Recent studies from mountainous areas of small spatial extent (<2500 km(2) ) suggest that fine-grained thermal variability over tens or hundreds of metres exceeds much of the climate warming expected for the coming decades. Such variability in temperature provides buffering to mitigate climate-change impacts. Is this local spatial buffering restricted to topographically complex terrains? To answer this, we here study fine-grained thermal variability across a 2500-km wide latitudinal gradient in Northern Europe encompassing a large array of topographic complexities. We first combined plant community data, Ellenberg temperature indicator values, locally measured temperatures (LmT) and globally interpolated temperatures (GiT) in a modelling framework to infer biologically relevant temperature conditions from plant assemblages within <1000-m(2) units (community-inferred temperatures: CiT). We then assessed: (1) CiT range (thermal variability) within 1-km(2) units; (2) the relationship between CiT range and topographically and geographically derived predictors at 1-km resolution; and (3) whether spatial turnover in CiT is greater than spatial turnover in GiT within 100-km(2) units. Ellenberg temperature indicator values in combination with plant assemblages explained 46-72% of variation in LmT and 92-96% of variation in GiT during the growing season (June, July, August). Growing-season CiT range within 1-km(2) units peaked at 60-65°N and increased with terrain roughness, averaging 1.97 °C (SD = 0.84 °C) and 2.68 °C (SD = 1.26 °C) within the flattest and roughest units respectively. Complex interactions between topography-related variables and latitude explained 35% of variation in growing-season CiT range when accounting for sampling effort and residual spatial autocorrelation. Spatial turnover in growing-season CiT within 100-km(2) units was, on average, 1.8 times greater (0.32 °C km(-1) ) than spatial turnover in growing-season GiT (0.18 °C km(-1) ). We conclude that thermal variability within 1-km(2) units strongly increases local spatial buffering of future climate warming across Northern Europe, even in the flattest terrains.

Background Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long‐term environmental change and deserve closer attention of vegetation ecologists. Methods Vegetation data obtained from the resurveying of non‐permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non‐permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions Re‐sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non‐permanent plots, while the latter are further divided into quasi‐permanent (with approximate relocation) and non‐traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses.

Biodiversity time series reveal global losses and accelerated redistributions of species, yet no net loss in local species richness. To better understand how these patterns are linked, we quantify how individual species trajectories scale up to diversity changes using data from 68 vegetation resurvey studies of seminatural forests in Europe. Herb-layer species with small geographic ranges are being replaced by more widely distributed species and our results suggest this is less due to species abundances than to species nitrogen (N) niches. N-deposition accelerates extinctions of small-ranged, N-efficient plants and colonization by broadly distributed, N-demanding plants including non-natives. Despite no net change in species richness at the spatial scale of a study site, losses of small-ranged species reduce biome-scale (gamma) diversity. These results provide one mechanism to explain the directional replacement of smallranged species within sites and thus patterns of biodiversity change across spatial scales.

More and more ecologists have started to resurvey communities sampled in earlier decades to determine long-term shifts in community composition and infer the likely drivers of the ecological changes observed. However, to assess the relative importance of, and interactions among, multiple drivers joint analyses of resurvey data from many regions spanning large environmental gradients are needed. In this paper we illustrate how combining resurvey data from multiple regions can increase the likelihood of driver-orthogonality within the design and show that repeatedly surveying across multiple regions provides higher representativeness and comprehensiveness, allowing us to answer more completely a broader range of questions. We provide general guidelines to aid implementation of multi-region resurvey databases. In so doing, we aim to encourage resurvey database development across other community types and biomes to advance global environmental change research.

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