Aim To analyse the effect of the inclusion of soil and land-cover data on the performance of bioclimatic envelope models for the regional-scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper
atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well-surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and
were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function
of: (1) climate, (2) climate and land-cover, (3) climate and soil, and (4) climate, land-cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion,
and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land-cover classes or predominant soil types led to similar improvements in the performance relative to the climate-only models for both taxonomic groups. In addition, the joint inclusion of land-cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models
incorporating only land-cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. KEYWORDS: Belgium, bioclimatic modelling, butterflies, envelope models, generalized additive models, grasshoppers, land-cover data, Orthoptera, Rhopalocera, species distribution modelling.
