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Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez‐Seoane et al. using habitat variables for three agro‐steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate‐habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate‐habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change.

Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez‐Seoane et al. using habitat variables for three agro‐steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate‐habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate‐habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change.

The effects of habitat fragmentation on species may change seasonally mainly due to variations in resource availability and biotic interactions. In critical periods, such as winter, when the importance of intraspecific competition diminish, species may relax their environmental requirements widening their ecological niche to exploit the scarcer trophic resources more efficiently in comparison with spring. Those variations in niche width may implicate seasonal expansions/retractions in species distribution. In this sense, an integrated knowledge on the spatial arrangement of breeding and wintering suitable patches is essential to infer seasonal movements (migratory connectivity). This paper shows that little bustard environmental preferences were more predictable and complex (controlled by a larger number of environmental factors) in spring than in winter, when potential distribution and ecological niche width were slightly larger. In spring, habitat variables (i.e. percentage of dry crops and pasturelands and altitude) ruled species’ distribution; while, winter pattern was driven by mixed criteria, based on both habitat and climate (i.e. percentage of dry crops and wastelands and winter rainfall). Suitable patches were more connected across spatial scales in winter than in spring, i.e. landscape was perceived as less fragmented. The overlap between potential breeding and wintering distribution areas was high. In fact, most of the predicted wintering areas coincided or showed high connectedness with predicted breeding patches. Conversely, there were significant breeding patches that were predicted with low suitability, showing little connectedness with potential winter areas. Spring habitat was a better predictor of little bustard’s wintering range than vice versa, which has clear management implications (preserving breeding sites closer to wintering areas ensures the conservation of a larger proportion of the total distribution range). This is an example of how pred

Little bustard populations have suffered reduction and isolation as a consequence of landscape transformations resulting from changes in traditional agricultural systems. Consequently, the species survives within reduced and fragmentary habitats, like islands isolated in a modified matrix. In this paper, we analyze the spatial variations in male density and habitat quality in a fragmented population located at the limit of the species’ Iberian range, which is affected by agricultural intensification, using a regional modelling approach. Habitat quality (quantified according to the species perception) and bird density decreased along the intensification gradient. However, in the most intensive agricultural zone, the quality of habitats selected by little bustard males increased, while density decreased, against the expected. In possible explanation, we suggest: (1) density is not necessarily a good indicator of habitat quality, (2) population could be under-saturated in this zone, (3) interannual variations in species distribution, or (4) other relevant variables related to the agricultural intensification process not included in this analysis, such as small-scale disturbances. Analysis of population distribution pattern showed a spatial configuration in which the most densely populated squares were located at the core of the biggest population patches, in contact with mid-density squares, and all surrounded by low-density squares. Fragmentation negatively affected habitat quality and male density. Largest population patches, containing higher density values, were located at the beginning of the intensification gradient. Preservation of little bustard densities is related to an adequate management of the farming system. Habitat fragmentation requires an urgent conservation strategy to prevent local and regional scale habitat deterioration, by reducing patch isolation to maintain genetic diversification and functional connectivity.