Abstract
State-and-Transition Simulation Modeling (STSM) is a quantitative analysis method that can consolidate a wide array of resource management issues under a “what-if” scenario exercise. STSM can be seen as an ensemble of models, such as climate models, ecological models, and economic models that incorporate human dimensions and management options. This chapter presents STSM as a tool to help synthesize information on social–ecological systems and to investigate some of the management issues associated with exotic annual Bromus species, which have been described elsewhere in this book. Definitions, terminology, and perspectives on conceptual and computer-simulated stochastic state-and-transition models are given first, followed by a brief review of past STSM studies relevant to the management of Bromus species. A detailed case study illustrates the usefulness of STSM for land management. As a whole, this chapter is intended to demonstrate how STSM can help both managers and scientists: (a) determine efficient resource allocation for monitoring nonnative grasses; (b) evaluate sources of uncertainty in model simulation results involving expert opinion, and their consequences for management decisions; and (c) provide insight into the consequences of predicted local climate change effects on ecological systems invaded by exotic annual Bromus species.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abatzoglou JT, Kolden CA (2011) Climate change in western US deserts: potential for increased wildfire and invasive annual grasses. Rangel Ecol Manag 64(5):471–478
Bagchi S, Briske DD, Bestelmeyer BT et al (2013) Assessing resilience and state-transition models with historical records of cheatgrass Bromus tectorum invasion in North American sagebrush-steppe. J Appl Ecol 50(5):1131–1141
Barrett T (2001) Models of vegetation change for landscape planning: a comparison of FETM, LANDSUM, SIMPPLLE, and VDDT. Gen Tech Rep RMRS-GTR-76-WWW. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT, p 14
Bestelmeyer BT, Herrick JE, Brown JR et al (2004) Land management in the American Southwest: a state-and-transition approach to ecosystem complexity. Environ Manag 34(1):38–51
Blankenship K, Smith J, Swaty R et al (2012) Modeling on the grand scale: LANDFIRE lessons learned. In: Kerns B, Shlisky AJ, Daniel CJ (eds) First landscape state-and-transition simulation modeling conference, 14–16 June 2011. Gen Tech Rep PNW-GTR-869. USDA, Forest Service, Pacific Northwest Research Station, Portland, OR, pp 43–56
Blankenship K, Provencher L, Frid L et al (2013) Human dimensions of state-and-transition simulation model applications to support decisions in wildland fire management. In: Proceedings of 3rd Human Dimensions of Wildland Fire Conference, Seattle, WA, 17–19 April 2012. International Association of Wildland Fire, Missoula, MT, pp 28–33
Bradley BA (2009a) Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity. Glob Change Biol 15(1):196–208
Bradley BA (2009b) Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA. Ecography 33(1):198–208
Bradley BA, Curtis CA, Chambers JC (2015) Bromus response to climate and projected changes with climate change. In: Germino MJ, Chambers JC, Brown CS (eds) Exotic brome-grasses in arid and semi-arid ecosystems of the Western US: causes, consequences and management implications. Springer, New York, NY (Chapter 9)
Briske DD, Bestelmeyer BT, Stringham TK et al (2008) Recommendations for development of resilience-based state-and-transition models. Rangel Ecol Manag 61(4):359–367
Brown TJ, Hall BL, Westerling AL (2004) The impact of twenty-first century climate change on wildland fire danger in the western United States: an applications perspective. Clim Change 62(1–3):365–388
Caudle D, DiBenedetto J, Karl M, Sanchez H, Talbot C (2013) Interagency ecological site handbook for rangelands. http://jornada.nmsu.edu/files/InteragencyEcolSiteHandbook.pdf. Accessed 2 Jun 2015
