fire

Article Propensities of Old Growth, Mature and Regrowth Wet Eucalypt Forest, and nitens , to Burn during Wildfire and Suffer Fire-Induced Crown Death

Suyanti Winoto-Lewin, Jennifer C. Sanger * and James B. Kirkpatrick Discipline of Geography and Spatial Sciences, University of , 7001, ; [email protected] (S.W.-L.); [email protected] (J.B.K.) * Correspondence: [email protected]; Tel.: +61-423-008-166

 Received: 7 April 2020; Accepted: 10 May 2020; Published: 14 May 2020 

Abstract: There are conflicting conclusions on how the flammability of wet eucalypt forests changes in the time after disturbances such as or wildfire. Some conclude that forests are most flammable in the decades following disturbance, while others conclude that disturbance has no effect on flammability. The comparative flammability of in the same environment as wet eucalypt forest is not known. We determined fire incidence and fire severity in regrowth, mature and old growth wet eucalypt forest, and E. nitens plantation, in the Huon Valley, Tasmania after the January–February 2019 wildfire. To control for topographic variation and fire weather, we randomly selected sites within the fire footprint, then randomly located a paired site for each in different forest types in the same topographic environment within 3 km. Each pair of sites was burned on the same day. Old growth forest and plantations were the least likely to burn. Old growth and mature forest exhibited scorched eucalypt crowns to a much lesser degree than regrowth forests. In a comparison of paired sites, plantation forest was less likely to burn than combined mature and old growth forests, but in all cases of detected ignition the canopy of plantation was scorched. The lower flammability of older forests, and their importance as an increasing store of carbon, suggests that a cessation of logging outside plantations might have considerable benefits.

Keywords: fire; logging; old-growth; plantation; regrowth; Tasmania; wet eucalypt forest

1. Introduction Unplanned forest fires frequently kill people and destroy property. For example, the 2009 Black Saturday fires caused the loss of 179 lives and more than 2000 homes [1]. Thirty-three people died (24th January 2020) during the 2019/2020 fires in and [2]. After extreme events like these, there is often a short-lived political urgency to take actions to prevent future ferocious fire events. Under change, fire incidence is expected to increase over the next century in both the Southern and Northern hemispheres, particularly in wet forests [3–5]. Fire hazard in forests, human land use and policy are connected in socio-ecological systems [6]. The effect of disturbance history on fire hazard in forests is specific to characteristics and fire regime [7]. UnderstandingRETRACTED the ways in which forest management affects fire behavior and incidence is imperative. The present study investigates the effect on fire incidence and severity of two forest management practices common in Tasmania; clearfelling and plantation establishment. The study area supports wet, temperate forests; specifically, wet Eucalyptus (predominantly and ) dominated forests which have an understorey of broad-leaved shrubs or rainforest . The wet forests that are dominated by large eucalypts more than 110 years old

Fire 2020, 3, 13; doi:10.3390/fire3020013 www.mdpi.com/journal/fire This paper has been retracted. A retraction notice was published on 28 August 2020 in Fire 2020, 3, 47; doi:10.3390/fire3030047 Fire 2020, 3, 13 2 of 11 have not been substantially disturbed by logging so are here described as ‘old-growth’ [8]. Clearfelling and the closely related ‘aggregated-retention’ (in which patches in the clearfelled area are retained) are used to harvest these forests. Clearfelling is followed by high intensity burning and aerial seeding of local eucalypt species [9]. This results in extremely dense regeneration followed by self-thinning [10,11]. Our study area contains a patchwork of coupes which have undergone clearfell logging at various times in the last 60 years, eucalypt plantations and old growth forest [8]. In wet forests in temperate zones, fire weather, ignition events and fuel moisture content limit fire activity [12–14]. This contrasts with dry ecosystems, in which fuel load, rather than fuel moisture content, is often a limiting factor [4,14]. In south-eastern Australia there is high daily variability of fire weather. Landscape fire events can occur in wet forests when there is an ignition on one of the infrequent days of extreme fire weather [15,16]. Analysis of fire weather data and active fire detections from moderate-resolution imaging spectroradiometer (MODIS) instruments has shown that fire weather is a strong driver of fire variability both on diurnal and larger time scales [17]. Post-fire analysis of fire severity in dry forests has found that weather is a stronger predictor of crown fire than topography and time since last fire [13]. In Australia there is yet to be a consensus on the effects of natural disturbance or clearfell logging on fire regimes. An analysis on fire feedbacks in the Australian Alps found regenerating tall wet forests were 8.3 times more likely to burn in the 58 years after fire than mature forests that had not been burned for much longer [18]. Surface fine fuels and overall fuel hazard have been found to accumulate to an equilibrium after disturbance [19,20]. Regenerating forests have been found to have a more vertically and horizontally continuous fuel layer which increases ease of fire spread [21]. The understorey species composition can change to more flammable species in response to changing fire regimes [11,19,22]. Species composition determines flame dimensions and therefore fire behaviour [23,24]. In contrast to the above, modeling of fire behavior related to fuel availability and structure of different forest age classes in south-east Australia found the highest flame height, probability of crown fire, and rate of spread to be in mature tall-mixed forests [25]. Surface fuel moisture has also been found to be higher in the first decade after fire, suggesting that young forests should be less available to burn [26]. Taylor et al. [11] found a strong relationship between stand age in Eucalyptus regnans forest in Victoria and the severity of fire, with the highest severity fires occurring in 7–36 year old stands. In the same forest type Bowman et al. [27] found that stand age only had a minor influence on fire severity compared to the strong influence of fire weather. Attiwill et al. [28] likewise claimed that there is no relationship between timber harvesting and fire extent and severity. Our study analyses the incidence and severity of the 2019 Riveaux Road fire in Southern Tasmania in four forest types: regrowth, mature forest, old growth forest and plantation. Fire severity (the effect of fire on the vegetation) rather than fire intensity (the energy produced by the fire) was analysed in three broad categories of unburnt, understorey fire or crown fire [29]. Fire severity is considered more pertinent than fire intensity in our analysis as it relates to how the forest responds to fire and therefore creates feedbacks. Fire severity can also indicate fire intensity when comparing similar vegetation types [30]. We consider regrowth forest to be undergoing the process of self-thinning after logging, while mature forest has largely completed self-thinning. Old-growth forest was defined as that which had less than 30 crowns per hectare and crowns of irregular shape indicating mature and senescing trees [31]. The plantations in the study are Eucalyptus nitens plantations of various ages. They have a uniform structure of evenly spaced trees and little to no understorey [8]. We expected to find that old-growth forests burnt at a lower rate and lower severity than younger forests. 2.RETRACTED Materials and Methods 2.1. Study Area The study area was 17,406 ha of the Huon Valley in southern Tasmania (Figure1). The mean daily minimum and maximum temperatures for January are 8.4 ◦C and 19.6 ◦C, respectively, and for July Fire 2020, 3, x FOR PEER REVIEW 3 of 13

