Open Access Journals

AIMS Environmental Science, 2015, 2(2): 180-202. doi: 10.3934/environsci.2015.2.180. Research article Special Issue

Export file:

Format

RIS(for EndNote,Reference Manager,ProCite)
BibTex
Text

Content

Citation Only
Citation and Abstract

Linking state-and-transition simulation and timber supply models for forest biomass production scenarios

Jennifer K. Costanza, Robert C. Abt, Alexa J. McKerrow, Jaime A. Collazo

1 North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology, North Carolina State University, Raleigh, NC 27695, USA;
2 Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA;
3 Core Science Analytics and Synthesis, U.S. Geological Survey, Raleigh, NC 27695, USA;
4 U.S. Geological Survey, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology, North Carolina State University, Raleigh, NC 27695, USA

Received date: , Accepted date: , Published date:

Special Issue: 2nd State-and-Transition Simulation Modeling Conference

Abstract Full Text(HTML) Figure/Table Supplementary Related pages

We linked state-and-transition simulation models (STSMs) with an economics-based timber supply model to examine landscape dynamics in North Carolina through 2050 for three scenarios of forest biomass production. Forest biomass could be an important source of renewable energy in the future, but there is currently much uncertainty about how biomass production would impact landscapes. In the southeastern US, if forests become important sources of biomass for bioenergy, we expect increased land-use change and forest management. STSMs are ideal for simulating these landscape changes, but the amounts of change will depend on drivers such as timber prices and demand for forest land, which are best captured with forest economic models. We first developed state-and-transition model pathways in the ST-Sim software platform for 49 vegetation and land-use types that incorporated each expected type of landscape change. Next, for the three biomass production scenarios, the SubRegional Timber Supply Model (SRTS) was used to determine the annual areas of thinning and harvest in five broad forest types, as well as annual areas converted among those forest types, agricultural, and urban lands. The SRTS output was used to define area targets for STSMs in ST-Sim under two scenarios of biomass production and one baseline, business-as-usual scenario. We show that ST-Sim output matched SRTS targets in most cases. Landscape dynamics results indicate that, compared with the baseline scenario, forest biomass production leads to more forest and, specifically, more intensively managed forest on the landscape by 2050. Thus, the STSMs, informed by forest economics models, provide important information about potential landscape effects of bioenergy production.

Figure/Table

Supplementary

Article Metrics

Keywords: bioenergy; forest dynamics; forest residues; land-use change; landscape dynamics; longleaf pine ecosystem; Southeastern U.S.; ST-Sim; SubRegional Timber Supply (SRTS) model; timber harvest

Citation: Jennifer K. Costanza, Robert C. Abt, Alexa J. McKerrow, Jaime A. Collazo. Linking state-and-transition simulation and timber supply models for forest biomass production scenarios. AIMS Environmental Science, 2015, 2(2): 180-202. doi: 10.3934/environsci.2015.2.180

References:

1. Sedjo RA, Sohngen B (2013) Wood as a Major Feedstock for Biofuel Production in the United States: Impacts on Forests and International Trade. J Sustain For 32: 195-211.
2. Goh CS, Junginger M, Cocchi M, et al. (2013) Wood pellet market and trade: a global perspective. Biofuels Bioprod Bioref 7: 24-42.
3. Immerzeel DJ, Verweij PA, van der Hilst F, et al. (2014) Biodiversity impacts of bioenergy crop production: A state-of-the-art review. GCB Bioenergy 6: 183-209.
4. McDonald RI, Fargione J, Kiesecker J, et al. (2009) Energy sprawl or energy efficiency: Climate policy impacts on natural habitat for the United States of America. PLoS One 4: e6802.
5. Stoms DM, Davis FW, Jenner MW, et al. (2012) Modeling wildlife and other trade-offs with biofuel crop production. GCB Bioenergy 4: 330-341.
6. Dale VH, Kline KL, Wiens J, et al. (2010) Biofuels: Implications for Land Use and Biodiversity. The Ecological Society of America Biofuels and Sustainability Reports. Available from: http://www.esa.org/biofuelsreports/files/ESA%20Biofuels%20Report_VH%20Dale%20et%20al.pdf.
7. Wiens J, Fargione J, Hill J (2011) Biofuels and biodiversity. Ecol Appl 21: 1085-1095.
8. Daystar J (2014) Environmental Impacts of Cellulosic Biofuels Made in the South East: Implications of Impact Assessment Methods and Study Assumptions. North Carolina State University: 264 pages.
9. Wear D, Abt R, Alavalapati J, et al. (2010) The South's Outlook for Sustainable Forest Bioenergy and Biofuels Production. The Pinchot Institute Report. Available from: http://www.pinchot.org/uploads/download?fileId=512.
10. Fletcher RJ, Robertson BA, Evans J, et al. (2011) Biodiversity conservation in the era of biofuels: risks and opportunities. Front Ecol Environ 9: 161-168.
11. Riffell S, Verschuyl J, Miller D, et al. (2011) Biofuel harvests, coarse woody debris, and biodiversity – A meta-analysis. For Ecol Manage 261: 878-887.
12. Dale VH, Lowrance R, Mulholland P, et al. (2010) Bioenergy Sustainability at the Regional Scale. Ecol Soc 15: 23.
13. Wear DN, Huggett R, Li R, et al. (2013) Forecasts of Forest Conditions in U.S. Regions under Future Scenarios: A Technical Document Supporting the Forest Service 2010 RPA Assessment. Gen Tech Rep SRS-170.
14. Lubowski RN, Plantinga AJ, Stavins RN (2008) What Drives Land-Use Change in the United States? A National Analysis of Landowner Decisions. Land Econ 84: 529-550.
15. Daniel CJ, Frid L (2012) Predicting Landscape Vegetation Dynamics Using State-and-Transition Simulation Models. Proc First Landsc State-and-Transition Simul Model Conf June 14-16 2011: 5-22.
16. Bestelmeyer BT, Herrick JE, Brown JR, et al. (2004) Land management in the American southwest: a state-and-transition approach to ecosystem complexity. Environ Manage 34: 38-51.
17. Costanza JK, Hulcr J, Koch FH, et al. (2012) Simulating the effects of the southern pine beetle on regional dynamics 60 years into the future. Ecol Modell 244: 93-103.
18. Wilson T, Costanza J, Smith J, et al. (2014) Second State-and-Transition Simulation Modeling Conference. Bull Ecol Soc Am 96: 174-175.
19. Halofsky J, Halofsky J, Burscu T, et al. (2014) Dry forest resilience varies under simulated climate-management scenarios in a central Oregon, USA landscape. Ecol Appl 24: 1908-1925.
20. Provencher L, Forbis TA, Frid L, et al. (2007) Comparing alternative management strategies of fire, grazing, and weed control using spatial modeling. Ecol Modell 209: 249-263.
21. Abt R, Cubbage F, Abt K (2009) Projecting southern timber supply for multiple products by subregion. For Prod J 59: 7-16.
22. Abt KL, Abt RC, Galik CS, et al. (2014) Effect of Policies on Pellet Production and Forests in the U.S. South: A Technical Document Supporting the Forest Service Update of the 2010 RPA Assessment. Gen Tech Rep GTR-SRS-202.
23. U.S. Geological Survey National Gap Analysis Program (2013) Protected Areas Database-US (PAD-US), Version 1.3. Available from: http://gapanalysis.usgs.gov/padus/.
24. Terando A, Costanza JK, Belyea C, et al. (2014) The southern megalopolis: using the past to predict the future of urban sprawl in the Southeast U.S. PLoS One 9: e102261.
25. Noss RF, Platt WJ, Sorrie BA, et al. (2015) How global biodiversity hotspots may go unrecognized: lessons from the North American Coastal Plain. Richardson D, ed. Divers Distrib 21: 236-244.
26. Southeast Gap Analysis Project (SEGAP) (2008) Southeast GAP regional land cover [digital data]. Available from: http://www.basic.ncsu.edu/segap/.
27. Burke S, Hall BR, Shahbazi G, et al. (2007) North Carolina's Strategic Plan for Biofuels Leadership. Available from: http://www.ces.ncsu.edu/fletcher/mcilab/publications/NC_Strategic_Plan_for_Biofuels_Leadership.pdf.
28. Forisk Consulting LLC (2014) Wood bioenergy US database 2013. Available by subscription.
29. Lal P, Alavalapati JRR, Marinescu M, et al. (2011) Developing Sustainability Indicators for Woody Biomass Harvesting in the United States. J Sustain For 30: 736-755.
30. Evans A, Perschel R, Kittler B, et al. (2010) Revised assessment of biomass harvesting and retention guidelines. For Guild, St Fe, NM, USA: 33.
31. Janowiak MK, Webster CR (2010) Promoting Ecological Sustainability in Woody Biomass Harvesting. J For 108: 16-23.
32. Apex Resource Management Solutions (2014) ST-Sim state-and-transition simulation model software. Available from: http//www.apexrms.com/stsm.
33. Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildl Fire 18: 235-249.
34. Comer P, Faber-Langendoen D, Evans R, et al. (2003) Ecological Systems of the United States: A Working Classification of U.S. Terrestrial Systems. Arlington, VA, USA. NatureServe, 82 pages.
35. Costanza JK, Terando AJ, McKerrow AJ, et al. (2015) Modeling climate change, urbanization, and fire effects on Pinus palustris ecosystems of the southeastern U.S. J Environ Manage 151: 186-199.
36. LANDFIRE (2014) LANDFIRE 2008 (version 1.1.0) Succession Class (S-Class) Layer. U.S. Department of Interior, Geological Survey. Available from: Http://landfire.cr.usgs.gov/viewer.
37. Multi-Resolution Land Characteristics Consortium (MRLC) (2011) National Land Cover Database, USFS Tree Canopy Cartographic, 2014. Available from: http://www.mrlc.gov/nlcd11_data.php.
38. Mackie R, Mason J, Curcio G (2007) LANDFIRE biophysical setting model for Southern Piedmont Dry Oak(-Pine) Forest. Available from: http://www.landfire.gov/national_veg_models_op2.php.
39. USDA Forest Service (2012) Forest Inventory and Analysis Data. Available from: http://apps.fs.fed.us/fiadb-downloads/datamart.html.
40. Young T, Wang Y, Guess F, et al. (2015) Understanding the Characteristics of Non-industrial Private Forest Landowners Who Harvest Trees. Small-scale For 1-13.
41. Hardie I, Parks P, Gottleib P, et al. (2000) Responsiveness of Rural and Urban Land Uses to Land Rent Determinants in the U.S. South. Land Econ 76: 659.
42. USDA Natural Resources Conservation Service (2000) 1997 National Resources Inventory Data, Revised December 2000.
43. Dale VH, Kline KL, Wright LL, et al. (2011) Interactions among bioenergy feedstock choices, landscape dynamics, and land use. Ecol Appl 21: 1039-1054.
44. Evans JM, Fletcher RJ, Alavalapati JRR, et al. (2013) Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity. Availbale from: http://www.nwf.org/News-and-Magazines/Media-Center/Reports/Archive/2013/12-05-13-Forestry-Bioenergy-in-the-Southeast.aspx.
45. Owens AK, Moseley KR, McCay TS, et al. (2008) Amphibian and reptile community response to coarse woody debris manipulations in upland loblolly pine (Pinus taeda) forests. For Ecol Manage 256: 2078-2083.
46. Otto CR V, Kroll AJ, McKenny HC (2013) Amphibian response to downed wood retention in managed forests: A prospectus for future biomass harvest in North America. For Ecol Manage 304: 275-285.
47. Davis JC, Castleberry SB, Kilgo JC (2010) Influence of coarse woody debris on herpetofaunal communities in upland pine stands of the southeastern Coastal Plain. For Ecol Manage 259: 1111-1117.
48. Wood P, Sheehan J, Keyser P, et al. (2013) Cerulean Warbler: Management Guidelines for Enhancing Breeding Habitat in Appalachian Hardwood Forests. American Bird Conservancy. The Plains, VA, USA. 28 Pages.
49. Perry RW, Thill RE (2013) Long-term responses of disturbance-associated birds after different timber harvests. For Ecol Manage 307: 274-283.
50. Wilson MD, Watts BD (2000) Breeding bird communities in pine plantations on the coastal plain of North Carolina. Chat 64: 1-14.
51. Peet RK, Allard DJ (1993) Longleaf Pine Vegetation of the Southern Atlantic an Eastern Gulf Coast Regions: A Preliminary Classification. In: Hermann SM, ed. Proceedings of the Tall Timbers Fire Ecology Conference, No. 18, The Longleaf Pine Ecosystem: Ecology, Restoration and Management. Tallahassee, FL, USA: Tall Timbers Research Station.; 1993: 45-81.

show all references

This article has been cited by:

1. Jessica M. Price, Janet Silbernagel, Kristina Nixon, Amanda Swearingen, Randy Swaty, Nicholas Miller, Collaborative scenario modeling reveals potential advantages of blending strategies to achieve conservation goals in a working forest landscape, Landscape Ecology, 2015, 10.1007/s10980-015-0321-2
2. Colin J. Daniel, Leonardo Frid, Benjamin M. Sleeter, Marie-Josée Fortin, Darren Kriticos, State-and-transition simulation models: a framework for forecasting landscape change, Methods in Ecology and Evolution, 2016, 10.1111/2041-210X.12597
3. Jennifer K. Costanza, Robert C. Abt, Alexa J. McKerrow, Jaime A. Collazo, Bioenergy production and forest landscape change in the southeastern United States, GCB Bioenergy, 2016, 10.1111/gcbb.12386
4. Nathan M. Tarr, Matthew J. Rubino, Jennifer K. Costanza, Alexa J. McKerrow, Jaime A. Collazo, Robert C. Abt, Projected gains and losses of wildlife habitat from bioenergy-induced landscape change, GCB Bioenergy, 2016, 10.1111/gcbb.12383
5. Anna S. Duden, Pita A. Verweij, H. Martin Junginger, Robert C. Abt, Jesse D. Henderson, Virginia H. Dale, Keith L. Kline, Derek Karssenberg, Judith A. Verstegen, André P.C. Faaij, Floor van der Hilst, Modeling the impacts of wood pellet demand on forest dynamics in southeastern United States, Biofuels, Bioproducts and Biorefining, 2017, 10.1002/bbb.1803
6. Anna S. Duden, Matthew J. Rubino, Nathan M. Tarr, Pita A. Verweij, Andre P. C. Faaij, Floor van der Hilst, Impact of increased wood pellet demand on biodiversity in the south-eastern United States, GCB Bioenergy, 2018, 10.1111/gcbb.12554
7. Vítor João Pereira Domingues Martinho, Interrelationships between renewable energy and agricultural economics: An overview, Energy Strategy Reviews, 2018, 22, 396, 10.1016/j.esr.2018.11.002

Reader Comments

your name: * your email: *

Copyright Info: 2015, Jennifer K. Costanza, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Associated material

Metrics