Unifying Wildfire Models from Ecology and Statistical Physics

Zinck, Richard D.; Grimm, Volker

doi:10.1086/605959

Cited by 69 publications

(29 citation statements)

References 41 publications

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“…Clear-cut partitioning of bottom-up vs. topdown controls is difficult (Peters et al 2004, Zinck andGrimm 2009), in part because bottomup factors usually interact amongst themselves, as well as with top-down controls (Gavin et al 2006, Parisien et al 2010. Simulation modeling provided the means, through input manipulation, to isolate the effect of bottom-up environmental factors on spatial fire likelihood in four fire-prone landscapes of North America.…”

Section: Resultsmentioning

confidence: 99%

Spatial bottom‐up controls on fire likelihood vary across western North America

2012

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Abstract. The unique nature of landscapes has challenged our ability to make generalizations about the effects of bottom-up controls on fire regimes. For four geographically distinct fire-prone landscapes in western North America, we used a consistent simulation approach to quantify the influence of three key bottom-up factors, ignitions, fuels, and topography, on spatial patterns of fire likelihood. We first developed working hypotheses predicting the influence of each factor based on its spatial structure (i.e., autocorrelation) in each of the four study areas. We then used a simulation model parameterized with extensive fire environment data to create high-resolution maps of fire likelihood, or burn probability (BP). To infer the influence of each bottom-up factor within and among study areas, these BP maps were compared to parallel sets of maps in which one of the three bottom-up factors was randomized. Results showed that ignition pattern had a relatively minor influence on the BP across all four study areas, whereas the influence of fuels was large. The influence of topography was the most equivocal among study areas; it had an insignificant influence in one study area and was the dominant control in another. We also found that the relationship between the influence of these factors and their spatial structure appeared nonlinear, which may have important implications for management activities aimed at attenuating the effect of fuels or ignitions on wildfire risk. This comparative study using landscapes with different biophysical and fire regime characteristics demonstrates the importance of employing consistent methodology to pinpoint the influence of bottom-up controls.

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Section: Resultsmentioning

confidence: 99%

Spatial bottom‐up controls on fire likelihood vary across western North America

2012

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“…Zinck & Grimm showed that these two additional patterns are also produced by the physicists' model if grid cells represent entire tree stands of several hectares instead of individual trees [35,44]. They also showed that the key process for reproducing all three patterns is a 'memory effect': flammability increases with the time since a site last burned.…”

Section: Examplesmentioning

confidence: 97%

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Grimm

Railsback

2012

Phil. Trans. R. Soc. B

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367

334

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Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

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“…Depending on the scenario intraspecific negative density regulation of seedlings is considered in the seed dispersal and seed production sub-model. Fire occurs every year (one ignition per year) somewhere on the 144-km 2 grid, the ignition location (cell) is chosen at random and the fire's spatial structure is determined by a percolation model [29], [30]. Not all fires spread across the metacommunity simulation arena containing the habitat patches.…”

Section: Methodsmentioning

confidence: 99%

Species-Specific Traits plus Stabilizing Processes Best Explain Coexistence in Biodiverse Fire-Prone Plant Communities

et al. 2013

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Coexistence in fire-prone Mediterranean-type shrublands has been explored in the past using both neutral and niche-based models. However, distinct differences between plant functional types (PFTs), such as fire-killed vs resprouting responses to fire, and the relative similarity of species within a PFT, suggest that coexistence models might benefit from combining both neutral and niche-based (stabilizing) approaches. We developed a multispecies metacommunity model where species are grouped into two PFTs (fire-killed vs resprouting) to investigate the roles of neutral and stabilizing processes on species richness and rank-abundance distributions. Our results show that species richness can be maintained in two ways: i) strictly neutral species within each PFT, or ii) species within PFTs differing in key demographic properties, provided that additional stabilizing processes, such as negative density regulation, also operate. However, only simulations including stabilizing processes resulted in structurally realistic rank-abundance distributions over plausible time scales. This result underscores the importance of including both key species traits and stabilizing (niche) processes in explaining species coexistence and community structure.

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Unifying Wildfire Models from Ecology and Statistical Physics

Cited by 69 publications

References 41 publications

Spatial bottom‐up controls on fire likelihood vary across western North America

Spatial bottom‐up controls on fire likelihood vary across western North America

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Species-Specific Traits plus Stabilizing Processes Best Explain Coexistence in Biodiverse Fire-Prone Plant Communities

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