Analysing eucalypt expansion in Portugal as a fire-regime modifier

Science of the Total Environment 666 (2019) 79–88

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Analysing eucalypt expansion in Portugal as a fire-regime modifier Paulo M. Fernandes a,⁎, Nuno Guiomar b, Carlos G. Rossa a a b

Centro de Investigação e de Tecnologias Agroambientais e Biológicas (CITAB), Universidade de Trás-os-Montes e Alto Douro, Quinta dos Prados, 5001-801 Vila Real, Portugal ICAAM–Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Ap. 94, 7002-554 Évora, Portugal

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Eucalypt expansion did not increase burned area in Portugal. • Fire size independent of forest composition • Slight decrease of mega-fire severity in eucalypt stands • Forest type is a minor influence on fuel hazard. • Fire activity reflects trade-offs between short-rotation forestry and fire behaviour.

a r t i c l e

i n f o

Article history: Received 16 January 2019 Received in revised form 13 February 2019 Accepted 15 February 2019 Available online 16 February 2019 Editor: G. Darrel Jenerette Keywords: Forest plantations Fire severity Land cover change Mediterranean climate Wildfire

a b s t r a c t Eucalypts, especially blue gum (Eucalyptus globulus), have been extensively planted in Portugal and nowadays dominate most of its forest landscapes. Large-scale forestation programs can intensify fire activity, and blue gum plantations are often viewed as highly flammable due to the nature and structure of the fuel complex. The role of eucalypt plantations in the fire regime of Mediterranean climate regions is increasingly debated following the recent catastrophic wildfires in Portugal and elsewhere. In this study we examined the effects of eucalypt forestation on burned area (BA), fire size, and fire severity in Portugal. This was based on fire and vegetation mapping and statistics, fire weather data, satellite imagery, and national forest inventory data. Eucalypt BA comprised an average of 12.5% of total BA (1980–2017) and did not increase over time and with eucalypt expansion. Eucalypt metrics did not explain interannual BA variability after accounting for the effects of other variables. Forest fires started within eucalypt stands were the least likely to become large, and large fire size was irresponsive to forest composition. Likewise, forest type was a generally minor influence in mega-fire severity and accounted for just 1.4–8.6% of surface fuel-hazard metrics variation. In general, large-scale conversion of maritime pine to eucalypt stands (1970–2015) implied lower fuel accumulation. Fire activity results are consistent with fuel hazard results and express trade-offs between short-rotation forestry and fire behaviour in blue gum stands, with high spotting potential versus modest crown fire likelihood. We found no support for the contention of a modified fire regime as a result of eucalypt forestation in Portugal, but the rising undermanaged and abandoned blue gum estate, especially after large-fire seasons, is a concern for the future. However, it remains to be determined whether post-fire eucalypt regrowth is a higher fire threat than native vegetation in the same context. © 2019 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author. E-mail addresses: [email protected] (P.M. Fernandes), [email protected] (N. Guiomar).

https://doi.org/10.1016/j.scitotenv.2019.02.237 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Eucalypts comprise the greatest area of non-native forests in Europe and are located mostly in the Iberian Peninsula, where they cover about

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1.5 million ha and are grown for pulpwood as even-aged monocultures coppiced on 10 to 12-year rotations (Silva and Tomé, 2016). Eucalypts expanded extensively in Portugal from the mid-twentieth century onwards, driven by the pulp industry development (Goes, 1977; Alves et al., 2007; Oliveira et al., 2017). The establishment of pulp mills fostered investment in eucalypt plantations and shifted management to an industrial perspective (Oliveira et al., 2017), such that the pulp and paper sector currently accounts for 4.4% of the Portuguese GDP (Silva and Tomé, 2016). Portugal burns at an annual rate of ~3% of the surface covered with forest or shrubland (Mateus and Fernandes, 2014). As a consequence, forest area decreased by 10% from 1990 to 2012, mostly because onefourth of the pine estate transitioned to open vegetation types (Fernandes and Guiomar, 2017). Still, eucalypts kept increasing in area, albeit at a slower rate than in the past (Oliveira et al., 2017), and partially replaced former pine stands (Oliveira et al., 2012; Meneses et al., 2017). As per the last National Forest Inventory, eucalypts (mainly blue gum, Eucalyptus globulus Labill.) occupy ~812 kha, i.e. 26% of the total forest area (ICNF, 2013) and 9.1% of Portugal mainland surface. Pulp companies manage 20% of the plantations, the remaining being owned and managed by individuals (Silva and Tomé, 2016). Rural depopulation in the Mediterranean Basin and the associated agricultural abandonment and forest expansion converged to increase landscape-scale fuel connectivity and accumulation and are contributing to more severe fire events and fire regimes (Pausas and Fernández-Muñoz, 2012; Viedma et al., 2015). Forestation and afforestation programmes, including the conversion of natural vegetation to exotic plantations (Kraaij et al., 2018; McWethy et al., 2018; Paritsis et al., 2018), can exacerbate fire activity. Such was the case of northern Portugal and Spain fuel-depleted shrublands planted with conifers in the second half of the 20th century (Fernandes et al., 2014; IriarteGoñi and Ayuda, 2018). However, impacts of forestation programmes on fire activity are expected to be system-specific (Paritsis et al., 2018). The role of large-scale industrial forestry in the contemporary fire regime is unclear, as the features of commercial plantations have conflicting effects on fire hazard (Cruz et al., 2018): fast growth, simplified (and time-dependent) stand structure and fuel stratification, and intensive management. In particular, short-rotation eucalypt plantations are established at full stocking, grow in height and achieve vertical discontinuity rapidly, and display variable fuel accumulation and structure depending on stand age, rotation, and management-related disturbances. It follows that the likelihood of an industrial plantation becoming a significant fire hazard under a fire-prone climate depends on whether the timing, intensity and frequency of fuel treatments is commensurate with fuel dynamics, e.g. Mirra et al. (2017). The fuel chemistry of eucalypts, through volatile oils, is commonly mentioned as a fire enhancer, namely in the frame of flammability experiments (e.g. Ganteaume et al., 2010). However, its influence is not quantified by fire behaviour models, and it has been dismissed as irrelevant because of very low oil content in the litter and the overriding effect of fuel load and structure (Hodgson, 1967; Scarff and Westoby, 2006). A second distinctive feature of smooth-barked eucalypts such as blue gum is the accumulation of long streamers of semi-detached bark that, given their aerodynamic properties and prolonged combustion time, can cause long-distance spot fires (Cruz et al., 2015). In Australia, fire hazard concerns with b7-year-old blue gum plantations are minor (McCaw et al., 2003), as substantial decreases in wildfire spread and intensity have been documented during that stage in comparison with the adjoining natural vegetation, e.g. Braun (2003). Fuel dynamics in Australian plantations favour high-intensity fire after six years post-establishment and during the first three years of the second rotation coppice (de Mar and Adshead, 2011). Earlier build-up of fuel hazard is likely where (as in southern Europe) a shrub layer promptly develops (Goodrick and Stanturf, 2012; Mirra et al., 2017). Destructive fuel sampling in Portugal revealed up to 25 t ha−1 of litter and

suspended bark in blue gum stands, plus a shrub understorey that characteristically constitutes 25% of the total fuel load (Fernandes et al., 2011), signifying faster fuel accumulation than in natural stands (Bresnehan, 2003). Notwithstanding higher variation of fuel load and structure within than between forest types (Fernandes, 2009), expert assessment attributes high stand flammability to eucalypt plantations in Europe, although lower than that of Mediterranean pines and shrublands (Xanthopoulos et al., 2012). Eucalypt stands in Portugal burned at an annual rate of 1.79% of the existing stock in the 1996–2014 period, which compares with incidences of 1.89% and 1.84% in deciduous oaks and maritime pine (Pinus pinaster Aiton), respectively (Fernandes and Guiomar, 2017). However, all Portuguese studies based on selection ratios show that fire has more preference for shrublands than for the most fire-prone forest types (pines and blue gum) (Nunes et al., 2005; Moreira et al., 2009; Barros and Pereira, 2014), which is the general rule in southern Europe (Oliveira et al., 2014). Mixed forest stands of maritime pine and blue gum, often resulting from fire or harvest, denote poor management (or lack thereof) and are the most susceptible to fire in Portugal (Moreira et al., 2009) and NW Spain (Calviño-Cancela et al., 2016). Post-fire abandonment of blue gum stands or a fire regime leading to irregular stands, therefore less attractive for industrial transformation, is expected to increase stand- and landscape-scale fire hazard (Rego et al., 2013). Blue gum plantations in Europe are often referred to as highly flammable (e.g., Oliveira et al., 2014; Valente et al., 2015; Nunes et al., 2016) and this perception seems to prevail among the population (Silva and Tomé, 2016; Calviño-Cancela and Cañizo-Novelle, 2018), mirroring similar concerns with pine plantations elsewhere, e.g. Australia (Forest Fire Management Group, 2007). The catastrophic wildfires of 2017 in Chile and Portugal raised increased concerns with the role of plantation forestry in large-scale fire events (Bowman et al., 2018; GómezGonzález et al., 2018). In fact, the perception of eucalypt plantations as a fire hazard is so acute that published opinion has suggested causeeffect relationships between eucalypt plantation and the 2017 wildfires death toll, e.g. Ames (2017). Improved understanding of the implications of widespread eucalypt plantation to wildfire activity and its socioecological consequences is needed to inform policies and practices. Here we set out to examine whether and to what extent blue gum planting (through the associated fuel conditions) is impacting the fire regime in Portugal. Specifically, and given the prevailing role of fire weather on fire spread and activity, we expect irrelevant impacts of eucalypt forestation on annual burned area (BA) and individual fire size; minor differences between forest types in the immediate effect of landscape fires on the aboveground vegetation, i.e. fire severity (Keeley, 2009); and, supporting the previous tenets, inconsequential differences in fuel hazard among forest types. 2. Methods 2.1. Data sources Our study area is the Portuguese mainland (89,089 km2). Eucalypt stands are significant, often dominant, in forest landscapes of the north- and central-western sectors of the country where the oceanic influence moderates the Mediterranean climate. Eucalypt forest area in Portugal for the available moments in time (1955–2015) was extracted from the literature (Goes, 1977; Fabião, 1987; Radich and Alves, 2000; Mendes et al., 2004), National Forest Inventories (1970, 1990, 2005, 2010) and from land use maps in the 1:25,000 scale (1995, 2007, 2015) (DGT, 2018), which were also the basis to quantify other land cover classes. We used land cover changes from 1970 to 1995 and from 1995 to 2015 to estimate area lost (or gained) to eucalypt plantations per cover type. Additional land cover data for individual large fires was obtained from the database used by (Fernandes et al., 2016a) and in reports about the 2017 mega-fires (CTI, 2017, 2018).

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The Portuguese rural fire database (http://www2.icnf.pt/portal/ florestas/dfci/inc) supplied the location and final size of individual fire events. Annual BA were calculated from the Portuguese atlas of burned perimeters (1980–2017) (http://www2.icnf.pt/portal/ florestas/dfci/inc). We compiled eucalypt BA (1996–2017) from pulp industry yearly reports available at http://www.celpa.pt/category/ boletins-estatisticos/, which also allowed separate quantification of fire extent in industry-owned stands and in other private property. For the 1980–1995 period, we produced estimates from the intersection of land cover maps and BA. We used the Canadian Forest Fire Weather Index System daily codes at 12 h UTC (Van Wagner, 1987) to describe fire weather as a BA driver. Computation of the indices (on a 0.04°-resolution grid) resulted from the ERA-Interim weather data reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) (Pinto et al., 2018). Fire severity was derived from Sentinel-2 Level-2A atmospherically corrected data (S2MSI2Ap product) for the extremely large fires of 2017. Fuel hazard was sourced from a database (Fernandes, 2009) for the Portuguese national forest inventory plots (2005–2006). 2.2. Burned area We correlated absolute and relative (as a % of the total fire extent) annual BA of eucalypt with time and the area occupied by the species for the 1980–2017 time series. Then, we modelled BA in Portugal to detect changes induced by eucalypt forestation; this required estimates of annual eucalypt areas that were generated by fitting a cubic spline regression to the available data. Modelled BA was a function of its three main drivers, respectively ignitions, weather, and fuels, following previous work at similar scales (Vilen and Fernandes, 2011; Fernandes et al., 2014; Price et al., 2015). Number of fires and BA recording criteria changed over time (Fernandes et al., 2017). For consistency, our dependent variable was annual BA as the sum of all fire patches ≥35 ha (minimum mapping unit prior to 1984), and the number of fires ≥1 ha was included among the independent variables. Antecedent BA was calculated for windows of 2–8 years. To describe fire weather we systematically sampled the grid of Canadian Forest Fire Weather Index codes but excluded locations in farm-, agroforestry- and urbandominated landscapes characterized by absent or low fire activity, resulting in n = 22, for which we calculated daily mean and median values used to derive annual statistics (mean and the 50th, 75th, 90th and 95th percentiles). Most BA in 2003 and 2017 resulted from highly unstable atmospheric conditions (including dry lightning) that led to very fast fire growth that fire danger rating depicted only in part, as revealed by exploratory analysis. To account for this influence we created a “plume-driven year” variable, categorized as 1 for 2003 and 2017 and 0 for the other years. BA was log-transformed and plotted against the putative explaining variables to decide on model form. We checked whether the independent variables required detrending due to correlation (p b 0.05) with time. Least squares fitting started by individually selecting the single best fire weather and fuel metrics and proceeded sequentially by adding the other candidate variables until no further variance could be explained. We used variance inflation factors (VIF) to diagnose collinearity problems and standardized partial regression coefficients (β) to quantify the relative influence of the independent variables. Model residuals were smoothed across time for graphical depiction and tested for correlation with time, eucalypt area, and eucalypt BA in % of total BA. 2.3. Fire size We used the Portuguese rural fire database (2001–2016) to compute the distribution of fire ignitions per land cover type of origin and the corresponding final BA for three fire-size thresholds (≥0.1 ha, ≥1 ha, and ≥100 ha) and mean fire size (≥0.1 ha). A thematic generalization in

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10 land cover classes was applied: urban and peri-urban areas, agriculture, evergreen oaks, deciduous oaks and chestnut (Castanea sativa Mill.), eucalypt, maritime pine, other broadleaves, other conifers, shrubland and grassland, and wetlands and water bodies. Large fire size (≥100 ha) was correlated with the % cover of the main vegetation types within the BA, including eucalypt. We used a representative sample of 609 fires occurring from 1998 to 2008 (Fernandes et al., 2016a), plus nine 2017 fires larger than 10 kha (CTI, 2017, 2018). 2.4. Fire severity Fire severity was estimated as the degree of fire-induced change using the Relative differenced Normalized Burn Ratio (RdNBR) that eliminates the bias inherent to pre-fire vegetation composition and structure (Miller and Thode, 2007). The RdNBR was derived from the near-infrared (NIR) and shortwave-infrared (SWIR-2) bands of Sentinel-2, with 10 and 20 m spatial resolution respectively. This information was acquired for seven 2017 mega-fires in the central region of Portugal, all exceeding 10 kha: Pedrogão Grande and Góis, that spread between June 17 and 24 (CTI, 2017); and Arganil, Sertã, Vouzela, Lousã and Figueira da Foz, 15–17 October (CTI, 2018). We restricted the sample to these fires to reveal differences in the most stringent context. We mapped mean fire severity for each cover type patch using pre- and post-fire images, acquired respectively on June 4 and October 12, and July 4 and October 22. We selected this post-fire dates to eliminate bias resulting from post-fire delayed processes with major effect on spectral signature such as tree harvest, vegetative regeneration, or rainfall. We used fire severity classification thresholds determined and validated for southern Spain (Botella-Martínez and Fernández-Manso, 2017); the three classes considered (Low, Moderate, High) correspond respectively to the prevalence of green (unaffected), brown (scorched), and black (burned) canopy. The overall areal distribution (%) of fire severity classes per land cover type was quantified and a fire severity index was calculated by scoring the severity classification as 1, 2, and 3, respectively for the Low, Moderate, and High classes. For each individual wildfire we fitted a least squares model weighted by patch size to mean log (RdNBR) as a function of cover type, obtained the amount of explained variation (R2), and tested for significant differences between the least square means of log (RdNBR) with the Tukey-HSD test (p b 0.05). The same analysis was carried out globally with wildfire as the random variable in a mixed model. 2.5. Fuel hazard and fire behaviour potential Fuel hazard was described through measured and estimated fuel characteristics for the Portuguese national forest inventory plots (2005–2006, n = 5367). Fuel hazard assessment integrated surface fuels, i.e. litter, herbs and forbs, and shrubs (up to a 2-m height), and refers to fine (b6-mm diameter) fuels only. Fuel load (t ha−1), dead fuel fraction, fuel depth (m), and bulk density (kg m−3) were estimated by combining the structural data (cover, depth) acquired in the field with quantitative relationships for each fuel layer and forest type (Fernandes, 2009), and summarized across four broad forest types, respectively evergreen broadleaves (n = 1799, mostly Quercus suber and Q. rotundifolia), deciduous broadleaves (n = 359, essentially Quercus pyrenaica, Q. robur, Q. faginea and chestnut), eucalypts (n = 1362, essentially blue gum), and pines (n = 1853, mostly maritime pine, P. pinea being also relevant). We adopted fireline intensity (Byram, 1959) as the fire behaviour potential metric. Fireline intensity (kW m−1) indicates fire suppression difficulty and aboveground fire effects and is the product of fire-spread rate, fuel consumption in the active flaming zone, and heat of combustion. To express the “pure” effect of fuel structure on fire behaviour, rate of spread in no-wind and no-slope conditions was estimated with the field-tested generic model of Rossa and Fernandes (2018), assuming

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Fig. 1. Continuous lines depict, from top to bottom, the evolution of eucalypt area in Portugal, resulting from spline smoothing to the observed and inferred (2017) values (points), yearly burned area (BA) by fires ≥35 ha, and yearly eucalypt BA. Residuals of the ≥35 ha BA model are shown as black points and are smoothed by the dashed line.

typical summer moisture contents of 8% (dead fuels) and 80% (live fuels). Fuel consumption was equated to fine fuel load. Heats of combustion were the heat contents in Fernandes (2009) adjusted for fuel moisture content. Linear models were fit to log-transformed fuel hazard metrics with forest type as the independent variable. We used the Kruskal-Wallis statistic to test for differences in fuel hazard metrics between forest types and then compared forest types with the nonparametric Steel-Dwass pairwise ranking test.

previous 6 years. The final four-variable model (Table 2) explained 72% of the existing interannual variability in BA and was fitted after adding the “plume-driven year” variable. A maximum VIF of 1.03 indicated near-total absence of multicollinearity in the model. Residuals of the BA model (Fig. 1) were uncorrelated with time (p = 0.6741), eucalypt area (p = 0.8443), and eucalypt BA as a % of the total BA (p = 0.6968). 3.2. Fire size

3. Results A large expansion of eucalypt plantations occurred from 1970 to 1995 (~480 kha) that essentially took the place of logged maritime pine (44.7% of the total), shrubland (23.7%), farmland (19.8%), and evergreen oak woodland (10.8%). Blue gum forestation decreased substantially in the 1995–2015 period (~186 kha) and mostly replaced maritime pine (71.8%), with farmland (21.5%) and shrubland (9.9%) being also relevant. 3.1. Burned area The annual BA of eucalypt stands in Portugal (Fig. 1) is unrelated with time or area occupied by eucalypt stands (Table 1). Cumulative eucalypt BA from 1980 to 2017 amounted to 677 kha, i.e. 18 kha year−1, corresponding to an annual average of 12.5% (range 1.0–34.0%) of the total BA in the country. The industry estate burned at an annual rate of 2.6% versus 3.2% in the remaining stands (1996–2017). The influence of fire weather on the log-transformed area burned by ≥35-ha fires was best described through the 95th percentile of the median daily Fire Weather Index, which had the greatest effect (β in Table 2). Annual number of fires was the second most influent variable in the model. The influence of previous fires in decreasing BA was weaker and was best expressed by the cumulative BA of the Table 1 Correlation coefficients (p-value) of eucalypt burned area (BA) with year and eucalypt area (1980–2017, n = 38). Eucalypt BA

Year

Eucalypt area

Hectares % of total area burned

0.22 (0.1904) −0.19 (0.2435)

0.20 (0.2246) −0.23 (0.1609)

Most fires in Portugal start in peri-urban areas or in farmland, accounting respectively for 65.2–76.4% and 63.8–65.1% of the total number of fires and area burned, depending on which size sub-set is considered (Table 3). Fires are much less likely to originate in any of the remaining land cover types, but shrublands and grasslands are somewhat relevant for fires N100 ha, with 13.2% and 12.1% of their total number and BA, respectively. Fires initiated in eucalypt stands are 4.2–5.2% of the total and comprise 6.1–6.5% of the BA. Interestingly, ignitions in eucalypt stands have the lowest (36.3%) and second lowest (1.4%, after urban and peri-urban areas) probabilities of reaching ≥1 ha and ≥ 100 ha, respectively. Finally, fires starting in evergreen oak woodland grow to a distinctively larger size; the corresponding mean fire sizes for all other forest types are lower than that for shrublandgrassland and vary in a narrow range (9.1–18.0 ha). Large-fire size as a function of eucalypt cover within the BA is plotted in Fig. 2, combining (Fernandes et al., 2016a) data with nine 2017 megafires, for which eucalypt cover varied between 0.6% and 46.4%. We found significant positive correlations, although very weak, between

Table 2 Linear regression model for log-transformed annual burned area by fires ≥35 ha in mainland Portugal (p b 0.0001, R2 = 0.721, n = 38). FWI95med is the 95th percentile of the daily median FWI. Log (BA6y) is the logarithmized cumulative burned area of the previous 6 years. Term

Estimate

Confidence interval

p

β

Intercept FWI95med Number of fires ≥1 ha Plume-driven year Log (BA6y)

13.6604 0.1275 0.0002 0.7752a −0.5095

7.1477–20.1731 0.0815–0.1735 0.0001–0.0002 1.1512–0.3992 −0.9955 to −0.0234

0.0002 b0.0001 b0.0001 0.0002 0.0405

– 0.527 0.445 0.391 −0.198

a The coefficient takes the values of 0.7752 or −0.7752, respectively for plume- and non-plume-driven years.

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Table 3 Distribution (%) of the number of fires (No.) and corresponding burned area (BA), and mean fire size, per cover-type point of origin and for each fire-size class (2001–2016). Cover type

Agriculture Eucalypt Evergreen oaks Maritime pine Other broadleaves Deciduous oaks and chestnut Other conifers Shrubland and grassland Urban and peri-urban areas Wetlands

≥0.1 ha (n = 157,068)

≥1 ha (n = 71,913)

≥100 ha (n = 2540)

Mean fire size (ha)

No.

BA

No.

BA

No.

BA

(≥0.1 ha)

25.7 5.2 1.0 6.0 2.6 1.5 0.3 6.7 50.7 0.3

24.9 6.1 4.3 7.8 2.7 1.7 0.2 11.8 40.2 0.3

25.8 4.2 1.4 5.8 2.8 1.8 0.4 8.5 49.1 0.3

24.9 6.1 4.3 7.8 2.7 1.7 0.2 11.9 40.1 0.3

24.3 4.7 3.1 7.0 3.1 2.9 0.4 13.2 40.9 0.4

24.8 6.5 4.9 8.1 2.7 1.5 0.1 12.1 39.0 0.3

fire size and the cover of maritime pine (r = 0.19, p b 0.0001), eucalypt (r = 0.16, p b 0.0001), and evergreen oaks (r = 0.12, p = 0.0003). The relationship of fire size with shrubland and grassland cover was steeper but negative (r = −0.28, p b 0.0001). It is clear from Fig. 2 that eucalypt can be either absent or relevant across the full large-fire size spectrum. 3.3. Fire severity High severity prevailed (≥68%) in all forest types burned by the 2017 mega-fires, with minor inter-type differences, and was overwhelmingly represented (93% of the total area) in shrubland (Table 4). The fire severity index was ranked as eucalypt b other broadleaves b deciduous oaks and chestnut b maritime pine b shrubland. Analysis of RdNBR values also indicates distinctively higher fire severity in shrubland but the forest type results are more nuanced than those for the fire severity classification (Table 5). Wildfire-level analysis generally shows little distinction between forest types, and variable relative position of eucalypt stands, from lowest to highest. Controlling for the random wildfire effect (“All fires” analysis) produces a RdNBR ranking where other broadleaves b eucalypt b other conifers b deciduous oaks and chestnut b maritime pine, but the range in mean RdNBR is extremely narrow (1021–1147). The explanation of fire severity variation due to cover type is variable and lower (8–39%) than the putative compounded influence of other factors.

13.3 16.1 58.3 18.0 14.4 15.6 9.1 24.4 10.9 13.6

3.4. Fuel hazard and fire behaviour potential Forest type accounted for very small portions of the observed variability in surface fuel hazard metrics, the R2 varying from 0.014 (fuel depth) to 0.086 (bulk density), with 0.039 for overall fuel hazard. Absolute differences in mean fuel hazard metrics between forest types were relatively small, yet large enough to establish statistically significant distinctions (Fig. 3). Eucalypt stands were intermediate in respect to fine fuel load and bulk density, did not differ from native broadleaves regarding the fine dead fraction, and had the second lowest fuel depth. Estimated fireline intensity was ranked as pine N deciduous broadleaves N eucalypt N evergreen broadleaves. 4. Discussion 4.1. Burned area and fire size We found annual BA in Portugal to be indifferent to the expansion of blue gum. This is in line with reports for Pinus radiata and eucalypt (E. globulus, E. nitens) stands in Chile (Úbeda and Sarricolea, 2016; McWethy et al., 2018; Gómez-González et al., 2019), even if the relative contribution of plantations to total BA doubled in recent years (Bowman et al., 2018). In north-western Spain, BA declined in the 1990s as eucalypts expanded (Moreno et al., 2014). In Portugal, forest contributes less to BA than open vegetation, offsetting the putative eucalypt influence, the exceptions being those years with abnormally high BA (2003, 2005, 2017) and characterized by extremely large fires that burn more forest than shrubland (Fernandes et al., 2016b). Long and severe drought periods enhance fire in more productive climates (Turco et al., 2018) and make plantation-dominated landscapes prone to particularly large fires (Bowman et al., 2018; González et al., 2018). Similarly to Chile (McWethy et al., 2018), a second explanation is that fire selectivity varies little among the more flammable forest types (Moreira et al., 2009) and disappears or is minimal for increasingly larger fires (Nunes et al., 2005; Barros and Pereira, 2014). It was more intriguing to find that blue gum BA did not increase across the study period, even if its uninterrupted expansion allowed for annual peaks in fire activity that would otherwise not occur. A probable justification

Table 4 Areal distribution of fire severity class and severity index per cover type for selected megafires in Portugal, 2017. Cover type

Fig. 2. Eucalypt composition within the area of fires ≥100 ha. Data for 1998–2008 (Fernandes et al., 2016a), except full circles, 2017 (CTI, 2017, 2018).

Deciduous oaks and chestnut Eucalypt Other broadleaves Maritime pine Shrubland

Area (ha)

Fire severity class (%) Low

Moderate

High

2954 59,147 14,693 81,751 20,158

9.9 7.5 9.2 5.8 2.1

15.8 24.7 19.8 15.5 5.3

74.3 67.7 71.0 78.6 92.6

Fire severity index 2.64 2.60 2.62 2.73 2.90

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Table 5 Effects of cover type on RdNBR for selected mega-fires in Portugal (2017), least square means (in original units), and explained variation per fire. The “All fires” row respects to a linear mixed model with wildfire as the random effect. Wildfire

Size (ha)

n

Pedrogão Grande Góis Vouzela complex Arganil complex Figueira da Foz Sertã complex Lousã All fires

28,586 15,413 14,973 46,560 13,561 31,469 44,148 194,710

2249 1291 4093 6243 1733 2750 7575 25,864

Deciduous oaks, chestnut

Eucalypt

Other broadleaves

Maritime pine

Other conifers

Shrubland

824 cd 878 bcd 1018 b 1418 b – 980 c 1120 b 1132 b

1069 b 1006 c 937 c 1063 e 917 c 1038 c 1024 c 1043 c

878 c 925 d 935 c 1157 d 1084 b 1098 c 1008 c 1021 d

1083 b 1150 b 993 b 1271 c 1214 a 1186 b 1112 b 1147 b

683 d – 768 abc 1358 b 974 abc 1282 abc 1008 c 1120 b

1248 a 1233 a 1686 a 1693 a 1332 a 1412 a 1465 a 1486 a

R2 0.135 0.166 0.389 0.301 0.178 0.218 0.080 0.282

Significantly different log (RdNBR) are denoted by different letters (Tukey HSD test, p b 0.05).

is that eucalypt expansion proceeded mostly where fire activity is low or is characterized by large but infrequent fires in comparison with the frequent-fire mountains of N-C Portugal (Fernandes et al., 2012; Oliveira et al., 2012). For Portugal and the short period of 2001–2003, Moreira et al. (2010) indicated that fires starting in forest and, especially, in shrubland, were more likely to grow to larger sizes than fires starting in farmland and in rural-urban interfaces, in generic agreement with

our results. Data in Table 3 and large-fire size in relation to land cover composition further indicate that variation in forest composition, both at the ignition location and within the burnt perimeter, is irrelevant to final fire size and is consistent with our burned-area findings and the existing fire selectivity studies. Using the 1998–2008 fires in Fig. 2 and boosted regression trees, large-fire size had been shown to be independent of land cover, except for a slight decrease at N70% shrubland-grassland cover; and a prime function of landscape-level

Fig. 3. Surface fuel hazard metrics per forest cover type: contour densities, means, and boxplots displaying the 10th, 25th, 50th, 75th and 90th percentiles. Means that are significantly different (p b 0.05, Steel-Dwass test) are followed by different letters.

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fuel connectivity and the complexity of fuel age mosaics, with increasing and decreasing effects respectively (Fernandes et al., 2016a). 4.2. Fire severity Land cover composition had a modest influence on fire severity variability in the Portuguese mega-fires of 2017. Dilution of the local controls of fire severity, i.e. vegetation/fuel and topography, is not unusual within large fires driven by extreme fire weather (Bradstock et al., 2010; Thompson and Spies, 2010; Dillon et al., 2011; FernándezAlonso et al., 2017; Zald and Dunn, 2018). Conversely, bottom-up influences can be more decisive to fire severity than top-down influences (Birch et al., 2015; Parks et al., 2018), and other studies assigned a prevailing or relevant role to fuel or stand structure (Oliveras et al., 2009; Viedma et al., 2014; Fernández-Alonso et al., 2017; Whitman et al., 2018; García-Llamas et al., 2019). Fire severity reflects the combination of multiple influences, thus finding a minor role for cover type is not unexpected, and more so because the effect is expressed through fire behaviour, which depends on highly variable fuel characteristics and stand structure rather than on vegetation type per se (Fernandes, 2009). 4.3. Fuel and fire behaviour considerations Wildfire extent and severity are contingent on fire behaviour characteristics such as rates of spread and energy release, which in turn assimilate the influences of weather, topography and vegetation. Fuel characteristics are then crucial to understand and explain how vegetation types differ in their relative fire incidence and fire characteristics (McWethy et al., 2018; Paritsis et al., 2018). Wide variation of fuelcomplex properties within a vegetation type, as we found based on extensive forest inventory data, hardly warrants clear-cut distinctions between the fuel hazard of each vegetation type. Hence the similarity in typical fireline intensity between forest types (Fig. 3). Nonetheless, the 75th and 90th percentiles of fireline intensity were substantially lower in eucalypt versus pine stands, plus median fuel load is almost half as low if canopy foliage is considered (Rosa et al., 2011). The large-scale replacement of maritime pine by blue gum stands thus suggests fuel load decreases at local to regional scales. Features that distinguish blue gum plantations can explain the disparity, namely short-rotation management that implies frequent disturbances of the fuel-complex and impedes maximum potential fuel accumulation (Rosa et al., 2011; Botequim et al., 2015; Mirra et al., 2017). Additionally, industrial blue gum stands were fuel-reduced at an annual rate of 14.1% from 2002 to 2017 (CELPA, 2018), and fuel treatments are probably more common in blue gum plantations than in pine stands. The observed and inferred decreases in fuel hazard at stand and national scales might be implicated in the irresponsiveness of blue gum BA to the species expansion. Litter-driven surface fires in pine plantations and in natural eucalypt forest spread at similar rates (Cheney, 1968), with the difference that pine litter sustains burning at higher moisture content (Forest Fire Management Group, 2007). Differences in the spread rates of high-intensity wildfires are marginal between conifers, eucalypts, and shrublands (Cruz and Alexander, personal communication). However, transition from surface to crown fire in pine plantations is a stepwise process and results in fire-spread rates increased by three-fold or more, whereas canopy burning in eucalypt forest develops steadily as surface fire intensity increases (Forest Fire Management Group, 2007). In contrast, comparing fire behaviour between blue gum plantations and other vegetation types is a futile exercise (Gould et al., 2001), as documented fire characteristics in the former remains scarce and limited to low-intensity experimental fires (e.g. Boness and van Etten, 1998) and occasional wildfire case studies (Braun, 2003; McCaw, 2006). Consequently, the predictive ability of models used to simulate fire behaviour in eucalypt plantations (Fernandes, 2009; Goodrick and

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Stanturf, 2012; Mirra et al., 2017) cannot be evaluated and no model can be recommended (Cruz et al., 2018). The mega-fires events of October 2017 in the eucalypt-dominated landscapes of central Portugal included spot fire formation up to distances of 2–3 km from the fire front (CTI, 2018), being of the same order of those produced by crowning in conifer forest (Albini et al., 2012). These distances are one order of magnitude lower than the maximum distances observed in eucalypt forests in Australia (Cruz et al., 2012), presumably because short-rotation forestry does not allow the build-up of extreme levels of bark fuel hazard (Goodrick and Stanturf, 2012; Mirra et al., 2017). Mid- to long-distance spotting challenges fire suppression operations, but it is debatable whether fire growth rate is significantly increased (Alexander and Cruz, 2006). More important is the coalescence of high-density short-distance spotting that characterizes eucalypt fire spread under extreme conditions (Cruz et al., 2015). The June and October 2017 mega fires in central Portugal resulted from a combination of atmospheric and drought circumstances enabling fast-growing fires, followed by downdrafts and area ignition, the phenomena that caused the human fatalities (CTI, 2017, 2018). Blue gum stands were widespread in the region, although not in all fires, and their spotting potential might have boosted the fires. However, firestorms causing comparable human life losses have originated from various vegetation types, such as in California recently (Nauslar et al., 2018). Crown fire runs in blue gum plantations occur given adequate combinations of fuel and stand structure, slope and weather (McCaw, 2006; Fig. 4a) but do not sustain over significant periods of time. Thus, in contrast with spotting, crowning is not a relevant factor in wildfire growth. Blue gum stands are on average the tallest in Portugal (Silva et al., 2009), and the canopy is relatively sparse and distant from the ground, with foliage described as “resistant to combustion” (Dickinson and Kirkpatrick, 1985). These joint characteristics, possibly reinforced by lesser fuel load,

Fig. 4. Wildfires in unmanaged blue gum stands in Portugal. a) Unthinned second-rotation coppice burned by crown fire in the Pedrogão Grande firestorm, June 2017. b) Regrowth burning 11 years after a previous fire, Terras de Bouro, September 2007.

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predispose to lower crowning potential than in native pine and evergreen oaks and explain why, overall, fire severity was lower in blue gum stands than in the remaining forest types, despite the equalizing effect of extreme fire weather conditions. 4.4. The future Questioning the role that blue gum stands might play in the fire regime of the future is legitimate, given the current extent of the species in Portugal and the fact that a significant fraction of its area is undermanaged or abandoned. This was already apparent in 2005, when 47% of the blue gum estate was in the form of mixed or irregular stands (Rego et al., 2013) and 21% was above rotation age, with the implied increased fuel loads (Mirra et al., 2017). Successive fires in temperate eucalypt forests create positive fire severity feedbacks (Barker and Price, 2018) through high-density regrowth (Bowman et al., 2016) (but see Attiwill et al., 2014). Blue gum regenerates easily after fire, both by resprouting (Catry et al., 2013) and seed germination (Águas et al., 2014), and a substantial part of the area burned in 2017 consisted of stands regrowing after the large fires of 2003 and 2005. If left unmanaged, regrowth of the extensive blue gum areas burned in 2016–2017 (165 kha) will increase forest continuity and uniformity, a previously on-going process (Fernandes and Guiomar, 2017) that subsequently induces potentially larger fires (Loepfe et al., 2010; Curt et al., 2013; Fernandes et al., 2016a). Further abandonment as a consequence of large-scale wildfires is conducive to higher burning likelihood and fire severity, because of: increased fuel loading in unharvested and top-killed stands, especially if comprised of smaller-sized trees (Catry et al., 2013); prevalence of low and dense stands (Botequim et al., 2013); rise in structurally heterogeneous stands (Viedma et al., 2014); increased mixtures of pine and eucalypt (Silva et al., 2009); higher shrub loading due to lower stand basal area (Fernandes and Guiomar, 2017); and facilitation of blue gum invasion, which is limited (Catry et al., 2015) but nonetheless can increase fuel hazard locally. Investigation of regrowth blue-gum stand structure trajectories and fuel dynamics is needed, similarly to recent work in native eucalypt forests (Cawson et al., 2018; Dixon et al., 2018). Additionally, climate change will promote forest abandonment and fuel accumulation by diminishing tree growth and the current range of the species (Costa et al., 2017; Deus et al., 2018), and through drought-induced insect defoliation and tree mortality (Fernández-Manjarrés et al., 2018). The enumerated future changes are generically valid for the Portuguese forest as a whole. Blue-gum area loss as a consequence of wildfire is quite low in comparison with native forest types (Fernandes and Guiomar, 2017). This, along with the regrowth characteristics of the species, suggests that unharvested and uncoppiced burnt stands will rapidly acquire marked three-dimensional fuel profiles (Fig. 4). However, it is currently unknown whether the corresponding fire hazard differs from native vegetation under similar circumstances. 5. Conclusion Eucalypt surface in mainland Portugal nearly tripled in three decades. Such expansion had no effect on total BA and, surprisingly, did not translate into increasingly higher eucalypt BA or share of total BA. Abnormal fire seasons characterized by mega-fire occurrence in central Portugal under extreme fire weather correspond with higher prevalence of forest, and of blue gum, in BA. However, this occasional but high-impact dominance of eucalypt in BA is a consequence of the regional extent, continuity and homogeneity of forest in general. The results of this study regarding fire size and fire severity mirror those obtained for BA, i.e. consequences of blue gum expansion on those fire regime metrics were not discernible, which is consistent with stand-level quantitative fuel data. Scaling-up of fire activity is a likely consequence of land cover conversion to forest plantations that replace less

flammable vegetation. However, that was not the case in Portugal, as blue gum stands mostly occupy land formerly covered by highly flammable shrubland and maritime pine. Comparative fire behaviour knowledge is scarce, but a trade-off is apparent between the higher spotting potential of blue gum and its lesser predisposition to crown fire, plus lower fuel accumulation inherent to short-rotation silviculture. Eucalypt forestation had no effect on the Portuguese fire regime, but the hypothesis of future impacts should not be excluded. The aftermath of previous years mega-fires spur rural abandonment in general, and of forest as an economic resource in particular, and homogenises and simplifies landscape structure, known to be the major factor in large fire growth. However, this concern is general independently of forest composition. Whether or not and to what extent fuel dynamics and fire behaviour differ between native vegetation types and abandoned blue gum stands, either after fire or harvest, is a subject that definitely warrants further research. CRediT authorship contribution statement Paulo M. Fernandes: Conceptualization, Supervision, Writing - original draft, Formal analysis, Investigation, Methodology, Writing - review & editing. Nuno Guiomar: Formal analysis, Investigation, Methodology, Writing - review & editing. Carlos G. Rossa: Formal analysis, Investigation, Methodology, Writing - review & editing. Acknowledgments Paulo Pereira suggested this study. We thank Miguel Pinto, Carlos da Câmara and Ricardo Trigo, and Hélder Fraga and João Santos for respectively supplying and extracting the Canadian fire weather indexes data. This work was supported by FCT - Portuguese Foundation for Science and Technology, with national funds under project UID/AGR/04033/ 2019; as a post-doctoral grant to C. Rossa (SFRH/BPD/84770/2012) through financing programs POPH and FSE; and in the frame of project FIREXTR (PTDC/ATPGEO/0462/2014), co-financed by national funds and the European Regional Development Fund (ERDF) through the COMPETE 2020 - Operational Program Competitiveness and Internationalization. References Águas, A., Ferreira, A., Maia, P., Fernandes, P.M., Roxo, L., Keizer, J., Silva, J.S., Rego, F.C., Moreira, F., 2014. Natural establishment of Eucalyptus globulus Labill. in burnt stands in Portugal. For. Ecol. Manag. 323, 47–56. https://doi.org/10.1016/j.foreco.2014.03.012. Albini, F.A., Alexander, M.E., Cruz, M.G., 2012. A mathematical model for predicting the maximum potential spotting distance from a crown fire. Int. J. Wildland Fire 21, 609–627. Alexander, M.E., Cruz, M.G., 2006. Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Can. J. For. Res. 36, 3015–3028. Alves, A.M., Pereira, J.S., Silva, J.M.N., 2007. A introdução e a expansão do eucalipto em Portugal. In: Alves, A.M., Pereira, J.S., Silva, J.M.N. (Eds.), O eucaliptal em Portugal, Impactes ambientais e investigação científica. ISAPress, Lisboa, pp. 13–24. Ames, P., 2017. Portugal's ‘killer forest’ deadly wildfire calls into question Portugal's embrace of eucalyptus. Available at. https://www.politico.eu/article/portugal-fire-eucalyptus-killer-forest/. Attiwill, P.M., Ryan, M.F., Burrows, N., Cheney, N.P., 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, 341–354. Barker, J.W., Price, O.F., 2018. Positive severity feedback between consecutive fires in dry eucalypt forests of southern Australia. Ecosphere 9, e02110. https://doi.org/10.1002/ ecs2.2110. Barros, A.M.G., Pereira, J.M.C., 2014. Wildfire selectivity for land cover type: does size matter? PLoS One 9, e84760. https://doi.org/10.1371/journal.pone.0084760. Birch, D.S., Morgan, P., Kolden, C.A., Abatzoglou, J.T., Dillon, G.K., Hudak, A.T., Smith, A.M.S., 2015. Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests. Ecosphere 6, art17. https://doi.org/10.1890/ ES14-00213.1. Boness, P., van Etten, E., 1998. Fire and fuel loads in blue gum plantations in South West Western Australia. Proc. 13th Fire and Forest Meteorology Conference. IAWF, Fairfield, USA, p. 551. Botella-Martínez, M.A., Fernández-Manso, A., 2017. Estudio de la severidad post-incendio en la Comunidad Valenciana comparando los índices dNBR, RdNBR y RBR a partir de imágenes Landsat 8. Rev. Teledetección 49, 33–47. https://doi.org/10.4995/ raet.2017.7095.

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