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Fire, Volume 1, Issue 3 (December 2018) – 19 articles

Cover Story (view full-size image): Typical Cerrado landscape, with a mosaic of different physiognomies: open savannas, wet grasslands, campos rupestres. In the open savanna areas, prescribed burnings experiments were established since 2013 to evaluate the effects of fire season and frequency on fuel load dynamics and vegetation responses. Reserva Natural Serra do Tombador, Central Brazil (January - wet season). View this paper.
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8 pages, 764 KiB  
Perspective
Wildland Fire Science Literacy: Education, Creation, and Application
by Devan Allen McGranahan and Carissa L. Wonkka
Fire 2018, 1(3), 52; https://doi.org/10.3390/fire1030052 - 19 Dec 2018
Cited by 9 | Viewed by 5242
Abstract
Wildland fire science literacy is the capacity for wildland fire professionals to understand and communicate three aspects of wildland fire: (1) the fundamentals of fuels and fire behavior, (2) the concept of fire as an ecological regime, and (3) multiple human dimensions of [...] Read more.
Wildland fire science literacy is the capacity for wildland fire professionals to understand and communicate three aspects of wildland fire: (1) the fundamentals of fuels and fire behavior, (2) the concept of fire as an ecological regime, and (3) multiple human dimensions of wildland fire and the socio-ecological elements of fire regimes. Critical to wildland fire science literacy is a robust body of research on wildland fire. Here, we describe how practitioners, researchers, and other professionals can study, create, and apply robust wildland fire science. We begin with learning and suggest that the conventional fire ecology canon include detail on fire fundamentals and human dimensions. Beyond the classroom, creating robust fire science can be enhanced by designing experiments that test environmental gradients and report standard data on fuels and fire behavior, or at least use the latter to inform models estimating the former. Finally, wildland fire science literacy comes full circle with the application of robust fire science as professionals in both the field and in the office communicate with a common understanding of fundamental concepts of fire behavior and fire regime. Full article
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<p>Four scales of control relevant to wildland fire. Many authors represent controls at each scale with triangles; this figure is a modified version of the modified version presented by Scott et al. [<a href="#B2-fire-01-00052" class="html-bibr">2</a>]. While these triangles together encompass most sources of biotic and abiotic variability in the wildland fire environment, the model fails to incorporate human dimensions of wildland fire.</p> Full article ">Figure 2
<p>Wildland fire science is practiced in two arenas—the field and the office—but few wildland fire professionals are confined to a single arena; they must both focus on relevant components of their specialty and translate this perspective to those with other specialties. This wallet card promotes fire science literacy by helping fire professionals from each arena identify characteristics of the fire environment or fire regime that dominate their colleagues’ perspective. The “wallet card” idea is a bit tongue-in-cheek, but what can it hurt to put this card in your pocket or above your desk?</p> Full article ">
16 pages, 3603 KiB  
Article
Contemporary Fire Regimes of the Arid Carnarvon Basin Region of Western Australia
by Megan Ladbrook, Eddie J. B. van Etten and William D. Stock
Fire 2018, 1(3), 51; https://doi.org/10.3390/fire1030051 - 14 Dec 2018
Cited by 3 | Viewed by 3739
Abstract
This study investigates the fire regime for the arid Carnarvon Basin region of Western Australia using remotely sensed imagery. A fire history database was constructed from satellite images to characterise the general fire regime and determine any effect of vegetation types and pre-fire [...] Read more.
This study investigates the fire regime for the arid Carnarvon Basin region of Western Australia using remotely sensed imagery. A fire history database was constructed from satellite images to characterise the general fire regime and determine any effect of vegetation types and pre-fire weather and climate. The study area was divided into two sections (northern and southern) due to their inherently different vegetation and climate. A total of 23.8% (15,646 km2) of the study area was burnt during the 39-year study period. Heathland vegetation (54%) burnt the most extensively in the southern study area, and hummock grasslands (68%) in the northern. A single, unusually large fire in 2012 followed exceptional rains in the previous 12 months and accounted for 55% of the total burnt area. This fire burnt mainly through Acacia shrublands and woodlands rather than hummock grasslands, as normally experienced in the northern study area. Antecedent rainfall and fire weather were found to be the main meteorological factors driving fire size. Both study areas showed a moderate to strong correlation between fire size and increased pre-fire rainfall in the year preceding the fire. Predicted future changes in climate may lead to more frequent and higher intensity fires. Full article
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<p>Area of study showing the major botanical provinces of Western Australia (shaded), with the study area (and two sub-areas) outlined, and inset showing location within Western Australia.</p> Full article ">Figure 2
<p>Vegetation groups of the northern study area.</p> Full article ">Figure 3
<p>Vegetation groups of the southern study area.</p> Full article ">Figure 4
<p>Total area burnt by bushfires and numbers of visible separate fire scars between 1973 and 2012 for total study area. Please note that no usable imagery was available for 1980 and 1985.</p> Full article ">Figure 5
<p>Distribution of fire size (<b>dots</b>) and fire count (<b>bars</b>) across the whole study area.</p> Full article ">Figure 6
<p>Northern study area showing the fire scars (shaded area) of the extensive 2012 bushfire.</p> Full article ">Figure 7
<p>Relationships between rainfall in the year preceding fire and fire size (expressed on a natural log scale), for all discrete fires &gt;20km<sup>2</sup> in size across both northern and southern study areas.</p> Full article ">
8 pages, 687 KiB  
Data Descriptor
Extensible Database of Validated Biomass Smoke Events for Health Research
by Ivan C. Hanigan, Geoffrey G. Morgan, Grant J. Williamson, Farhad Salimi, Sarah B. Henderson, Murray R. Turner, David M. J. S. Bowman and Fay H. Johnston
Fire 2018, 1(3), 50; https://doi.org/10.3390/fire1030050 - 6 Dec 2018
Cited by 4 | Viewed by 4902
Abstract
The extensible Biomass Smoke Validated Events Database is an ongoing, community driven, collection of air pollution events which are known to be caused by vegetation fires such as bushfires (also known as wildfire and wildland fires), or prescribed fuel reduction burns, and wood [...] Read more.
The extensible Biomass Smoke Validated Events Database is an ongoing, community driven, collection of air pollution events which are known to be caused by vegetation fires such as bushfires (also known as wildfire and wildland fires), or prescribed fuel reduction burns, and wood heaters. This is useful for researchers of health impacts who need to distinguish smoke from vegetation versus other sources. The overarching aim is to study statistical associations between biomass smoke pollution and health. Extreme pollution events may also be caused by dust storms or fossil fuel smog events and so validation is necessary to ensure the events being studied are from biomass. This database can be extended by contribution from other researchers outside the original team. There are several available protocols for adding validated smoke events to the database, to ensure standardization across datasets. Air pollution data can be included, and free software was created for identification of extreme values. Protocols are described for reference material needed as supporting evidence for event days. The utility of this database has previously been demonstrated in analyses of hospitalization and mortality. The database was created using open source software that works across operating systems. The prospect for future extensions to the database is enhanced by the description in this paper, and the availability of these data on the open access Github repository enables easy addition to the database with new data by the research community. Full article
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<p>Schematic diagram of the processes for extending the database.</p> Full article ">
11 pages, 5857 KiB  
Case Report
The Year 2017: Megafires and Management in the Cerrado
by Alessandra Fidelis, Swanni T. Alvarado, Ana Carolina S. Barradas and Vânia R. Pivello
Fire 2018, 1(3), 49; https://doi.org/10.3390/fire1030049 - 5 Dec 2018
Cited by 78 | Viewed by 7713
Abstract
The year 2017 was a megafire year, when huge areas burned on different continents. In Brazil, a great extension of the Cerrado burned, raising once more the discussion about the “zero-fire” policy. Indeed, most protected areas of the Cerrado adopted a policy of [...] Read more.
