Sets of presence records used to model species’ distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species’ distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non‐presence background data. We tested a new method, which we call ‘background thickening’, in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods – target group background selection and presence thinning – using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.

Aim Climate and land use are key determinants of biodiversity, with past and ongoing changes posing serious threats to global ecosystems. Unlike most other organism groups, plant species can possess dormant life‐history stages such as soil seed banks, which may help plant communities to resist or at least postpone the detrimental impact of global changes. This study investigates the potential for soil seed banks to achieve this. Location Europe. Time period 1978–2014. Major taxa studied Flowering plants. Methods Using a space‐for‐time/warming approach, we study plant species richness and composition in the herb layer and the soil seed bank in 2,796 community plots from 54 datasets in managed grasslands, forests and intermediate, successional habitats across a climate gradient. Results Soil seed banks held more species than the herb layer, being compositionally similar across habitats. Species richness was lower in forests and successional habitats compared to grasslands, with annual temperature range more important than mean annual temperature for determining richness. Climate and land‐use effects were generally less pronounced when plant community richness included seed bank species richness, while there was no clear effect of land use and climate on compositional similarity between the seed bank and the herb layer. Main conclusions High seed bank diversity and compositional similarity between the herb layer and seed bank plant communities may provide a potentially important functional buffer against the impact of ongoing environmental changes on plant communities. This capacity could, however, be threatened by climate warming. Dormant life‐history stages can therefore be important sources of diversity in changing environments, potentially underpinning already observed time‐lags in plant community responses to global change. However, as soil seed banks themselves appear, albeit less, vulnerable to the same changes, their potential to buffer change can only be temporary, and major community shifts may still be expected.

Questions: Which environmental and management factors determine plant species composition in semi-natural grasslands within a local study area? Are vegetation and explanatory factors scale-dependent? Location: Semi-natural grasslands in Laerdal, Sogn og Fjordane County, western Norway. Methods:We recorded plant species composition and explanatory variables in six grassland sites using a hierarchically nested sampling design with three levels: plots randomly placed within blocks selected within sites. We evaluated vegetationenvironment relationships at all three levels by means of DCA ordination and split-plot GLM analyses. Results:The most important complex gradient determining variation in grassland species composition showed a broadscale relationship with management. Soil moisture conditions were related to vegetation variation on block scale, whereas element concentrations in the soil were significantly related to variation in species composition on all spatial scales. Our results show that vegetation-environment relationships are dependent on the scale of observation. We suggest that scalerelated (and therefore methodological) issues may explain the wide range of vegetation-environment relationships reported in the literature, for semi-natural grassland in particular but also for other ecosystems. Conclusions: Interpretation of the variation in species composition of semi-natural grasslands requires consideration of the spatial scales on which important environmental variables vary.

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