Wildfires are a primary disturbance in the Sierras de Córdoba, Argentina, with approximately 2 152 000 ha burned between 1993 and 2012. However, little is known about the spatial and temporal patterns of fires and their relationship with climate and vegetation in this area. Such information is of great value for fire risk assessment and the development of strategies for fire management. Our main objective was to analyze fire activity in four sierran ranges, assessing which weather and climate conditions were mostly related to fire activity, and which land cover types were mostly burned. We used a fire database of mid-high spatial resolution and a land cover map derived from Landsat imagery. Fire regimes were different among the different sierran ranges. The Sierras Chicas range was the most affected by fires, with the largest number of fire events, burned area, and fire frequency. Although large fires represented 3 % to 5 % of fire events, they accounted for 60 % to 86 % of total burned area in different sierran ranges. Sierras of lower elevation had a winter seasonality of fires, while sierras of higher elevation had a winter-spring or spring fire seasonality. The number of fire events was positively correlated with preceding periods that were wetter than normal, while the burned area was mainly associated with midterm weather conditions. Fires occurred mainly in grasslands and shrublands, but the area of burned forests was important, too. Our results will be useful to determine the times and conditions in which fire risk is highest, and also to identify where preventive efforts should be focused.

Many wild species are affected by human activities occurring at broad spatial scales. For instance, in South America, habitat loss threatens Greater Rhea (Rhea americana) populations, making it important to model and map their habitat to better target conservation efforts. Spatially explicit habitat modeling is a powerful approach to understand and predict species occurrence and abundance. One problem with this approach is that commonly used land cover classifications do not capture the variability within a given land cover class that might constitute important habitat attribute information. Texture measures derived from remote sensing images quantify the variability in habitat features among and within habitat types; hence they are potentially a powerful tool to assess species-habitat relationships. Our goal was to explore the utility of texture measures for habitat modeling and to develop a habitat suitability map for Greater Rheas at the home range level in grasslands of Argentina. Greater Rhea group size obtained from aerial surveys was regressed against distance to roads, houses, and water, and land cover class abundance (dicotyledons, crops, grassland, forest, and bare soil), normalized difference vegetation index (NDVI), and selected first- and second-order texture measures derived from Landsat Thematic Mapper (TM) imagery. Among univariate models, Rhea group size was most strongly positively correlated with texture variables derived from near infrared reflectance measurement (TM band 4). The best multiple regression models explained 78% of the variability in Greater Rhea group size. Our results suggest that texture variables captured habitat heterogeneity that the conventional land cover classification did not detect. We used Greater Rhea group size as an indicator of habitat suitability; we categorized model output into different habitat quality classes. Only 16% of the study area represented high-quality habitat for Greater Rheas (group size > or =15). Our results stress the potential of image texture to capture within-habitat variability in habitat assessments, and the necessity to preserve the remaining natural habitat for Greater Rheas.

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