The year 2017 was a megafire year, when huge areas burned on different continents. In Brazil, a great extension of the Cerrado burned, raising once more the discussion about the “zero-fire” policy. Indeed, most protected areas of the Cerrado adopted a policy of fire exclusion and prevention, leading to periodic megafire events. Last year, 78% of the Chapada dos Veadeiros National Park burned at the end of the dry season, attracting media attention. Furthermore, 85% of the Reserva Natural Serra do Tombador burned as a result of a large accumulation of fuel caused by the zero-fire policy. In 2014, some protected areas started to implement the Integrate Fire Management (IFM) strategy. During 2017, in contrast to other protected areas, the Estação Ecológica Serra Geral do Tocantins experienced no megafire events, suggesting that a few years of IFM implementation led to changes in its fire regime. Therefore, we intended here to compare the total burned area and number of fire scars between the protected areas where IFM was implemented and those where fire exclusion is the adopted policy. The use of fire as a management tool aimed at wildfire prevention and biodiversity preservation should be reconsidered by local managers and environmental authorities for most Cerrado protected areas, especially those where open savanna physiognomies prevail. Changing the paradigm is a hard task, but last year’s events showed the zero-fire policy would bring more damage than benefits to Cerrado protected areas. Full article
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Graphical abstract
Full article ">Figure 1
<p>Annual active fires (<b>A</b>) and burned area (<b>B</b>) for the six Brazilian biomes from 2001 to 2017 (Source: [<a href="#B28-fire-01-00049" class="html-bibr">28</a>]).</p> Full article ">Figure 2
<p>Location and surroundings of the Cerrado protected areas: Chapada dos Veadeiros National Park (CVNP), Reserva Natural Serra do Tombador (RNST), Estação Ecológica Serra Geral do Tocantins (ESEC-SGT), Serra da Canastra National Park (SCaNP) and Serra do Cipó National Park (SCNP). Polygons in brighter green indicate the protected areas. Source (background image): Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN and the GIS User Community.</p> Full article ">Figure 3
<p>Fire counts in 2010–2017 for Chapada dos Veadeiros National Park and Estação Ecológica Serra Geral do Tocantins (<b>left</b>); time since last fire, only for the burned area in 2017 (<b>middle</b>); annual percentage of burned area in 2010–2017 (<b>right</b>).</p> Full article ">Figure 4
<p>Relationship between the number of fire scars per year and the average area of scars in the Chapada dos Veadeiros National Park (<b>A</b>) and Estação Ecológica Serra Geral do Tocantins (<b>B</b>) (Source: DIMIF/ICMBio). (Burn scars were not detected for the CVNP in 2011).</p> Full article ">Figure 5
<p>Relationship between the number of fire scars per year and the average extension of burn scars in protected and non-protected areas in the Cerrado between 2001 and 2016. Information about the protected status (protected/non-protected) comes from the World Database on Protected Areas (WDPA) [<a href="#B55-fire-01-00049" class="html-bibr">55</a>]. Data source: FRY Global fire patch morphology database [<a href="#B56-fire-01-00049" class="html-bibr">56</a>].</p> Full article ">
8 pages, 476 KiB  
Article
Body Composition Changes of United States Smokejumpers during the 2017 Fire Season
by Callie N. Collins, Randall H. Brooks, Benjamin D. Sturz, Andrew S. Nelson and Robert F. Keefe
Fire 2018, 1(3), 48; https://doi.org/10.3390/fire1030048 - 1 Dec 2018
Cited by 4 | Viewed by 4890
Abstract
Wildland firefighting is arduous work with extreme physical and nutritional demands that often exceeds those of athletes competing in sports. The intensity and duration of job demands, impacts the amount of calories burned, which can influence body composition. The purpose of this study [...] Read more.
Wildland firefighting is arduous work with extreme physical and nutritional demands that often exceeds those of athletes competing in sports. The intensity and duration of job demands, impacts the amount of calories burned, which can influence body composition. The purpose of this study was to determine if the body composition of nine wildland firefighters working as smokejumpers changed throughout the 2017 fire season. Subjects (n = 9) for the study ranged in age from 24–49 (age 30.1 ± 8.3 y). Height (177 ± 18.8 cm) and weight (81.32 ± 6.39 kg) was recorded during initial body composition testing and body fat percentage was determined pre and post-season using Lange skinfold calipers. Outcomes were evaluated using a paired t-test. Body fat percentage was significantly different between pre and post-season (average body fat percentage increase = 1.31%; t = 2.31, p = 0.04, alpha = 0.05). Body weight increased slightly from pre to post-season (average increase in body weight: 0.17 kg), although the differences were not significant (t = 2.31, p = 0.78). Change in body fat percentage without change in body weight suggest that monitoring of WLFF body composition and fitness may be needed help inform dietary and fitness interventions to insure that nutritional demands of this population are sufficient to support physical work on the fireline. Full article
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<p>Pre and post season fat mass. Each set of paired bars represents an individual smokejumper.</p> Full article ">Figure 2
<p>Pre and post season weight (kg). Each set of paired bars represents and individual smokejumper.</p> Full article ">
22 pages, 10687 KiB  
Article
Winds and Gusts during the Thomas Fire
by Robert G. Fovell and Alex Gallagher
Fire 2018, 1(3), 47; https://doi.org/10.3390/fire1030047 - 30 Nov 2018
Cited by 27 | Viewed by 7155 | Correction
Abstract
We analyze observed and simulated winds and gusts occurring before, during, and immediately after the ignition of the Thomas fire of December 2017. This fire started in Ventura county during a record-long Santa Ana wind event from two closely located but independent ignitions [...] Read more.
We analyze observed and simulated winds and gusts occurring before, during, and immediately after the ignition of the Thomas fire of December 2017. This fire started in Ventura county during a record-long Santa Ana wind event from two closely located but independent ignitions and grew to become (briefly) the largest by area burned in modern California history. Observations placed wind gusts as high as 35 m/s within 40 km of the ignition sites, but stations much closer to them reported much lower speeds. Our analysis of these records indicate these low wind reports (especially from cooperative “CWOP” stations) are neither reliable nor representative of conditions at the fire origin sites. Model simulations verified against available better quality observations indicate downslope wind conditions existed that placed the fastest winds on the lee slope locations where the fires are suspected to have started. A crude gust estimate suggests winds as fast as 32 m/s occurred at the time of the first fire origin, with higher speeds attained later. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Model terrain height (shaded) for the 2-km Domain 4 indicating available observation locations marked by their network affiliations. Location of the innermost nest is identified, along with locations of ASOS stations KDAG and KLAX.</p> Full article ">Figure 2
<p>Time series of differences in (<b>a</b>) mean sea level pressure (ΔSLP, hPa), and (<b>b</b>) 700 hPa temperature (ΔT, K), between locations corresponding to Daggett/Barstow (KDAG) and Los Angeles International (KLAX) airports during the Thomas Fire period (black, spanning 00 UTC 3–10 December 2017, lower abscissa), and the historic fire season of late October 2007 (grey, spanning 00 UTC 20–27 October 2007, upper abscissa). ΔSLP is derived from station observations while ΔT was computed using the NARR reanalysis. Note to facilitate comparison, ΔSLP is KDAG-KLAX and ΔT is KLAX-KDAG, and the October series is slightly shifted chronologically.</p> Full article ">Figure 3
