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• W2007069787 abstract "The quantification of wildfire regimes, especially the relationship between the frequency with which events occur and their size, is of particular interest to both ecologists and wildfire managers. Recent studies in cellular automata (CA) and the fractal nature of the frequency–area relationship they produce has led some authors to ask whether the power-law frequency–area statistics seen in the CA might also be present in empirical wildfire data. Here, we outline the history of the debate regarding the statistical wildfire frequency–area models suggested by the CA and their confrontation with empirical data. In particular, the extent to which the utility of these approaches is dependent on being placed in the context of self-organized criticality (SOC) is examined. We also consider some of the other heavy-tailed statistical distributions used to describe these data. Taking a broadly ecological perspective we suggest that this debate needs to take more interest in the mechanisms underlying the observed power-law (or other) statistics. From this perspective, future studies utilizing the techniques associated with CA and statistical physics will be better able to contribute to the understanding of ecological processes and systems. In many regions of the world, wildfires are common and are considered an integral component of ecosystem functioning. However, wildfires also pose a threat to humans, their activities and livelihoods, and repeated fires can negatively affect ecosystem functioning (Bond & van Wilgen 1996). Thus, understanding and managing the relationships between wildfires, ecological systems and human activity is important. The combination of the timing, frequency and magnitude of all disturbances occurring in a given region is known as the ‘disturbance regime’. Recently, much research has considered one particular aspect of the disturbance regime: the frequency–area distribution of wildfires in a given area. Here, we will focus on disturbance by wildfires. Examination of these statistics in the context of wildfire activity is not new (e.g. Minnich 1983; Baker 1989; Strauss et al. 1989, among many others), but recently there has been considerable debate regarding the ‘heavy-tailed’ (i.e. the tail decreases at a relatively slow rate) nature of these frequency–area distributions. One specific class of heavy-tailed distribution is a power-law (fractal) where the frequency–area distribution has no inherent scale (it is scale-invariant). The presence of such scaling relationships has been noted widely in many features of biological and ecological systems (e.g. Brown et al. 2002). In the wildfire literature, discussion has particularly addressed whether these heavy-tailed frequency–area distributions are power-law in nature, and what the implications of such a power-law distribution might be. Much of the present debate on the heavy-tailed nature of ‘real’ wildfire areas is the result of research in the early 1990s, where simple ‘forestfire’ cellular-automata (CA) models were found to produce power-law size frequency distribution – a characteristic linked with self-organized criticality (SOC) (Bak et al. 1990; Drossel & Schwabl 1992; Clar et al. 1996). Malamud et al. (1998) then produced the first detailed research showing that both the forest-fire CA model and ‘real-world’ wildfires exhibit robust power-law frequency–area distributions. Since then, other authors have presented data and analyses with the aim of variously confirming or refuting the assertion that real-world wildfire frequency–area distributions follow a power-law distribution (e.g. Ricotta et al. 1999, 2001; Cumming 2001; Ward et al. 2001; Reed & McKelvey 2002; Schoenberg et al. 2003). In this paper, we will examine the history and nature of this discussion, before suggesting what direction it might take, or be most useful to take in the future. We approach this topic from a broadly ecological perspective, emphasizing the need for consideration of the ecological (or otherwise) mechanisms driving observed wildfire frequency–area distributions. We will begin by From: CELLO, G. & MALAMUD, B. D. (eds) 2006. Fractal Analysis for Natural Hazards. Geological Society, London, Special Publications, 261, 155–167. 0305-8719/06/$15.00 # The Geological Society of London 2006. examining the most recent papers in this area, before establishing the state of current research in this area and suggesting what avenues of future research on this topic might prove fruitful. Ecological examination of wildfire frequency–area distributions Consideration of wildfire and other disturbance regimes (the spatio-temporal dynamics of recurrent disturbance events) has a long history in ecology. The dynamics of succession–disturbance in ecology is important when considering wildfires. Succession is the change in ecological community composition (in essence the relative abundance of the different species in the community) and function (the ways in which the abiotic and biotic components of the community are linked) through time. Disturbance is the disruption of an ecosystem, community, or species’ populations by any relatively discrete event in space and time, with a resultant change in the physical environment (White & Pickett 1985). Until the 1950s and 1960s, ecologists’ views on succession–disturbance dynamics were dominated by the perspective of Frederick Clements (1916, 1928, 1936). Clements’ conceptualization of the community emphasized equilibrium and stability, as encapsulated by the ‘balance of nature paradigm’; in this view disturbance events were seen as unnatural, as they moved the system away from its ‘natural’ equilibria (the so-called ‘climax’ condition). Ecosystem management conducted from this perspective, therefore, aimed to minimize disturbance events and their impacts, resulting in policies such as fire suppression. More recently, ecologists have accepted the fundamental importance of apparently random events, such as disturbance, in structuring ecosystems, and have adopted a more scale-sensitive, disequilibrial view (Wu & Loucks 1995; Perry 2002). With this shift has come increasing interest in characterizing the three key dimensions of the disturbance regime: size, frequency, and intensity. Recently, there has been some attention focused on determining whether large, infrequent disturbance have a qualitatively different effect than small, frequent ones (Romme et al. 1998; Turner et al. 1998). It is with this historical perspective in mind that we need to consider ecological approaches to quantifying wildfire regimes, in contrast to the model-based approaches we discuss later. The frequency of disturbance events is very important in terms of the evolution of the reproductive strategies that different species adopt (e.g. the number and size of offspring produced, the energy invested per reproductive event, and so on); these reproductive strategies are sometimes also known as life history traits (Bond & Keeley 2005). For example, the optimal time after disturbance for a species to maximize seed storage (in either the crown or soil seedbank) will be influenced by the average time between wildfire events (Enright et al. 1998a, b). What constitutes a ‘frequent’ wildfire will vary from ecosystem to ecosystem, depending on factors such as rates of biomass production, the nature of other disturbance agents operating alongside fire (e.g. wind-throw) and regeneration rates. An intriguing body of ecological theory suggests, however, that intermediate disturbance frequencies will promote the highest levels of biodiversity (the ‘intermediate disturbance hypothesis’, Connell 1978). Early quantitative studies of the wildfire regime, conducted in the 1950s and 1960s, emphasized frequency – in essence an estimate of the probability distribution of survival or mortality from wildfire(s) (Johnson & Gutsell 1994). Early efforts (e.g. Spurr 1954) were often somewhat ad hoc studies of wildfire occurrence, and are perhaps better seen as wildfire ‘history’ studies. However, Heinselman (1973), in a seminal study, mapped the time-since-wildfireyear, on the basis of stand ages, in the Boundary Waters Canoe Area in Minesotta (USA). On the basis of this map Heinselmann estimated survivorship from wildfires in the landscape. Since the late 1970s, a number of statistical methods and distributions that might be suitable for describing wildfire frequency have been developed and applied, with much emphasis on the Weibull and negative exponential distributions (see Johnson & van Wagner 1985). These statistical models allow empirical assessment of relationships between spatio-temporal variation in wildfire frequency and other environmental factors (Johnson & Gutsell 1994). Considerable debate remains over the drivers of spatio-temporal variability in wildfire frequency (in particular the relative roles of weather v. fuels), and unravelling these patterns is a focus of current work (e.g. Bessie & Johnson 1995). Although sophisticated statistical tools are available for modelling fire frequency (e.g. Presiler et al. 2004; Reed & Johnson 2004), the stumblingblock is often collecting adequate empirical data to represent the processes and designing adequate sampling strategies for this data collection (Johnson & Gutsell 1994). Although much research effort has focused on the frequency component of the wildfire regime, other ecologists have considered the size (i.e. burned area, often equated with severity) component of the wildfire regime. As different ecosystems respond differently to wildfires, what constitutes a severe event will also vary (Moritz 1997). The diverse effects of wildfire suppression efforts have received considerable attention in this context. J. D. A. MILLINGTON ET AL. 156" @default.
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• W2007069787 title "Models, data and mechanisms: quantifying wildfire regimes" @default.
• W2007069787 cites W1273791837 @default.
• W2007069787 cites W1605323303 @default.
• W2007069787 cites W1965599912 @default.
• W2007069787 cites W1965846649 @default.
• W2007069787 cites W1968695733 @default.
• W2007069787 cites W1975444608 @default.
• W2007069787 cites W1975714642 @default.
• W2007069787 cites W1978849186 @default.
• W2007069787 cites W1979716985 @default.
• W2007069787 cites W1984661972 @default.
• W2007069787 cites W1985769566 @default.
• W2007069787 cites W1998908151 @default.
• W2007069787 cites W1999662432 @default.
• W2007069787 cites W2003320658 @default.
• W2007069787 cites W2003510327 @default.
• W2007069787 cites W2008879046 @default.
• W2007069787 cites W2009831767 @default.
• W2007069787 cites W2020837601 @default.
• W2007069787 cites W2025650736 @default.
• W2007069787 cites W2027053301 @default.
• W2007069787 cites W2028780775 @default.
• W2007069787 cites W2031983852 @default.
• W2007069787 cites W2033904185 @default.
• W2007069787 cites W2041181492 @default.
• W2007069787 cites W2045073912 @default.
• W2007069787 cites W2045860903 @default.
• W2007069787 cites W2048840954 @default.
• W2007069787 cites W2051678993 @default.
• W2007069787 cites W2053923735 @default.
• W2007069787 cites W2063027387 @default.
• W2007069787 cites W2063697020 @default.
• W2007069787 cites W2067817178 @default.
• W2007069787 cites W2068602646 @default.
• W2007069787 cites W2068892214 @default.
• W2007069787 cites W2072225385 @default.
• W2007069787 cites W2075514521 @default.
• W2007069787 cites W2079163277 @default.
• W2007069787 cites W2083335707 @default.
• W2007069787 cites W2087809153 @default.
• W2007069787 cites W2090293022 @default.
• W2007069787 cites W2095809560 @default.
• W2007069787 cites W2096851650 @default.
• W2007069787 cites W2104356015 @default.
• W2007069787 cites W2105137615 @default.
• W2007069787 cites W2106973087 @default.
• W2007069787 cites W2108204502 @default.
• W2007069787 cites W2109602569 @default.
• W2007069787 cites W2111496070 @default.
• W2007069787 cites W2116032305 @default.
• W2007069787 cites W2118354697 @default.
• W2007069787 cites W2125408438 @default.
• W2007069787 cites W2127140203 @default.
• W2007069787 cites W2134266071 @default.
• W2007069787 cites W2134886615 @default.
• W2007069787 cites W2136559979 @default.
• W2007069787 cites W2142429223 @default.
• W2007069787 cites W2143618255 @default.
• W2007069787 cites W2159674046 @default.
• W2007069787 cites W2166722801 @default.
• W2007069787 cites W2172060360 @default.
• W2007069787 cites W2180765141 @default.
• W2007069787 cites W2227563457 @default.
• W2007069787 cites W2326497574 @default.
• W2007069787 cites W2478227765 @default.
• W2007069787 cites W3023731239 @default.
• W2007069787 cites W3123425126 @default.
• W2007069787 cites W4232587822 @default.
• W2007069787 cites W4247194611 @default.
• W2007069787 cites W6230103 @default.
• W2007069787 cites W656244872 @default.
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