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Exploring the key drivers of forest flammability in wet eucalypt forests using expert-derived conceptual models

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Abstract

Context

Fire behaviour research has largely focused on dry ecosystems that burn frequently, with far less attention on wetter forests. Yet, the impacts of fire in wet forests can be high and therefore understanding the drivers of fire in these systems is vital.

Objectives

We sought to identify and rank by importance the factors plausibly driving flammability in wet eucalypt forests, and describe relationships between them. In doing so, we formulated a set of research priorities.

Methods

Conceptual models of forest flammability in wet eucalypt forests were elicited from 21 fire experts using a combination of elicitation techniques. Forest flammability was defined using fire occurrence and fireline intensity as measures of ignitability and heat release rate, respectively.

Results

There were shared and divergent opinions about the drivers of flammability in wet eucalypt forests. Widely agreed factors were drought, dead fine fuel moisture content, weather and topography. These factors all influence the availability of biomass to burn, albeit their effects and interactions on various dimensions of flammability are poorly understood. Differences between the models related to lesser understood factors (e.g. live and coarse fuel moisture, plant traits, heatwaves) and the links between factors.

Conclusions

By documenting alternative conceptual models, we made shared and divergent opinions explicit about flammability in wet forests. We identified four priority research areas: (1) quantifying drought and fuel moisture thresholds for fire occurrence and intensity, (2) modelling microclimate in dense vegetation and rugged terrain, (3) determining the attributes of live vegetation that influence forest flammability, (4) evaluating fire management strategies.

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References

  • Abel N, Ross H, Walker P (1998) Mental models in rangeland research, communication and management. Rangeland J 20:77–91

    Google Scholar 

  • Alexander ME, Cruz MG (2013) Assessing the effect of foliar moisture on the spread rate of crown fires. Int J Wildl Fire 22(4):415–427

    Google Scholar 

  • Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock RA, Fogarty L, Gould J, McCarthy G, Marden-Smedley JB, Matthews S, Mattingley G, Pearce HG, van Wilgen BW (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. Int J Wildl Fire 24:443–460

    Google Scholar 

  • Arienti MC, Cumming SG, Boutin S (2006) Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthropogenic linear features, fire weather, and management. Can J For Res Rev Can Rech For 36(12):3155–3166

    Google Scholar 

  • Ashton DH (1975) Studies of litter in Eucalyptus regnans forests. Aust J Bot 23(3):413–433

    CAS  Google Scholar 

  • Ashton DH (1981) Fire in tall open-forests (wet sclerophyll forests). In: Gill AM, Groves RH, Noble IR (eds) Fire and the Australian Biota. Australian Academy of Science, Canberra, pp 340–366

    Google Scholar 

  • Ashton DH (2000) The Big Ash forest, Wallaby Creek, Victoria—changes during one lifetime. Aust J Bot 48:1–26

    Google Scholar 

  • Ashton DH, Attiwill PM (1994) Tall Open Forests. In: Groves RH (ed) Australian vegetation, Second edn. Cambridge University Press, Cambridge, pp 157–196

    Google Scholar 

  • Attiwill PM, Ryan MF, Burrows N, Cheney NP, McCaw L, Neyland M, Read S (2014) Timber harvesting does not increase fire risk and severity in Wet Eucalypt Forests of Southern Australia. Conserv Lett 7(4):341–354

    Google Scholar 

  • Aussenac G (2000) Interactions between forest stands and microclimate: ecophysiological aspects and consequences for silviculture. Ann For Sci 57(3):287–301

    Google Scholar 

  • Benyon RG, Lane PNJ (2013) Ground and satellite-based assessments of wet eucalypt forest survival and regeneration for predicting long-term hydrological responses to a large wildfire. For Ecol Manage 294:197–207

    Google Scholar 

  • Biggs D, Abel N, Knight AT, Leitch A, Langston A, Ban NC (2011) The implementation crisis in conservation planning: could “mental models” help? Conserv Lett 4(3):169–183

    Google Scholar 

  • Bilgili E, Saglam B (2003) Fire behavior in maquis fuels in Turkey. For Ecol Manage 184(1–3):201–207

    Google Scholar 

  • Boer MM, Nolan RH, Resco de Dios V, Clarke H, Pric EO, Bradstock RA (2017) Changing weather extreme call for early warning of potential for catastrophic fire. Earth’s Future 5:1196–1202