2. Materials and Methods

2.1. Study Area Fire 2020, 3, 13 3 of 11 The study area was 17,406 ha of the Huon Valley in southern Tasmania (Figure 1). The mean daily minimum and maximum temperatures for January are 8.4 ℃ and 19.6 ℃, respectively, and for

Julyare 2.4are◦ C2.4 and ℃ 8.6and◦ C,8.6 respectively. ℃, respectively. The averageThe average annual annual rainfall rainfall is 1762 is mm 1762 (Warra mm (Warra weather weather station, ° ° station,43.06◦ S,43.06 146.70 S, ◦146.70E; Australian E; Australian Bureau Bureau of Meteorology, of Meteorology, Jan 2020). Jan 2020). The natural The natural vegetation vegetation of the of study the studyarea is area wet is eucalypt wet eucalypt forest, forest, mostly mostly dominated dominated by Eucalyptus by Eucalyptus regnans regnansor Eucalyptus or Eucalyptus obliqua .obliqua Old-growth. Old- growthforests forests in this regionin this oftenregion contain often contain extremely extrem tallely eucalypt tall eucalypt trees (mature trees (mature trees often trees between often between 60 and 60100 and m) 100 and m) have and an have understorey an understorey dominated dominated by cool temperateby cool rainforest species (mixed species forests) (mixed or forests)broad-leaved or broad-leaved tall shrubs tall and shrubs small treesand small (wet sclerophyll trees (wet forests)sclerophyll [32]. forests) The area [32]. has The been area managed has been for managedtimber extraction for timber for extraction many decades for many and containsdecades aand mosaic contains of old-growth a mosaic forests,of old-growth native forestforests, regrowth native forestand Eucalyptus regrowth nitensand Eucalyptusplantations nitens [8,32 plantations]. Sixty-four [8,32]. thousand Sixty-four hectares thousand were burned hectares in the were Riveaux burned Road in thefire, Riveaux which started Road fire, on 15th which of Januarystarted on and 15th continued of January burning and untilcontinued mid-February burning until 2019 [mid-February33]. Lightning 2019strikes [33]. ignited Lightning several strikes fires toignited the west several of Geeveston fires to the which west amalgamatedof Geeveston andwhich spread amalgamated to the east and and spreadsouth. to The the fires east of and the south. 2018–19 The season fires of in the Tasmania 2018–19 caused season the in destructionTasmania caused of six homesthe destruction and burned of six14% homes of Tasmania’s and burned tall forests14% of asTasmania’s well as significant tall forests areas as well of Gondwanan as significant myrtle-beech areas of Gondwanan forests and myrtle-beechalpine vegetation forests [33 and]. The alpine region vegetation experienced [33]. record The region low rainfall experienced during record January low 2019 rainfall and summerduring Januarytemperatures 2019 and 1.6 ◦summerC above temperatures average [33]. 1.6 °C above average [33].

FigureFigure 1. 1. StudyStudy area area and and sample sample sites sites in in the Huon Valley inin southernsouthern TasmaniaTasmania and and the the fire fire footprint footprint of ofthe the Riveaux Riveaux fire fire which which burned burned in in January–February January–February 2019. 2019.

