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Keywords:

  • Belgium;
  • bioclimatic modelling;
  • butterflies;
  • envelope models;
  • generalized additive models;
  • grasshoppers;
  • land-cover data;
  • Orthoptera;
  • Rhopalocera;
  • species distribution modelling

Abstract

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 (= 366 for butterflies and = 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.

Main conclusions  These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land-cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional-scale species–climate impact models, particularly when predicting future range shifts of species under climate change.