Search

We analyzed wildland fire occurrence and size data from Sardinia, Italy, and Corsica, France, to examine spatiotemporal patterns of fire occurrence in relation to weather, land use, anthropogenic features, and time of year. Fires on these islands are largely human caused and can be attributed to negligence, agro-pastoral land use, and arson. Of particular interest was the predictive value of a fire weather index (FWI) that is widely used by fire managers to alert suppression crews. We found that an increase in the FWI from 30 to 60 produced on average an approximate eightfold increase in the odds of a large fire, regardless of the time of year during the fire season or land use type. Total area burned per fire season was positively correlated with the number of days with FWI > 40 over the period studied. Strong interactions between time of year and land type were also observed for both the probability of ignition and large fire. For example, the estimated odds of a large fire on agricultural lands in southern Sardinia was approximately 10 times larger than the forest and shrubland land type for areas close to roads, early (May) in the fire season. Conversely, toward the end of the fire season (September), we estimated the odds of a large fire in these same areas at about half the value estimated for the forest land classes. Of the explanatory variables analyzed, only FWI had an effect on the probability of a large fire (P

SUMMARY Forest fires are an important societal problem. They cause extensive damage and substantial funds are spent preparing for and fighting them. This work develops a stochastic model useful for probabilistic risk assessment, specifically to estimate chances of fires at a future time given explanatory variables. Questions of interest include: Are random effects needed in the risk model? and if yes, How is the analysis to be implemented? An exploratory data analysis approach is taken using both fixed and random effects models for data concerning the Federal Lands in the state of California during the period 2000–2003. Published in 2006 by John Wiley & Sons, Ltd.

SUMMARY We describe the use of bivariate stochastic differential equations (SDE) for modeling movements of 216 radiocollared female Rocky Mountain elk at the Starkey Experimental Forest and Range in northeastern Oregon. Spatially and temporally explicit vector fields were estimated using approximating difference equations and nonparametric regression techniques. Estimated vector fields of movement were mapped onto the project area at selected times of the day to examine spatial patterns of movement in relation to topography. Using the concept of a potential function, we were able to study the influence of roads and grassland foraging areas on elk movements. Doing so we identified broad spatial patterns of elk movements and showed the time dependent effects of habitat features within the habitat mosaic at Starkey. Our analyses quantify the cycles of movements in spring and summer in terms of attraction or repulsion to specific habitat features, and illustrate the magnitude, timing and direction of these movements. An extensive list of references is included. Published in 2004 by John Wiley & Sons, Ltd.