2.2.2.2. Study Study Design Design SamplingSampling was was designed designed to to minimise minimise the the effects effects of of variation variation in in topography topography and and fire fire weather weather on on thethe relationships relationships between between disturbance disturbance history history and and bo bothth fire fire incidence incidence and and fire fire severity. severity. This This outcome outcome waswas achieved achieved by by randomly randomly selecting selecting 50 50 pairs pairs of of to topographicallypographically similar similar site sites.s. The The study study area area was was 2 griddedgridded in in 8 8 km km2 cells.cells. Initial Initial sample sample points points were were randomly randomly located, located, with with a a maximum maximum of of one one in in each each gridgrid cell. cell. Sample Sample points points were were located located on on pre-fire pre-fire imagery imagery [34] [34 ]from from which which we we characterised characterised the the forest forest type.type. The The slope, slope, slope slope position, position, aspect aspect and and elevat elevationion were were determined determined from from Google Google Maps. Maps. A A paired paired randomrandomRETRACTED location inin anotheranother forest forest type type was was sought sought within within a 3 km a 3 radius km radius of the initialof the selection. initial selection. Random selections were made until the second site had no more than 40 m difference in elevation, 90◦ difference in aspect and 20% difference in slope. The pairs also had to occur in the same slope position (ridge, upper slope, lower slope, valley) or an adjacent one. Plantations were identified through TasVeg Live [35]. Other forest types were determined from high resolution aerial photography. Regrowth forests were defined as those with either more than Fire 2020, 3, 13 4 of 11

100 visible crowns per hectare or very young regenerating stands with ground visible between the trees. Mature forests had 30 to 99 eucalypt crowns per hectare and rounded regular canopies indicating even age. Old growth forests had some (up to 29 per ha) emergent, unevenly shaped eucalypt crowns and smaller trees beneath. Post-fire aerial ESRI images [36] were analysed to determine fire severity in the 0.1 ha around the point and check for any changes of forest type. Completely green forest was regarded as ‘unburned’. Plots labelled ‘understorey fire’ had at least some remaining green in the crowns but were brown or black beneath. Completely brown or black forest was labelled as ‘crown fire’. Some of the apparent crown scorch in these cases may have occurred between the fire and the taking of the image, and not have resulted from fire in the canopy, but rather from heat from below or the effects of ringbarking by fire. Active fire detections obtained from moderate resolution imaging spectroradiometer (MODIS) instruments on the Terra and Aqua satellites were used to estimate the time frame in which each point burnt [37]. Each sample point was estimated to have burnt between the earliest and latest dates of the five closest MODIS hotspot detections. Cloud cover can result in false negative detections, and this has, in some cases, resulted in a broad potential burn period [38]. If the sample point occurred within 300 m of a MODIS hotspot detection it was estimated to have burnt on the same day/night as the hotspot detection, as fire severity has been found to be spatially dependent within this distance [13]. Forest Fire Danger Index ratings were calculated in three hourly increments using wind velocity, temperature and relative humidity data from Warra weather station (Warra weather station, 43.15◦ S, 146.90◦ E; Australian Bureau of Meteorology, Mar 2020). factor was calculated from data from Geeveston (Fourfoot Rd Weather Station, 43.06◦ S, 146.70◦ E; Australian Bureau of Meteorology, Jan 2020), using soil dryness index (SDI) as the drought factor input. The mean and maximum FFDIs over the estimated time frame at which each point was burned were determined.

2.3. Statistical Analyses The data were analysed as a full set, and for individual pairs of forest types. Chi2 was used to determine whether the relationship between fire severity classes and forest types deviated from random. It was also used to determine if the relationship between topographic position classes and forest types deviated from random. One-way analysis of variance was used to determine if forest types differed in elevation, slope, aspect, latitude and longitude, as well as maximum and mean FFDI. Minitab18 was used for analyses.

3. Results There were no significant differences between forest types in any of the environmental conditions for the data set as a whole (Table1) and for any of the paired forest types. There were no significant differences in mean or maximum FFDI experienced in sampled forest types (Table1). The maximum potential FFDI for old growth, regrowth and mature forest was 38.78, and the minimum possible was 0.52. For plantations the maximum potential FFDI was 25.3 and the minimum was 0.54. For the full data set, there was strong variation in severity between forest types. Old growth forests burned less than mature forests, which in turn burned slightly less than regrowth (Table1, Figure2). Plantations burned in the same proportion as old growth (Table1, Figure2). However, in both plantation and regrowth, fires crowned to a much greater extent than in mature and old growth forest (Table1, Figure3). There were few crown fires in old growth forest (Table1, Figure3). In the pairedRETRACTED comparisons between forest types, old growth forest burned less than mature forest but not regrowth forests (Table2). However, old growth forest experienced fewer crown fires than regrowth (Table2). Mature forests also experienced fewer crown fires than regrowth (Table A1). Pooled with mature forest, old growth forest burned more than plantations, although the incidence of crown fire did not differ (Table2). Fire 2020, 3, 13 5 of 11

Table 1. Fire severity and environmental conditions by forest type, showing significance of differences (Chi2 (C) and ANOVA). Column percentages are shown for the Chi2 analyses.

Old Growth Mature Regrowth Plantation P Unburned (%) 41 21 17 40 Understorey fire (%) 48 50 22 0 Crown fire (%) 11 29 60 60 0.001 (C) Valley (%) 22 32 29 50 Lower slope (%) 56 46 54 40 Upper slope (%) 22 21 17 10 0.783 (C) Elevation (m) 233 187 197 150 0.185 Slope (%) 21 17 24 16 0.822 Aspect (◦) 128 144 137 113 0.922 Average maximum potential FFDI 20.32 21.13 24.54 19.22 0.17 Mean potential FFDI 11.07 11.05 12.15 12.94 0.51 Fire 2020, 3, x FOR PEER REVIEW 6 of 13 Fire 2020, 3, x FOR PEER REVIEW 6 of 13 100 100 80 80 60 60

Burnt (%) Burnt 40

Burnt (%) Burnt 40 20 20 0 0 Old Growth Mature Forest Regrowth Plantation OldForest Growth Mature Forest Regrowth Plantation Forest

Figure 2. The percentage of sites burned in each forest type. FigureFigure 2. The2. The percentage percentage ofof sitessites burned in in each each forest forest type. type.