    Google Scholar 

  • Bowman D, Murphy BP, Neyland DLJ, Williamson GJ, Prior LD (2014) Abrupt fire regime change may cause landscape-wide loss of mature obligate seeder forests. Glob Change Biol 20(3):1008–1015

    Google Scholar 

  • Bowman D, Williamson GJ, Prior LD, Murphy BP (2016) The relative importance of intrinsic and extrinsic factors in the decline of obligate seeder forests. Glob Ecol Biogeogr 25(10):1166–1172

    Google Scholar 

  • Bradshaw LS, Deeming JE, Burgan JE, Cohen JD (1984) The 1978 National Fire-Danger Rating System: technical documentation. General Technical Report INT-169. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT

  • Bradstock RA (2010) A biogeographic model of fire regimes in Australia: current and future implications. Glob Ecol Biogeogr 19(2):145–158

    Google Scholar 

  • Bradstock RA, Hammill KA, Collins L, Price O (2010) Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landscape Ecol 25(4):607–619

    Google Scholar 

  • Burrows N, Gill M, Sharples J (2018) Development and validation of a model for predicting fire behaviour in spinifex grasslands of arid Australia. Int J Wildl Fire 27(4):271–279

    Google Scholar 

  • Burton J, Cawson J, Noske P, Sheridan G (2019) Shifting states, altered fates: divergent fuel moisture responses after high frequency wildfire in an obligate seeder eucalypt forest. Forests 10:436

    Google Scholar 

  • Cawson JG, Duff TJ, Swan MH, Penman TD (2018) Wildfire in wet sclerophyll forests: the interplay between disturbances and fuel dyanmics. Ecosphere 9(5):e02211

    Google Scholar 

  • Cawson JG, Duff TJ, Tolhurst KG, Baillie CC, Penman TD (2017) Fuel moisture in Mountain Ash forests with contrasting fire histories. For Ecol Manage 400:568–577

    Google Scholar 

  • Chen JQ, Saunders SC, Crow TR, Naiman RJ, Brosofske KD, Mroz GD, Brookshire BL, Franklin JF (1999) Microclimate in forest ecosystem and landscape ecology—variations in local climate can be used to monitor and compare the effects of different management regimes. Bioscience 49(4):288–297

    Google Scholar 

  • Cheney NP (1981) Fire behaviour. In: Gill AM, Groves RH, Noble IR (eds) Fire and the Australian biota. Australian Academy of Science, Canberra

    Google Scholar 

  • Cheney NP, Gould JS, Catchpole WR (1998) Prediction of fire spread in grasslands. Int J Wildl Fire 8(1):1–13

    Google Scholar 

  • Cheney NP, Gould JS, McCaw WL, Anderson WR (2012) Predicting fire behaviour in dry eucalypt forest in southern Australia. For Ecol Manage 280:120–131

    Google Scholar 

  • Clarke H, Gibson R, Cirulis B, Bradstock RA, Penman TD (2019) Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia. J Environ Manage 235:34–41

    PubMed  Google Scholar 

  • Clarke H, Penman TD, Boer M, Cary GJ, Fontaine JB, Price O, Bradstock R (2020) The proximal drivers of large fires: a pyrogeography study. Frontiers in Earth Science. https://doi.org/10.3389/feart.2020.00090

    Article  Google Scholar 

  • Cochrane MA (2003) Fire science for rainforests. Nature 421(6926):913–919

    CAS  PubMed  Google Scholar 

  • Collins KM, Price OF, Penman TD (2015) Spatial patterns of wildfire ignitions in south-eastern Australia. Int J Wildl Fire 24:1098–1108

    Google Scholar 

  • Cruz MG, Sullivan AL, Gould JS, Sims NC, Bannister AJ, Hollis JJ, Hurley RJ  (2012) Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia. For Ecol Manage 284:269–285

    Google Scholar 

  • Dent JM, Buckley HL, Lustig A, Curran TJ (2019) Flame temperatures saturate with increasing dead material in Ulex europaeus, but flame duration, fuel consumption and overall flammability continue to increase. Fire 2(6):8

    Google Scholar 

  • Department of Environment Land Water and Planning (2015) Strategic bushfire management plant. East Central Victorian State Goverment, Melbourne. http://www.delwp.vic.gov.au/__data/assets/pdf_file/0006/318849/DELWP0016F_BMP15_EastCentral_web_v2.pdf