Chambers J (2008) Invasive plant species and the Great Basin. In: Chambers J, Devoe N, Evenden A (eds) Collaborative management and research in the Great Basin – examining the issues and developing a framework for action. Gen Tech Rep RMRS-GTR-204. USDA, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, pp 38–41
Chambers JC, Miller RF, Board DI et al (2014) Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments. Rangel Ecol Manag 67(5):440–454
Creutzburg MK, Halofsky JE, Halofsky JS et al (2014) Climate change and land management in the rangelands of central Oregon. Environ Manag 55:1–13
Creutzburg MK, Halofsky JS, Hemstrom MA (2012) Using state-and-transition models to project cheatgrass and juniper invasion in southeastern Oregon sagebrush steppe. In: Kerns BK, Shlisky AJ, Daniel CJ (eds) First landscape state-and-transition simulation modeling conference, 14–16 June 2011. USDA, Forest Service, Pacific Northwest Research Station, Portland, OR, pp 73–84
Czembor CA, Morris WK, Wintle BA et al (2011) Quantifying variance components in ecological models based on expert opinion. J Appl Ecol 48(3):736–745
Czembor CA, Vesk PA (2009) Incorporating between-expert uncertainty into state-and-transition simulation models for forest restoration. Forest Ecol Manag 259(2):165–175
Daniel CJ, Frid L (2012) Predicting landscape vegetation dynamics using state-and-transition simulation models. In: Kerns B, Shlisky AJ, Daniel CJ (eds) First landscape state-and-transition simulation modeling conference, 14–16 June 2011. Gen Tech Rep PNW-GTR-869. USDA, Forest Service, Pacific Northwest Forest and Range Experiment Station, Portland, OR, pp 5–22
ESSA Technologies Ltd. (2007) Vegetation dynamics development tool user guide, Version 6.0. ESSA Technologies Ltd, Vancouver
Evers L, Miller RF, Hemstrom M et al (2011) Estimating historical sage-grouse habitat abundance using a state-and-transition model. Nat Resour Environ Iss 17(1):115–129
Evers LB, Miller RF, Doescher PS et al (2013) Simulating current successional trajectories in sagebrush ecosystems with multiple disturbances using a state-and-transition modeling framework. Rangel Ecol Manag 66(3):313–329
Faraway JJ (2006) Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman Hall/CRC, Boca Raton, FL
Forbis TA, Provencher L, Frid L et al (2006) Great Basin land management planning using ecological modeling. Environ Manag 38(1):62–83
Frid L, Hanna D, Korb N et al (2013a) Evaluating alternative weed management strategies for three Montana landscapes. Invasive Plant Sci Manag 6(1):48–59
Frid L, Holcombe T, Morisette JT et al (2013b) Using state-and-transition modeling to account for imperfect detection in invasive species management. Invasive Plant Sci Manag 6(1):36–47
Frid L, Wilmshurst JF (2009) Decision analysis to evaluate control strategies for crested wheatgrass (Agropyron cristatum) in Grasslands National Park of Canada. Invasive Plant Sci Manag 2(4):324–336
Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New York, NY
Grant CD (2006) State-and-transition successional model for bauxite mining rehabilitation in the Jarrah forest of western Australia. Restor Ecol 14(1):28–37
Halofsky JE, Hemstrom MA, Conklin DR et al (2013) Assessing potential climate change effects on vegetation using a linked model approach. Ecol Model 266:131–143
Hann WJ, Bunnell DL (2001) Fire and land management planning and implementation across multiple scales. Int J Wildl Fire 10(3–4):389–403
Hardesty J, Adams J, Gordon D et al (2000) Simulating management with models: lessons from ten years of ecosystem management at Eglin Air Force Base. Conserv Pract 1(1):26–32
Hayes MJ, Svoboda MD, Wilhite DA et al (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80(3):429–438
Heddinghaus T, Sabol P (1991) A review of the Palmer drought severity index and where do we go from here? 7th Conference on Applied Climatology, Salt Lake City, UT, 10–13 September 1991. American Meteorological Society, Boston, MA, pp 242–246
Hemstrom M, Ager AA, Vavra M et al (2004) A state and transition approach for integrating models. In: Hayes J, Ager AA, Barbour RJ (eds) Methods for integrated modeling of landscape change: interior northwest landscape analysis system. Gen Tech Rep PNW-GTR-610. USDA, Forest Service, Pacific Northwest Research Station, Portland, OR, p 218
