A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington


Autoři: T. Sheehan aff001;  D. Bachelet aff003
Působiště autorů: Conservation Biology Institute, Corvallis, Oregon, United States of America aff001;  Environmental Sciences Program, Oregon State University, Corvallis, Oregon, United States of America aff002;  Department of Biological and Ecological Engineering, Oregon State University, Corvallis, Oregon, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222051

Souhrn

Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.

Klíčová slova:

Biomass – Carbon dioxide – Climate change – Climate modeling – Fire suppression technology – Oregon – Wildfires – Fuzzy logic


Zdroje

1. Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, et al. 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. IPCC; 2014.

2. Urban D, Roberts MJ, Schlenker W, Lobell DB. Projected temperature changes indicate significant increase in interannual variability of US maize yields. Climatic Change. 2012 May 1;112(2):525–33.

3. Houston L, Capalbo S, Seavert C, Dalton M, Bryla D, Sagili R. Specialty fruit production in the Pacific Northwest: adaptation strategies for a changing climate. Climatic Change. 2018 Jan 1;146(1–2):159–71.

4. Leng G, Huang M. Crop yield response to climate change varies with crop spatial distribution pattern. Scientific Reports. 2017 May 3;7(1):1463. doi: 10.1038/s41598-017-01599-2 28469171

5. Tito R, Vasconcelos HL, Feeley KJ. Global climate change increases risk of crop yield losses and food insecurity in the tropical Andes. Global Change Biology. 2018 Feb;24(2):e592–602. doi: 10.1111/gcb.13959 29055170

6. Thom D, Rammer W, Dirnböck T, Müller J, Kobler J, Katzensteiner K, et al. The impacts of climate change and disturbance on spatio‐temporal trajectories of biodiversity in a temperate forest landscape. Journal of Applied Ecology. 2017 Feb 1;54(1):28–38. doi: 10.1111/1365-2664.12644 28111479

7. Fernández-Manjarrés J, Ruiz-Benito P, Zavala M, Camarero J, Pulido F, Proença V, et al. Forest adaptation to climate change along steep ecological gradients: the case of the Mediterranean-temperate transition in South-Western Europe. Sustainability. 2018 Sep;10(9):3065.

8. Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications. 2015 Jul 14;6:7537. doi: 10.1038/ncomms8537 26172867

9. Abatzoglou JT, Williams AP. Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences. 2016 Oct 18;113(42):11770–5.

10. Stevens‐Rumann CS, Kemp KB, Higuera PE, Harvey BJ, Rother MT, Donato DC, et al. Evidence for declining forest resilience to wildfires under climate change. Ecology Letters. 2018 Feb;21(2):243–52. doi: 10.1111/ele.12889 29230936

11. May C, Luce C, Casola J, Chang M, Cuhaciyan J, Dalton M, et al. Northwest. In Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KL, Maycock TK, et al. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. 2018.

12. Abatzoglou JT, Rupp DE, Mote PW. Seasonal climate variability and change in the Pacific Northwest of the United States. Journal of Climate. 2014 Mar;27(5):2125–42.

13. Kolb TE, Fettig CJ, Ayres MP, Bentz BJ, Hicke JA, Mathiasen R, et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. Forest Ecology and Management. 2016 Nov 15;380:321–34.

14. Sheehan T, Bachelet D, Ferschweiler K. Projected major fire and vegetation changes in the Pacific Northwest of the conterminous United States under selected CMIP5 climate futures. Ecological Modelling. 2015 Dec 10;317:16–29.

15. Rehfeldt GE, Crookston NL, Warwell MV, Evans JS. Empirical analyses of plant-climate relationships for the western United States. International Journal of Plant Sciences. 2006 Nov;167(6):1123–50.

16. Rehfeldt GE, Jaquish BC, López-Upton J, Sáenz-Romero C, St Clair JB, Leites LP, et al. Comparative genetic responses to climate for the varieties of Pinus ponderosa and Pseudotsuga menziesii: Realized climate niches. Forest Ecology and Management. 2014 Jul 15;324:126–37.

17. Rehfeldt GE, Leites LP, St Clair JB, Jaquish BC, Sáenz-Romero C, López-Upton J, et al. Comparative genetic responses to climate in the varieties of Pinus ponderosa and Pseudotsuga menziesii: Clines in growth potential. Forest Ecology and Management. 2014 Jul 15;324:138–46.

18. Rehfeldt GE, Jaquish BC, Sáenz-Romero C, Joyce DG, Leites LP, St Clair JB, et al. Comparative genetic responses to climate in the varieties of Pinus ponderosa and Pseudotsuga menziesii: reforestation. Forest Ecology and Management. 2014 Jul 15;324:147–57.

