Instituto de Diversidad y Ecología Animal (IDEA), CONICET, Facultad de Ciencias Exactas Físicas y Naturales, Universidad Nacional de Córdoba, Av. Vélez Sarsfield 299, 5000, Córdoba, Argentina.
Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos (Centro de Investigación en Recursos Naturales, CIRN-IRB), De los Reseros y Las Cabañas S/N, HB1712WAA, Hurlingham, Buenos Aires, Argentina.
Sci Total Environ. 2015 Jul 1;520:1-12. doi: 10.1016/j.scitotenv.2015.02.081. Epub 2015 Mar 14.
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.
火灾是阿根廷中部半干旱查科山脉经常发生的干扰事件。多种因素的相互作用导致火灾在时空上呈现出不同的发生模式。了解不同空间尺度上的主要火灾驱动因素是将火灾负面影响降到最低的基本目标。我们的目标是确定阿根廷中部半干旱查科地区火灾的生物物理和人为驱动因素及其对火灾活动的个别影响,以便确定火灾发生有利或不利的驱动因素阈值和/或范围。我们使用火灾频率作为响应变量,以及一组 28 个潜在的预测变量,其中包括气候、人为、地形、生物和水文因素。使用 Boosted Regression Trees 分析数据,使用近 10500 个采样点的数据。我们的模型准确地识别了火灾驱动因素(解释了 75.6%的方差)。尽管人类是大多数点火的原因,但气候变量,如年降水量、年潜在蒸散量和温度季节性,是火灾频率的最重要决定因素,其次是人为因素(人口密度和距离废物处理场)和生物因素(NDVI)预测因子。一般来说,在降水和初级生产力的中等水平以及靠近城市固体废物处理场的地方,火灾活动较高。在温度和生产力变化较大的地区,火灾也更容易发生。Boosted Regression Trees 被证明是一种有用且准确的工具,可以确定火灾控制因素和驱动因素有利于火灾活动的范围。我们的方法为了解我们研究区域和其他具有类似特征的景观中的火灾生态学提供了有价值的见解,并且研究结果将有助于制定管理政策,并预测在不同气候变化和发展情景下火灾活动的变化。