Horn HS (1975) Markovian properties of forest successions. In: Cody M, Diamond JM (eds) Ecology and the evolution of communities. Harvard University Press, Cambridge, pp 196–211
Intergovernmental Panel on Climate Change (IPCC) (2013) Climate change 2013: the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York
Johnson CJ, Gillingham MP (2004) Mapping uncertainty: sensitivity of wildlife habitat ratings to expert opinion. J Appl Ecol 41(6):1032–1041
Keane RE, Cary GJ, Davies ID et al (2004) A classification of landscape fire succession models: spatial simulations of fire and vegetation dynamics. Ecol Model 179(1):3–27
Kelly AE, Goulden ML (2008) Rapid shifts in plant distribution with recent climate change. Proc Natl Acad Sci U S A 105(33):11823–11826
Knapp CN, Fernandez-Gimenez M, Kachergis E et al (2011a) Using participatory workshops to integrate state-and-transition models created with local knowledge and ecological data. Rangel Ecol Manag 64(2):158–170
Knapp CN, Fernandez-Gimenez ME, Briske DD et al (2011b) An assessment of state-and-transition models: perceptions following two decades of development and implementation. Rangel Ecol Manag 64(6):598–606
Knapp PA (1996) Cheatgrass (Bromus tectorum L.) dominance in the Great Basin Desert – History, persistence, and influences to human activities. Glob Environ Change 6(1):37–52
Kurz WA, Beukema SJ, Klenner W et al (2000) TELSA: the tool for exploratory landscape scenario analyses. Comput Electron Agric 27(1–3):227–242
Littell JS, McKenzie D, Peterson DL et al (2009) Climate and wildfire area burned in western U. S. ecoprovinces, 1916–2003. Ecol Appl 19(4):1003–1021
Low G, Provencher L, Abele S (2010) Enhanced conservation action planning: assessing landscape condition and predicting benefits of conservation strategies. J Conserv Plan 6:36–60
Maxwell BD, Lehnhoff E, Rew LJ (2009) The rationale for monitoring invasive plant populations as a crucial step for management. Invasive Plant Sci Manag 2(1):1–9
McCarthy M (2007) Bayesian methods for ecology. Cambridge University Press, Cambridge
Merzenich J, Kurz WA, Beukema SJ et al (1999) Long-range modeling of stochastic disturbances and management treatments using VDDT and TELSA. Society of American Foresters National Convention: Landscape Analysis session, Portland, OR, 14 September 1999
Miller C (2007) Simulation of the consequences of different fire regimes to support wildland fire use decisions. Fire Ecol 3:83–102
Monsen S, Stevens R, Shaw NL (2004) Restoring western ranges and wildlands. Gen Tech Rep RMRS-GTR-136-vol. 1. USDA, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, pp 89–100
Moody ME, Mack RN (1988) Controlling the spread of plant invasions – the importance of nascent foci. J Appl Ecol 25(3):1009–1021
National Environmental Policy Act (NEPA) (1969) Enacted by the senate and house of representatives of the United States of America in Congress assembled: as amended, Pub. L. 91–190, 42 U.S.C. 4321–4347, 1 Jan 1970, as amended by Pub. L. 94–52, 3 Jul 1975, Pub. L. 94–83, 9 Aug 1975, and Pub. L. 97–258, § 4(b), 13 Sept 1982
Natural Resources Conservation Service (NRCS) (1998) National Forestry Manual. USDA. http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_050983.pdf. Accessed 2 Jun 2015
Natural Resources Conservation Service (NRCS) (2003) National Range and Pasture Handbook, revision 1. USDA, Grazing Lands Technology Institute. http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/landuse/rangepasture/. Accessed 2 Jun 2015
Nixon K, Silbernagel J, Price J et al (2014) Habitat availability for multiple avian species under modeled alternative conservation scenarios in the Two Hearted River watershed in Michigan, USA. J Nat Conserv 22(4):302–317
Nowak RS, Ellsworth DS, Smith SD (2004) Functional responses of plants to elevated atmospheric CO2 – do photosynthetic and productivity data from FACE experiments support early predictions? New Phytol 162(2):253–280