  • Department of Environment Land Water and Planning (2016) Native Vegetation—Modelled 2005 Ecological Vegetation Classes (with Bioregional Conservation Status). Victorian State Goverment (data.vic.gov.au), Melbourne,

  • Department of Primary Industries Parks Water and Environment (2013) TASVEG 3.0. Tasmanian Vegetation Monitoring and Mapping Program. Resource Management and Conservation Division, Hobrt,

  • Duff TJ, Cawson JG, Harris S (2018) Dryness thresholds for fire occurrence vary by forest type along an aridity gradient: evidence from Southern Australia. Landscape Ecology 33:1369–1383

    Google Scholar 

  • Fedrigo M, Kasel S, Bennett LT, Roxburgh SH, Nitschke CR (2014) Carbon stocks in temperate forests of south-eastern Australia reflect large tree distribution and edaphic conditions. For Ecol Manage 334:129–143

    Google Scholar 

  • Fernandes PAM (2001) Fire spread prediction in shrub fuels in Portugal. For Ecol Manage 144(1–3):67–74

    Google Scholar 

  • Fernandes PM, Botelho HS, Rego FC, Loureiro C (2009) Empirical modelling of surface fire behaviour in maritime pine stands. Int J Wildl Fire 18(6):698–710

    Google Scholar 

  • Florence RG (1996) Ecology and silviculture of eucalypt forests. CSIRO Publishing Collingwood, Victoria

  • Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian forest fire behaviour prediction system. Forestry Canada, Science and Sustainable Development Directorate, Ottawa

    Google Scholar 

  • Gambiza J, Campbell BM, Moe SR, Frost PGH (2005) Fire behaviour in a semi-arid Baikiaea plurijuga savanna woodland on Kalahari sands in western Zimbabwe. S Afr J Sci 101(5–6):239–244

    Google Scholar 

  • Gill AM, Zylstra P (2005) Flammability of Australian forests. Australian Forestry 68(2):87–93

    Google Scholar 

  • Gould JS, McCaw WL, Cheney NP (2011) Quantifying fine fuel dynamics and structure in dry eucalypt forest (Eucalyptus marginata) in Western Australia for fire management. For Ecol Manage 262:531–564

    Google Scholar 

  • Hemming V, Burgman MA, Hanea AM, McBride MF, Wintle BC (2018a) A practical guide to structured expert elicitation using the IDEA protocol. Methods Ecol Evol 9:169–181

    Google Scholar 

  • Hemming V, Walshe TV, Hanea AM, Fidler F, Burgman MA (2018b) Eliciting improved quantitative judgements using the IDEA protocol: a case study in natural resource management. PLoS ONE 13(6):34

    Google Scholar 

  • Hines F, Tolhurst KG, Wilson AAG, McCarthy GJ (2010) Overall fuel hazard assessment guide, 4th edn. Fire Management Branch, Department of Sustainability and Environment, Melbourne, VIC

    Google Scholar 

  • Holden ZA, Jolly WM (2011) Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support. For Ecol Manage 262(12):2133–2141

    Google Scholar 

  • Holgate CM, De Jeu RAM, van Dijk AIJM, Liu YY, Renzullo LJ, Vinodkumar, Dharssi I, Parinussa RM, Van Der Schalie R, Gevaert A, Walker J, McJannet D, Cleverly J, Haverd V, Trudinger CM, Briggs PR (2016) Comparison of remotely sensed and modelled soil moisture data sets across Australia. Remote Sens Environ 186:479–500

    Google Scholar 

  • Hollis JJ, Matthews S, Ottmar RD, Prichard SJ, Slijepcevic A, Burrows ND, Ward B, Tolhurst KG, Anderson WR, Gould JS (2010) Testing woody fuel consumption models for application in Australian southern eucalypt forest fires. For Ecol Manage 260:948–964

    Google Scholar 

  • Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DMJS (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun 6:7537

    Google Scholar 

  • Jolly WM, Johnson DH (2018) Pyro-Ecophysiology: shifting the paradigm of live wildland fuel research. Fire 1:8

    Google Scholar 

  • Keane RE (2015) Wildland fuel fundamentals and application. Springer International Publishing, Switzerland

    Google Scholar 

  • Keeley JE (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildl Fire 18:116–126

