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In this paper, we tested whether the spatial distribution of a given species in more or less fragmented and disturbed landscapes depends on the species habitat specialization. We studied 891 spatial replicates from the French Breeding Bird Survey (FBBS) monitored at least two years during 2001–2005, and two independent landscape databases measuring respectively landscape fragmentation and recent landscape disturbance on each FBBS replicate. We used a continuous habitat specialization index for the 105 most common bird species monitored by the FBBS. We further modelled the spatial variation in abundance of each species according to fragmentation and disturbance across FBBS replicates, accounting for habitat differences and spatial trends. We then tested whether more or less specialized species responded to landscape fragmentation and disturbance. We found that the more specialist a species, the more negative its spatial response to landscape fragmentation and disturbance. Although there was a very high variation around these tendencies indicating that there are many other drivers of species distribution, our results suggest that measuring specialization may be helpful in predicting which species are likely to thrive in human degraded landscapes. We also emphasize the need to consider both positive and negative species responses when assessing consequences of habitat change in communities. [ABSTRACT FROM AUTHOR]

Species distributions often show an aggregated pattern, which can be due to a number of endo- and exogenous factors. While autologistic models have been used for modelling such data with statistical rigour, little emphasis has been put on disentangling potential causes of aggregation. In this paper we ask whether it is possible to infer sources of aggregation in species distributions from a single set of occurrence data by comparing the performance of various autologistic models. We create simulated data sets, which show similar occupancy patterns, but differ in the process that causes the aggregation. We model the distribution of these data with various autologistic models, and show how the relative performance of the models is sensitive to the factor causing aggregation in the data. This information can be used when modelling real species data, where causes of aggregation are typically unknown. To illustrate, we use our approach to assess the potential causes of aggregation in data of seven bird species with contrasting statistical patterns. Our findings have important implications for conservation, as understanding the mechanisms that drive population fluctuations in space and time is critical for the development of effective management actions for long-term conservation. [ABSTRACT FROM AUTHOR]