Effects of spatial autocorrelation and imperfect detection on species distribution models

1. Species distribution models (SDMs) are widely used in ecology and related fields. They are frequently adopted to predict the expected occurrence (presence/absence) or abundance over large spatial scales, that is, to produce a species distribution map. Two issues that almost universally affect these models are measurement errors (especially imperfect detection) and residual spatial autocorrelation. 2. We explored the effects of imperfect detection and autocorrelation in abundance models by simulating datasets which did or did not contain these two effects and analysing them with four different models that did or did not accommodate them. Specifically, we used a Poisson GLM as a baseline, an N-mixture model accounting only for imperfect detection and two N-mixture models that accounted for imperfect detection, but differed in their specification of spatial autocorrelation (CAR random effects vs. two-dimensional splines). 3. In a case study, we then applied these models to Common Redstart Phoenicurus phoenicurus data from the second Swiss Breeding Bird Atlas (1993-1996) and validated them using an independent monitoring dataset. 4. We found that both imperfect detection and autocorrelation strongly affected the quality and the uncertainty of abundance maps, especially when they occurred together. Spatial N-mixture models were well able to estimate the true abundance maps. Explicit modelling of measurement error and spatial autocorrelation can thus greatly improve the quality of SDMs and should not be ignored when producing large-scale abundance or occurrence maps.