60 60

40 40

Crown Fire (%) Fire Crown 20 Crown Fire (%) Fire Crown 20

0 RETRACTED0 Old Growth Mature Forest Regrowth Plantation OldForest Growth Mature Forest Regrowth Plantation Forest

Figure 3. The percentage of sites that experienced crown fire. Figure 3. The percentage of sites that experienced crown fire. Figure 3. The percentage of sites that experienced crown fire. 4. Discussion 4. Discussion Much of the world’s forest estate (and 12% of Tasmania’s land mass) is managed for timber productionMuch ofin athe context world’s in whichforest unplannedestate (and forest12% offires Tasmania’s are likely landto increase mass) inis frequencymanaged foras a timber result ofproduction climate change in a context [3–5,39]. in whichWe sought unplanned to consolidat forest efires understanding are likely to ofincrease the effects in frequency of both the as absence a result of climateclearfelling change and [3–5,39]. the time We since sought clearfelling to consolidat on thee understanding fire hazard of of commercial the effects of and both reserved the absence wet eucalyptof clearfelling forests. and Using the time a methodological since clearfelling approa on thech firethat hazard minimised of commercial the effects and of reservedtopographic wet variationeucalypt andforests. fire weatherUsing a onmethodological our comparisons, approa we chaddressed that minimised the question the ‘doeseffects clearfelling of topographic affect flammability?’variation and fire asked weather by Taylor on our et al.comparisons, [11], Bowman we et addressed al. [27] and the Attiwill question et ‘doesal. [28] clearfelling in similar forestaffect types.flammability?’ Our results asked are byconsistent Taylor et with al. [11],those Bowman of Taylor et et al. al. [27] [11], and in Attiwillthat they et show al. [28] a higher in similar incidence forest oftypes. canopy Our scorchresults orare consumption consistent with by thosefire in of regr Taylorowth et forests al. [11], than in that in boththey showmature a higherand old incidence growth forests.of canopy scorch or consumption by fire in regrowth forests than in both mature and old growth forests.As the majority of our samples burned under relatively moderate fire weather conditions (averageAs themaximum majority FFDIs of our did samplesnot exceed burned 25), our under results relatively pertain to moderate moderate fire rather weather than extreme conditions fire weather(average conditions.maximum FFDIsFire behaviour did not exceed is less 25), sensitive our results to variations pertain to inmoderate fuel under rather extreme than extreme conditions fire [13,40].weather For conditions. example, Fire fuel behaviour age was found is less to sensitive have no to effect variations on fire in severity fuel under in extreme extreme fire conditions weather [13,40]. For example, fuel age was found to have no effect on fire severity in extreme fire weather

Fire 2020, 3, 13 6 of 11

Table 2. Percentage of stands that were burned and percentage that had crown fires by forest type, showing significance of differences between forest types (Chi2).

Old Growth Mature Regrowth Plantation P Old growth vs. mature (n = 10) Burned 0 60 na na 0.038 Crown fire 0 0 na na 1.000 Old growth vs. regrowth (n = 38) Burned 68 na 79 na 0.461 Crown 11 na 53 na 0.005 Mature and old growth vs. plantation (n = 20) Burned 100 na 60 0.025 Crown 40 Na 60 0.371 Mature vs. regrowth (n = 32) Burned na 75 88 na 0.365 Crown Na 31 69 na 0.034

