 135,291

Northern Pontotoc Ridge 26,622

Southern Clay Hills 143,193

Southern Loam Hills 416,310

Southern Loessial Plains 186,152

Upper Clay Hills 42,761

Upper Loam Hills 1,821

National Forests in Mississippi Total 1,191,104

National Forests in North Carolina Carolina Slate-North 46,543

Lower Terraces 97,368

Metasedimentary Mountains 350,079

Midland Plateau Central Uplands-North 7,939

Southern Blue Ridge Mountains 684,495

Southern Triassic Uplands 4,855

Southern Region

Tidal Area 63,275

Water 0

National Forests in North Carolina Total 1,254,552

National Forests in Texas Blackland Prairie 10,427

Eastern Rolling Plains 93

Piney Woods Transition 295,537

Red River Alluvial Plain 7,430

Sabine Alluvial Valley 35,843

Sand Hills 17,420

South Central Arkansas 69,201

Southern Loam Hills 220,281

Southwest Flatwoods 1,114

Texas Grand Prairie 497

Western Cross Timbers 19,513

National Forests in Texas Total 677,357

Ouachita National Forest Athens Piedmont Plateau 97,188

East Central Ouachita Mountains 349,787

Eastern Arkansas Valley and Ridges 89

Fourche Mountains 700,267

Mount Magazine 9,670

Red River Alluvial Plain 15,152

Southwestern Arkansas 11,614

West Central Ouachita Mountains 600,146

Ouachita National Forest Total 1,783,914

Ozark-St. Francis National Forest Boston Hills 564,674

Southern Region Broad-scale Monitoring Evaluation Report

Boston Mountains 294,953

Crowley's Ridge 19,614

Eastern Arkansas Valley and Ridges 29,313

Mount Magazine 100,935

North Mississippi River Alluvial Plain 3,163

Springfield Plateau 48,942

White River Hills 98,847

Ozark-St. Francis National Forest Total 1,160,441

Grand Total 13,378,937

Southern Region

Legend (For all charts)

1a. 1b.

1c. 1d.

Figure 1. Average Daily Maximum Temperature. (1a) Ozark-St. Francis National Forest – Boston Hills; (1b) National Forests in Texas – Piney Woods Transition; (1c) National Forests in North Carolina – Southern Blue Ridge Mountains; (1d) National Forests in Alabama – Upper Clay Hills.

Southern Region Broad-scale Monitoring Evaluation Report

2a. 2b.

2c. 2d.

Figure 2. Average Daily Minimum Temperature. (2a) Ozark-St. Francis National Forest – Boston Hills; (2b) National Forests in Texas – Piney Woods Transition; (2c) National Forests in North Carolina – Southern Blue Ridge Mountains; (2d) National Forests in Alabama – Upper Clay Hills.

3a. 3b.

Southern Region

3c. 3d.

Figure 3. Days per Year with a Maximum Temperature above 90 degrees F. (3a) Ozark- St. Francis National Forest – Boston Hills; (3b) National Forests in Texas – Piney Woods Transition; (3c) National Forests in North Carolina – Southern Blue Ridge Mountains; (3d) National Forests in Alabama – Upper Clay Hills.

4a. 4b.

Southern Region Broad-scale Monitoring Evaluation Report

4c. 4d.

Figure 4. Days per Year with a Minimum Temperature below 32 degrees F. (4a) Ozark-St. Francis National Forest – Boston Hills; (4b) National Forests in Texas – Piney Woods Transition; (4c) National Forests in North Carolina – Southern Blue Ridge Mountains; (4d) National Forests in Alabama – Upper Clay Hills.

5a. 5b.

5c. 5d.

Southern Region

Figure 5. Dry Days. (5a) Ozark-St. Francis National Forest – Boston Hills; (5b) National Forests in Texas – Piney Woods Transition; (5c) National Forests in North Carolina – Southern Blue Ridge Mountains; (5d) National Forests in Alabama – Upper Clay Hills.

6a. 6b.

6c. 6d.

Figure 6. Total Precipitation. (6a) Ozark-St. Francis National Forest – Boston Hills; (6b) National Forests in Texas – Piney Woods Transition; (6c) National Forests in North Carolina – Southern Blue Ridge Mountains; (6d) National Forests in Alabama – Upper Clay Hills.

Southern Region Broad-scale Monitoring Evaluation Report

7a. 7b.

7c. 7d.

Figure 7. Days per Year with more than 2in Precipitation. (7a) Ozark-St. Francis National Forest – Boston Hills; (7b) National Forests in Texas – Piney Woods Transition; (7c) National Forests in North Carolina – Southern Blue Ridge Mountains; (7d) National Forests in Alabama – Upper Clay Hills.

Southern Region

Appendix D: Summary of Southern Region National Forest Potential Effects of Climate Change Assessments

Indicator Impact Response NATIONAL FORESTS IN ALABAMA NATIONAL FORESTS IN ALABAMA Invasive and aggressive plant and insect species may increasingly CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS Non-native outcompete or negatively affect native species in the future. Winter CHEROKEE NATIONAL FOREST Invasive freezes currently limit many forest pests, but higher temperatures will CROATAN NATIONAL FOREST Species likely allow these species to increase. Destructive insects, such as NATIONAL FORESTS IN FLORIDA southern pine beetle will be better able to take advantage of forests GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS Biological due to factors such as increased drought. Certain invasive plant KISATCHIE NATIONAL FOREST Diversity species found in these forests, including kudzu are expected to THE LAND BETWEEN THE LAKES RECREATION AREA increase dramatically as they are able to tolerate a wide range of NATIONAL FORESTS IN MISSISSIPPI harsh conditions, allowing them to rapidly move into new areas. OUACHITA NATIONAL FOREST OZARK-ST. FRANCIS NATIONAL FORESTS CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS SUMTER NATIONAL FOREST OUACHITA NATIONAL FOREST NATIONAL FORESTS AND GRASSLANDS IN TEXAS Invasive and aggressive plant and insect species may increasingly UWHARRIE NATIONAL FOREST outcompete or negatively affect native species in the future. Winter Manage tree densities through practices such as thinning and freezes currently limit many forest pests, but higher temperatures will prescribed fire to maximize carbon sequestration and reduce the likely allow these species to increase. Certain invasive plant species vulnerability of forest stands to water stress, insect and disease found in these forests, including Japanese honeysuckle are expected outbreaks, and fire. to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas. Monitor for new invasive species moving into areas where they were not traditionally found, especially following events such as hurricanes CHEROKEE NATIONAL FOREST and fire. CROATAN NATIONAL FOREST NATIONAL FORESTS IN FLORIDA EL YUNQUE NATIONAL FOREST NATIONAL FORESTS IN MISSISSIPPI NANTAHALA and PISGAH NATIONAL FORESTS SUMTER NATIONAL FOREST

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response Invasive and aggressive plant and insect species may increasingly Monitor for new invasive species moving into areas where they were outcompete or negatively affect native species in the future. Winter traditionally not found, especially following hurricane events in high- freezes currently limit many forest pests, but higher temperatures will elevation communities. likely allow these species to increase. Destructive insects, such as southern pine beetles, will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species found in this forest, including kudzu and Japanese FRANCIS MARION NATIONAL FOREST honeysuckle are expected to increase dramatically as they are able Monitor for new invasive species moving into areas where they were to tolerate a wide range of harsh conditions, allowing them to rapidly not traditionally found, especially following events such as hurricanes move into new areas. and fire. NANTAHALA and PISGAH NATIONAL FORESTS DANIEL BOONE NATIONAL FOREST Monitor for new invasive species moving into areas where they were Invasive and aggressive plant and insect species may increasingly traditionally not found, especially in high-elevation communities. outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Extremes in temperature or rainfall also stress forest vegetation making it more likely to die when attacked by insects and disease, such as the hemlock woolly adelgid and the two-lined chestnut borer.

GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Destructive insects, such as southern pine beetles, will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species found in these forests, including kudzu, Japanese honeysuckle, and Amur honeysuckle are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

KISATCHIE NATIONAL FOREST Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Higher temperatures will likely allow these species to increase. Destructive insects, such as southern pine beetles, will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species found in this forest, including kudzu, are expected to increase dramatically as they are able to tolerate a

Southern Region

Indicator Impact Response wide range of harsh conditions, allowing them to rapidly move into new areas.

THE LAND BETWEEN THE LAKES RECREATION AREA Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Destructive insects will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species, including the Japanese honeysuckle, are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

OZARK-ST. FRANCIS NATIONAL FORESTS Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Destructive insects, such as the red oak beetle and southern pine beetle will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species found in these forests, including kudzu are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

EL YUNQUE NATIONAL FOREST Biological Diversity – Plants and animals at risk will respond to environmental changes by adapting, moving, or declining. Species with high genetic variation will be better able to survive in new conditions. Higher temperatures will cause many species to shift ranges up in elevation. However, in some cases, the rate of warming combined with land use changes will restrict the ability of plants and animals to move into suitable habitat. Highland species with restricted habitats and are more likely to be negatively impacted by climate change than are lower elevation species. In montane cloud forests in 44

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response El Yunque, narrow thermal ranges may pressure these high elevation species beyond their upper elevational limits.

FRANCIS MARION NATIONAL FOREST Biological Diversity - Plants and animals at risk will respond to environmental changes by adapting, moving, or declining. Species with high genetic variation will be better able to survive in new conditions. Higher temperatures will cause many species to shift ranges, generally moving to track their suitable habit (e.g., northward or up in elevation). However, in some cases, the rate of warming combined with landuse changes will restrict the ability of plants and animals to move into suitable habitat. The species most likely to be negatively impacted by climate change will be highly specialized and habitat restricted, effecting threatened and endangered species more than common species.

Forest Health - Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, and higher temperatures will likely allow these species to increase in number. Destructive insects, such as bark beetles, will be better able to take advantage of forests stressed by more frequent drought. Certain invasive plant species, including cogongrass, are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

NANTAHALA and PISGAH NATIONAL FORESTS Biological Diversity - Plants and animals at risk will respond to environmental changes by adapting, moving, or declining. Species with high genetic variation will be better able to survive in new conditions. Higher temperatures will cause many species to shift ranges, generally moving to track their suitable habit (e.g., north or up in elevation). However, in some cases, the rate of warming combined with land use changes will restrict the ability of plants and animals to move into suitable habitat. The species most likely to be negatively impacted by climate change will be highly specialized and habitat restricted.

Forest Health - Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the

Southern Region

Indicator Impact Response future. Winter freezes currently limit many forest pests, and higher temperatures will likely allow these species to increase in number. Destructive insects, such as bark beetles, will be better able to take advantage of forests stressed by more frequent drought. Certain invasive plant species, including kudzu, are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions and already cover a large expanse, allowing them to rapidly move into new areas.

NATIONAL FORESTS AND GRASSLANDS IN TEXAS Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Destructive insects, such as southern pine beetles, will be better able to take advantage of forests due to factors such as increased drought. Certain invasive plant species, found in these grasslands, including Chinese tallow trees, are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas.

UWHARRIE NATIONAL FOREST Invasive and aggressive plant and insect species may increasingly outcompete or negatively affect native species in the future. Winter freezes currently limit many forest pests, but higher temperatures will likely allow these species to increase. Destructive insects, such as bark beetles, will be better able to take advantage of forests stressed by more frequent drought. Certain invasive plant species found in the Uwharrie, including kudzu and honeysuckle, are expected to increase dramatically as they are able to tolerate a wide range of harsh conditions, allowing them to rapidly move into new areas. REGION WIDE REGION WIDE Southeast forests will be affected by many factors including extreme Develop a coordinated system of monitored and controlled entrance Forest weather, shifts in plant hardiness zones, sea level rise and saltwater points that control the majority of water flow inland from the shoreline Health intrusion, and increased pressure from invasive plants and pests, and high-value water and land restoration areas in order to reduce drought, and wildfire frequency. Increasing temperatures will worsen salt-intrusion as well as to preserve marshes and swamps. 46

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response Region disturbance due to invasive plants and insects. Warmer temperatures Wide due to climate change are converting saltwater marsh to mangroves Efforts to restore ecological integrity to impacted ecosystems (ex. by and shifting where the marsh to mangrove ecotone exists. Sea level managing for longleaf and shortleaf pine) can have positive effects on rise will increase soil salinity levels in coastal communities. Coastal disease and pest resistance, as well as wildfire and drought forest retreat due to saltwater intrusion and the formation of “ghost resilience. forests” has been documented along the Southeast U.S. coastline. In addition, coastal wetlands have seen plant community shifts due to higher levels of salinity.

REGION WIDE REGION WIDE Conserve buffer areas along riparian habitats to provide habitat for Some bird species along the coast have been negatively affected by Forest amphibian species. the development of ghost forests and consequent habitat loss. Health Certain amphibian and insect species such as the red legged High elevation areas are crucial refugia for many species. Preventing salamander or the Diana Fritillary that are highly dependent on Animal the addition of new roads and heavy equipment in these areas can elevation are becoming more and more isolated due to habitat Communities maintain habitat connectivity. fragmentation and loss.

Create habitat corridors, assist in species movement, increase NATIONAL FORESTS IN ALABAMA National Forest management unit sizes, and identify high- value Wildlife species will be affected in different ways. Amphibians may be conservation lands adjacent to National Forests. most at risk, due to dependencies on moisture and cool temperatures

that could be altered. The endangered gopher tortoise will likely be NATIONAL FORESTS IN ALABAMA severely affected by increasing drought conditions due to climate CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS change. Bird species, such as red cockaded woodpeckers, may see CHEROKEE NATIONAL FOREST a decrease in population as vegetation types change and heat stress CROATAN NATIONAL FOREST makes food more difficult to come by. NATIONAL FORESTS IN FLORIDA

GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS CHEROKEE NATIONAL FOREST KISATCHIE NATIONAL FOREST Wildlife species will be affected in different ways. Amphibians may be NATIONAL FORESTS IN MISSISSIPPI most at risk, due to dependencies on moisture and cool temperatures OUACHITA NATIONAL FOREST that could be altered. Bird species, such as the hooded warbler, are OZARK-ST. FRANCIS NATIONAL FORESTS threatened by impacts of climate change. Available habitat for the SUMTER NATIONAL FOREST Carolina northern flying squirrel may disappear completely by the EL YUNQUE NATIONAL FOREST year 2060. Alternatively, mammals such as deer and bears may UWHARRIE NATIONAL FOREST increase due to higher survival rates during warmer winters. Maintain piles of natural woody debris in areas of high amphibian

diversity to supplement habitats that retain cool, moist conditions. CROATAN NATIONAL FOREST

SUMTER NATIONAL FOREST Create habitat corridors, assist in species movement, increase NATIONAL FORESTS AND GRASSLANDS IN TEXAS National Forest management unit sizes, and identify high- value UWHARRIE NATIONAL FOREST conservation lands adjacent to National Forests.

Southern Region

Indicator Impact Response Wildlife species will be affected in different ways. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures THE LAND BETWEEN THE LAKES RECREATION AREA that could be altered. Bird species, such as red cockaded FRANCIS MARION NATIONAL FOREST woodpeckers, may see a decrease in population as vegetation types NANTAHALA and PISGAH NATIONAL FORESTS change and heat stress makes their food sources more difficult to NATIONAL FORESTS AND GRASSLANDS IN TEXAS come by. Alternatively, mammals such as deer and bears may Maintain piles of natural woody debris in areas of high amphibian increase. diversity to supplement habitats that retain cool, moist conditions.

DANIEL BOONE NATIONAL FOREST EL YUNQUE NATIONAL FOREST Wildlife species will be affected in different ways. Amphibians may be Enhance landscape connectivity by maintaining natural migration most at risk, due to dependencies on moisture and cool temperatures corridors between lowland and upland forests to allow species to that could be altered. Greater ambient temperatures may be harmful move up-slope into cooler environments as climate warms. to the endangered Indiana bat and the Virginia big-eared bat as well. Alternatively, mammals such as deer and black bears may increase NANTAHALA and PISGAH NATIONAL FORESTS due to higher survival rates resulting from warmer winters. Enhance riparian corridors to provide shade to moderate increases in water temperature and stream flow that could decrease water quality NATIONAL FORESTS IN FLORIDA and harm native trout populations. NATIONAL FORESTS IN MISSISSIPPI Wildlife species will be affected in different ways. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. Bird species, such as red cockaded woodpeckers, may see a decrease in population as vegetation types change and heat stress makes food sources more difficult to come by. The endangered gopher tortoise will likely be severely affected by increasing drought conditions due to climate change. Alternatively, mammals such as deer and black bears may increase due to higher survival rates during warmer winters.

GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS Wildlife species will be affected in different ways. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. Increasing water temperatures will also likely decrease populations of brook trout and greater ambient temperatures may also be harmful to the endangered Indiana bat. Alternatively, mammals such as deer and black bears may increase due to higher survival rates during warmer winters. 48

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response

KISATCHIE NATIONAL FOREST Wildlife species will be affected in different ways. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. Avian species, such as the federally listed endangered red cockaded woodpeckers, may see a decrease in population as vegetation types change and heat stress makes their food sources more difficult to come by. Alternatively, mammals such as deer may increase due to higher survival rates during warmer winters.

THE LAND BETWEEN THE LAKES RECREATION AREA Wildlife species will be affected in different ways. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. Greater ambient temperatures may be harmful to the endangered northern long-eared bat.

OUACHITA NATIONAL FOREST Wildlife species will be affected in different ways. Amphibians such as salamanders may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. Bird species, such as red cockaded woodpeckers, may see a decrease in population as vegetation types change and heat stress makes their food sources more difficult to come by.

OZARK-ST. FRANCIS NATIONAL FORESTS Wildlife species will be affected in different ways. Amphibians such as salamanders may be most at risk, due to dependencies on moisture and cool temperatures that could be altered. The Ozark hellbender is one such amphibian seeing a rapid decline in population and may be particularly affected. Greater ambient temperatures may be harmful to mammals such as the endangered Indiana bat.

EL YUNQUE NATIONAL FOREST Wildlife – Wildlife species will be affected in different ways, depending on their needs. Climatic warming may push the narrow thermal tolerances of many species in tropical environments above their upper limits, prompting population losses and habitat changes that will affect animal communities. Because of their cool-adapted, range-restricted nature, high elevation amphibians, including Puerto

Southern Region

Indicator Impact Response Rican Coquí frogs, are especially vulnerable to future changes. More frequent drought conditions may increase the vulnerability of both reptiles and amphibians to water loss, disease, and parasites. Reduced rainfall may lead to decreased habitat quality for neotropical bird migrants wintering in El Yunque, while cavity- nesting birds, including the Puerto Rican Parrot, could see an increase in habitat competition and nesting predation with an increase in major hurricane disturbances.

FRANCIS MARION NATIONAL FOREST Animal Communities - Wildlife species will be affected in different ways, depending on their needs. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered in a future climate. Bird species, even those that are presently increasing such as red cockaded woodpeckers, may see a decrease in population size as vegetation types change and heat stress makes migration more difficult. In order to adapt, arrival date and nesting times of some common birds may start earlier in the year. On the other hand, populations of large mammals such as deer and bears may increase with warmer winter temperatures due to a higher winter survival rate.

NANTAHALA and PISGAH NATIONAL FORESTS Animal Communities - Wildlife species will be affected in different ways, depending on their needs. Amphibians may be most at risk, due to dependencies on moisture and cool temperatures that could be altered in a future climate. Populations of large mammals such as deer and bears may increase with warmer winter temperatures due to a higher winter survival rate. Birds, on the other hand, may decrease in population size as vegetation types change and heat stress makes migration more difficult. In order to adapt, arrival date and nesting times of some common birds may start earlier in the year.

Fish - Warmer air and water temperatures and changes in stream flow will affect the abundance and distribution of fish species. With higher water temperatures, fish communities in northern streams will 50

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response begin to resemble communities in more southerly locations. Altered stream flow patterns can lead to decreases in water quality and oxygen content. Cold-water species, such as trout, will be the most vulnerable to population declines with future warming. The native brook trout may be most at risk, as warmer stream temperature and competition with invasive species will continue to reduce their populations. REGION WIDE REGION WIDE Suitability conditions are projected to change for different tree Manage for tree species with high adaptive capacity. Forest species, with certain species having more adaptive capacity Health (southern pines, oaks, and hickories) than others (balsam fir, red Early detection and rapid response (EDDR) is the most effective way spruce, eastern hemlock, and sugar maple) due to pests and climate to respond to invasive species and should be implemented where Plant competition. Changes in growing season and flowering dates are also possible. Communities possible with increasing minimum temperatures. Projected increase in temperatures can allow invasive pests and plants to increase their NATIONAL FORESTS IN ALABAMA spread. CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS CHEROKEE NATIONAL FOREST NATIONAL FORESTS IN ALABAMA CROATAN NATIONAL FOREST Heat stress may limit the growth of some southern pines and NATIONAL FORESTS IN FLORIDA hardwood species. Stresses from drought and wide- scale pest KISATCHIE NATIONAL FOREST outbreaks have the potential to cause large areas of forest THE LAND BETWEEN THE LAKES RECREATION AREA dieback. Intensified extreme weather events, such as hurricanes, ice NATIONAL FORESTS IN MISSISSIPPI storms, and fire, are also expected to lead to changes in plant UWHARRIE NATIONAL FOREST community composition. Species more resistant to these Focus restoration efforts in hurricane-resistant forests, such as disturbances, such as longleaf pine, will be more resilient to a longleaf pine as well as sweetgum or red oak hardwood, and promote changing climate. Populations of other plants, including the the planting of longleaf pines over loblolly pine where feasible. endangered Alabama leather flower, will be particularly vulnerable to drier conditions. Include a range of ages and species in forests to lessen potential loss from drought or infestation. CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS Heat stress may limit the growth of some southern pines and CHEROKEE NATIONAL FOREST hardwood species. Stresses from drought and wide- scale pest Focus restoration efforts in mixed shortleaf pine, known for their short outbreaks have the potential to cause large areas of forest dieback foliage, strong wood, and resistance to disease in order to lessen Intensified extreme weather events, such as hurricanes, ice storms, vulnerability to the southern pine beetle, fungus, and impacts from and fire, are also expected to lead to changes in plant community severe storms. composition. Species more resistant to these disturbances, such as shortleaf pine, will be more resilient to a changing climate. GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS Populations of other plants, including the threatened large-flowered OUACHITA NATIONAL FOREST skullcap, may be particularly vulnerable because invasive species like Include a range of ages and species in forests to lessen potential loss the Japanese honeysuckle out-compete the native plant. from drought or infestation.