<p>Telescoping configuration employed for most of the WRF simulations in this study (see <a href="#fire-01-00047-t001" class="html-table">Table 1</a>), consisting of five (54, 18, 6, 2, and 0.667 km horizontal grid spacing) domains and based on [<a href="#B6-fire-01-00047" class="html-bibr">6</a>,<a href="#B15-fire-01-00047" class="html-bibr">15</a>], except that the innermost nest was shifted over the Thomas Fire area. Topography of outermost domain shown (shaded), except where superimposed with 2 km (Domain 4) terrain. Verifications against observations discussed herein were performed in Domain 4.</p> Full article ">Figure 4
<p>Model terrain height (shaded) for the 667-m Domain 5 along with maximum gusts (m/s) reported during the simulation period (0000 UTC 4 December to 0600 UTC 6 December 2017, inclusive), with markers indicating network affiliation. Stations specifically referenced in the analysis are identified. Note some stations have significant reporting gaps during this period. Red stars mark the locations of fire ignitions #1 and #2 presumed for this analysis, and cross-sections A-A′ and B-B′ denote the locations of cross sections examined later.</p> Full article ">Figure 5
<p>Time series of observed sustained wind speed (black dots) and gusts (white dots), and forecasted sustained wind speed (red curves), at the four locations with the highest reported gusts in <a href="#fire-01-00047-f004" class="html-fig">Figure 4</a> during the simulation period. Shown are (<b>a</b>) F0112 (CWOP), (<b>b</b>) AT184 (CWOP), (<b>c</b>) WLYC1 (RAWS), and (<b>d</b>) KNTD (ASOS). For KNTD, gaps in the 1-min record were filled in with hourly METAR reports where available, and note that due to a small average gust factor that gusts are not well separated from sustained winds. Although all available observations are plotted only those closest to the top of each hour were used in verification statistics.</p> Full article ">Figure 6
<p>Time series of observed (black or grey dots) and forecasted (red or grey curves) sustained winds for stations near the fires and/or along the cross-sections shown in <a href="#fire-01-00047-f004" class="html-fig">Figure 4</a>: (<b>a</b>) D7412 (CWOP), (<b>b</b>) AT490 and C7664 (both CWOP), (<b>c</b>) E4795 (CWOP), and (<b>d</b>) WTPC1 (RAWS). 10 m wind predictions at the ignition sites #1 and #2 are included on panels (<b>a</b>) and (<b>b</b>), respectively, as blue curves. Note CWOP forecasts are valid at 10 m AGL while for the RAWS site (<b>d</b>) both 6.1 m (solid) and 26 m (dashed) forecasts are provided; see text. Note vertical scale differs from <a href="#fire-01-00047-f005" class="html-fig">Figure 5</a> and observed gusts are not shown.</p> Full article ">Figure 7
<p>Station mean gust factors (GFs) for the simulation period, presented in rank order within each network, for the (<b>a</b>) ASOS 1-min (black), SDG &amp; E (red), RAWS (blue), and (<b>b</b>) CWOP (black), networks. Note panels have very different vertical scales.</p> Full article ">Figure 8
<p>Network-averaged sustained wind observations (black dots) and forecasts (red curves) for (<b>a</b>) ASOS + AWOS, (<b>b</b>) ASOS 1-min, (<b>c</b>) RAWS, and (<b>d</b>) CWOP over the simulation period. Vertical grey lines denote ±1 standard deviation around the observations. In (<b>a</b>,<b>d</b>), network averages disregarding calm observations and corresponding forecasts are represented as grey dots and lines, respectively; for (<b>c</b>) one forecast hour was neglected owing to observation dropouts.</p> Full article ">Figure 9
<p>Comparison of network-averaged CWOP (horizontal axis) vs. ASOS + AWOS (vertical axis) observations (black dots) and corresponding forecasts (red dots), hourly between forecast hours 12–54, inclusive. Observation (grey dots) and forecast (orange dots) comparisons after calm and/or low quality observations were removed are also shown. The linear fits are depicted as solid lines corresponding to their subset’s colors. The black dashed line represents one-to-one correspondence.</p> Full article ">Figure 10
<p>Station-average sustained wind forecast bias vs. observed sustained wind speed (black or grey dots) along with least squares fits (red lines) for the (<b>a</b>) ASOS+AWOS and ASOS 1-min, (<b>b</b>) RAWS, (<b>c</b>) CWOP, and (<b>d</b>) SDG &amp; E networks. Regressions are shown to emphasize correlations but are not used in this analysis.</p> Full article ">Figure 11
<p>Vertical cross-sections showing horizontal wind speed (shaded) and potential temperature (black contours, called isentropes) for the control run valid at 0220 UTC 5 December 2017, along the two dashed lines indicated on <a href="#fire-01-00047-f004" class="html-fig">Figure 4</a>: (<b>a</b>) A-A′, and (<b>b</b>) B-B′. Terrain is grey shaded, presumed ignition sites are denoted with vertical dotted lines, and the locations of nearby stations are also marked. Note some stations are slightly outside the plane depicted. Horizontal spans in (<b>a</b>) and (<b>b</b>) are 69 and 60.5 km, respectively.</p> Full article ">Figure 12
<p>Similar to <a href="#fire-01-00047-f011" class="html-fig">Figure 11</a>a, for these times during the control run: (<b>a</b>) 0020, (<b>b</b>) 0120, (<b>c</b>) 0220, (<b>d</b>) 1000, (<b>e</b>) 1400, and (<b>f</b>) 1700 UTC, all on 5 December 2017. Note the difference in time intervals between the left (one hour) and right (three hours) columns.</p> Full article ">Figure 13
<p>Similar to <a href="#fire-01-00047-f011" class="html-fig">Figure 11</a>a, but from selected sensitivity experiments, all valid at time 0220 UTC 5 December 2017. (<b>a</b>) The mean of five trials using SKEBS perturbations. (<b>b</b>) Run using the Noah z_0mod LSM with the MYNN PBL. (<b>c</b>) Simulation using the Noah z_0mod LSM with the YSU PBL. (<b>d</b>) Run using the unmodified Noah LSM with the YSU PBL. (<b>e</b>) Simulation initialized with NAM at 1200 UTC 4 December. (<b>f</b>) Run initialized with GFS at 0000 UTC 4 December. (<b>g</b>) Simulation initialized with HRRR at 0000 UTC 4 December. (<b>h</b>) Run initialized with NARR at 0000 UTC 4 December. Left column simulations shared the control run’s initialization, while those in the right column employed the control run’s model physics.</p> Full article ">Figure 14
<p>Time series of near-surface wind maximum (NSMAX, m/s), the maximum simulated sustained wind forecast within the lowest 600 m AGL, from the control run (black), SKEBS-perturbed simulations (grey), initialization experiment runs (blue), and simulations varying either model physics and/or version (green). See <a href="#fire-01-00047-t001" class="html-table">Table 1</a>. For scale, the ensemble standard deviation (grey dashed curve) is provided in dm/s, or at ten times its actual value in m/s, and fire #1’s presumed ignition time is denoted by the vertical red dashed line.</p> Full article ">
23 pages, 2843 KiB  
Article
The Impacts of Wildfire Characteristics and Employment on the Adaptive Management Strategies in the Intermountain West
by Liana Prudencio, Ryan Choi, Emily Esplin, Muyang Ge, Natalie Gillard, Jeffrey Haight, Patrick Belmont and Courtney Flint
Fire 2018, 1(3), 46; https://doi.org/10.3390/fire1030046 - 30 Nov 2018
Cited by 3 | Viewed by 4840
Abstract
Widespread development and shifts from rural to urban areas within the Wildland-Urban Interface (WUI) has increased fire risks to local populations, as well as introduced complex and long-term costs and benefits to communities. We use an interdisciplinary approach to investigate how trends in [...] Read more.