19. Littell JS, Oneil EE, McKenzie D, Hicke JA, Lutz JA, Norheim RA, et al. Forest ecosystems, disturbance, and climatic change in Washington State, USA. Climatic Change. 2010 Sep 1;102(1–2):129–58.

20. Rogers BM, Neilson RP, Drapek R, Lenihan JM, Wells JR, Bachelet D, et al. Impacts of climate change on fire regimes and carbon stocks of the US Pacific Northwest. Journal of Geophysical Research: Biogeosciences. 2011 Sep 1;116(G3). doi: 10.1029/2011jg001641

21. Coops NC, Waring RH, Beier C, Roy‐Jauvin R, Wang T. Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process‐based growth model and decision tree analysis. Applied Vegetation Science. 2011 Aug;14(3):402–14.

22. Creutzburg MK, Halofsky JE, Halofsky JS, Christopher TA. Climate change and land management in the rangelands of central Oregon. Environmental Management. 2015 Jan 1;55(1):43–55. doi: 10.1007/s00267-014-0362-3 25216989

23. Whitlock C, Shafer SL, Marlon J. The role of climate and vegetation change in shaping past and future fire regimes in the northwestern US and the implications for ecosystem management. Forest Ecology and Management. 2003 Jun 3;178(1–2):5–21.

24. Latta G, Temesgen H, Barrett TM. Mapping and imputing potential productivity of Pacific Northwest forests using climate variables. Canadian Journal of Forest Research. 2009 Jun 17;39(6):1197–207.

25. Sheehan T, Bachelet D, Ferschweiler K. Fire, CO2, and climate effects on modeled vegetation and carbon dynamics in western Oregon and Washington. PloS One. 2019 Jan 25;14(1):e0210989. doi: 10.1371/journal.pone.0210989 30682107

26. Keenan RJ. Climate change impacts and adaptation in forest management: a review. Annals of Forest Science. 2015 Mar 1;72(2):145–67.

27. Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software. 2016 May 1;79:214–32.

28. Littell JS, McKenzie D, Kerns BK, Cushman S, Shaw CG. Managing uncertainty in climate‐driven ecological models to inform adaptation to climate change. Ecosphere. 2011 Sep;2(9):1–9.

29. Luo Y, Ahlström A, Allison SD, Batjes NH, Brovkin V, Carvalhais N, et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochemical Cycles. 2016 Jan 1;30(1):40–56.

30. Williams BK. Adaptive management of natural resources—framework and issues. Journal of Environmental Management. 2011 May 1;92(5):1346–53. doi: 10.1016/j.jenvman.2010.10.041 21075505

31. Millar CI, Stephenson NL, Stephens SL. Climate change and forests of the future: managing in the face of uncertainty. Ecological Applications. 2007 Dec 1;17(8):2145–51. doi: 10.1890/06-1715.1 18213958

32. National Research Council. Adaptive Management for Water Resources Planning. The National Academies Press, Washington, DC. 2004.

33. Bachelet D, Ferschweiler K, Sheehan TJ, Sleeter BM, Zhu Z. Projected carbon stocks in the conterminous USA with land use and variable fire regimes. Global Change Biology. 2015 Dec;21(12):4548–60. doi: 10.1111/gcb.13048 26207729

34. Hudiburg T, Law B, Turner DP, Campbell J, Donato D, Duane M. Carbon dynamics of Oregon and Northern California forests and potential land‐based carbon storage. Ecological Applications. 2009 Jan 1;19(1):163–80. doi: 10.1890/07-2006.1 19323181

35. Omernik JM, Griffith GE. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental Management. 2014 Dec 1;54(6):1249–66. doi: 10.1007/s00267-014-0364-1 25223620

36. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society. 2008 Dec;28(15):2031–64.

37. Taylor KE, Stouffer RJ, Meehl GA (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society. 2012 April1;93(4):485–498.

38. Sheehan T, Gough M. A platform-independent fuzzy logic modeling framework for environmental decision support. Ecological Informatics. 2016 Jul 1;34:92–101.

39. Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on systems, Man, and Cybernetics. 1973 Jan(1):28–44.

40. Giles R. Łukasiewicz logic and fuzzy set theory. International Journal of Man-Machine Studies. 1976 May 1;8(3):313–27.

41. Masson D, Knutti R. Climate model genealogy. Geophysical Research Letters. 2011 Apr 28;38(8).

42. Neary DG, Ice GG, Jackson CR. Linkages between forest soils and water quality and quantity. Forest Ecology and Management. 2009 Oct 30;258(10):2269–81.