Southern Region

Indicator Impact Response

CHEROKEE NATIONAL FOREST OUACHITA NATIONAL FOREST Heat stress may limit the growth of some southern pines and OZARK-ST. FRANCIS NATIONAL FORESTS hardwood species. Stresses from drought and wide- scale pest SUMTER NATIONAL FOREST outbreaks have the potential to cause large areas of forest dieback. Focus restoration efforts in wind resistant forests, such as shortleaf Populations of some plants, including the threatened blue ridge pines as well as native hardwoods, and promote the planting of goldenrod, may be particularly vulnerable to warmer weather shortleaf pines over loblolly pine. Hardwood- dominated forests may experience stress from higher temperatures, allowing pines and other fast-growing species to Include a range of ages and species in forests to lessen potential loss become more dominant at the expense of slower-growing species from drought or infestation. such as hickories and oaks. OZARK-ST. FRANCIS NATIONAL FORESTS CROATAN NATIONAL FOREST Replace fescue with native warm season grasses because they are GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS more drought tolerant and endemic. SUMTER NATIONAL FOREST NATIONAL FORESTS AND GRASSLANDS IN TEXAS FRANCIS MARION NATIONAL FOREST Heat stress may limit the growth of some southern pines and Manage tree densities where needed through sound forest hardwood species. Stresses from drought and wide- scale pest management practices such as thinning and prescribed fire to outbreaks have the potential to cause large areas of forest maximize carbon sequestration while reducing the susceptibility of dieback. Intensified extreme weather events, such as hurricanes, ice forest stands to water stress, insect and disease outbreaks, and storms, and fire, are also expected to lead to changes in plant wildfire. community composition. Populations of some rare or endemic plants may be disproportionately impacted. Species more resistant to these Focus restoration efforts in longleaf pine forests and promote the disturbances, such as longleaf pine, will be more resilient to a planting of longleaf pine over loblolly pine where feasible. changing climate. NANTAHALA and PISGAH NATIONAL FORESTS DANIEL BOONE NATIONAL FOREST Manage tree densities where needed through sound forest Heat stress may limit the growth of some trees. Stresses from management practices to maximize carbon sequestration while drought and wide-scale pest outbreaks have the potential to cause reducing the susceptibility of forest stands to water stress, insect and large areas of forest to die. Intense weather events, ice storms and disease outbreaks, and wildfire. fire, are also expected to lead to changes in plant community composition by knocking down the forest canopy and allowing Focus restoration efforts in cove forests where cool microclimates aggressive species to invade an area. buffer the effects of future warming and water stress.

NATIONAL FORESTS IN FLORIDA NATIONAL FORESTS AND GRASSLANDS IN TEXAS NATIONAL FORESTS IN MISSISSIPPI 52

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response Heat stress may limit the growth of some southern pines and Include a range of ages and species in forests to lessen potential loss hardwood species. Stresses from drought and wide- scale pest from drought or infestation. outbreaks have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as hurricanes, ice storms, and fire, are also expected to lead to changes in plant community composition. Populations such as the endangered green pitcher plant require moisture-rich soils and may decline due to increasing droughts. Species more resistant to these disturbances, such as longleaf pine, will be more resilient to a changing climate.

THE LAND BETWEEN THE LAKES RECREATION AREA Heat stress may limit the growth of some southern pines and hardwood species. Stresses from drought and wide- scale pest outbreaks have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as storms and fire, are also expected to lead to changes in plant community composition. Populations of some rare or endemic plants may be particularly vulnerable. Hardwood-dominated forests may experience stress from higher temperatures, allowing pines and other fast-growing species to become more dominant at the expense of slower-growing species such as hickories and oaks.

OUACHITA NATIONAL FOREST OZARK-ST. FRANCIS NATIONAL FORESTS Heat stress may limit the growth of some southern pines and hardwood species. Stresses from drought and wide- scale pest outbreaks have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as tornadoes, ice storms, and fire, are also expected to lead to changes in plant community

Southern Region

Indicator Impact Response composition. Populations of some rare or endemic plants may be disproportionately impacted. Species more resistant to these disturbances, such as shortleaf pine will be more resilient to a changing climate.

EL YUNQUE NATIONAL FOREST Terrestrial Ecosystems – Higher temperatures, changes in precipitation patterns, and any alteration in cloud cover will affect plant communities and ecosystem processes. Increasing night- time temperatures may affect tropical tree growth and induce tree mortality. Both intensified extreme weather events and progressively drier summer months in the Caribbean are expected to alter the distribution of tropical forest life-zones, potentially allowing low- elevation Tabonuco forest species to colonize areas currently occupied by Colorado forest. El Yunque’s tropical montane cloud forests are among the world’s most sensitive ecosystems to climate change. Cloud forest epiphytes may experience moisture stress due to higher temperatures and less cloud cover with a rising cloud base, affecting epiphyte growth and flowering. Plant communities on isolated mountain peaks will be most vulnerable, as they will not be able to adapt to the shifting-cloud base by moving to higher elevations.

FRANCIS MARION NATIONAL FOREST Plant Communities - Heat stress may limit the growth of some southern pines and hardwood species. Additional stresses from drought, in combination with wide-scale pest outbreaks, have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as hurricanes, ice storms, and fire, are also expected to lead to changes in plant community composition. An increase in disturbance may promote the establishment of longleaf pine at the expense of loblolly pine. Populations of bald cypress may be particularly vulnerable to future changes, including higher air and water temperatures.

NANTAHALA and PISGAH NATIONAL FORESTS 54

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response Plant Communities - Changing temperature and rainfall patterns may threaten the survival of high-elevation communities in mountain forests. Rising temperatures will allow species from lower elevations to migrate up-slope, changing the forest communities seen today. Populations of species now existing on mountain peaks, including spruce-fir forests, will be most at risk in the future. Hardwood- dominated forests may experience stress from higher temperatures, allowing pines and other fast-growing species to become more dominant at the expense of slower-growing species such as hickories and oaks.

UWHARRIE NATIONAL FOREST Heat stress may limit the growth of some southern pines and hardwood species. Stresses from drought and wide- scale pest outbreaks have the potential to cause large areas of forest dieback. Intensified extreme weather events, such as hurricanes, ice storms, and fire, are also expected to lead to changes in plant community composition. Species more resistant to these isturbances, such as longleaf pine, will be more resilient to a changing climate. Populations of other plants, including the endangered Schweinitz’s Sunflower, may be particularly vulnerable to future changes. REGION WIDE REGION WIDE Reduce impact on aquatic ecosystems affected by drought by With climate change projected to cause warmer temperatures and Forest favoring tree species that are fire tolerant and have relatively low variable precipitation in the future, water resources will likely be even Health water use (e.g. longleaf pine). more affected by drought and extreme weather events. Severe

drought impacts could lower streamflow in forested watersheds. Water Remove invasive species that use more water to reduce stress on the Increased water temperature due to warming climate can potentially Resources aquatic ecosystems. lead to an increase in toxic algal blooms in lakes.

NATIONAL FORESTS IN ALABAMA NATIONAL FORESTS IN ALABAMA CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS NATIONAL FORESTS IN FLORIDA CROATAN NATIONAL FOREST GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS NATIONAL FORESTS IN FLORIDA KISATCHIE NATIONAL FOREST GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS THE LAND BETWEEN THE LAKES RECREATION AREA NATIONAL FORESTS IN MISSISSIPPI NATIONAL FORESTS IN MISSISSIPPI OUACHITA NATIONAL FOREST OUACHITA NATIONAL FOREST SUMTER NATIONAL FOREST OZARK-ST. FRANCIS NATIONAL FORESTS NATIONAL FORESTS AND GRASSLANDS IN TEXAS SUMTER NATIONAL FOREST UWHARRIE NATIONAL FOREST NATIONAL FORESTS AND GRASSLANDS IN TEXAS

