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Context: Better balancing agricultural production and biodiversity conservation is a central goal for many landscapes. Yet, empirical work on how to best achieve such a balance has focused mainly on the local scale, thereby disregarding that landscape context might mediate biodiversity-agriculture trade-offs. Objectives: Focusing on vertebrates in the Argentine Chaco, we evaluate how trade-offs between agriculture and biodiversity vary with landscape context, from landscapes where agricultural and natural areas are separated to landscapes where both are interspersed. Methods: We modelled the distributions of 226 vertebrates and use the resulting maps to describe the species richness of ecosystem-service providing guilds. We calculated three agricultural intensity metrics, and evaluated how both species richness and agricultural intensity vary along a gradient of landscape configuration, while controlling for landscape composition. Results: Species richness and agricultural yields both varied with landscape configuration. Biodiversity was highest in mixed landscapes where agricultural and natural area are interspersed, whereas agricultural yields showed a more heterogeneous response, with some yield metrics highest in mixed and others in separated landscapes. As a result, agriculture/biodiversity trade-offs depended strongly on landscape configuration, irrespective of landscape composition. We also identified large areas with low vertebrate richness and agricultural yields, suggesting considerable potential for improving in at least one dimension. Conclusions: Agriculture/biodiversity trade-offs varied with landscape configuration, suggesting that landscape design can balance these trade-offs. Our simple and broadly applicable approach can provide baseline information for landscape planning aimed at realizing co-benefits between agriculture and biodiversity—in the Gran Chaco and elsewhere. [ABSTRACT FROM AUTHOR]

Three major problems are faced when mapping natural vegetation with mid-resolution satellite images using conventional supervised classification techniques: defining the adequate hierarchical level for mapping; defining discrete land cover units discernible by the satellite; and selecting representative training sites. In order to solve these problems, we developed an approach based on the: (1) definition of ecologically meaningful units as mosaics or repetitive combinations of structural types, (2) utilization of spectral information (indirectly) to define the units, (3) exploration of two alternative methods to classify the units once they are defined: the traditional, Maximum Likelihood method, which was enhanced by analyzing objective ways of selecting the best training sites, and an alternative method using Discriminant Functions directly obtained from the statistical analysis of signatures. The study was carried out in a heterogeneous mountain rangeland in central Argentina using Landsat data and 251 field sampling sites. On the basis of our analysis combining terrain information (a matrix of 251 stands×14 land cover attributes) and satellite data (a matrix of 251 stands×8 bands), we defined 8 land cover units (mosaics of structural types) for mapping, emphasizing the structural types which had stronger effects on reflectance. The comparison through field validation of both methods for mapping units showed that classification based on Discriminant Functions produced better results than the traditional Maximum Likelihood method (accuracy of 86% vs. 78%). [Copyright &y& Elsevier]