Summary1. Predictive models of species' distributions are used increasingly in ecological studies investigating features as varied as biodiversity, habitat selection and interspecific competition. In a pilot study, we based a successful model for the great bustard Otis tarda on advanced very high resolution radiometer (AVHRR) satellite data, which offer attractive predictor variables because of the global coverage, high temporal frequency of overpasses and low cost. We wished to assess whether the approach could be applied at very large spatial scales, and whether the coarse resolution of the imagery (1 km 2 ) would limit application to those bird species with large home ranges or to simple recognition of broad habitat types. 2. We modelled the distributions of three agricultural steppe birds over the whole of Spain using a common set of predictor variables, including AVHRR imagery. The species, great bustard, little bustard Tetrax tetrax and calandra lark Melanocorhypha calandra , have similar habitat requirements but differently sized home ranges, and are all species of conservation concern. Good models would reveal differences in distribution between the species and have high predictive power despite the large geographical extent covered. 3. Generalized additive models (GAMs) were built with the presence-absence of the species as the response variable. Individual species' responses to the habitat variables were identified using partial fits and compared with each other. We found that this modelling framework could successfully distinguish the habitats selected by the three species, while the response curves indicated how the habitats differed. Model fits and cross-validations assessed using receiver operating characteristic (ROC) plots showed the models to be successful and robust. 4. We overlaid the predictive maps to identify key areas for agricultural steppe birds in Spain and compared these with the present network of protected sites in two sample regions. In Castilla León the provision of protected sites appears appropriate, but in Castilla La Mancha large areas of apparently suitable habitat have no protection. 5. These results confirm that large-scale models are able to increase our understanding of species' ecology and provide data for conservation planning. AVHRR imagery, in combination with other variables, has sufficient resolution to model a range of bird species, and GAMs have the flexibility to model subtle species-habitat responses.

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Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non‐stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non‐stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non‐stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better‐fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.