Southern Region

Indicator Impact Response Shifts in rainfall patterns will lead to periods of flooding and drought UWHARRIE NATIONAL FOREST that can significantly impact water resources. Increases in heavy Focus attention on and near smaller, isolated water systems that are downpours and more intense hurricanes can lead to greater erosion more vulnerable and may not be able to absorb and benefit from and more sedimentation in waterways. Increased periods of drought wildfires and heavy rains that cause large floods or debris flow. may lead to poor water quality. Relieve groundwater and large reservoir use when there is ample CHEROKEE NATIONAL FOREST surface water during wet periods or times of high water flow to In the Southern Appalachian Mountains, high- elevation streams are recharge aquifers, provide temporary irrigation, decrease stored most susceptible to acidification. As stream temperatures continue to sediment loss, and construct small reservoirs. rise, species shifting to higher elevations will be constrained by the acidification process. This increases the likelihood of local and CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS regional extinction. CHEROKEE NATIONAL FOREST GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS DANIEL BOONE NATIONAL FOREST KISATCHIE NATIONAL FOREST Shifts in rainfall patterns will lead to periods of flooding and drought THE LAND BETWEEN THE LAKES RECREATION AREA that can significantly affect depth and volume of water in lakes, OUACHITA NATIONAL FOREST streams, wetlands and underground water systems. Heavy OZARK-ST. FRANCIS NATIONAL FORESTS downpours may lead to erosion and sedimentation in waterways as NATIONAL FORESTS AND GRASSLANDS IN TEXAS well as flooding and damage to forest roads and recreation sites. UWHARRIE NATIONAL FOREST Periods of drought between rain events may affect species of fish, Restore and reinforce vegetation in headwater and marsh areas to mussels and amphibians that are sensitive to fluctuations in water help alleviate runoff of sediment during heavy rain, reduce climate- temperature and depth. induced warming of water, and decrease water sensitivity to changes in air temperature KISATCHIE NATIONAL FOREST Shifts in rainfall patterns will lead to periods of flooding and drought CHEROKEE NATIONAL FOREST that can significantly impact water resources. Increases in heavy To reduce acidity in headwaters, use liming techniques. To reduce downpours and more intense storms can lead to greater erosion and temperatures, canopy enhancement is a primary strategy. more sedimentation in waterways. Increased periods of drought may lead to poor water quality, more variable stream flows, and loss of CROATAN NATIONAL FOREST quality aquatic habitat. Focus attention on and near smaller, isolated water systems that are more vulnerable and may not be able to absorb and benefit from THE LAND BETWEEN THE LAKES RECREATION AREA wildfires and heavy rains that cause large floods or debris flow. Shifts in rainfall patterns will lead to periods of flooding and drought that can significantly impact water resources. Increases in heavy Develop a coordinated system of monitored and controlled entrance downpours and more intense storms are leading to greater erosion points that control the majority of water flow inland from the shoreline and more sedimentation in waterways. 56

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response and high-value water and land restoration areas in order to reduce OZARK-ST. FRANCIS NATIONAL FORESTS salt-intrusion as well as to preserve marshes and swamps. Shifts in rainfall patterns will lead to periods of flooding and drought that can significantly impact water resources. Increases in heavy FRANCIS MARION NATIONAL FOREST downpours and more intense storms can lead to greater erosion and Protect existing coastal marshes by promoting healthy vegetation and more sedimentation in waterways. Increased periods of drought may restoring natural hydrology, and maintain coastal land buffers to allow lead to poor water quality. for the natural inland migration of salt marshes as sea levels rise.

EL YUNQUE NATIONAL FOREST Aquatic Ecosystems – Shifts in rainfall patterns due to climate change will lead to periods of flooding and drought that can significantly impact aquatic ecosystems and water resources. Increases in heavy downpours and more intense hurricanes in the wet season can lead to greater erosion and sedimentation in waterways. Riparian areas will see changes in structure and composition due to altered temperature, precipitation and run-off regimes as well as changes in the distribution of plant and animal species. Extended droughts in the dry season may significantly affect water resources by decreasing dissolved oxygen content. Freshwater aquatic communities during drought will experience crowding of species, leading to habitat squeezes and a decrease in reproductive output.

FRANCIS MARION NATIONAL FOREST Coastal Ecosystems - Coastal areas in the Southeast have already experienced an average of one inch of sea level rise per decade over the 20th century, a rate that will continue to increase in the future. Rising seas, in combination with more intense hurricanes, will alter the composition of coastal marshes. As saltwater flooding expands, low-lying coastal wet forests could become marshland where landuse barriers do not exist. Sea level rise can also increase the potential for saltwater intrusion into coastal freshwater tables. Increasing salinity of coastal aquifers may affect groundwater resources within three miles of the coast.

Southern Region

Indicator Impact Response such as Carolina bays, will be particularly vulnerable to changing climate as temperature and rainfall changes have the potential to lower groundwater table levels, altering the length of time that wetlands hold standing water.

NANTAHALA and PISGAH NATIONAL FORESTS Water Resources - Shifts in rainfall patterns will lead to periods of flooding and drought that can significantly impact water resources. Increases in heavy downpours and more intense hurricanes can lead to greater erosion and more sedimentation in our waterways. Increased periods of drought may lead to decreasing dissolved oxygen content and poor water quality in some areas. Groundwater- fed wetlands such as high-elevation bogs will be particularly vulnerable to changing climate as temperature and rainfall changes have the potential to lower groundwater table levels, altering the length of time that wetlands hold standing water. Prescribed See Forest Health – Plant Communities See Forest Health – Plant Communities Fire REGION WIDE REGION WIDE Changes in precipitation due to drought could negatively impact water Enact monitoring to determine when it is safe for recreational activities Recreational based outdoor recreation like canoeing, kayaking, and motorized to take place in water recreation areas and communicate effectively to Use and activities. Increase in temperature can impact visitors comfort. visitors the potential risks of higher temperature or high water levels. Satisfaction Climate change can also have impacts on culturally significant natural resources. Work with local indigenous populations and cultural groups to provide resources for them to adapt to the climate. NATIONAL FORESTS IN ALABAMA CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS NATIONAL FORESTS IN ALABAMA CHEROKEE NATIONAL FOREST CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS CROATAN NATIONAL FOREST CHEROKEE NATIONAL FOREST NATIONAL FORESTS IN FLORIDA CROATAN NATIONAL FOREST GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS NATIONAL FORESTS IN FLORIDA KISATCHIE NATIONAL FOREST KISATCHIE NATIONAL FOREST NATIONAL FORESTS IN MISSISSIPPI THE LAND BETWEEN THE LAKES RECREATION AREA OUACHITA NATIONAL FOREST NATIONAL FORESTS IN MISSISSIPPI OZARK-ST. FRANCIS NATIONAL FORESTS OUACHITA NATIONAL FOREST SUMTER NATIONAL FOREST OZARK-ST. FRANCIS NATIONAL FORESTS 58

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response NATIONAL FORESTS AND GRASSLANDS IN TEXAS SUMTER NATIONAL FOREST UWHARRIE NATIONAL FOREST NATIONAL FORESTS AND GRASSLANDS IN TEXAS Environmental changes may negatively impact recreational UWHARRIE NATIONAL FOREST experiences due to changes in the plant and animal communities that Communicate early warnings for extreme weather to protect make those experiences unique. More days above freezing could vulnerable groups from health impacts, such as heat illnesses, and increase tick and mosquito populations throughout the year, leading monitor for early outbreaks of disease to an increase in vector-borne illness. With more days of extreme heat, recreation areas could see decreased use in the summer if CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS temperatures impact visitor comfort. CHEROKEE NATIONAL FOREST NATIONAL FORESTS IN FLORIDA DANIEL BOONE NATIONAL FOREST GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS Changes in plant and animal communities as mentioned above may NATIONAL FORESTS IN MISSISSIPPI make some areas less attractive to recreation users. Tick and SUMTER NATIONAL FOREST mosquito populations may increase due to warmer winters and extreme heat may result in less visitors during high heat conditions. Examine the goals for a water system or area of land when considering changing dynamics. For example, a stream managed EL YUNQUE NATIONAL FOREST mostly for recreation must balance the demand for rainbow trout from Recreation – The Caribbean region, where year-round warm weather anglers with other aquatic and terrestrial impacts. is the principle tourism resource, may see increasing competition from other regions as warm seasons expand globally. Sea level rise EL YUNQUE NATIONAL FOREST will affect coastal resorts, which may affect tourist and recreationist Anticipate changes in visitor behavior and proactively plan to mitigate preferences throughout Puerto Rico. Climate change may affect any seasonal increases in use. recreation in El Yunque through changes to local ecosystems and resources that impact scenic values, as well as changes to weather patterns that may disrupt recreational activities and lead to changes in visitor use. Visitors to El Yunque may see impacts to the local plant and animal communities that make the forest unique. An increase in extreme weather events may increase damage to facilities and structures, reduce tourist access in some areas, and increase the need for road repairs.

FRANCIS MARION NATIONAL FOREST Recreation - Environmental changes may negatively impact recreational experiences due to changes in the plant and animal communities that make those experiences unique. Fishing in coastal marshes could be affected, as intense storm events and rising sea levels may lead to degraded habitat conditions for game fish. More days above freezing could increase tick and mosquito populations throughout the year, leading to an increase in vector-borne illness.

Southern Region

Indicator Impact Response With more days with extreme heat, recreation areas could see decreased use in the summer if temperatures impact visitor comfort.

NANTAHALA and PISGAH NATIONAL FORESTS Recreation - Environmental changes may negatively impact recreational experiences due to changes to the plant and animal communities that make those recreational experiences unique, along with an increase in haze that may reduce the visibility of mountain views. While more days above freezing could increase use in some forest areas in the cooler seasons, more days with extreme heat could decrease use in the summer if temperatures impact visitor comfort. The fall foliage season may be affected as leaves change color later in the season and increasing stresses on forests impact the vividness of fall foliage displays. CHEROKEE NATIONAL FOREST CHEROKEE NATIONAL FOREST Air Quality Wildfire Air Quality As climate change increases the potential for wildfire and prescribed Since future emissions from wildfires will likely increase, emissions fires become a forest management tool, smoke Appalachian Trail can have important air quality impacts both regionally and management tools can help managers with decisions on how to locally. balance fire-related smoke and the importance of air quality in affected areas. Jobs and See Economics Analysis See Economics Analysis Income Phenology See Forest Health – Plant Communities See Forest Health – Plant Communities REGION WIDE REGION WIDE Extreme precipitation events are becoming more likely; however, Manage tree densities through practices such as thinning and there are longer dry periods between storms. Increasing drought prescribed fire to maximize carbon sequestration and reduce the frequency and a projected increase in dry season, as much as 156 vulnerability of forest stands to water stress, insect and disease days in some areas, will increase the risk of wildfires. Not only are outbreaks, and fire. Extreme extreme precipitation events becoming more likely, hurricanes are Weather becoming more severe and are able to sustain damaging conditions Communicate early warnings to visitors of extreme weather events. for longer periods of time. These events have large impacts on nearby estuaries and coastal waters, including negatively impacting NATIONAL FORESTS IN ALABAMA carbon sources and sinks. CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS NATIONAL FORESTS IN FLORIDA

Southern Region Broad-scale Monitoring Evaluation Report

Indicator Impact Response NATIONAL FORESTS IN ALABAMA GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS CHATTAHOOCHEE AND OCONEE NATIONAL FORESTS KISATCHIE NATIONAL FOREST CROATAN NATIONAL FOREST NATIONAL FORESTS IN MISSISSIPPI NATIONAL FORESTS IN FLORIDA OZARK-ST. FRANCIS NATIONAL FORESTS NATIONAL FORESTS IN MISSISSIPPI SUMTER NATIONAL FOREST SUMTER NATIONAL FOREST NATIONAL FORESTS AND GRASSLANDS IN TEXAS NATIONAL FORESTS AND GRASSLANDS IN TEXAS Identify areas that provide particularly valuable ecosystem services, The potential for severe storms is expected to increase in the future, like timber harvest or carbon sequestration, and are also vulnerable to including more intense hurricanes making landfall in the southern US. extreme weather, like hurricanes or fires. Then plan conservation Extended periods of extreme high temperature and drought may lead strategies accordingly to mitigate for extreme weather impacts and to drier forest fuels which will burn more easily and contribute to payment for ecosystem service programs. larger and more frequent wildfires. More cloud-to-ground lightning due to warming may also increase wildfire ignitions. Prescribed burning can also be a management option for reducing the impacts of any future increases in wildfire potential emanating from DANIEL BOONE NATIONAL FOREST climate change. GEORGE WASHINGTON AND JEFFERSON NATIONAL FORESTS KISATCHIE NATIONAL FOREST CROATAN NATIONAL FOREST OUACHITA NATIONAL FOREST Focus attention on and near smaller, isolated water systems that are OZARK-ST. FRANCIS NATIONAL FORESTS more vulnerable and may not be able to absorb and benefit from The potential for severe storms is expected to increase in the future. wildfires and heavy rains that cause large floods or debris flow. Extended periods of extreme high temperature and drought may lead to drier forest fuels which will burn more easily and contribute to Relieve groundwater and large reservoir use when there is ample larger and more frequent wildfires. More cloud-to-ground lightning surface water during wet periods or times of high water flow to due to warming may also increase wildfire ignitions. recharge aquifers, provide temporary irrigation, decrease stored sediment loss, and construct small reservoirs. EL YUNQUE NATIONAL FOREST Climate Trends – Average temperatures in El Yunque have increased NATIONAL FORESTS IN FLORIDA over the past 30 years, and scientists predict warming will continue at NATIONAL FORESTS IN MISSISSIPPI an accelerated pace, however, climate models vary in the Develop a coordinated system of mature and healthy coastal degree of warming. Projected decreases in precipitation in the mangroves, dunes, and wetlands that are resilient and resistant to the Caribbean suggest drier wet seasons, and even drier dry seasons. stress of climate change and protect against storm surge. This system Increasing sea surface temperatures may lift the base altitude of provides valuable and cost-effective ecosystem services and many cloud formation and alter atmospheric circulation patterns. Any ancillary benefits. change in the cloud base height will further decrease precipitation in El Yunque. THE LAND BETWEEN THE LAKES RECREATION AREA Prescribed burning can also be a management option for reducing the Extreme Weather – In the Caribbean, the occurrence of very warm impacts of any future increases in wildfire potential emanating from days and nights is accelerating, while very cool days and nights are climate change. becoming less common, increasing the likelihood of extreme heat waves. The frequency of extreme precipitation events is expected to OUACHITA NATIONAL FOREST

Southern Region

Indicator Impact Response increase, leading to potential increases in inland flooding and Identify areas that provide particularly valuable ecosystem services, landslides. Hurricane events are likely to become less frequent but like timber harvest or carbon sequestration, and are also vulnerable to more severe, with increased wind speeds, rainfall intensity, and storm extreme weather, like tornadoes or fires. Then plan conservation surge height. As annual rainfall decreases over time in the Caribbean strategies accordingly to mitigate for extreme weather impacts and region, longer periods of drought are expected in the future. In Puerto payment for ecosystem service programs. Rico, where nearly all wildfires are associated with human activity, the interactions between climate warming and drying and increased EL YUNQUE NATIONAL FOREST human development have the potential to increase the effects of fire. Mitigate future negative effects on sensitive species following intense hurricane events by promoting the establishment of more disturbance- FRANCIS MARION NATIONAL FOREST resistant species, such as palms Extreme Weather - The potential for severe storms is expected to increase in the future, including more intense hurricanes making landfall in the southern US, with potential increases in both inland flooding and coastal storm surge events. Under a more variable climate, extended periods of extreme high temperature and drought may lead to drier forest fuels which will burn more easily and at hotter temperatures and contribute to larger and more frequent wildfires. More cloud-to-ground lightning due to warming may also increase wildfire ignitions.