43. Hurteau MD, Koch GW, Hungate BA. Carbon protection and fire risk reduction: toward a full accounting of forest carbon offsets. Frontiers in Ecology and the Environment. 2008 Nov;6(9):493–8.

44. Fischer AP, Spies TA, Steelman TA, Moseley C, Johnson BR, Bailey JD, et al. Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment. 2016 Jun;14(5):276–84.

45. D'Amato AW, Bradford JB, Fraver S, Palik BJ. Effects of thinning on drought vulnerability and climate response in north temperate forest ecosystems. Ecological Applications. 2013 Dec 1;23(8):1735–42. doi: 10.1890/13-0677.1 24555305

46. Bradley St Clair J, Howe GT. Genetic maladaptation of coastal Douglas‐fir seedlings to future climates. Global Change Biology. 2007 Jul;13(7):1441–54.

47. Chmura DJ, Anderson PD, Howe GT, Harrington CA, Halofsky JE, Peterson DL, et al. Forest responses to climate change in the northwestern United States: ecophysiological foundations for adaptive management. Forest Ecology and Management. 2011 Apr 1;261(7):1121–42.

48. Hann WJ, Bunnell DL. Fire and land management planning and implementation across multiple scales. International Journal of Wildland Fire. 2001;10(4):389–403.

49. Rupp DE, Abatzoglou JT, Hegewisch KC, Mote PW. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research: Atmospheres. 2013 Oct 16;118(19):10–884.

50. Adriaenssens V, De Baets B, Goethals PL, De Pauw N. Fuzzy rule-based models for decision support in ecosystem management. Science of the Total Environment. 2004 Feb 5;319(1–3):1–2. doi: 10.1016/S0048-9697(03)00433-9 14967497

51. Barbosa AM, Real R. Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians. The Scientific World Journal. 2012;2012.

52. Petrou ZI, Kosmidou V, Manakos I, Stathaki T, Adamo M, Tarantino C, et al. A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic. Pattern Recognition Letters. 2014 Oct 15;48:24–33.

53. Forio MA, Mouton A, Lock K, Boets P, Nguyen TH, Ambarita MN, et al. Fuzzy modelling to identify key drivers of ecological water quality to support decision and policy making. Environmental Science & Policy. 2017 Feb 1;68:58–68.

54. Soto ME. The identification and assessment of areas at risk of forest fire using fuzzy methodology. Applied Geography. 2012 Nov 1;35(1–2):199–207.

55. Smith MJ, Wagner C, Wallace KJ, Pourabdollah A, Lewis L. The contribution of nature to people: Applying concepts of values and properties to rate the management importance of natural elements. Journal of Environmental Management. 2016 Jun 15;175:76–86. doi: 10.1016/j.jenvman.2016.02.007 27056439

56. Meigs G, Krawchuk M. Composition and Structure of Forest Fire Refugia: What Are the Ecosystem Legacies across Burned Landscapes?. Forests. 2018 May;9(5):243.

57. Meddens AJ, Kolden CA, Lutz JA, Smith AM, Cansler CA, Abatzoglou JT, et al. Fire Refugia: What Are They, and Why Do They Matter for Global Change?. BioScience. 2018 Oct 3;68(12):944–54.

58. Krawchuk MA, Haire SL, Coop J, Parisien MA, Whitman E, Chong G, et al. Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America. Ecosphere. 2016 Dec 1;7(12).

59. Morelli TL, Daly C, Dobrowski SZ, Dulen DM, Ebersole JL, Jackson ST, et al. Managing climate change refugia for climate adaptation. PLoS One. 2016 Aug 10;11(8):e0159909. doi: 10.1371/journal.pone.0159909 27509088

60. Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS One. 2015 Dec 7;10(12):e0143619. doi: 10.1371/journal.pone.0143619 26641818

61. Zabihi K, Paige GB, Hild AL, Miller SN, Wuenschel A, Holloran MJ. A fuzzy logic approach to analyse the suitability of nesting habitat for greater sage-grouse in western Wyoming. Journal of Spatial Science. 2017 Jul 3;62(2):215–34.

62. Krosby M, Breckheimer I, Pierce DJ, Singleton PH, Hall SA, Halupka KC, et al. Focal species and landscape “naturalness” corridor models offer complementary approaches for connectivity conservation planning. Landscape Ecology. 2015 Dec 1;30(10):2121–32.

63. Gavin MC, McCarter J, Mead A, Berkes F, Stepp JR, Peterson D, Tang R. Defining biocultural approaches to conservation. Trends in Ecology & Evolution. 2015 Mar 1;30(3):140–5.


Článek vyšel v časopise

PLOS One


2019 Číslo 10