Widespread development and shifts from rural to urban areas within the Wildland-Urban Interface (WUI) has increased fire risks to local populations, as well as introduced complex and long-term costs and benefits to communities. We use an interdisciplinary approach to investigate how trends in fire characteristics influence adaptive management and economies in the Intermountain Western US (IMW). Specifically, we analyze area burned and fire frequency in the IMW over time, how fires in urban or rural settings influence local economies and whether fire trends and economic impacts influence managers’ perspectives and adaptive decision-making. Our analyses showed some increasing fire trends at multiple levels. Using a non-parametric event study model, we evaluated the effects of fire events in rural and urban areas on county-level private industry employment, finding short- and long-term positive effects of fire on employment at several scales and some short-term negative effects for specific sectors. Through interviewing 20 fire managers, we found that most recognize increasing fire trends and that there are both positive and negative economic effects of fire. We also established that many of the participants are implementing adaptive fire management strategies and we identified key challenges to mitigating increasing fire risk in the IMW. Full article
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<p>We address the overarching research question (top in bold) through investigating the sub-questions in the three boxes. The solid arrows show the connections that this interdisciplinary study addresses and are further discussed later in the paper. We acknowledge that other feedbacks exist between these questions (dashed arrows), such as managers’ decisions and economies impacting fire trends.</p> Full article ">Figure 2
<p>Fires over ~400 ha over a 32-year period (1984–2015), broadly classified as either “urban” (&lt;2.4 km from high-density census-blocks) or “rural.”.</p> Full article ">Figure 3
<p>Increasing Focal Counties (Arizona [n = 2], Idaho [n = 7], Montana [n = 1], Nevada [n = 1], Utah [n = 2] and Wyoming [n = 1]) have experienced increasing trends for area burned, fire frequency, or both from 1984–2015. When ranking the 281 counties’ regression slopes from highest to lowest, the Increasing Focal Counties are in the top 5 percent of slopes.</p> Full article ">Figure 4
<p>Example from two Arizona counties (Apache County—FIPS 4001; Cochise County—FIPS 4003) showing employment trends for the Leisure and Hospitality sector (2001–2015). Triangles represent urban fires, while dots represent rural fires. Different sizes of dots or triangles represent differing fire size. Fires were sorted according to size. Green dots/triangles represent the upper 25th percentile of fires, followed by the 50th–75th percentile in blue and lower 25th percentile in red.</p> Full article ">Figure 5
<p>State-level linear trends in percentage of area burned for rural and urban fires.</p> Full article ">Figure 6
<p>State-level linear trends in fire frequency for rural and urban fires.</p> Full article ">
51 pages, 1725 KiB  
Editorial
Recognizing Women Leaders in Fire Science: Revisited
by Alistair M.S. Smith and Eva K. Strand
Fire 2018, 1(3), 45; https://doi.org/10.3390/fire1030045 - 21 Nov 2018
Cited by 3 | Viewed by 12912
Abstract
In August, 2018, an editorial in Fire entitled Recognizing Women Leaders in Fire Science was published. This was intended to ignite a conversation into diversity in fire science by highlighting several women leaders in fire research and development. This editorial was released alongside [...] Read more.
In August, 2018, an editorial in Fire entitled Recognizing Women Leaders in Fire Science was published. This was intended to ignite a conversation into diversity in fire science by highlighting several women leaders in fire research and development. This editorial was released alongside a new Topical Collection in Fire called Diversity Leaders in Fire Science. The response on social media was fantastic, leading to numerous recommendations of women leaders in fire science that had been inadvertently missed in the first editorial. In this editorial, we acknowledge 145 women leaders in fire science to promote diversity across our disciplines. Fire is continually committed to improving diversity and inclusion in all aspects of the journal and welcomes perspectives, viewpoints, and constructive criticisms to help advance that mission. Full article
(This article belongs to the Collection Diversity Leaders in Fire Science)
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<p>Global distribution and impact of woman leaders in fire science. Woman leaders in fire science, color coded by H-index, worldwide (<b>A</b>), in the conterminous United States (<b>B</b>), and in Western Europe (<b>C</b>). Map source: National Geographic, via Environmental Systems Research Institute (ESRI).</p> Full article ">
7 pages, 1775 KiB  
Communication
Wildfire Impact and the “Fire Paradox” in a Natural and Endemic Pine Forest Stand and Shrubland
by José Ramón Arévalo and Agustín Naranjo-Cigala
Fire 2018, 1(3), 44; https://doi.org/10.3390/fire1030044 - 13 Nov 2018
Cited by 15 | Viewed by 5249
Abstract
Fire is a powerful force that has shaped forests for thousands of years. It also provokes widespread social concern due to possible economic damage, social effects, impact on homes and properties, and other social effects including fatalities. Regions with seasonal variations in aridity [...] Read more.
Fire is a powerful force that has shaped forests for thousands of years. It also provokes widespread social concern due to possible economic damage, social effects, impact on homes and properties, and other social effects including fatalities. Regions with seasonal variations in aridity have a fire regime dependent on climate resulting from the role of precipitation and temperature in fire occurrence, implying a synchrony of fire occurrence at regional scale. This spatial and temporal variation of fire regimes regulates the structure, diversity, regeneration dynamics, and nutrient cycle of an area. In the Canary Islands, fires are recurrent in pine forests, although their occurrence in the same area more than once within a 20-year period is rare. The main aim of this work is to reveal, over a 50-year period, fire occurrence and impact on the Canary Islands and how the islands are immersed in a “fire paradox”—a process typical of protected areas, where fire suppression becomes one of the main aims of forest management. Full article
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<p>Conceptual model of forest fire patterns in developed and developing countries in the last five decades (bars are total surface hectares burned and the line indicates the number of fires) that follows the fire paradox hypothesis. Modified from the article by García-Domínguez (2010) [<a href="#B11-fire-01-00044" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>The Canary Islands and surface area of pine forest on the different islands with this forest stand.</p> Full article ">Figure 3
<p>Information on the number of fires (line) and total surface hectares burned (bars) for the Canary Islands (information provided by the government of the Canary Islands—Vicenconsejería de Política Territorial y Medio Ambiente and Ministerio de Agricultura del Gobierno de España).</p> Full article ">Figure 4
<p>Number of years per decade with forest fire extensions over 1000 ha.</p> Full article ">
5 pages, 215 KiB  
Perspective
Live Fuel Moisture Content: The ‘Pea Under the Mattress’ of Fire Spread Rate Modeling?
by Carlos G. Rossa and Paulo M. Fernandes
Fire 2018, 1(3), 43; https://doi.org/10.3390/fire1030043 - 12 Nov 2018
Cited by 19 | Viewed by 4489
Abstract
Currently, there is a dispute on whether live fuel moisture content (FMC) should be accounted for when predicting a real-world fire-spread rate (RoS). The laboratory and field data results are conflicting: laboratory trials show a significant effect of live FMC on RoS, which [...] Read more.
Currently, there is a dispute on whether live fuel moisture content (FMC) should be accounted for when predicting a real-world fire-spread rate (RoS). The laboratory and field data results are conflicting: laboratory trials show a significant effect of live FMC on RoS, which has not been convincingly detected in the field. It has been suggested that the lack of influence of live FMC on RoS might arise from differences in the ignition of dead and live fuels: flammability trials using live leaves subjected to high heat fluxes (80–140 kW m−2) show that ignition occurs before all of the moisture is vaporized. We analyze evidence from recent studies, and hypothesize that differences in the ignition mechanisms between dead and live fuels do not preclude the use of overall fine FMC for attaining acceptable RoS predictions. We refer to a simple theory that consists of two connected hypotheses to explain why the effect of live FMC on field fires RoS has remained elusive so far: H1, live tree foliage FMC remains fairly constant over the year; and H2, the seasonal variation of live shrubs’ FMC correlates with the average dead FMC. As a result, the effect of live FMC is not easily detected by statistical analysis. Full article
4 pages, 179 KiB  
Perspective
Increasing Editorial Diversity: Strategies for Structural Change
by Annabel L. Smith
Fire 2018, 1(3), 42; https://doi.org/10.3390/fire1030042 - 8 Nov 2018
Cited by 3 | Viewed by 3998
Abstract
Editorial boards should be representative of the people doing science but they are often plagued with inequality. This article presents some starting points towards increasing editorial diversity, hoping to spark new initiatives to recruit people of under-represented groups to editorial boards. I argue [...] Read more.
Editorial boards should be representative of the people doing science but they are often plagued with inequality. This article presents some starting points towards increasing editorial diversity, hoping to spark new initiatives to recruit people of under-represented groups to editorial boards. I argue there should be a greater focus on what journals and publishers should do instead of focusing on stories and celebrations of extraordinary individuals overcoming barriers. Transparent reporting, diversity targets, strategic invitations, mentoring programs, self-assigned workloads are all strategies which might lead to structural change. New, creative ways to recruit editors are needed so that women and all under-represented groups are given more opportunities to shape the direction of science. Full article
(This article belongs to the Collection Diversity Leaders in Fire Science)
13 pages, 2753 KiB  
Article
A Global Analysis of Hunter-Gatherers, Broadcast Fire Use, and Lightning-Fire-Prone Landscapes
by Michael R. Coughlan, Brian I. Magi and Kelly M. Derr
Fire 2018, 1(3), 41; https://doi.org/10.3390/fire1030041 - 25 Oct 2018
Cited by 22 | Viewed by 7124
Abstract
We examined the relationships between lightning-fire-prone environments, socioeconomic metrics, and documented use of broadcast fire by small-scale hunter-gatherer societies. Our approach seeks to re-assess human-fire dynamics in biomes that are susceptible to lightning-triggered fires. We quantify global lightning-fire-prone environments using mean monthly lightning [...] Read more.
We examined the relationships between lightning-fire-prone environments, socioeconomic metrics, and documented use of broadcast fire by small-scale hunter-gatherer societies. Our approach seeks to re-assess human-fire dynamics in biomes that are susceptible to lightning-triggered fires. We quantify global lightning-fire-prone environments using mean monthly lightning and climatological flammability, and then compare how well those environments and socioeconomic variables (population density, mobility, and subsistence type) serve as predictors of observed broadcast fire use from the ethnographic data. We use a logistic model for all vegetated, forested, and unforested biomes. Our global analysis of human-fire-landscape interaction in three hundred and thirty-nine hunter-gatherer groups demonstrates that lightning-fire-prone environments strongly predict for hunter-gatherer fire use. While we do not maintain that lightning-fire-prone environments determine the use of fire by small societies, they certainly appear to invite its use. Our results further suggest that discounting or ignoring human agency contradicts empirical evidence that hunter-gatherers used fire even in locations where lightning could explain the presence of fire. Paleoecological research on fire and hypothesis testing using global fire modeling should consider insights from human ecology in the interpretation of data and results. More broadly, our results suggest that small-scale societies can provide insight into sustainable fire management in lightning-fire-prone landscapes. Full article
(This article belongs to the Special Issue Land-Use and Fire around the World from the Past to the Present)
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<p>Map showing Forested and Unforested superbiomes, with the locations of the documented hunter-gatherer societies used in this study. Sites with BFU are marked by red circles, and those without documented BFU are marked by yellow circles.</p> Full article ">Figure 2
<p>Map of lightning-fire prone (LFP) environments, calculated as the temporal correlation of mean monthly flammability and lightning. Red means the seasons are in phase, and blue means the seasons are out of phase.</p> Full article ">Figure 3
<p>Z statistics (the logistic regression coefficient for each of the predictors divided by the uncertainty in the coefficient) for each predictor variable, using data from All biomes, Forested biomes, and Unforested biomes.</p> Full article ">Figure 4
<p>Results of the logistic regression showing the normalized histogram of modeled broadcast fire use (BFU), when observed BFU is zero (light gray, meaning no recorded evidence of fire use), and when observed BFU is one (dark gray, meaning there is recorded evidence of fire use). The graphs represent results using data from (<b>a</b>) All biomes, (<b>b</b>) Forested biomes, and (<b>c</b>) Unforested biomes.</p> Full article ">Figure 5
<p>Evaluating the sensitivity of the logistic regression to randomly exclude different amounts of data, in Western North America (WNA). (<b>A</b>) Map depicts WNA exclusion area and hunter gatherer groups, and results from (<b>B</b>) global (All biomes), (<b>C</b>) Forested biome, and (<b>D</b>) Unforested biome. The x-axis shows how much of the WNA data was included in the logistic regression. Points represent the Z statistic for each predictor, for each biome, for 1000 different randomized WNA data exclusions.</p> Full article ">
28 pages, 8202 KiB  
Article
Geographic Patterns of Fire Severity Following an Extreme Eucalyptus Forest Fire in Southern Australia: 2013 Forcett-Dunalley Fire
by Mercy N. Ndalila, Grant J. Williamson and David M. J. S. Bowman
Fire 2018, 1(3), 40; https://doi.org/10.3390/fire1030040 - 22 Oct 2018
Cited by 38 | Viewed by 9105
Abstract
Fire severity is an important characteristic of fire regimes; however, global assessments of fire regimes typically focus more on fire frequency and burnt area. Our objective in this case study is to use multiple lines of evidence to understand fire severity and intensity [...] Read more.
Fire severity is an important characteristic of fire regimes; however, global assessments of fire regimes typically focus more on fire frequency and burnt area. Our objective in this case study is to use multiple lines of evidence to understand fire severity and intensity patterns and their environmental correlates in the extreme 2013 Forcett-Dunalley fire in southeast Tasmania, Australia. We use maximum likelihood classification of aerial photography, and fire behavior equations, to report on fire severity and intensity patterns, and compare the performance of multiple thresholds of the normalised burn ratio (dNBR) and normalized difference vegetation index (dNDVI) (from pre- and post-fire Landsat 7 images) against classified aerial photography. We investigate how vegetation, topography, and fire weather, and therefore intensity, influenced fire severity patterns. According to the aerial photographic classification, the fire burnt 25,950 ha of which 5% burnt at low severities, 17% at medium severity, 32% at high severity, 23% at very high severities, while 22% contained unburnt patches. Generalized linear modelling revealed that fire severity was strongly influenced by slope angle, aspect, and interactions between vegetation type and fire weather (FFDI) ranging from moderate (12) to catastrophic (>100). Extreme fire weather, which occurred in 2% of the total fire duration of the fire (16 days), caused the fire to burn nearly half (46%) of the total area of the fireground and resulted in modelled extreme fireline intensities among all vegetation types, including an inferred peak of 68,000 kW·m−1 in dry forest. The best satellite-based severity map was the site-specific dNBR (45% congruence with aerial photography) showing dNBR potential in Eucalyptus forests, but the reliability of this approach must be assessed using aerial photography, and/or ground assessment. Full article
(This article belongs to the Special Issue Extreme Fire Events, Ecosystem Resilience, and Human Well-Being)
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<p>Geographic context of the Forcett-Dunalley fireground: (<b>a</b>) Annual rainfall (in millimetres) and elevation (in metres) across Tasmania and the location of major fires in the 2013 fire season: 1–Forcett-Dunalley; 2–Giblin River; 3–Repulse; 4–Bicheno; 5–Montumana. (<b>b</b>) Dominant vegetation in the Forestier and Tasman Peninsulas based on TASVEG 3.0, an integrated vegetation map of Tasmania [<a href="#B49-fire-01-00040" class="html-bibr">49</a>]. (<b>c</b>) Elevation displayed as hillshaded Digital Elevation Model, and mean annual rainfall across the Forestier and Tasman Peninsulas.</p> Full article ">Figure 2
<p>Schematic workflow of overall methodology of the study. For the five objectives of this study, the various data streams and analytical steps are shown, as is the way these steps are interrelated. ML refers to maximum likelihood classification; RH to relative humidity, and temp to temperature.</p> Full article ">Figure 3
<p>Temporal trends of fire weather and drought: (<b>a</b>) forest fire danger index (FFDI) for the duration of the fire, calculated from 30-min weather data (temperature, relative humidity, wind speed, and soil moisture) from the nearest automatic weather stations (Hobart airport and Stroud Point stations). (<b>b</b>) Soil dryness index (SDI) also obtained from the Australian Bureau of Meteorology, three months before and during the fire. The period of the fire is indicated by dashed lines and the double arrow.</p> Full article ">Figure 4
<p>Environmental conditions that influenced the Forcett-Dunalley fire behavior: (<b>a</b>) maximum FFDI representing continuous spatial data downscaled using a numerical weather model, and (<b>b</b>) fire history map showing previous areas burnt by wild and prescribed fires between 1967 and 2013 and the 2013 fire perimeter.</p> Full article ">Figure 5
<p>Fire severity maps of the fire from: (<b>a</b>) a classified aerial photo using maximum likelihood classification method, and (<b>b</b>) dNBR using class thresholds generated based on knowledge of fireground, (<b>c</b>) maximum likelihood classification of raw dNBR, (<b>d</b>) using dNBR class ranges used by Key and Benson [<a href="#B67-fire-01-00040" class="html-bibr">67</a>] for western U.S. forests, and (<b>e</b>) using dNDVI class ranges used by Hammill and Bradstock [<a href="#B10-fire-01-00040" class="html-bibr">10</a>].</p> Full article ">Figure 6
<p>Summary of fire severity and fire weather during the fire. (<b>a</b>) Temporal variation of the five fire severity classes. The width of block in the <span class="html-italic">x</span>-axis corresponds to the duration of isochrone. (<b>b</b>) FFDI is displayed in three ways: (1) gridded FFDI corresponding with midpoint time between two consecutive fire isochrones, (2) station FFDI obtained from the nearby meteorological station, which correspond with the midpoint time between the fire isochrones, and (3) trace FFDI, which is raw FFDI as recorded at the nearby meteorological station.</p> Full article ">Figure 7
<p>Temporal variation of fire severity classes stratified by different vegetation types. For each vegetation type (total area indicated on right-hand side of the graph) and the proportion (%) of the area in each fire severity class for each isochrone is indicated.</p> Full article ">Figure 8
<p>Scatterplot of percentage area of each severity class in each vegetation type in aerial photographic classification relative to the percentage of each class in the dNBR map. The 1:1 line shown in the graphs represents situations where corresponding points between the aerial photo and dNBR data are equal.</p> Full article ">Figure 9
<p>Per-severity class spatial congruence between aerial and dNBR satellite analysis of fire severity across the vegetation types. The x-axis represents severity class in the dNBR map while the y-axis is the classification accuracy (congruence) using aerial photographic classification.</p> Full article ">Figure 10
<p>Predicted probability of congruence of aerial photography and dNBR maps from AIC analysis of the best model based on the interaction between vegetation and FFDI (<a href="#fire-01-00040-t006" class="html-table">Table 6</a>a). The bars represent the prediction of congruence for increasing FFDI classes (low FFDI = 11, high FFDI = 24, very high FFDI = 49, and severe = 64).</p> Full article ">
14 pages, 2480 KiB  
Review
Do Mixed Fire Regimes Shape Plant Flammability and Post-Fire Recovery Strategies?
by Helen M. Poulos, Andrew M. Barton, Jasper A. Slingsby and David M.J.S. Bowman
Fire 2018, 1(3), 39; https://doi.org/10.3390/fire1030039 - 22 Oct 2018
Cited by 24 | Viewed by 7369
Abstract
The development of frameworks for better-understanding ecological syndromes and putative evolutionary strategies of plant adaptation to fire has recently received a flurry of attention, including a new model hypothesizing that plants have diverged into three different plant flammability strategies due to natural selection. [...] Read more.
The development of frameworks for better-understanding ecological syndromes and putative evolutionary strategies of plant adaptation to fire has recently received a flurry of attention, including a new model hypothesizing that plants have diverged into three different plant flammability strategies due to natural selection. We provide three case studies of pyromes/taxa (Pinus, the Proteaceae of the Cape Floristic Region, and Eucalyptus) that, contrary to model assumptions, reveal that plant species often exhibit traits of more than one of these flammability and post-fire recovery strategies. We propose that such multiple-strategy adaptations have been favoured as bet-hedging strategies in response to selective pressure from mixed-fire regimes experienced by these species over evolutionary time. Full article
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<p>(<b>a</b>) Revised version of the conceptual flammability model of Pausas, Keeley, and Schwilk [<a href="#B20-fire-01-00039" class="html-bibr">20</a>] depicting where pines with multiple fire-adapted traits fall on the three axes of variation in plant flammability, and (<b>b</b>) non-metric multidimensional scaling (nMDS) of fire-adapted traits for the members of the <span class="html-italic">Pinus</span> genus with (1) multiple fire-adapted traits listed in <a href="#fire-01-00039-t001" class="html-table">Table 1</a>A and (2) traits for taxa with only single flammability strategy taken from Appendix 21.1 in Keeley and Zedler [<a href="#B26-fire-01-00039" class="html-bibr">26</a>]. Species with multiple flammability strategies in this ordination were identified through an exhaustive review of the literature on <span class="html-italic">Pinus</span>. Species are plotted in trait space and are displayed according to flammability strategy via the nMDS of species by traits (i.e., presence/absence) using relative Euclidean distances and PC-Ord software [<a href="#B36-fire-01-00039" class="html-bibr">36</a>]. The final stress for the ordination was 0.0749. While some members of the Pinaceae fall at the extremes, the ordination diagram clearly shows that many species fall at the center of the nMDS and possess multiple fire-adapted traits.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Revised version of the conceptual flammability model of Pausas, Keeley, and Schwilk [<a href="#B20-fire-01-00039" class="html-bibr">20</a>] depicting where pines with multiple fire-adapted traits fall on the three axes of variation in plant flammability, and (<b>b</b>) non-metric multidimensional scaling (nMDS) of fire-adapted traits for the members of the <span class="html-italic">Pinus</span> genus with (1) multiple fire-adapted traits listed in <a href="#fire-01-00039-t001" class="html-table">Table 1</a>A and (2) traits for taxa with only single flammability strategy taken from Appendix 21.1 in Keeley and Zedler [<a href="#B26-fire-01-00039" class="html-bibr">26</a>]. Species with multiple flammability strategies in this ordination were identified through an exhaustive review of the literature on <span class="html-italic">Pinus</span>. Species are plotted in trait space and are displayed according to flammability strategy via the nMDS of species by traits (i.e., presence/absence) using relative Euclidean distances and PC-Ord software [<a href="#B36-fire-01-00039" class="html-bibr">36</a>]. The final stress for the ordination was 0.0749. While some members of the Pinaceae fall at the extremes, the ordination diagram clearly shows that many species fall at the center of the nMDS and possess multiple fire-adapted traits.</p> Full article ">Figure 2
<p>Four post-fire regeneration strategies according the classification of Nicolle (2006) (<b>a</b>–<b>h</b>): (<b>a</b>) and (<b>b</b>) obligate seeder, (<b>c</b>,<b>d</b>) lignotuber resprouter, (<b>f</b>) stem resprouter, and (<b>e</b>–<b>h</b>) combination resprouter for 723 taxa (species, subspecies and hybrids) within the eucalypt clade. The continental distributions of the four regeneration strategies and the tropical (green), arid (yellow) and temperate (blue) Koppen regions are shown in the maps. Photographs of each of these four regeneration strategies are also shown for (<b>a</b>) the obligate seeder <span class="html-italic">E. regnans</span> forest above a <span class="html-italic">Nothofagus</span> rainforest in western Tasmania (=non-flammable strategy of Pausas, Keeley, and Schwilk [<a href="#B19-fire-01-00039" class="html-bibr">19</a>]); (<b>c</b>) lignotuberous ‘mallee’ <span class="html-italic">E. socialis</span> in semi-arid southeastern Australia (=hot flammable strategy [<a href="#B19-fire-01-00039" class="html-bibr">19</a>]); (<b>e</b>) stem resprouting in the combination resprouter <span class="html-italic">E. globulus</span> forests in dry temperate Tasmania (=hot flammable strategy [<a href="#B19-fire-01-00039" class="html-bibr">19</a>]); and (<b>h</b>) combination resprouter <span class="html-italic">E. tetrodonta</span> in tropical savanna (=fast flammable strategy [<a href="#B19-fire-01-00039" class="html-bibr">19</a>]). The distribution data are sourced from the Atlas of Living Australia (ALA, <a href="http://www.ala.org.au." target="_blank">http://www.ala.org.au.</a> Accessed 8 December 2017). Note some hybrids listed by Nicolle [<a href="#B67-fire-01-00039" class="html-bibr">67</a>] were not available in ALA.</p> Full article ">
10 pages, 1091 KiB  
Article
Changes in Lightning Fire Incidence in the Tasmanian Wilderness World Heritage Area, 1980–2016
by Jenny Styger, Jon Marsden-Smedley and Jamie Kirkpatrick
Fire 2018, 1(3), 38; https://doi.org/10.3390/fire1030038 - 19 Oct 2018
Cited by 36 | Viewed by 7518
Abstract
The Tasmanian Wilderness World Heritage Area (TWWHA) has globally significant natural and cultural values, some of which are dependent on the absence of fire or the presence of particular fire regimes. Planned burning is currently used to reduce the risk of loss of [...] Read more.
The Tasmanian Wilderness World Heritage Area (TWWHA) has globally significant natural and cultural values, some of which are dependent on the absence of fire or the presence of particular fire regimes. Planned burning is currently used to reduce the risk of loss of world heritage values from unplanned fires, but large and damaging fires still occur, with lightning as the primary ignition source. Lightning-caused fire was rare in the TWWHA before 2000. There has since been an increase in both the number of fires following lightning storms and the area burnt by these fires. In the absence of a direct measurement of lightning strike incidence, we tested whether changes in rainfall, soil dryness and fuel load were responsible for these changes in fire incidence and extent. There were no relationships between these variables and the incidence of fires associated with lightning, but the variability in the Soil Dryness Index and the mean of 25% of driest values did predict both the number and area of fires. Thus, it appears that an increase in the proportion of lightning strikes that occur in dry conditions has increased ignition efficiency. These changes have important implications for the management of the TWWHA’s values, as higher projected fuel loads and drier climates could result in a further increase in the number of fires associated with lightning. Full article
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<p>Tasmanian Wilderness World Heritage Area (TWWHA). SR = Savage River, LM = Lake Margaret and SV = Strathgordon Village.</p> Full article ">Figure 2
<p>Average number of lightning fires per fire season for five-year periods between 1980/1981, 2014/2015 and 2015/2016. Lowess (segmented regression) line is shown.</p> Full article ">Figure 3
<p>Average size (ha) of lightning fires per fire season for five-year periods between 1980/1981, 2014/2015, and 2015/2016. Lowess (segmented regression) line is shown.</p> Full article ">Figure 4
<p>Average area burnt per fire season (ha) by lightning fires for five yearly periods between 1980/1981–2014/2015 and 2015/2016. Lowess (segmented regression) line is shown.</p> Full article ">
16 pages, 3435 KiB  
Article
Slash-and-Burn Practices Decrease Arbuscular Mycorrhizal Fungi Abundance in Soil and the Roots of Didierea madagascariensis in the Dry Tropical Forest of Madagascar
by Alícia Donnellan Barraclough and Pål Axel Olsson
Fire 2018, 1(3), 37; https://doi.org/10.3390/fire1030037 - 1 Oct 2018
Cited by 11 | Viewed by 5868
Abstract
Deforestation and the use of fire to clear land have drastic effects on ecosystem functioning and compromise essential ecosystem services, especially in low-income tropical countries such as Madagascar. We evaluated the effects of local slash-and-burn practices on soil nutrients and arbuscular mycorrhizal (AM) [...] Read more.
Deforestation and the use of fire to clear land have drastic effects on ecosystem functioning and compromise essential ecosystem services, especially in low-income tropical countries such as Madagascar. We evaluated the effects of local slash-and-burn practices on soil nutrients and arbuscular mycorrhizal (AM) fungi abundance in a southwestern Madagascar forest. Nine sampling plot pairs were established along the border of a reserve within the Fiherenana–Manombo (pk-32) complex, where soil and seedling root samples of the endemic tree Didierea madagascariensis were taken. We analysed soil extractable PO43−, NH4+, and NO3− as well as total soil carbon and nitrogen. We analysed AM fungal abundance in soil and roots through fatty acid marker analysis (NLFA and PLFA 16:1ω5), spore extraction, and root staining. Slash-and-burn caused an increase in pH and doubled the plant available nutrients (from 7.4 to 13.1 µg PO43− g−1 and from 6.9 to 13.2 µg NO3− g−1). Total C and total N increased in deforested soil, from 0.6% to 0.84% and from 0.06% to 0.08%, respectively. There was a significant decline in AM fungi abundance in soil, with a decrease in soil NLFA 16:1ω5 from 0.2 to 0.12 nmol/g. AM fungi abundance in D. madagascariensis roots was also negatively affected and colonization decreased from 27.7% to 16.9% and NLFA 16:1ω5 decreased from 75.7 to 19 nmol/g. Together with hyphal network disruption, increased nutrient availability caused by burning is proposed as an explanation behind AM decline in soil and roots of D. madagascariensis. This is the first study to report the effects of slash-and-burn on AM symbiosis in Madagascar’s dry forests, with likely implications for other tropical and subtropical dryland forests worldwide where slash-and-burn is practiced. Full article
(This article belongs to the Special Issue Land-Use and Fire around the World from the Past to the Present)
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<p>The sampling site situated 1 km from the coast town of Mangily (<b>indicated by a cross</b>). Dark areas indicate intact forest and light areas indicate burned deforested areas. The vertical line indicates the position of the first sampling pair transect (<b>a</b>) and the blue line indicates the position of the last transect (<b>i</b>).</p> Full article ">Figure 2
<p>Linear regression showing changes in plant available nutrients with distance from the town of Mangily in meters. (<b>a</b>) Negative correlation between distance and extractable orthophosphate as measured by two different methods (Bray-1 and NaFS) in forested (Bray-1 y = −0.0053x + 16.25 <span class="html-italic">R</span><sup>2</sup> = 0.58, NaFS y = −0.001x + 2.65 <span class="html-italic">R</span><sup>2</sup> = 0.62 (p &lt; 0.05)) and deforested areas (Bray-1 y = −0.0094x + 28.84, <span class="html-italic">R</span><sup>2</sup> = 0.82, NaFS y = −0.0016x + 4.71 <span class="html-italic">R</span><sup>2</sup> = 0.63 (<span class="html-italic">p</span> &lt; 0.05)). (<b>b</b>) Extractable N in forested (NO<sub>3</sub><sup>−</sup> y = −0.0078x + 19.97 <span class="html-italic">R</span><sup>2</sup> = 0.53 (<span class="html-italic">p</span> &lt; 0.05), NH<sub>4</sub><sup>+</sup> y = −0.0006x + 2.99 <span class="html-italic">R</span><sup>2</sup> = 0.23 (<span class="html-italic">p</span> &gt; 0.05)) and deforested areas (NO<sub>3</sub><sup>−</sup> y = −0.0123x + 33.738 <span class="html-italic">R</span><sup>2</sup> = 0.54 (<span class="html-italic">p</span> &lt; 0.05), NH<sub>4</sub><sup>+</sup> y = −0.0004x + 2.81 <span class="html-italic">R</span><sup>2</sup> = 0.31 (<span class="html-italic">p</span> &gt; 0.05)).</p> Full article ">Figure 3
<p>Effect of deforestation on arbuscular mycorrhizal (AM) fungi variables of <span class="html-italic">Didierea madagascariensis</span> in deforested (D) and forested (F) areas, fatty acid 16:1ω5 content in roots of <span class="html-italic">D. madagascariensis</span> (nmol/g dried root) in forested and deforested areas (upper panel), and fatty acid 16:1ω5 in soil (nmol/g dry soil) collected in forested and deforested areas (lower panel). Error bars indicate 95% confidence interval and different letters above the bars indicate a significant difference (paired <span class="html-italic">t</span>-test, n = 9, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Effect of deforestation on AM fungi root colonization levels (%) in <span class="html-italic">D. madagascariensis</span> in deforested (D) and forested (F) areas. Error bars indicate 95% confidence interval and different letters above the bars indicate a significant difference (paired <span class="html-italic">t</span>-test, n = 9, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Spore densities in 100 g of dry soil in deforested area (D) and forested area (F): (<b>a</b>) total spores, (<b>b</b>) Class A spores (<b>c</b>), Class B spores (<b>d</b>), and Class C spores. Error bars indicate 95% confidence interval. In all cases, differences were nonsignificant according to a paired <span class="html-italic">t</span>-test.</p> Full article ">
20 pages, 3682 KiB  
Article
Assessing the Impact of Climate Variability on Wildfires in the N’Zi River Watershed in Central Côte d’Ivoire
by Jean-Luc Kouakou Kouassi, Narcisse Eboua Wandan and Cheikh Mbow
Fire 2018, 1(3), 36; https://doi.org/10.3390/fire1030036 - 28 Sep 2018
Cited by 13 | Viewed by 6632 | Correction
Abstract
This study evaluates the impact of climate variability on wildfire regime in the N’Zi River Watershed (NRW) in central Côte d’Ivoire. For that purpose, MODIS active fire and monthly burned area data are used to evaluate wildfire occurrence, impacts and trends. Wildfire data [...] Read more.
This study evaluates the impact of climate variability on wildfire regime in the N’Zi River Watershed (NRW) in central Côte d’Ivoire. For that purpose, MODIS active fire and monthly burned area data are used to evaluate wildfire occurrence, impacts and trends. Wildfire data are compared to past trends of different climatic parameters extracted from long-term meteorological records. Generalized additive models and Spearman correlations are used to evaluate the relationships between climate variables and wildfire occurrence. Seasonal Kendall and Sen’s slope methods were used for trend analysis. Results showed that from 2001 to 2016, 19,156 wildfire occurrences are recorded in the NRW, of which 4443 wildfire events are observed in forest, 9536 in pre-forest, and 5177 in Sudanian zones. The burned areas are evaluated at 71,979.7 km2, of which 10,488.41 km2 were registered in forest, 33,211.96 km2 in pre-forest, and 28,279.33 km2 in Sudanian zones. A downward trend is observed in fire records. The results indicates a strong correlation between some climatic variables and wildfire regime in this ecoregion. These correlations can be used to develop models that could be used as prediction tools for better management of fire regimes and support decision-making in the NRW. Full article
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Figure 1
<p>Map of N’Zi River Watershed, showing synoptic stations along with elevation.</p> Full article ">Figure 2
<p>Monthly series of the total annual burned area (SB) and number of wildfires (NF) in the (<b>a</b>) forest zone, (<b>b</b>) preforest zone, (<b>c</b>) Sudanian zone and (<b>d</b>) the whole N’Zi River Watershed.</p> Full article ">Figure 3
<p>Annual series of the total annual burned area (SB) and number of wildfires (NF) in the (<b>a</b>) forest zone, (<b>b</b>) preforest zone, (<b>c</b>) Sudanian zone and (<b>d</b>) the whole N’Zi River Watershed.</p> Full article ">Figure 4
<p>Spearman correlation matrix between wildfire variables and climatic variables in the (<b>a</b>) forest zone; (<b>b</b>) preforest zone; (<b>c</b>) Sudanian zone and (<b>d</b>) the whole N’Zi River Watershed. Significant positive correlations are displayed in blue, significant negative correlations in red and non-significant correlations are in blank (<span class="html-italic">p</span> &gt; 0.05). The intensity of the colour is proportional to the correlation coefficients.</p> Full article ">Figure 4 Cont.
<p>Spearman correlation matrix between wildfire variables and climatic variables in the (<b>a</b>) forest zone; (<b>b</b>) preforest zone; (<b>c</b>) Sudanian zone and (<b>d</b>) the whole N’Zi River Watershed. Significant positive correlations are displayed in blue, significant negative correlations in red and non-significant correlations are in blank (<span class="html-italic">p</span> &gt; 0.05). The intensity of the colour is proportional to the correlation coefficients.</p> Full article ">Figure 5
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the Forest zone of the N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">Figure 5 Cont.
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the Forest zone of the N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">Figure 6
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the Preforest zone of the N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">Figure 6 Cont.
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the Preforest zone of the N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">Figure 7
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the Sudanian zone of the N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">Figure 8
<p>Smoothed fits of covariates modelling (<b>a</b>) the number of wildfires and (<b>b</b>) the burned area in the whole N’Zi River Watershed. Central (bold) line show the best fit and the shaded areas show the 95% confidence intervals of the model. Tick marks on the x-axis represent observations. The y-axis represents the spline function.</p> Full article ">
14 pages, 1062 KiB  
Article
Grass Canopy Architecture Influences Temperature Exposure at Soil Surface
by Xiulin Gao and Dylan W. Schwilk
Fire 2018, 1(3), 35; https://doi.org/10.3390/fire1030035 - 26 Sep 2018
Cited by 12 | Viewed by 5215
Abstract
There is increasing recognition that plant traits contribute to variations in fire behavior and fire regime. Diversity across species in litter flammability and canopy flammability has been documented in many woody plants. Grasses, however, are often considered homogeneous fuels in which any flammability [...] Read more.
There is increasing recognition that plant traits contribute to variations in fire behavior and fire regime. Diversity across species in litter flammability and canopy flammability has been documented in many woody plants. Grasses, however, are often considered homogeneous fuels in which any flammability differences across species are attributable to biomass differences alone and therefore are of less ecological interest, because biomass is hugely plastic. We examined the effect of grass canopy architecture on flammability across eight grass species in short grass steppe of New Mexico and Texas. To characterize grass canopy architecture, we measured biomass density and “biomass-height ratio” (the ratio of canopy biomass above 10 cm to that of biomass below 10 cm). Indoor flammability experiments were performed on air-dried individual plants. As expected, plant biomass influenced all flammability measures. However, biomass-height ratio had additional negative effect on temperature exposure at soil surface (accumulation of mean temperature >100 °C) in well-cured grasses, which is an important fire behavior metric predicting soil heating and meristem survival. This canopy architecture effect, however, needs further investigation to be isolated from biomass density due to correlation of these two traits. This result demonstrates the potential for species-specific variation in architecture to influence local fire effects in grasses. Full article
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Figure 1

Figure 1
<p>Experimental apparatus for flammability experiments. Temperature was read every second at soil surface (locations a &amp; b), 10 cm, 20 cm, and 40 cm relative to ground by k-type thermocouple temperature sensors that were placed at the center of plant. A metal ruler was placed by the plant as a reference for maximum flame height assessment. A cardboard tray caught biomass that fell during combustion. The balance was connected to a computer to record every second during combustion (code at <a href="https://github.com/schwilklab/serial-balance" target="_blank">https://github.com/schwilklab/serial-balance</a>).</p> Full article ">Figure 2
<p>Principal component analysis biplot of flammability measurements. Flammability measurements include duration of heating above 100 °C at 25 cm, temperature integration above 100 °C at 25 cm, total mass combusted, and maximum mass-loss rate. The first two axes account for 87.0% of total variance.</p> Full article ">Figure 3
<p>Relationship between total above-ground biomass and temperature integration above 100 °C at soil surface and 25 cm. Lines indicate the best fit linear models (soil surface: <span class="html-italic">p</span> &lt; 0.0001, adjusted R<sup>2</sup> = 0.58; 25 cm: <span class="html-italic">p</span> &lt; 0.0001, and adjusted R<sup>2</sup> = 0.59).</p> Full article ">Figure 4
<p>Relationship between biomass-height ratio and biomass-corrected temperature integration above 100 °C at soil surface across eight grass species. The line indicates the best fitted linear mixed model with species as random intercept effect (<span class="html-italic">p</span> = 0.038). Small points in background are individual observations, and large points are species means.</p> Full article ">Figure 5
<p>Relationship between total above-ground biomass and maximum mass-loss rate. Line indicates the best fit linear model (<span class="html-italic">p</span> = 0.02, adjusted R<sup>2</sup> = 0.044).</p> Full article ">
2 pages, 146 KiB  
Book Review
Between Two Fires: A Fire History of Contemporary America by Dr. Stephen J. Pyne, 1st ed.; University of Arizona Press: Tucson, AZ, USA, 2015
by Stephen D. Fillmore
Fire 2018, 1(3), 34; https://doi.org/10.3390/fire1030034 - 25 Sep 2018
Viewed by 2673
Abstract
Between Two Fires [1] is one of many books that Dr. Stephen J. Pyne has published about the wildland fire scene.[...] Full article
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