Modeling fire spread as an infection process is intuitive: an ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce non-linear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n=1131) in grassy ecosystems across a precipitation gradient (496-1442mm mean annual precipitation), and evaluating how these scaled regionally (across 533 sites) and across time (1989-2012, 2016-2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate non-linear fire spread thresholds, but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

Full details on the methodology can be found in the published article's main text and supplementary material.

1. Data on individual fires at each of the study sites ("Cardoso.etal_PNAS_fire_data.csv"). Please see README and the original publication for more details. 

2. Data on each of the grass biomass monitoring plots in Kruger National Park ("Cardoso.etal_PNAS_vca.plots_data.csv"). Please see metadata see README and the original publication for more details. 

3. Data on windspeed at fine scale resolution ("Cardoso.etal_PNAS_finescale.wind_data.csv"). Please see README and the original publication for more details.