    Google Scholar 

  • Keetch JJ, Byram GM (1968) A drought index for forest fire control, USDA Forest Service Research Paper SE-38. Department of Agriculture - Forest Service, Asheville, North Carolina, U.S, p 33

    Google Scholar 

  • Keith DA, Simpson CC (2011) Vegetation formations and classes of NSW (version 3.03). Department of Planning Industry and Environment, Sydney, NSW

  • Kirkpatrick JB, Nunez M (1980) Vegetation-radiation relationships in mountainous terrain: eucalypt-dominated vegetation in the Risdon Hills. Tasmania J Biogeogr 7(2):197–208

    Google Scholar 

  • Krawchuk MA, Moritz MA (2011) Constraints on global fire activity vary across a resource gradient. Ecology 92(1):121–132

    PubMed  Google Scholar 

  • Kumar V, Dharssi I (2019) Evaluation and calibration of a high-resolution soil moisture product for wildfire prediction and management. Agric For Meteorol 264:27–39

    Google Scholar 

  • Lindenmayer DB (2009) Old forest, new perspectives—insights from the Mountain Ash forests of the Central Highlands of Victoria, south-eastern Australia. For Ecol Manag 258:357–365

    Google Scholar 

  • Littell JS, Peterson DL, Riley KL, Liu YQ, Luce CH (2016) A review of the relationships between drought and forest fire in the United States. Glob Change Biol 22(7):2353–2369

    Google Scholar 

  • Little JK, Prior LD, Williamson GJ, Williams SE, Bowman DM (2012) Fire weather risk differs across rain forest-savanna boundaries in the humid tropics of north-eastern Australia. Aust Ecol 37(8):915–925

    Google Scholar 

  • Lowe TD, Lorenzoni I (2007) Danger is all around: eliciting expert perceptions for managing climate change through a mental models approach. Global Environ Change Hum Policy Dimen 17(1):131–146

    Google Scholar 

  • Markóczy L, Goldberg J (1995) A method for eliciting and comparing causal maps. J Manag 21(2):305–333

    Google Scholar 

  • Matthews S (2014) Dead fuel moisture research: 1991–2012. Int J Wildl Fire 23(1):78–92

    Google Scholar 

  • McArthur AG (1967) Fire behaviour in eucalypt forests. Leaflet No. 107. Department of National Development, Forestry and Timber Bureau, Canberra,

  • McCarthy GJ, Tolhurst KG, Wouters M (2003) Prediction of firefighting resources for suppression operations in Victoria’s parks and forests, No. 56. Department of Sustainability and Environment, East Melbourne, Victoria,

  • Meyn A, White PS, Buhk C, Jentsch A (2007) Environmental drivers of large, infrequent wildfires: the emerging conceptual model. Prog Phys Geogr 31(3):287–312

    Google Scholar 

  • Moon K, Adams VM (2016) Using quantitative influence diagrams to map natural resource managers’ mental models of invasive species management. Land Use Policy 50:341–351

    Google Scholar 

  • Moon K, Duff TJ, Tolhurst KG (2019a) Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation. Fire Saf J 105:320–329

    Google Scholar 

  • Moon K, Guerrero AM, Adams VM, Biggs D, Blackman DA, Craven L, Dickinson H, Ross H (2019b) Mental models for conservation research and practice. Conserv Lett 2019:e12642

    Google Scholar 

  • Nolan RH, Boer MM, de Dios VR, Caccamo G, Bradstock RA (2016) Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia. Geophys Res Lett 43(9):4229–4238

    Google Scholar 

  • Nyman P, Baillie CC, Duff TJ, Sheridan GJ (2018) Eco-hydrological controls on microclimate and surface fuel evaporation in complex terrain. Agric For Meteorol 252:49–61

    Google Scholar 

  • Pausas JG, Keeley JE, Schwilk DW (2017) Flammability as an ecological and evolutionary driver. J Ecol 105(2):289–297

    Google Scholar 

  • Peacock RJ (2019) Managing wildfire risk to fire sensitive rainforest vegetation. In: Fuel of today—fire behaviour of tomorrow, 6th international fire behaviour and fuel conference, Sydney 2019. International Associaton of Widland Fire

  • Penman TD, Bradstock RA, Price O (2013) Modelling the determinants of ignition in the Sydney Basin, Australia: implications for future management. Int J Wildl Fire 22:469–478

