Pulsatilla patens (L.) Mill. (Ranunculaceae) is a threatened plant which in Fennoscandia favours south-facing, warm slopes of pine-dominated esker forests. The cessation of cattle grazing, modern forestry practices, and especially efficient fire prevention have resulted in closure of undergrowth vegetation in these forests. Using generalized linear models (GLM), we studied the relationships between habitat factors (covers of field and ground layer and amount of litter) and the population structure of P. patens in 48 populations in Finland in order to identify favourable conditions for regeneration. The largest populations occurred at sites with intermediate values of both ground and field layer. The number of juvenile plants was also highest at intermediate values of ground layer. Dense moss layer and abundant litter had a negative effect on the flowering of P. patens. In conclusion, creation and maintenance of habitat heterogeneity to prevent the closure of undergrowth vegetation is of paramount importance for the successful reproduction of P. patens.

Assisted migration (AM) has been suggested as a management strategy for aiding species in reaching newly suitable locations as climate changes. Species distribution models (SDMs) can provide important insights for decisions on whether to assist a species in its migration; however, their application includes uncertainties. In this study, we use consensus SDMs to model the future suitable areas for 13 vascular plant species with poor dispersal capacity. Based on the outputs of SDMs under different climate change scenarios and future times, we quantify the predicted changes in suitable area by calculating metrics that describe need and potential for migration. We find that, by the end of the 21 st century, one of the species would benefit from AM under mild climate change, seven under moderate change, and for 12 out of 13 species studied AM appears to be a relevant conservation method under strong climate change. We also test the effect of different modeling attributes on the metrics and find little variation between SDMs constructed using different combinations of modeling methods and variable sets. However, choice of climate variables had a larger influence on the level of the metrics than did the modeling method. We therefore suggest that choice of climate variables should receive ample attention when measuring climate change threat using SDMs and that experiments aiming to uncover critical environmental factors for individual species should be extensively conducted. This study illustrates that dispersal assistance may be needed for many species under a wide range of possible future climates.

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.

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The aims of this study were (1) to examine the geographic distribution of red-listed species of agricultural environments and identify their national threat spots (areas with high diversity of threatened species) in Finland and (2) to determine the main environmental variables related to the richness and occurrence patterns of red-listed species. Atlas data of 21 plant, 17 butterXy and 11 bird species recorded using 10 km grid squares were employed in the study. Generalized additive models (GAMs) were constructed separately for species richness and occurrence of individual species of the three species groups using climate and land cover predictor variables. The predictive accuracy of models, as measured using correlation between the observed and predicted values and AUC statistics, was generally good. Temperature-related variables were the most important determinants of species richness and occurrence of all three taxa. In addition, land cover variables had a strong eVect on the distribution of species. Plants and butterXies were positively related to the cover of grasslands and birds to small-scale agricultural mosaic as well as to arable land. Biodivers Conserv (2008) 17:3289-3305 1 C Spatial coincidence of threat spots of plants, butterXies and birds was limited, which emphasizes the importance of considering the potentially contrasting environmental requirements of diVerent taxa in conservation planning. Further, it is obvious that the maintenance of various non-crop habitats and heterogeneous agricultural landscapes has an essential role in the preservation of red-listed species of boreal rural environments.