Summary• Shrub expansion in alpine and arctic areas is a process with possibly profound implications for ecosystem functioning. The recent shrub expansion has been mainly documented by remote sensing techniques, but the drivers for this process largely remain hypotheses.• Here, we outline a dendrochronological method, adapted to shrubs, to address these hypotheses and then present a mechanism for the current shrub expansion by linking recent climate change to shrub growth performance in northern Sweden.• A pronounced increase in radial and vertical growth during recent decades along an elevational gradient from treeline to shrubline indicates an ongoing shrub expansion. Age distribution of the shrub population indicates the new colonization of shrubs at high elevations.• Shrub growth is correlated with warm summers and winter snow cover and suggests the potential for large-scale ecosystem changes if climate change continues as projected.

Summary1 Much ecological theory is based on the characterization of ecological habits of species as 'generalist' or 'specialist', but standard measures for placing species along a generalist-specialist gradient do not exist. 2 We introduce a method for quantifying habitat specialization (i.e. relative niche widths) using species co-occurrence data. Generalists should co-occur with many species, whereas specialists should co-occur with relatively few species, given equal plot occurrences. We quantify this concept using a generalist-specialist metric ( θ ) derived from a beta diversity statistic. 3 We evaluate the ability of our generalist-specialist metric to correctly rank species according to simulated (known) niche widths. Our technique is generally robust to a wide variety of niche distribution structures and sampling designs, but surveys strongly biased toward certain habitats can undermine the ability of θ to accurately describe niche widths for underrepresented species. 4 We apply our technique to three spatially nested surveys of the large woody flora (> 1 cm d.b.h.) of the south-eastern USA. For each dataset we rank the generalistspecialist tendencies of all species of non-trivial occurrences, including 113 species across the Southeast, 71 species of southern Appalachian forests, and 44 species of the 6800-ha Joyce Kilmer-Slickrock Wilderness Area (NC and TN, USA). 5 Rankings of species' θ -values were generally consistent among datasets of different spatial extent. Generalist species (e.g. Ilex opaca , Ulmus rubra , Morus rubra , Prunus serotina , Acer rubrum ) were often those with large geographical ranges, particularly for θ -values from the largest dataset, and overall were more likely to be bird-dispersed, deciduous, and shade tolerant. South-eastern specialist species (e.g. Taxodium spp., Abies fraseri , Quercus laevis , Pinus pungens , Pinus palustris ) were those associated with stressful or unusual conditions, such as a long duration of flooding, high fire frequency, or extreme cold or dry climates. 6 Our study demonstrates that increasingly available, large-survey datasets can contribute niche-related species information in the absence of detailed environmental or habitat measurements. Applications include new assessments of relationships between species traits, ecological and environmental tolerances, and species packing in different assemblages.

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Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm2 to 1,024 m2. Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.

Summary 1. Zeleny (2008) demonstrated that the co-occurrence based assessment of species habitat specialization (introduced by Fridley et al . 2007) depends on the size of the species pool. To correct for the effect of the species pool on the estimation of species niche width, Zeleny suggested a modification of the original algorithm by replacing additive partitioning as a measure of beta diversity with Whittaker's beta. 2. We used simulated data to show that the alternative index proposed by Zeleny (2008) will poorly represent the niche widths of species inhabiting a set of plots with a highly skewed distribution of local richness values. We therefore expand on Zeleny's (2008) analysis by considering two additional metrics of beta diversity based on compositional similarity and by testing the performance of these indices under different local-regional richness relationships. 3. Synthesis. None of the four tested metrics of beta diversity produced unbiased estimates of niche width under curvilinear local-regional richness relationships. In this context, we provide additional guidance to potential users of co-occurrence based niche width estimates by specifying the conditions under which certain indices of beta diversity best represent niche width information.

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