    Google Scholar 

  • Pivello VR (2011) The use of fire in the Cerrado and amazonian rainforests of brazil: past and present. Fire Ecol 7(1):24–39

    Google Scholar 

  • Plucinski MP (2012) Factors affecting containment area and time of Australian forest fires features aerial suppression. For Sci 58:390–398

    Google Scholar 

  • Plucinski MP (2019) Fighting flames and forging firelines: wildfire suppression effectiveness at the fire edge. Curr For Rep 5(1):1–19

    Google Scholar 

  • Plucinski MP, McCarthy GJ, Hollis JJ, Gould JS (2012) The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel. Int J Wildl Fire 21:219–229

    Google Scholar 

  • Price OF, Bradstock RA (2012) The efficacy of fuel treatment in mitigating property loss during wildfires: insights from analysis of the severity of the catastrophic fires in 2009 in Victoria, Australia. J Environ Manage 113:146–157

    PubMed  Google Scholar 

  • Rawson RP, Billing PR, Duncan SF (1983) The 1982-83 forest fires in Victoria. Aust For 46(3):163–172

    Google Scholar 

  • Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628

    Google Scholar 

  • Riley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA (2013) The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984-2008: the role of temporal scale. Int J Wildl Fire 22(7):894–909

    Google Scholar 

  • Rothermel R (1972) A mathematical model of predicting fire spread in wildland fuels. Research Paper INT-115. USDA Forest Service, Intermountain forest and range experiment station, Ogden, Utah,

  • Runge MC, Converse SJ, Lyons JE (2011) Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biol Conserv 144:1214–1223

    Google Scholar 

  • Schwilk DW (2003) Flammability is a niche construction trait: canopy architecture affects fire intensity. Am Nat 162(6):725–733

    PubMed  Google Scholar 

  • Schwilk DW, Caprio AC (2011) Scaling from leaf traits to fire behaviour: community composition predicts fire severity in a temperate forest. J Ecol 99(4):970–980

    Google Scholar 

  • Sharples JJ (2009) An overview of mountain meteorological effects relevant to fire behaviour and bushfire risk. Int J Wildl Fire 18(7):737–754

    Google Scholar 

  • Sharples JJ, Cary GJ, Fox-Hughes P, Mooney S, Evans JP, Fletcher M-S, Fromm M, Grierson PF, McRae R, Baker P (2016) Natural hazards in Australia: extreme bushfire. Clim Change 139(1):85–99

    Google Scholar 

  • Sharples JJ, Mills GA, McRae RHD (2012) Extreme drying events in the Australian high-country and their implications for bushfire risk management. Aust Meteorol Oceanogr J 62:157–170

    Google Scholar 

  • Shea K, Runge MC, Pennell D, Probert WJM, Li S-L, Tildesley M, Ferrari M (2020) Harnessing multiple models for outbreak management. Science 368(6461):577–579

    CAS  PubMed  Google Scholar 

  • Sillett S, Van Pelt R, Kramer RD, Carroll AL, Koch GW (2015) Biomass and growth potential of Eucalyptus regnans up to 100 m tall. For Ecol Manage 348:78–91

    Google Scholar 

  • Slijepcevic A, Anderson WR, Matthews S, Anderson DH (2018) An analysis of the effect of aspect and vegetation type on fine fuel moisture content in eucalypt forest. Int J Wildl Fire 27(3):190–202

    Google Scholar 

  • Sneeuwjagt R, Peet GB (1985) Forest fire behaviour tables for Western Australia, 3rd edn. Western Australian Department of Conservation and Land Management, Perth

    Google Scholar 

  • Solomon RJ, Dell AR (1967) The Hobart bushfires of February, 1967. Aust Geographer 10:306–308

    Google Scholar 

  • Sullivan AL (2009) Wildland surface fire spread modelling, 1990-2007. 2: Empirical and quasi-empirical models. Int J Wildl Fire 18(4):369–386

    Google Scholar 

  • Sullivan AL (2017) Inside the inferno: fundamental processes of wildland fire behaviour. Part 2: heat transfer and interactions. Curr For Rep 3:150–171

    Google Scholar 

  • Sullivan AL, Matthews S (2013) Determining landscape fine fuel moisture content of the Kilmore East ‘Black Saturday’ wildfire using spatially-extended point-based models. Environ Modell Softw 40:98–108

