Human and biophysical drivers of fires in Semiarid Chaco mountains of Central Argentina

• DOI: 10.1016/j.scitotenv.2015.02.081
• Publication Date: 2015-03-14T13:01:34Z
• Authors: Juan P. Argañaraz; Gregorio Gavier P...; Marcelo Zak; Marcos A. Landi; Laura M. Bellis
• Keywords: Chaco Serrano; 0106 biological sciences; Fire Frequency; 15. Life on land; Sierras de Córdoba; 01 natural sciences; 12. Responsible consumption; Fire Drivers; https://purl.org/becyt/ford/1.5; 13. Climate action; https://purl.org/becyt/ford/1.6; 11. Sustainability; Boosted Regression Trees; Semiarid Chaco; Fire Ecology; https://purl.org/becyt/ford/1

Fires are a recurrent disturbance in Semiarid Chaco mountains of central Argentina. The interaction of multiple factors generates variable patterns of fire occurrence in space and time. Understanding the dominant fire drivers at different spatial scales is a fundamental goal to minimize the negative impacts of fires. Our aim was to identify the biophysical and human drivers of fires in the Semiarid Chaco mountains of Central Argentina and their individual effects on fire activity, in order to determine the thresholds and/or ranges of the drivers at which fire occurrence is favored or disfavored. We used fire frequency as the response variable and a set of 28 potential predictor variables, which included climatic, human, topographic, biological and hydrological factors. Data were analyzed using Boosted Regression Trees, using data from near 10,500 sampling points. Our model identified the fire drivers accurately (75.6% of deviance explained). Although humans are responsible for most ignitions, climatic variables, such as annual precipitation, annual potential evapotranspiration and temperature seasonality were the most important determiners of fire frequency, followed by human (population density and distance to waste disposals) and biological (NDVI) predictors. In general, fire activity was higher at intermediate levels of precipitation and primary productivity and in the proximity of urban solid waste disposals. Fires were also more prone to occur in areas with greater variability in temperature and productivity. Boosted Regression Trees proved to be a useful and accurate tool to determine fire controls and the ranges at which drivers favor fire activity. Our approach provides a valuable insight into the ecology of fires in our study area and in other landscapes with similar characteristics, and the results will be helpful to develop management policies and predict changes in fire activity in response to different climate changes and development scenarios.