Olsson AD, Betancourt JL, Crimmins MA et al (2012) Constancy of local spread rates for buffelgrass (Pennisetum ciliare L.) in the Arizona Upland of the Sonoran Desert. J Arid Environ 87:136–143
Palmer WC (1965) Meteorological drought. Research Paper No 45. US Department of Commerce, Office of Climatology, Weather Bureau Washington, DC, USA
Pennisi E (2010) Western US forests suffer death by degrees. Science 323(5913):447
Pimentel D, Zuniga R, Morrison D (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol Econ 52(3):273–288
Price J, Silbernagel J, Miller N et al (2012) Eliciting expert knowledge to inform landscape modeling of conservation scenarios. Ecol Model 229:76–87
Provencher L, Anderson T, Low G et al (2013) Landscape conservation forecasting™ for Great Basin National Park. Park Sci 30:56–67
Provencher L, Forbis TA, Frid L et al (2007) Comparing alternative management strategies of fire, grazing, and weed control using spatial modeling. Ecol Model 209(2–4):249–263
Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, Cambridge
Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12(2):618–628
Rehfeldt GE, Crookston NL, Warwell MV et al (2006) Empirical analyses of plant-climate relationships for the western United States. Int J Plant Sci 167(6):1123–1150
Rollins MG (2009) LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildl Fire 18(3):235–249
Rumpff L, Duncan DH, Vesk PA et al (2011) State-and-transition modelling for adaptive management of native woodlands. Biol Conserv 144(4):1224–1236
Scheller RM, Domingo JB, Sturtevant BR et al (2007) Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecol Model 201(3–4):409–419
Smith SD, Huxman TE, Zitzer SF et al (2000) Elevated CO2 increases productivity and invasive species success in an arid ecosystem. Nature 408(6808):79–82
Speirs-Bridge A, Fidler F, McBride M et al (2010) Reducing overconfidence in the interval judgments of experts. Risk Anal 30(3):512–523
Stringham TK, Krueger WC, Shaver PL (2003) State and transition modeling: an ecological process approach. J Range Manag 56(2):106–113
Tausch R, Nowak RS (1999) Fifty years of ecotone change between shrub and tree dominance in the Jack Springs pinyon research natural area. In: McArthur E, Ostler WK, Wambodt CL (eds) Shrubland Ecotones, Ephraim, UT. USDA, Forest Service, Rocky Mountain Research Station, Ogden, UT, pp 71–77
Taylor AH, Beaty RM (2005) Climatic influences on fire regimes in the northern Sierra Nevada Mountains, Lake Tahoe Basin, Nevada, USA. J Biogeogr 32(3):425–438
Thacker ET, Ralphs MH, Call CA et al (2008) Invasion of broom snakeweed (Gutierrezia sarothrae) following disturbance: evaluating change in a state-and-transition model. Rangel Ecol Manag 61(3):263–268
Twidwell D, Allred BW, Fuhlendorf SD (2013) National-scale assessment of ecological content in the world’s largest land management framework. Ecosphere 4(8):94
Vavra M, Hemstrom MA, Wisdom M (2007) Modeling the effects of herbivores on the abundance of forest overstory states using a state-transition approach in the upper Grande Ronde River Basin, Oregon, USA. Landscape Urban Plan 80(3):212–222
Westerling A (2009) Climate change impacts on wildfire. In: Schneider M, Rosencranz A, Mastrandrea MD et al (eds) Climate change science and policy. Island Press, Washington, DC
Westerling AL, Bryant BP (2008) Climate change and wildfire in California. Clim Change 87:S231–S249
Westoby M, Walker B, Noy-Meir I (1989) Opportunistic management for rangelands not at equilibrium. J Range Manag 42:266–274
Acknowledgments
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
1.1 Calculating the Palmer Drought Severity Index
The Palmer Drought Severity Index (PDSI) time series was used to calculate the temporal multipliers for replacement fire, drought, annual grass invasion, and tree invasion. Drought is a major influence for these disturbances. PDSI measures long-term soil drought and is updated monthly (Palmer 1965; Heddinghaus and Sabol 1991). Positive values indicate above average soil moisture (>3 is very wet), whereas negative values represent droughty soil (<−3 is very dry). A PDSI of zero is average soil moisture. The formula for PDSI at time t (month) is as follows:
