A Neutral Model for Low-Severity Fire Regimes

Climate, topography, fuel loadings, and human activities all affect spatial and temporal patterns of fire occurrence. Because fire occurrence is a stochastic process, an understanding of baseline variability is necessary in order to identify constraints on surface fire regimes. With a suitable null, or neutral, model, characteristics of natural fire regimes estimated from fire history data can be compared to a “null hypothesis.” We generated random landscapes of fire-scarred trees via a point process with sequential spatial inhibition. Random ignition points, fire sizes, and fire years were drawn from uniform and exponential family probability distributions. For this paper we focused on two sets of statistics commonly computed in fire history studies. Composite fire records and Weibull median probability intervals (WMPIs) were calculated at multiple spatial scales for random subsets of each landscape, and parameters of the Weibull distribution were estimated for each simulated “fire history” and tested for significance. We compared results from simulations to fire-history data from a watershed in eastern Washington. Strong nonlinear relationships were evident between and area sampled and WMPIs for a range of fire sizes for both real and simulated data. Patterns of significance of Weibull “shape” parameters were distinctly different between real and simulated landscapes. The clear patterns on neutral landscapes suggest that deviations from them in empirical data represent real constraints on fire regimes (e.g., topography, fuels) rather than sampling artifacts. Neutral models show promise for investigating low-severity fire regimes to separate intrinsic properties of stochastic processes from the effects of climate, fuel loadings, topography, and management.