4. Discussion Much of the world’s forest estate (and 12% of Tasmania’s land mass) is managed for timber production in a context in which unplanned forest fires are likely to increase in frequency as a result of climate change [3–5,39]. We sought to consolidate understanding of the effects of both the absence of clearfelling and the time since clearfelling on the fire hazard of commercial and reserved wet eucalypt forests. Using a methodological approach that minimised the effects of topographic variation and fire weather on our comparisons, we addressed the question ‘does clearfelling affect flammability?’ asked by Taylor et al. [11], Bowman et al. [27] and Attiwill et al. [28] in similar forest types. Our results are consistent with those of Taylor et al. [11], in that they show a higher incidence of canopy scorch or consumption by fire in regrowth forests than in both mature and old growth forests. As the majority of our samples burned under relatively moderate fire weather conditions (average maximum FFDIs did not exceed 25), our results pertain to moderate rather than extreme fire weather conditions. Fire behaviour is less sensitive to variations in fuel under extreme conditions [13,40]. For example, fuel age was found to have no effect on fire severity in extreme fire weather conditions during Victoria’s Black Saturday fires [41]. This may explain the inconsistency of our results from those of Bowman et al. [27] and Attiwill et al. [28], as those studies find the effects of stand age minimal in comparison to the full range of fire weather. Our results are also consistent with some other observations of changes in flammability after disturbance from mainland Australia. Extremely dense regrowth, up to 200,000 stems per ha in the first 7 years, undergoes thinning in the next 40 to 70 years, resulting in the build-up of dry fuel [42,43]. Younger stands of Eucalyptus trees have a more vertically continuous fuel load than older forests as they have a lower canopy, resulting in more frequent canopy consumption than in older stands [11,22,42]. Fuel load reaches an equilibrium after 40–70 years [19], but fuel moisture may continue to increase with age as the understorey trees and shrubs form closed canopies and water-holding moss mats and fern beds develop [19,44]. As fuel moisture is the limiting factor for fire in wet forests [4], fire hazards will stabilise or continue to decrease after the fuel load has stabilised. However, in a study with a small sample size, Cawson et al. [26] observed that fuel moisture was higher in forests recently burnt in a high severity fire than in long unburnt forests and forests recently burnt at low intensity. Modelling usingRETRACTED fuel structure as the input has also predicted higher fire severity in mature tall mixed forests [25]. These discrepancies indicate the difficulty in predicting the effect of disturbance history on fire using single predictors. Landscape traps are phenomena whereby due to a multitude of feedbacks between human and natural disturbance regimes, ecosystems in a landscape change to, and are maintained in, a new and compromised structural and functional state [44]. This concept relates to Jackson’s ‘alternative stable Fire 2020, 3, 13 7 of 11 states’ model of vegetation and fire dynamics in , in which a vegetation type will remain in a landscape due to stabilising feedbacks up to a threshold level of fire disturbance (or lack thereof), after which it will switch into a new stable state, in turn maintained by stabilising feedbacks [45]. Our study predicts a positive feedback mechanism by which the high intensity fire that is used to regenerate clearfelled coupes in wet forests increases the severity of fire in regenerating forests. This feedback will cause wet eucalypt forests to be maintained in a younger age class. If the frequency of canopy-consuming fire increases to more than two fires within 20–30 years, this may cause the localised of obligate seeders, such as E. regnans [21,27,44]. We found that plantations were less likely to burn than other forest types, but if ignition was detected, the incidence of crown scorch or consumption was 100%. plantations in southern Australia can support a crown consuming fire at six years after planting. At this age there is ample leaf litter and ribbons of oil-rich bark hanging from the trees [46,47]. Eucalyptus nitens trees have similarly ribboning bark. The low observed occurrence of fire in plantations in our study may reflect the highly managed state of plantation forests, with surrounding roads and tracks acting as fire breaks and little understorey and no self-thinning, as well as the recency of establishment of some of the plantations. The dense canopy of older plantations may have reduced ability to detect understorey fires from aerial imagery. Our study has a number of caveats. We assume random distribution of different fire weather conditions (quantified as FFDI values) across the different forest types sampled to make our conclusions on the relative impacts of fire on forests of different disturbance histories. Our assumption appears to be correct, though the precision of data on fire progression, and therefore the FFDI at which each point burnt, is limited. High resolution mapping of fire progress, severity and behavior in conjunction with meteorological and environmental factors has strengthened our understanding of recent Australian fires such as the 2017 Dunally fires [48] and the 2009 Black Saturday fires [49]. Such detailed research on the Riveaux Road fire would be highly valuable. There may be some error in the detection of understorey fires due to reliance on satellite imagery. Undergrowth fires were identified by death of understorey species. This may have been missed when obscured by a dense canopy, or in plantations where understorey vegetation is sparse. The distinctions between different forest types are arbitrary, but consistent and are based on understood processes in aging eucalypt forests.

5. Conclusions Our results show a clear relationship between disturbance history and fire incidence and severity under moderate fire weather conditions. Fire severity is related to intensity, which is important for ease of suppression [29,30]. Thus, the retention of older forests across the landscape may decrease fire risk. Allowing eucalypt forests to mature further than the normal cycles of 40 to 90 years could help reduce fire hazards. This is especially pertinent around built assets and vegetation types vulnerable to fire. Our results are consistent with the ‘landscape trap’ theory [21,44]. In addition to providing a valuable damper on fire incidence and severity, old growth Eucalyptus regnans wet forests store the highest density of carbon of any forests in the world [50]. Carbon continues to be captured in the aging old growth forests, rather than reaching an asymptote [51]. Soil carbon in wet eucalypt forest is a substantial store, its magnitude related to the above ground biomass of the forest [52]. The slow release of carbon from the soil as a result of previous conversion from primary forest to production forest, can only be prevented by allowing forest regrowth to continue to old age [52]. RETRACTEDTo refine our understanding of the relationship between disturbance history and fire we need to ask: how localised is the effect of patches of more flammable regrowth in a landscape—does a patchwork of forest types make a whole region more susceptible to fire; at what point after clearfell logging does a forest become less flammable; and, is there a way to manage the forest up to this point to reduce fire hazards while maintaining natural values? Fire 2020, 3, 13 8 of 11

Author Contributions: Conceptualization, S.W.-L., J.C.S. and J.B.K.; methodology, S.W.-L. and J.B.K.; software, J.B.K.; validation, S.W.-L., J.C.S. and J.B.K.; formal analysis, J.B.K.; investigation, J.C.S.; data curation, S.W.-L.; writing—original draft preparation, S.W.-L. and J.C.S.; writing—review and editing, S.W.-L., J.C.S. and J.B.K.; supervision, J.B.K.; project administration, S.W.-L. and J.C.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Publication Fees were paid for by The Bob Brown Foundation and The Wilderness Society. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. List of sample points with coordinates and forest type.