    Google Scholar 

  • Swan M, Sitters H, Cawson J, Duff TJ, Wibisono Y, York A (2018) Fire planning for multispecies conservation: integrating growth stage and fire severity. For Ecol Manage 415–416:85–97

    Google Scholar 

  • Syphard AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK (2008) Predicting spatial patterns of fire on a southern California landscape. Int J Wildl Fire 17:602–613

    Google Scholar 

  • Taylor C, McCarthy MA, Lindenmayer DB (2014) Nonlinear effects of stand age on fire severity. Conserv Lett 7(4):355–370

    Google Scholar 

  • Tng DYP, Jordan GJ, Bowman D (2013) Plant traits demonstrate that temperate and tropical giant eucalypt forests are ecologically convergent with rainforest not savanna. PLoS ONE 8(12):13

    Google Scholar 

  • Tng DYP, Williamson GJ, Jordan GJ, Bowman D (2012) Giant eucalypts—globally unique fire-adapted rain-forest trees? New Phytol 196(4):1001–1014

    Google Scholar 

  • Tolhurst KG, Anderson WR, Gould J (2006) Woody fuel consumption experiments in an undisturbed forest. In: Viegas DX (ed) V international conference on forest fire research, Coimbra, Portugal, pp. 14

  • Tumino BJ, Duff TJ, Goodger JQD, Cawson JG (2019) Plant traits linked to field-scale flammability metrics in prescribed burns in Eucalyptus forest. PLoS ONE 14(8):e0221403

    CAS  PubMed  PubMed Central  Google Scholar 

  • Turner PAM, Balmer J, Kirkpatrick JB (2009) Stand-replacing wildfires? The incidence of multi-cohort and single-cohort Eucalyptus regnans and E. obliqua forests in southern Tasmania. For Ecol Manag 258(4):366–375

    Google Scholar 

  • Van Wagner CE (1987) Development and structure of the Canadian forest fire weather index system. Technical report 35. Canadian Forestry Service, Ottawa, ON

  • Viney NR (1991) A review of fine fuel moisture modelling. Int J Wildl Fire 1(4):215–234

    Google Scholar 

  • Walsh SF, Nyman P, Sheridan GJ, Baillie CC, Tolhurst KG, Duff TJ (2017) Hillslope-scale prediction of terrain and forest canopy effects on temperature and near-surface soil moisture deficit. Int J Wildl Fire 26(3):191–208

    Google Scholar 

  • Walshe T, Burgman M (2010) A framework for assessing and managing risks posed by emerging diseases. Risk Anal 30(2):236–249

    PubMed  Google Scholar 

  • Wood SW, Bowman D, Prior L, Lindenmayer D, Wardlaw T, Robinson R (2014) 13 Tall eucalypt forests. In: Burns E, Lindenmayer D, Lowe A, Thurgate N (eds) Biodiversity and environmental change: monitoring, challenges and direction. CSIRO Publishing, Collingwood

    Google Scholar 

  • Yeo CS, Kepert JD, Hicks R (2015) Fire danger indices: current limitations and a pathway to better indices. Bushfire and Natural Hazards CRC, Melbourne

    Google Scholar 

  • Zylstra P, Bradstock RA, Bedward M, Penman TD, Doherty MD, Weber RO, Gill AM, Cary GJ (2016) Biophysical mechanistic modelling quantifies the effects of plant traits on fire severity: species, not surface fuel loads, determine flame dimensions in eucalypt forests. PLoS ONE 11(8):e0160715

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This research was part of a project titled “Relationships between soil and fuel drying—flammability switch in ash and damper foothill forests” managed within the Integrated Forest Ecosystem Research program, a research program conducted by the University of Melbourne and funded by the Victorian Government’s Department of Environment, Water, Land and Planning. We would like to thank Andrew Sullivan and Nigel Brennan who were participants in the workshop but who felt their contributions were not sufficient to warrant authorship of this paper. Human ethics approval was obtained to conduct this research (ethics approval# 1853368).

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Cawson, J.G., Hemming, V., Ackland, A. et al. Exploring the key drivers of forest flammability in wet eucalypt forests using expert-derived conceptual models. Landscape Ecol 35, 1775–1798 (2020). https://doi.org/10.1007/s10980-020-01055-z

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