where P t is precipitation during month t, P t is average (historic) precipitation for month t, and k is a monthly climatic coefficient that weighs the local importance of (P t − P t ) (Palmer 1965). For example, k might imply that (P t − P t ) in January does not contribute as much to PDSI as the same deviation in precipitation observed in August (Palmer 1965). Although we downloaded monthly precipitation values and obtained monthly P t from historic precipitation data (respectively, month, precipitation [mm/day]: January, 0.8004; February, 0.8368; March, 1.0234; April, 0.9310; May, 0.9612; June, 0.6130; July, 0.6356; August, 0.7394; September, 0.6876; October, 0.7502; November, 0.7476; December, 0.6858), the value of k t is unknown and requires complicated field estimation based, among others, on evapotranspiration (Palmer 1965). (To remove this complication and need for a heuristic equation, future projects will use the Standard Precipitation Index [Hayes et al. 1999]). Therefore, we made several arbitrary assumptions to imitate k using the month’s temperature differential. Specifically,
where MaxT = 31 (°C) is the maximum temperature observed, and T t is the average temperature during month t. In this heuristic equation, higher temperatures cause smaller values to multiply (P t − P t ) when monthly precipitation is higher and thus PDSI becomes smaller (more evapotranspiration). The coefficients 1.5 and −0.15 are fitting constants we iteratively selected that allow the PDSI to vary within the observed range and be responsive to changes in precipitation, primarily, and secondarily to temperature. Using the latest observed monthly PDSI from March 2012 as the first PDSI t−1 , we estimated future monthly PSDIs per replicate for 75 years using Eqs. (13.1) and (13.2) for both without and with climate change. Compared to the PDSI replicates without climate change, it is noticeable that three temporal replicates of PDSI estimated for climate change effects were drier during certain decades only (replicates #1, 4, and 5), whereas the third replicate was wetter and the second replicate neutral (Fig. 13.6).
Because PDSI can be negative and the STSM software requires positive values, heuristic functions (arbitrary coefficients) were developed for drought, replacement fire, invasive annual grass expansion, and tree expansion that transformed negative values into positive values while maintaining the role of PDSI on the intensity of the disturbance. Not many flexible functions allow the conversion of negative values into positive ones while also accepting positive values; therefore, these curve fitting requirements led us to adopt functions with exponential components that could be easily calibrated. These functions do not calculate the rate of the disturbance, which is found in the STSM, but the temporal variability of the disturbance. All equations generated non-dimensional values and the final temporal multipliers were also non-dimensional.
1.2 Drought Disturbance
Because PDSI can be negative, therefore incompatible with PATH’s format for temporal multipliers, we chose a negative exponential function for drought to create positive values that increased exponentially with more negative (drier) PDSI values:
The parameters of this function (0.6 and −0.6) were chosen such that PDSI values close to −3 (very dry) were slightly greater than 3 (actually, 3.63) and that very severe droughts with PDSI of −5 (extreme drought) translated into slightly more than doubling of the function (12). Another consideration for curve fitting was that a mild drought characterized by a PDSI of −1 would be about equal to a neutral value of 1. Equation 13.3 is not the final temporal multiplier, however, because it is not divided by its average. In the absence of climate change effects, yearly values of Eq. (13.1) were divided by their temporal average over 75 years, whereas each yearly value of Eq. (13.3) with climate change was divided by the no-climate change average to reflect the hypothesis of altered levels.