Figure Type Latitude Longitude 43.08303 146.64068 − 43.1454 146.6975 − 43.18016 146.69161 − 43.07995 146.71744 − 43.09978 146.73659 − 43.14320117 146.7217038 − 43.11016 146.76972 − 43.14243 146.75797 − 43.11317 146.82207 − 43.12675716 146.7633651 − 43.0801363 146.7491124 − 43.11205417 146.6818051 − 43.19338837 146.706203 − 43.15118863 146.7092791 Mature Forest N = 28 − 43.09835272 146.813395 − 43.10181905 146.6846046 − 43.0960364 146.7176345 − 43.16487128 146.8008163 − 43.12446248 146.805289 − 43.12257778 146.732741 − 43.05575735 146.7370589 − 43.06846053 146.7907608 − 43.06377419 146.8190625 − 43.15994797 146.8146598 − 43.14499515 146.803759 − 43.09941536 146.6808368 − 43.08424 146.67764 − 43.07956 146.77419 − 43.08515 146.64824 − 43.12263 146.6476 − 43.0655074 146.7038219 − 43.08431 146.70658 − 43.12503 146.70664 − 43.08882475 146.6577918 − 43.19767106 146.6904315 − 43.17274107 146.7948133 − 43.06194675 146.7333377 − 43.1395778 146.7008108 − 43.1061844 146.6928751 − 43.12869 146.75099 − 43.05016 146.78821 − Old Growth N = 27 43.1146543 146.6837872 − 43.17519516 146.7040771 − 43.20208056 146.7122196 − 43.21392569 146.6989478 − 43.04989754 146.7596096 − 43.09795 146.75448 − 43.13735028 146.7635581 − 43.05699434 146.7330971 − RETRACTED43.08764566 146.8224413 − 43.07408108 146.7430955 − 43.07691053 146.7238735 − 43.17294907 146.8107889 − 43.08509 146.68944 − 43.07502 146.7679 − Fire 2020, 3, 13 9 of 11

Table A1. Cont.

Figure Type Latitude Longitude 43.15255 146.69509 − 43.18547 146.69224 − 43.11973 146.70522 − 43.10448 146.73063 − 43.11764 146.81243 Plantation N = 10 − 43.07688353 146.749029 − 43.11737 146.74405 − 43.05044 146.80162 − 43.1008982 146.8223385 − 43.06859812 146.7831244 − 43.11947 146.65409 − 43.06204 146.70617 − 43.13761358 146.7234643 − 43.11815 146.76958 − 43.15569 146.75947 − 43.08713092 146.665777 − 43.12284072 146.7610805 − 43.20281548 146.693155 − 43.16940257 146.7962776 − 43.0633208 146.7311239 − 43.13596986 146.6994332 − 43.11101999 146.7008032 − 43.18401673 146.6963299 − 43.21111571 146.7092367 − 43.21876749 146.696354 − 43.18973554 146.7034153 − 43.15551434 146.7100481 Regrowth N = 35 − 43.05176174 146.7614738 − 43.1013635 146.6893879 − 43.09937212 146.7055105 − 43.10931557 146.7421104 − 43.15635164 146.7914153 − 43.14272755 146.7683749 − 43.12690012 146.8009437 − 43.11625033 146.7345929 − 43.05748329 146.7374861 − 43.05095175 146.7339691 − 43.06637026 146.8211688 − 43.08214941 146.8213097 − 43.15929051 146.8167518 − 43.14340951 146.8013612 − 43.07417457 146.7459314 − 43.07860165 146.7252177 − 43.17019969 146.8031068 − 43.09698709 146.6768909 −

References

1. Whittaker, J. Ten years after the Black Saturday fires, what have we learnt from post-fire research? Aust. J. Emerg. Manag. 2019, 34, 32–37. 2. Bushfires Death Toll Rises to 33 after Body Found in Burnt-Out House near MORUYA. Available online: https://www.theguardian.com/australia-news/2020/jan/24/bushfires-death-toll-rises-to-33-after-body-found-in-burnt- out-house-near-moruya (accessed on 24 January 2020). 3. Cochrane, M.A.; Barber, C.P. Climate change, human land use and future fires in the Amazon. Glob. Chang. Biol. 2009, 15, 601–612. [CrossRef] 4. Mariani, M.; Holz, A.; Veblen, T.; Williamson, G.; Fletcher, M.-S.; Bowman, D. Climate Change Amplifications of Climate-Fire Teleconnections in the . Geophys. Res. Lett. 2018, 45, 5071–5081. [CrossRef] 5.RETRACTEDHalofsky, J.E.; Peterson, D.L.; Harvey, B.J. Changing wildfire, changing forests: The effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecol. 2020, 16, 4. [CrossRef] 6. Spies, T.A.; White, E.M.; Kline, J.D.; Fischer, A.P.; Ager, A.; Bailey, J.; Bolte, J.; Koch, J.; Platt, E.; Olsen, C.S.; et al. Examining fire-prone forest landscapes as coupled human and natural systems. Ecol. Soc. 2014, 19, 9. [CrossRef] Fire 2020, 3, 13 10 of 11

