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

Peatlands are characteristic features of the Fennoscandian landscape, about one-quarter of the land surface being classified as peatland. The aim of this work was to determine the extent to which the distribution of four main mire complex types (aapa mire, blanket bog, palsa mire, raised bog) can be modelled on the basis of climatological parameters. Additionally, the relative importance of different climatological variables in influencing the distribution of different mire complex types was scrutinized using the variation partitioning method. Variation partitioning is a novel statistical approach that provides deeper understanding of the importance of different explanatory variable groups for geographical patterns than traditional regression methods. The variation in the distribution of mire complex types was decomposed into independent and joint effects of temperature, precipitation and spatial variables.The distributional limits of aapa mires, palsa mires and raised bogs were primarily associated with thermal factors, whereas moisture regime also played an obvious role for blanket bogs. A considerable amount of variation in the distribution of mire complex types was accounted for by the joint effects of explanatory variables and may thus be causally related to two or all three groups of variables. Although the present distribution of mire complex types corresponded well to the contemporary climate in Fennoscandia, our results indicate that the climate envelopes of palsa mires are narrow. Thus, they can be expected to be extremely sensitive to changes in future climatic conditions.

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Aim Understanding the spatial patterns of species distribution and predicting the occurrence of high biological diversity and rare species are central themes in biogeography and environmental conservation. The aim of this study was to model and scrutinize the relative contributions of climate, topography, geology and land-cover factors to the distributions of threatened vascular plant species in taiga landscapes in northern Finland.Location North-east Finland, northern Europe. MethodsThe study was performed using a data set of 28 plant species and environmental variables at a 25-ha resolution. Four different stepwise selection algorithms [Akaike information criterion (AIC), Bayesian information criterion (BIC), adaptive backfitting, cross selection] with generalized additive models (GAMs) were fitted to identify the main environmental correlates for species occurrences. The accuracies of the distribution models were evaluated using fourfold cross-validation based on the area under the curve (AUC) derived from receiver operating characteristic plots. The GAMs were tentatively extrapolated to the whole study area and species occurrence probability maps were produced using GIS techniques. The effect of spatial autocorrelation on the modelling results was also tested by including autocovariate terms in the GAMs. ResultsAccording to the AUC values, the model performance varied from fair to excellent. The AIC algorithm provided the highest mean performance (mean AUC = 0.889), whereas the lowest mean AUC (0.851) was obtained from BIC. Most of the variation in the distribution of threatened plant species was related to growing degree days, temperature of the coldest month, water balance, cover of mire and mean elevation. In general, climate was the most powerful explanatory variable group, followed by land cover, topography and geology. Inclusion of the autocovariate only slightly improved the performance of the models and had a minor effect on the importance of the environmental variables. Main conclusionsThe results confirm that the landscape-scale distribution patterns of plant species can be modelled well on the basis of environmental parameters. A spatial grid system with several environmental variables derived from remote sensing and GIS data was found to produce useful data sets, which can be employed when predicting species distribution patterns over extensive areas. Landscape-scale maps showing the predicted occurrences of individual or multiple threatened plant species may provide a useful basis for focusing field surveys and allocating conservation efforts.