Valid auto‐models for spatially autocorrelated occupancy and abundance data. Issue 10 (26th June 2015) - Bardos, David C.; Guillera‐Arroita, Gurutzeta; Wintle, Brendan A.
Summary: Spatially autocorrelated species abundance or distribution data sets typically generate spatially autocorrelated residuals in generalized linear models; a broader modelling framework is therefore required. Auto‐logistic and related auto‐models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial population processes. The auto‐logistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland ( Journal of Applied Ecology, 1996, 33, 339) analysed red deer census data using a hybrid estimation approach, combining maximum pseudo‐likelihood estimation with Gibbs sampling of missing data. However, Dormann ( Ecological Modelling, 2007, 207, 234) questioned the validity of auto‐logistic regression even for fully observed data, giving examples of apparent underestimation of covariate parameters in analysis of simulated 'snouter' data. Dormann et al . ( Ecography, 2007, 30, 609) extended this critique to auto‐Poisson and certain auto‐normal models, finding again that autocovariate‐regression estimates for covariate parameters bore little resemblance to values employed to generate 'snouter' data. We note that all the above studies employ neighbourhood weighting schemes inconsistent with auto‐model definitions; in the auto‐Poisson case, a further inconsistency was the failure to exclude cooperative interactions. We investigate the impact of these implementation errors on auto‐model estimation using both empirical and simulated data sets. We show that when 'snouter' data are reanalysed using valid weightings, very different estimates are obtained for covariate parameters. For auto‐logistic and auto‐normal models, the new estimates agree closely with values used to generate the 'snouter' simulations. Re‐analysis of the red deer data shows that invalid neighbourhood weightings generate only small estimation errors for the full data set, but larger errors occur on geographic subsamples. A substantial fraction of papers employing auto‐logistic regression use these invalid neighbourhood weightings, which were embedded as default options in the widely used 'spdep' spatial dependence package for R. Auto‐logistic analyses conducted using invalid neighbourhood weightings will be erroneous to an extent that can vary widely. These analyses can easily be corrected by using valid neighbourhood weightings available in 'spdep'. The hybrid estimation approach for missing data is readily adapted for valid neighbourhood weighting schemes and is implemented here in R for application to sparse presence–absence data.