Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available software, to examine breeding habitat relationships for three common Neotropical migrant songbirds in the southern Appalachian Mountains of North Carolina and Tennessee, USA.In preliminary models that ignored space, the abundance of all three species was correlated with both local-and landscape-scale habitat variables. These models were then modified to account for broadscale spatial trend (via trend surface analysis) and fine-scale autocorrelation (via an autoregressive spatial covariance matrix). Residuals from ordinary least squares regression models were autocorrelated, indicating that the assumption of independent errors was violated. In contrast, residuals from autoregressive models showed little spatial pattern, suggesting that these models were appropriate.The magnitude of habitat effects tended to decrease, and the relative importance of different habitat variables shifted when we incorporated broadscale and then fine-scale space into the analysis. The degree to which habitat effects changed when space was added to the models was roughly correlated with the amount of spatial structure in the habitat variables.Spatial pattern in the residuals from ordinary least squares models may result from failure to include or adequately measure autocorrelated habitat variables. In addition, contagious processes, such as conspecific attraction, may generate spatial patterns in species abundance that cannot be explained by habitat models. For our study species, spatial patterns in the ordinary least squares residuals suggest that a scale of 500-1000 m would be appropriate for investigating possible contagious processes.

Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available software, to examine breeding habitat relationships for three common Neotropical migrant songbirds in the southern Appalachian Mountains of North Carolina and Tennessee, USA. In preliminary models that ignored space, the abundance of all three species was correlated with both local‐ and landscape‐scale habitat variables. These models were then modified to account for broadscale spatial trend (via trend surface analysis) and fine‐scale autocorrelation (via an autoregressive spatial covariance matrix). Residuals from ordinary least squares regression models were autocorrelated, indicating that the assumption of independent errors was violated. In contrast, residuals from autoregressive models showed little spatial pattern, suggesting that these models were appropriate. The magnitude of habitat effects tended to decrease, and the relative importance of different habitat variables shifted when we incorporated broadscale and then fine‐scale space into the analysis. The degree to which habitat effects changed when space was added to the models was roughly correlated with the amount of spatial structure in the habitat variables. Spatial pattern in the residuals from ordinary least squares models may result from failure to include or adequately measure autocorrelated habitat variables. In addition, contagious processes, such as conspecific attraction, may generate spatial patterns in species abundance that cannot be explained by habitat models. For our study species, spatial patterns in the ordinary least squares residuals suggest that a scale of 500–1000 m would be appropriate for investigating possible contagious processes.

We examined the relationship between songbird relative abundance and local and landscape‐scale habitat variables in two predominately mid‐ to late‐successional managed National Forests in the southern Appalachian Mountains, USA. We used partial‐regression analysis to remove correlations between habitat variables measured at different spatial scales (local habitat and square landscape regions with sides of 0.5, 1, and 2 km) and between landscape composition (proportion of different land cover types) and pattern (spatial arrangement of land cover) variables. To account for spatial autocorrelation, we used autoregressive models that incorporated information on bird abundance in the spatial neighborhood surrounding each sample point. Most species, especially Neotropical migrants, were significantly correlated with at least one landscape variable. These correlations included both composition and pattern variables at 0.5–2 km scales. However, landscape effects explained only a small amount of the variation in bird abundance that could not be explained by local habitat. Our results are consistent with other studies of songbird abundance in large managed forests that have found weak or moderate landscape effects. These studies suggest that songbird abundance in forested landscapes will primarily reflect the quantity of different habitats in the landscape rather than the spatial arrangement of those habitats. Although some studies have suggested consolidating clearcuts in large managed forests to reduce edge and landscape heterogeneity, much of the current evidence does not support this management recommendation. An important future challenge in avian conservation is to better understand how the importance of landscape effects varies in relation to (1) the amount of suitable habitat in the landscape, and (2) land use patterns at broader spatial scales.

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