7. Schoennagel, T.; Veblen, T.T.; Romme, W.H. The Interaction of Fire, Fuels, and Climate across Rocky Mountain Forests. BioScience 2004, 54, 661–676. [CrossRef] 8. Hickey, J.E.; Wilkinson, G.R. The development and current implementation of silvicultural practices in native forests in Tasmania. Aust. For. 1999, 62, 245–254. [CrossRef] 9. Forestry Tasmania. Lowland Wet Eucalypt Forest; Native Forest Silviculture Technical Bulletin No. 8; Forestry Tasmania: Hobart, Australia, 1998. 10. Volkova, L.; Aparicio, A.G.W.; Weston, C.J. Fire intensity effects on post-fire fuel recovery in Eucalyptus open forests of south-eastern Australia. Sci. Total Environ. 2019, 670, 328–336. [CrossRef] 11. Taylor, C.; McCarthy, M.A.; Lindenmayer, D.B. Nonlinear effects of stand age on fire severity. Conserv. Lett. 2014, 7, 355–370. [CrossRef] 12. Bradstock, R.A. A biogeographic model of fire regimes in Australia: Current and future implications. Glob. Ecol. Biogeogr. 2010, 19, 145–158. [CrossRef] 13. Bradstock, R.A.; Hammill, K.A.; Collins, L.; Price, O. Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landsc. Ecol. 2010, 25, 607–619. [CrossRef] 14. Meyn, A.; White, P.S.; Buhk, C.; Jentsch, A. Environmental drivers of large, infrequent wildfires: The emerging conceptual model. Prog. Phys. Geogr. Environ. 2007, 31, 287–312. [CrossRef] 15. Fox-Hughes, P. A fire danger climatology for Tasmania. Aust. Meteorol. Manag. 2008, 57, 109–120. 16. Attiwill, P.M.; Adams, M.A. Mega-fires, inquiries and politics in the eucalypt forests of Victoria, south-eastern Australia. For. Ecol. Manag. 2013, 294, 45–53. [CrossRef] 17. Williamson, G.J.; Prior, L.D.; Jolly, W.M.; Cochrane, M.A.; Murphy, B.P.; Bowman, D.M. Measurement of inter-and intra-annual variability of landscape fire activity at a continental scale: The Australian case. Environ. Res. Lett. 2016, 11, 035003. [CrossRef] 18. Zylstra, P.J. Flammability dynamics in the Australian Alps. Austral Ecol. 2018, 43, 578–591. [CrossRef] 19. Cawson, J.G.; Duff, T.J.; Swan, M.H.; Penman, T.D. Wildfire in wet sclerophyll forests: The interplay between disturbances and fuel dynamics. Ecosphere 2018, 9, e02211. [CrossRef] 20. Mackey, B.; Lindenmayer, D.; Gill, M.; Lindesay, J. Wildlife, Fire & Future Climate: A Forest Ecosystem Analysis; CSIRO Publishing: Victoria, Australia, 2020; p. 30. 21. Bowman, D.M.; Murphy, B.P.; Neyland, D.L.J.; Williamson, G.J.; Prior, L.D. Abrupt fire regime change may cause landscape-wide loss of mature obligate seeder forests. Glob. Chang. Biol. 2014, 20, 1008–1015. [CrossRef] 22. Serong, M.; Lill, A. The timing and nature of floristic and structural changes during secondary succession in wet forests. Aust. J. Bot. 2008, 56, 220–231. [CrossRef] 23. Zylstra, P.; Bradstock, R.A.; Bedward, M.; Penman, T.D.; Doherty, M.D.; Weber, R.O.; Cary, G.J. Biophysical mechanistic modelling quantifies the effects of traits on fire severity: Species, not surface fuel loads, determine flame dimensions in eucalypt forests. PLoS ONE 2016, 11, e0160715. [CrossRef] 24. Lindenmayer, D.B.; Hunter, M.L.; Burton, P.J.; Gibbons, P. Effects of logging on fire regimes in moist forests. Conserv. Lett. 2009, 2, 271–277. [CrossRef] 25. McColl-Gausden, S.C.; Penman, T.D. Pathways of change: Predicting the effects of fire on flammability. J. Environ. Manag. 2019, 232, 243–253. [CrossRef][PubMed] 26. Cawson, J.G.; Duff, T.J.; Tolhurst, K.G.; Baillie, C.C.; Penman, T.D. Fuel moisture in Mountain Ash forests with contrasting fire histories. For. Ecol. Manag. 2017, 400, 568–577. [CrossRef] 27. Bowman, D.M.; Williamson, G.J.; Prior, L.D.; Murphy, B.P. The relative importance of intrinsic and extrinsic factors in the decline of obligate seeder forests. Glob. Ecol. Biogeogr. 2016, 25, 1166–1172. [CrossRef] 28. Attiwill, P.M.; Ryan, M.F.; Burrows, N.; Cheney, N.P.; McCaw, L.; Neyland, M.; Read, S. Timber harvesting does not increase fire risk and severity in wet eucalypt forests of southern Australia. Conserv. Lett. 2014, 7, 341–354. [CrossRef] 29. Keeley, J.E. Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland RETRACTEDFire 2009, 18, 116–126. [CrossRef] 30. Hammill, K.A.; Bradstock, R.A. Remote sensing of fire severity in the Blue Mountains: Influence of vegetation type and inferring fire intensity. Int. J. Wildland Fire 2006, 15, 213–226. [CrossRef] 31. Woodgate, P.W.; Peel, B.D.; Coram, J.E.; Farrell, S.J.; Ritman, K.T.; Lewis, A. Old-growth forest studies in Victoria, Australia Concepts and principles. For. Ecol. Manag. 1996, 85, 79–94. [CrossRef] Fire 2020, 3, 13 11 of 11

32. Kirkpatrick, J.B.; Peacock, R.J.; Cullen, P.J.; Neyland, M.G. The Wet Eucalypt Forests of Tasmania; Tasmanian Conservation Trust: Hobart, Australia, 1988. 33. Australasian Fire and Emergency Service Authorities Council. AFAC Independent Operational Review: A Review of the Management of the Tasmanian Fires of December 2018–March 2019. Available online: http://www.fire.tas.gov.au/userfiles/AFAC/AFAC_Review.pdf (accessed on 1 March 2020). 34. LIST State Aerial Photo Basemap, Accessed through QGIS. 2019. Available online: https://www.thelist.tas.gov. au/app/content/data/geo-meta-data-record?detailRecordUID=46146ba8-2485-411b-bd06-e89447b7329b (accessed on 30 January 2020). 35. Department of Primary Industries, Parks, Water & Environment. TasVeg Live: The Digital Vegetation Map of Tasmania; : Hobart, Australia, 2014. 36. ESRI Images, Accessed on QGIS. 2019. Available online: https://maps.thelist.tas.gov.au/listmap/app/list/map (accessed on 1 March 2020). 37. MODIS Collection 6 NRT Hotspot/Active Fire Detections MCD14ML. Available online: https://earthdata. nasa.gov/firms (accessed on 30 January 2020). 38. Hantson, S.; Padilla, M.; Corti, D.; Chuvieco, E. Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence. Remote Sens. Environ. 2013, 131, 152–159. [CrossRef] 39. Department of State Growth, Tasmanian Government. Tasmania’s Forest Management System: An Overview. 2017. Available online: https://www.stategrowth.tas.gov.au/__data/assets/pdf_file/0018/154620/5_Overview_ Tasmania_Forest_Management_System_2017.pdf (accessed on 21 January 2020). 40. Clarke, P.J.; Knox, K.J.E.; Bradstock, R.A.; Munoz-Robles, C.; Kumar, L. Vegetation, terrain and fire history shape the impact of extreme weather on fire severity and ecosystem response. J. Veg. Sci. 2014, 25, 1033–1044. [CrossRef] 41. Price, O.F.; Bradstock, R.A. 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. Manag. 2012, 113, 146–157. [CrossRef][PubMed] 42. Ashton, D.H. The development of even-aged stands of Eucalyptus regnans F. Muell. in central Victoria. Aust. J. Bot. 1976, 24, 397–414. [CrossRef] 43. Florence, R.G. The ecological basis of forest fire management in New South Wales. In The Burning Continent: Forest Ecosystems and Fire Management in Australia; Attiwill, P.M., Florence, R.G., Hurditch, W.E., Eds.; Institute of Public Affairs: Jolimont, Australia, 1994. 44. Lindenmayer, D.B.; Hobbs, R.J.; Likens, G.E.; Krebs, C.J.; Banks, S.C. Newly discovered landscape traps produce regime shifts in wet forests. Proc. Natl. Acad. Sci. USA 2011, 108, 15887–15891. [CrossRef][PubMed] 45. Jackson, W.D. Fire, air, water and earth—An elemental . Proc. Ecol. Soc. Australia 1968, 3, 9–16. 46. McCaw, L. Understanding the level of fire hazard in a blue gum plantation. Aust. For. Grow. 2003, 25, 18–19. 47. McCaw, L.; Smith, B. Fire Behaviour in a 6 Year Old Eucalyptus Globulus Plantation during Conditions of Extreme Fire Danger—A Case Study from South-; Science Division, Department of Conservation and Land Management: Manjimup Australia, 2005. 48. Ndalila, M.N.; Williamson, G.J.; Bowman, D.M. Geographic patterns of fire severity following an extreme eucalyptus forest fire in southern Australia: 2013 Forcett-Dunalley fire. Fire 2018, 1, 40. [CrossRef] 49. Cruz, M.G.; Sullivan, A.L.; Gould, J.S.; Sims, N.C.; Bannister, A.J.; Hollis, J.J.; Hurley, R.J. Anatomy of a catastrophic wildfire: The Black Saturday Kilmore East fire in Victoria, Australia. For. Ecol. Manag. 2012, 284, 269–285. [CrossRef] 50. Keith, H.; Mackey, B.G.; Lindenmayer, D.B. Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests. Proc. Natl. Acad. Sci. USA 2009, 106, 11635–11640. [CrossRef] 51. Sillett, S.C.; Van Pelt, R.; Kramer, R.D.; Carroll, A.L.; Koch, G.W. Biomass and growth potential of Eucalyptus regnans up to 100 m tall. For. Ecol. Manag. 2015, 348, 78–91. [CrossRef] 52.RETRACTEDDean, C.; Kirkpatrick, J.B.; Friedland, A. Conventional intensive logging promotes loss of organic carbon from the mineral soil. Glob. Chang. Biol. 2017, 23, 1. [CrossRef]

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