International audienceSpatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species. North-eastern Finland, Europe. The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well‐sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine‐learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression‐based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over‐prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.

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Vulnerability of 100 European butterfly species to climate change was assessed using 13 different criteria and data on species distributions, climate, land cover and topography from 1,608 grid squares 30 0 9 60 0 in size, and species characteristics increasing the susceptibility to climate change. Four bioclimatic model-based criteria were developed for each species by comparing the present-day distribution and climatic suitability of the occupied grid cells with projected distribution and suitability in the future using the HadCM3-A2 climate scenario for 2051-2080. The proportions of disadvantageous land cover types (bare areas, water, snow and ice, artificial surfaces) and cultivated and managed land in the occupied grid squares and their surroundings were measured to indicate the amount of unfavourable land cover and dispersal barriers for butterflies, and topographical heterogeneity to indicate the availability of potential climatic refugia. Vulnerability was also assessed based on species dispersal ability, geographical localization and habitat specialization. Northern European species appeared to be amongst the most vulnerable European butterflies. However, there is much species-to-species variation, and species appear to be threatened due to different combinations of critical characteristics. Inclusion of additional criteria, such as life-history species characteristics, topography and land cover to complement the bioclimatic model-based species vulnerability measures can significantly deepen the assessments of species susceptibility to climate change.