Loading [Contrib]/a11y/accessibility-menu.js
Research article Special Issues

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

  • Received: 30 January 2015 Accepted: 18 March 2015 Published: 24 March 2015
  • 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.

    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[J]. AIMS Environmental Science, 2015, 2(2): 180-202. doi: 10.3934/environsci.2015.2.180

    Related Papers:

    [1] Michael Herty, Lorenzo Pareschi, Sonja Steffensen . Mean--field control and Riccati equations. Networks and Heterogeneous Media, 2015, 10(3): 699-715. doi: 10.3934/nhm.2015.10.699
    [2] Nastassia Pouradier Duteil . Mean-field limit of collective dynamics with time-varying weights. Networks and Heterogeneous Media, 2022, 17(2): 129-161. doi: 10.3934/nhm.2022001
    [3] Seung-Yeal Ha, Jeongho Kim, Jinyeong Park, Xiongtao Zhang . Uniform stability and mean-field limit for the augmented Kuramoto model. Networks and Heterogeneous Media, 2018, 13(2): 297-322. doi: 10.3934/nhm.2018013
    [4] Martino Bardi . Explicit solutions of some linear-quadratic mean field games. Networks and Heterogeneous Media, 2012, 7(2): 243-261. doi: 10.3934/nhm.2012.7.243
    [5] András Bátkai, Istvan Z. Kiss, Eszter Sikolya, Péter L. Simon . Differential equation approximations of stochastic network processes: An operator semigroup approach. Networks and Heterogeneous Media, 2012, 7(1): 43-58. doi: 10.3934/nhm.2012.7.43
    [6] Fabio Camilli, Italo Capuzzo Dolcetta, Maurizio Falcone . Preface. Networks and Heterogeneous Media, 2012, 7(2): i-ii. doi: 10.3934/nhm.2012.7.2i
    [7] Olivier Guéant . New numerical methods for mean field games with quadratic costs. Networks and Heterogeneous Media, 2012, 7(2): 315-336. doi: 10.3934/nhm.2012.7.315
    [8] Michele Gianfelice, Enza Orlandi . Dynamics and kinetic limit for a system of noiseless $d$-dimensional Vicsek-type particles. Networks and Heterogeneous Media, 2014, 9(2): 269-297. doi: 10.3934/nhm.2014.9.269
    [9] Mattia Bongini, Massimo Fornasier, Oliver Junge, Benjamin Scharf . Sparse control of alignment models in high dimension. Networks and Heterogeneous Media, 2015, 10(3): 647-697. doi: 10.3934/nhm.2015.10.647
    [10] Maria Teresa Chiri, Xiaoqian Gong, Benedetto Piccoli . Mean-field limit of a hybrid system for multi-lane car-truck traffic. Networks and Heterogeneous Media, 2023, 18(2): 723-752. doi: 10.3934/nhm.2023031
  • 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.


    [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. doi: 10.1080/10549811.2011.652049
    [2] Goh CS, Junginger M, Cocchi M, et al. (2013) Wood pellet market and trade: a global perspective. Biofuels Bioprod Bioref 7: 24-42. doi: 10.1002/bbb.1366
    [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. doi: 10.1111/gcbb.12067
    [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. doi: 10.1371/journal.pone.0006802
    [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. doi: 10.1111/j.1757-1707.2011.01130.x
    [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. doi: 10.1890/09-0673.1
    [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. doi: 10.1890/090091
    [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. doi: 10.1016/j.foreco.2010.12.021
    [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. doi: 10.1016/j.ecolmodel.2012.06.037
    [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. doi: 10.1890/13-1653.1
    [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. doi: 10.1016/j.ecolmodel.2007.06.030
    [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. doi: 10.1371/journal.pone.0102261
    [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. doi: 10.1111/ddi.12278
    [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. doi: 10.1080/10549811.2011.571581
    [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. doi: 10.1071/WF08088
    [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. doi: 10.1016/j.jenvman.2014.12.032
    [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. doi: 10.2307/3146958
    [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. doi: 10.1890/09-0501.1
    [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. doi: 10.1016/j.foreco.2008.07.030
    [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. doi: 10.1016/j.foreco.2013.04.023
    [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. doi: 10.1016/j.foreco.2009.12.024
    [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. doi: 10.1016/j.foreco.2013.07.026
    [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.
  • This article has been cited by:

    1. Michael Herty, Dante Kalise, 2018, Suboptimal nonlinear feedback control laws for collective dynamics, 978-1-5386-6089-8, 556, 10.1109/ICCA.2018.8444303
    2. Melanie Harms, Simone Bamberger, Eva Zerz, Michael Herty, On d-Collision-Free Dynamical Systems, 2022, 55, 24058963, 25, 10.1016/j.ifacol.2022.11.303
    3. Fuguo Xu, Qiaobin Fu, Tielong Shen, PMP-based numerical solution for mean field game problem of general nonlinear system, 2022, 146, 00051098, 110655, 10.1016/j.automatica.2022.110655
    4. M. K. Banda, M. Herty, T. Trimborn, 2020, Chapter 7, 978-3-030-50449-6, 133, 10.1007/978-3-030-50450-2_7
    5. Michael Herty, Anna Thunen, 2021, Consistent Control of a Stackelberg Game with Infinitely many Followers, 978-1-6654-3659-5, 918, 10.1109/CDC45484.2021.9682798
    6. Michael Herty, Hui Yu, 2016, Boundary stabilization of hyperbolic conservation laws using conservative finite volume schemes, 978-1-5090-1837-6, 5577, 10.1109/CDC.2016.7799126
    7. Giacomo Albi, Michael Herty, Dante Kalise, Chiara Segala, Moment-Driven Predictive Control of Mean-Field Collective Dynamics, 2022, 60, 0363-0129, 814, 10.1137/21M1391559
    8. Giacomo Albi, Emiliano Cristiani, Lorenzo Pareschi, Daniele Peri, 2020, Chapter 8, 978-3-030-50449-6, 159, 10.1007/978-3-030-50450-2_8
    9. Michael Herty, Sonja Steffensen, Anna Thünen, Multiscale control of Stackelberg games, 2022, 200, 03784754, 468, 10.1016/j.matcom.2022.04.028
    10. Marco Caponigro, Benedetto Piccoli, Francesco Rossi, Emmanuel Trélat, Mean-field sparse Jurdjevic–Quinn control, 2017, 27, 0218-2025, 1223, 10.1142/S0218202517400140
    11. Bertram Düring, Lorenzo Pareschi, Giuseppe Toscani, Kinetic models for optimal control of wealth inequalities, 2018, 91, 1434-6028, 10.1140/epjb/e2018-90138-1
    12. Yan Ma, Minyi Huang, Linear quadratic mean field games with a major player: The multi-scale approach, 2020, 113, 00051098, 108774, 10.1016/j.automatica.2019.108774
    13. Michael Herty, Mattia Zanella, Performance bounds for the mean-field limit of constrained dynamics, 2017, 37, 1553-5231, 2023, 10.3934/dcds.2017086
    14. Aylin Aydoğdu, Marco Caponigro, Sean McQuade, Benedetto Piccoli, Nastassia Pouradier Duteil, Francesco Rossi, Emmanuel Trélat, 2017, Chapter 3, 978-3-319-49994-9, 99, 10.1007/978-3-319-49996-3_3
    15. Giacomo Albi, Lorenzo Pareschi, Mattia Zanella, Boltzmann Games in Heterogeneous Consensus Dynamics, 2019, 175, 0022-4715, 97, 10.1007/s10955-019-02246-y
    16. Michael Herty, Lorenzo Pareschi, Sonja Steffensen, 2019, Chapter 5, 978-3-030-20296-5, 149, 10.1007/978-3-030-20297-2_5
    17. A. Medaglia, G. Colelli, L. Farina, A. Bacila, P. Bini, E. Marchioni, S. Figini, A. Pichiecchio, M. Zanella, Uncertainty quantification and control of kinetic models of tumour growth under clinical uncertainties, 2022, 141, 00207462, 103933, 10.1016/j.ijnonlinmec.2022.103933
    18. Giacomo Albi, Federica Ferrarese, Chiara Segala, 2021, Chapter 5, 978-3-030-91645-9, 97, 10.1007/978-3-030-91646-6_5
    19. Minyi Huang, Mengjie Zhou, Linear Quadratic Mean Field Games: Asymptotic Solvability and Relation to the Fixed Point Approach, 2020, 65, 0018-9286, 1397, 10.1109/TAC.2019.2919111
    20. Eva Zerz, Michael Herty, Collision-Free Dynamical Systems , 2019, 52, 24058963, 72, 10.1016/j.ifacol.2019.11.029
    21. Giacomo Albi, Michael Herty, Chiara Segala, Robust Feedback Stabilization of Interacting Multi-agent Systems Under Uncertainty, 2024, 89, 0095-4616, 10.1007/s00245-023-10078-2
    22. Xiaoqian Gong, Michael Herty, Benedetto Piccoli, Giuseppe Visconti, Crowd Dynamics: Modeling and Control of Multiagent Systems, 2023, 6, 2573-5144, 261, 10.1146/annurev-control-060822-123629
    23. Christian Fiedler, Michael Herty, Sebastian Trimpe, Mean-Field Limits for Discrete-Time Dynamical Systems via Kernel Mean Embeddings, 2023, 7, 2475-1456, 3914, 10.1109/LCSYS.2023.3341280
    24. Martin Gugat, Michael Herty, Jiehong Liu, Chiara Segala, The turnpike property for high‐dimensional interacting agent systems in discrete time, 2024, 45, 0143-2087, 2557, 10.1002/oca.3172
    25. Michael Herty, Yizhou Zhou, Exponential turnpike property for particle systems and mean-field limit, 2025, 0956-7925, 1, 10.1017/S0956792524000871
    26. Giacomo Albi, Sara Bicego, Michael Herty, Yuyang Huang, Dante Kalise, Chiara Segala, 2025, Chapter 2, 978-3-031-85255-8, 29, 10.1007/978-3-031-85256-5_2
    27. Giacomo Albi, Sara Bicego, Dante Kalise, Control of high-dimensional collective dynamics by deep neural feedback laws and kinetic modelling, 2025, 539, 00219991, 114229, 10.1016/j.jcp.2025.114229
  • Reader Comments
  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(7526) PDF downloads(1456) Cited by(15)

Article outline

Figures and Tables

Figures(7)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog