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Cade, Brian ABSTRACT- Estimates of animal responses to their physical environment in spatially structured landscapes commonly have hidden biases because all factors limiting the organism are not measured and accounted for in statistical models. One possible approach to help account for effects of important unmeasured factors is to include a spatial component in the model, based on the assumption that unmeasured factors are spatially structured. A simple way to implement spatial structuring in a regression model is by including a spatial trend surface as some low order polynomial function of latitudinal and longitudinal coordinates of sample locations. Simulations with quantile regression demonstrate that less biased estimates of effects of environmental predictors and more variation in animal response were explained by models that included terms for the spatial trend. Considerable heterogeneity in responses remain unless the unmeasured factors are strongly correlated ( Key words: regression, limiting factors, quantiles, habitat |