NANTAHALA and PISGAH NATIONAL FORESTS Extreme Weather - The potential for severe storms is expected to increase in the future, including more intense hurricanes making landfall in the southern US, with potential increases in flooding and landslides in mountainous landscapes. Conversely, extended periods of drought and forest stress may lead to drier fuels which will burn more easily and at hotter temperatures, and contribute to more and larger wildfires. More cloud-to- ground lightning due to warming may increase wildfire ignitions, even in mountainous areas where fires are historically lesscommon

Region 8 Broad Scale Monitoring

Economic Indicators

Prepared by: Allison Borchers Economist

for: US Forest Service, Region 8

August 30, 2018

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 ...... 3 Demographics ...... 3 Economy ...... 4 Forest Operations...... 8 Economic Contribution Analysis ...... 9 Employment by Program Area ...... 10 Labor Income by Program Area ...... 10 Summary / Conclusion ...... 11 References Cited ...... Error! Bookmark not defined. Appendix A. Counties by Planning Unit ...... Error! Bookmark not defined. Appendix B. Total Number of Jobs Contributed, by Resource Program, 2015 ...... 1 Appendix C. Total Labor Income Contributed, by Resource Program, 2015 ...... 4 Appendix D. Unemployment rate...... 5

i Economic Indicators Region 8

Tables

Table 1. National Forest Planning Units in Region 8 ...... 2 Table 2. Population and population change...... 4 Table 3. Unemployment rate, 2016 ...... 5 Table 4. Per capita income and population poverty levels, 2016 ...... 6 Table 5. Secure Rural Schools (SRS) Act Payments and 1908 Act 25 Percent Payments, 2017 .... 7 Table 6. Expenditure by Forest Planning Unit, FY2016 ...... 8 Table 7. Total number of jobs contributed by program area, Region 8, 2015 ...... 10 Table 8. Total labor income, by program area ...... 10

ii Economic Indicators Region 8

Economic Monitoring Indicators Introduction The U.S. Forest Service manages approximately x acres of public land across xx National Forests and Grasslands in 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 both provide a regional assessment of changes in x, but also offer needed information and analysis for Forest units undertaking similar monitoring efforts. The indicators are intended to cover the forests economic contribution and the economic 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 the forest’s 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.

Relevant indicators to measure human uses and values of the National Forests and Grasslands include: payments to states and counties, and Forest Service expenditures.

Baseline demographic and economic data are drawn from federal sources, such as the U.S. Census Bureau and the Bureau of Economic Analysis. El Yunque NF, located in Puerto Rico, at times required a different source to obtain similar indicators. Therefore, the indicators for El Yunque NF 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 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

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 1). 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.

Table 1. National Forest Planning Units in Region 8

Planning Unit Grouped National Forests (when applicable) NFs in Alabama Bankhead, Talladega, Tuskegee, Conecuh Chattahoochee-Oconee NFs Cherokee NF Kisatchie NF Daniel Boone NF Land Between the Lakes RNA El Yunque NF NFs in Florida Apalachicola, Oseola, Ocala Francis Marion NF George Washington NF Jefferson NF NFs in Mississippi Bienville, Chickasawhay Delta, Do Soto, Holly Springs, Homochito, Tombigbee Croatan NF Nantahala and Pisgah NFs Uwharrie NF Ozark-St. Francis NFs Ouachita NF Sumter NF NFG 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 1) 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 listing of counties by forest planning unit.

2 Economic Indicators Region 8

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 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 economic area of influences, as determined by the modeling needs, than the population and income tables. 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 forests and grasslands have seen significant population growth. Ozark-St.Francis, Chattahoochee-Oconee, NF 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 Forests (Sumter, Kiskatchie, Daniel Boone and El Yunque NFs) 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 forests and grasslands have seen population growth above this average. And most had growth greater than the US non-metro average. This likely reflects the more urban nature of many communities surrounding Region 8 lands. This serves to highlight the pressures

3 Economic Indicators Region 8

population growth will likely have on Forest Service managed lands and the need for management to address the challenges population growth can pose.

Table 2. Population and population change

Percent Change Planning Unit 2000 2016 2000-2016 NF Alabama 736,578 768,901 4% Chattahoochee-Oconee 879,200 1,081,647 23% Cherokee NF 578,052 622,432 8% Croatan 161,649 182,180 13% Daniel Boone NF 445,397 447,315 0% El Yunque NF 113,188 101,114 -11% NF Florida 1,232,584 1,631,303 32% Francis Marion 966,212 1,298,705 34% George Washington 587,309 673,028 15% Jefferson 779,875 810,922 4% Kiskatchie 395,644 400,706 1% Land Between the Lakes 138,430 146,896 6% NF of Mississippi 1,079,419 1,136,356 5% Nantahala-Pisgah NF 820,564 943,759 15% Ouachita 545,030 597,548 10% Ozark-St. Francis 678,374 867,283 28% Sumter 405,695 414,921 2% NF in TX 1,083,846 1,429,561 32% Uwharrie 305,600 335,760 10% Region 8 (excl. El Yunque) 10,740,464 12,348,312 15% United States 282,162,411 323,127,513 15% US (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.

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

4 Economic Indicators Region 8 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’s area of influence in Region 8 is also within an acceptable range. Three planning areas—Kiskatchie NF, NF of Alabama, and Daniel Boone NF—have unemployment rates above six percent. These are regions which maybe more sensitive to changes in Forest management which impact the local economy.

Table 3. Unemployment rate, 2016

Location 2016

NF Alabama 6.5% Chattahoochee-Oconee 5.3% Cherokee NF 5.4% Croatan 5.2% Daniel Boone NF 7.2% El Yunque NF 23.1% NF Florida 4.9% Francis Marion 5.0% George Washington 3.9% Jefferson 4.8% Kiskatchie 6.8% Land Between the Lakes 5.6% NF of Mississippi 6.0% Nantahala-Pisgah NF 4.6% Ouachita 4.4% Ozark-St. Francis 3.5% Sumter 5.3% NF in TX 5.4% Uwharrie 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.

5 Economic Indicators Region 8

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 4 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 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 4). 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 NFS 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 4. Per capita income and population poverty levels, 2016 Percent of Planning Unit Per capita population below income poverty level NF Alabama $ 34,211 21% Chattahoochee-Oconee $ 36,464 18% Cherokee NF $ 37,054 19% Croatan $ 44,153 15% Daniel Boone NF $ 30,908 29% El Yunque a $ 9,968 47% NF Florida $ 39,664 17% Francis Marion $ 41,187 17% George Washington $ 40,378 11% Jefferson $ 39,750 15%

6 Economic Indicators Region 8

Kiskatchie $ 38,961 23% Land Between the Lakes $ 36,928 18% NF of Mississippi $ 34,252 23% Nantahala-Pisgah NF $ 37,623 18% Ouachita $ 36,141 19% Ozark-St. Francis $ 47,198 17% Sumter $ 34,872 21% NF in TX $ 44,573 16% Uwharrie $ 35,575 17% Region 8 $ 38,237 18% US (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 Bureau, 2012-2016 American Community Survey 5-Year Estimates, available at https://factfinder.census.gov. Downloaded June 7, 2018.

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. PILT 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 5 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 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 5. Secure Rural Schools (SRS) Act Payments and 1908 Act 25 Percent Payments, 2017

Total Average payment National Forest Acres payment per Acre NFs in Alabama 670,804 1,572,325 $ 2.34

7 Economic Indicators Region 8

Chattahoochee-Oconee NFs 867,841 1,358,396 $ 1.57 Cherokee NF 657,324 908,446 $ 1.38 Kisatchie NF 608,565 1,556,216 $ 2.56 Daniel Boone NF 711,230 1,377,585 $ 1.94 Land Between the Lakes RNA 171,251 167,416 $ 0.98 El Yunque NF 28,709 128,632 $ 4.48 NFs in Florida 1,203,413 2,303,596 $ 1.91 Francis Marion NF 260,495 411,491 $ 1.58 George Washington NF 1,067,079 776,193 $ 0.73 Jefferson NF 726,778 831,087 $ 1.14 NFs in Mississippi * 1,191,094 4,764,452 $ 4.00 Croatan NF 161,325 148,190 $ 0.92 Nantahala and Pisgah NFs 1,043,297 1,354,315 $ 1.30 Uwharrie NF 51,398 75,615 $ 1.47 Ozark-St. Francis NFs 22,827 71,717 $ 3.14 Ouachita NF 1,785,583 4,480,368 $ 2.51 Sumter NF 372,972 1,149,341 $ 3.08 NF 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 forest. Some of the least affluent areas--for example, National Forests of 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 SRS and other county payments, such as Payments in Lieu of Taxes (PILT), 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 6. Expenditure by Forest Planning Unit, FY2016 Planning Unit Salary Nonsalary NFs in Alabama $ 11,742,580.92 $ 7,186,656.96 Chattahoochee-Oconee NFs $ 10,005,183.81 $ 5,269,440.15

8 Economic Indicators Region 8

Cherokee NF $ 11,739,961.80 $ 7,158,868.86 Kisatchie NF $ 12,722,862.62 $ 5,903,195.28 Daniel Boone NF $ 10,582,339.08 $ 4,599,586.71 El Yunque NF NA NA Land Between the Lakes RNA $ 4,401,967.73 $ 8,205,400.21 NFs in Florida $ 15,507,263.20 $ 14,314,412.37 Francis Marion & Sumter NF $ 11,767,310.36 $ 7,121,841.05 George Washington & Jefferson NF $ 16,339,132.90 $ 9,825,710.84 NFs in Mississippi $ 17,640,918.59 $ 10,722,997.83 NF in North Carolina $ 16,726,864.58 $ 13,162,510.27 Ozark-St. Francis NFs $ 15,617,260.62 $ 10,055,547.41 Ouachita NF $ 19,441,214.58 $ 14,177,367.85 NFG 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 forest. 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

9 Economic Indicators Region 8

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 activity in the region. Based on IMPLAN analysis, Table 7 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 7. 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 8 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 7 indicated program area contributions to regional employment, Table 8 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 8. Total labor income, by program area Total Labor Income (thousands of Program area 2015 dollars) Recreation $454,544

10 Economic Indicators Region 8

Grazing $1,162 Timber $216,010 Minerals $17,657 Payments to States/Counties $50,441 Forest Service Expenditures $271,820 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 economic 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 2). 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 Forests experienced even higher poverty levels (Table 4). 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 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. 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.

PILT is not reported above, but is 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 longer time trends should be informative. Similar to the current comparisons to national averages, comparing trends to

Employment by sector, and relative size of the sector, is an area which may be of interest in future iterations. This could illustrate the size and importance of the timber sector, for example, and therefore

11 Economic Indicators Region 8 help understand the relative importance of forest product removal and changing relationship to public lands. These are tricky connections to draw. Range is a relatively small resource program in Region 8 and therefore the agricultural sector may be less important to watch. 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. Regardless, a thoughtful assessment of sector employment trends is an area which may of interest in the future.

Trends in resource use could also be informative in this report. For example, AUMS, recreation visits, mineral products and timber forest products and special forest products. Most of these are inputs into the contribution analysis so therefore trends in these underlying resource use explain trends in the economic contributions. They also help illustrate how different resource relationships might be changing over time.

12 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 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.

13 Economic Indicators Region 8

Appendix A. Counties by Planning Unit Planning Unit Counties NFs in Alabama Alabama: Bibb, Calhoun, Cherokee, Chilton, Clay, Cleburne, Covington, Dallas, Escambia, Franklin, Hale, Lawrence, Macon, Perry, Talladega, Tuscaloosa, Winston

Chattahoochee- Georgia: BANKS, CATOOSA, CHATTOOGA, DAWSON, FANNIN, FLOYD, GILMER, GORDON, GREENE, HABERSHAM, Oconee NFs HALL, JASPER, JONES, LUMPKIN, MORGAN, MURRAY, OCONEE, OGLETHORPE, PUTNAM, RABUN, STEPHENS, TOWNS, UNION, WALKER, WHITE, WHITFIELD Cherokee NF Tennessee: Carter, Cocke, Greene, Johnson, McMinn, Monroe, Polk, Sullivan, Unicol, Washington

North Carolina: Ashe Kisatchie NF Louisiana: Claiborne, Lincoln, Jackson, Winn, Grent, Rapdes, Vernon, Natchitoches, Red River, Bienville, Webster

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

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

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

1 Economic Indicators Region 8

Croatan NF North Carolina: Carteret, Craven, Jones Nantahala and Pisgah North Carolina: Cherokee, Clay, Graham, Swain, Macon, Jackson, Haywood, Transylvania, Henderson, Buncombe, Madison, NFs Yancey, McDowell, Burke, Caldwell, Watauga, Avery, Mitchell

Uwharrie NF North Carolina: Montgomery, Randolph, Davidson Ozark-St. Francis NFs Arkansas: Baxter , Benton , Conway , Crawford, Franklin, Johnson, Logan, Madison, Marion, Newton, Pope, Searcy, Stone, Van Buren, Washington, Yell, Lee, Philips

Ouachita NF Arkansas: Ashley, Garland, Hot Spring, Howard, Logan, Montgomery, Perry, Pike, Polk, Saline, Scott, Sebastian, Yell

Oklahoma: LeFlore, McCurtain Sumter NF South Carolina: Abbeville, Chester, Edgefield, Fairfield, Greenwood, Laurens, McCormick, Newberry, Oconee, Saluda, Union NFG in Texas Texas: Angelina, Fannin, Houston, Jasper, Montague, Montgomery, Nacogdoches, Newton, Sabine, San Augustine, San Jacinto, Shelby, Trinity, Tyler, Walker, Wise

Note: 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.

2 Economic Indicators Region 8

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

Total Number of Jobs Contributed

Forest Total Payments to Planning Unit Recreation Grazing Timber Minerals Service Forest States/Counties Expenditures Management

Chattahoochee- 1,364 6 67 0 66 253 1,756 Oconee Cherokee NF 566 0 49 0 35 268 918 Daniel Boone NF 597 0 62 3 50 239 952 El Yunque 661 0 281 0 1 55 997 Francis Marion 197 0 334 0 38 281 850 &Sumter George Washington & 776 33 197 1 102 378 1487 Jefferson Kisatchie 80 1 480 4 47 291 903 Land Between the 544 0 45 0 12 120 722 Lakes NF Alabama 166 0 224 0 49 264 702 NF Florida 485 2 148 0 88 463 1187 NF in Mississippi 467 0 997 0 108 421 1993 NF in North Carolina 6064 0 95 2 116 402 6679 NF in Texas 446 15 270 125 62 248 1167 Ouschita 727 11 548 0 157 462 1905 Ozark St Francis 1089 12 411 39 107 391 2050 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

3 Economic Indicators Region 8

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

Forest Payments to Total Forest Planning Unit Recreation Grazing Timber Minerals Service States/Counties Management Expenditures

Chattahoochee-Oconee $46,869 $88 $3,359 $0 $3,395 $15,800 $69,510 Cherokee NF $16,608 $0 $2,117 $0 $1,591 $13,552 $33,868 Daniel Boone NF $17,831 $0 $2,544 $187 $2,254 $13,438 $36,254 El Yunque $19,245 $0 $16,476 $0 $42 $3,784 $39,547 Francis Marion &Sumter $6,516 $0 $16,246 $0 $1,894 $17,519 $42,175 George Washington & Jefferson $26,504 $434 $8,111 $96 $5,428 $22,810 $63,382 Kisatchie $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 NF Alabama $5,658 $0 $11,960 $0 $2,515 $16,441 $36,574 NF Florida $15,975 $33 $7,660 $0 $4,287 $27,265 $55,219 NF in Mississippi $15,463 $1 $48,787 $0 $5,107 $25,671 $95,029 NF in North Carolina $192,534 $0 $4,519 $104 $5,869 $24,174 $227,200 NF in Texas $19,355 $237 $17,136 $15,428 $3,662 $17,386 $73,206 Ouschita $20,067 $175 $28,205 $8 $6,706 $27,552 $82,714 Ozark St Francis $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

4 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% Region 8** (excl. El Yunque) 5.1% US (Non Metro) 6.7% 4.6% 9.9% 7.7% 6.4% 5.7% 5.4% 4.7% **2016 unemployment 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.

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.