Importance of spatial autocorrelation in modeling bird distributions at a continental scale Volker Bahn, Raymond J. O’Connor $ and William B. Krohn Bahn, V., O’Connor, R. J. and Krohn, W. B. 2006. Importance of spatial autocorrelation in modeling bird distributions at a continental scale.  Ecography 29: 835  844. Spatial autocorrelation in species’ distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to thevariation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike’s information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species’ distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distri- bution of species. Spatially explicit models are expected to yield better predic- tions especially for mobile species such as birds, even in coarse-grained models with a large extent. V. Bahn (volker.bahn@gmx.net) and R. J. O’Connor, Dept of Wildlife Ecology, Univ. of Maine, Orono, ME 04469-5755, USA(present address of V.B.: Dept of Biology, McGill Univ., Stewart Biol. Bldg., 1205 avenue Docteur Penfield, Montreal, QC H3A 1B1, Canada).  W. B. Krohn, U.S. Geological Survey, Maine Cooperative Fish and Wildlife Research Unit, Orono, ME 04469-5755, USA. Documenting and understanding the distributions of organisms in space and time are central to the fields of biogeography, ecology, and conservation biology. Ecol- ogy has been defined as the study of the distribution and abundance of organisms (Andrewartha and Birch 1954, 1984, Krebs 1972). In conservation biology, knowledge of the actual or potential distribution of a species is indispensable for threatened and endangered species management and protected area planning (Scott and Csuti 1997). However, at most times the actual locations of individual organisms are unknown. The discipline of distribution modeling strives to fill this void by making probabilistic statements about the geographic distribu- tion of species (Scott et al. 2002). Accepted 19 September 2006 $ deceased Copyright # ECOGRAPHY 2006 ISSN 0906-7590 ECOGRAPHY 29: 835  844, 2006 ECOGRAPHY 29:6 (2006) 835