1.3 Annual Grass Invasion and Tree Invasion Disturbances
The temporal multipliers for invasive annual grass expansion and tree expansion were calculated from two heuristic Gompertz equations (not including the CO2 fertilization). The Gompertz equation is highly flexible for curve fitting and a special case of it is the negative exponential:
where TMCO2 is the temporal multiplier for CO2 levels, which is <2 for any yearly value with climate change and equal to one without climate change. In accordance with our hypothesized relationship between species expansion and soil moisture and CO2 levels, the effect of CO2 levels as expressed by its temporal multiplier (between 0 and 1) on variability is proportional, whereas the effect of PDSI is exponential (i.e., greater). We arbitrarily dampened the effect of CO2 fertilization on trees by taking the square root of the CO2 temporal multiplier. The Gompertz equations allow for some expansion during even dry years (PDSI < 0), average expansion (temporal multiplier close to 1) during average moisture years, and a rapid rise of expansion (multiplier increasing to 4.5 and 2.5), respectively, for invasive annual grass expansion and tree expansion during very wet years. The parameters 4.5 and 2.5 were chosen to match values from the initial Park’s study by Provencher et al. (2013). Equations 13.4 and 13.5 are not temporal multipliers, however, because they are not divided by their averages. In the absence of climate change effects, yearly values of Eqs. (13.4) and (13.5), respectively, were each divided by their temporal average over 75 years, whereas each yearly value of Eqs. (13.4) and (13.5) with climate change, respectively, was divided by the no-climate change average to reflect the hypothesis of altered levels.
1.4 Fire
The shrubland–woodland fire temporal multipliers considered the roles of 3 years of PDSI, more specifically that fine fuels will more likely burn in the current dry year immediately following two previous and consecutive wetter-than-average years where fine fuels accumulated. The equation to calculate the temporal multipliers from shrubland fire contained two Gompertz functions to account for 3 years of PDSI:
where MaxFire = 1547 hectares and is 10 % of the area sum of all shrubland–woodland ecological systems. Equation 13.6 combines two Gompertz functions to accommodate negative and positive values of PDSI. The first part of Eq. (13.6) after MaxFire, representing fine fuels production, is a classic Gompertz function where a weighted sum is applied to soil moisture during 2 previous years (70 % of PDSI in year t−1 and 30 % of PDSI in year t−2). Wetter years (PDSI > 0) increase the value of this function (fine fuels accumulation) to a maximum of one. The first part is multiplied by the second function representing the current year, which is one minus another Gompertz function bound between zero and one. Increasingly drier soil moisture (PDSI < 0) causes the second part of Eq. (13.6) to increase to a maximum of one (maximum ignition probability). The PDSI values from the scenarios without and with climate change were used to calculate future area burned. Equation 13.6 is not the final temporal multiplier, however, because it is not divided by its average. In the absence of climate change effects, yearly values of Eq. (13.6) were divided by their temporal average over 75 years, whereas each yearly value of Eq. (13.6) with climate change was divided by the no-climate change average to reflect the hypothesis of altered levels.
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Provencher, L., Frid, L., Czembor, C., Morisette, J.T. (2016). State-and-Transition Models: Conceptual Versus Simulation Perspectives, Usefulness and Breadth of Use, and Land Management Applications. In: Germino, M., Chambers, J., Brown, C. (eds) Exotic Brome-Grasses in Arid and Semiarid Ecosystems of the Western US. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-24930-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-24930-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24928-5
Online ISBN: 978-3-319-24930-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)