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Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution. ISSN:1354-1013 ISSN:1365-2486

Dust in space is subject to various interactions of photons and charged particles. Whereas photoemission is rather well understood, the charging of dust grains due to the interaction with charged particles has many variations and was not study in detail so far. We report the first laboratory experiment dealing with some less pronounced and often neglected secondary processes, e.g., secondary electron emissions due to ion, electron or positron impacts. We employed a quadrupole trap to store a single micrometer-sized silicate dust grain and measure variations of its charge with an accuracy of one elementary charge. The determined yields of secondary processes exhibit surprisingly large values. We discuss the experimental results and formulate possible consequences for space dust charging.

Species’ environmental niches are conventionally modelled using coarse-grained macroclimate data. These data are known to deviate substantially from local, near-ground and proximal conditions (i.e., the microclimate), especially so below forests canopies. Here we aimed to assess the impact of using gridded microclimate data instead of gridded macroclimate data on the performance of species distribution models (SDMs), as well as on the predicted geographical distribution and the derived species response curves of 140 forest specialist plant species across Europe over the 2000-2020 period. We performed a comparative study between SDMs constructed with different sets of bioclimatic predictors to separately test the effect of using (i) proximal climate data instead of conventional macroclimatic data and (ii) high-resolution proximal climate data rather than coarse-gridded macroclimatic data. Therefore, we challenged SDMs with: (1) a macroclimatic dataset at a spatial resolution of 1 km × 1 km; (2) an aggregated microclimatic dataset matching the same resolution of 1 km × 1 km; and (3) a microclimatic dataset at a much finer spatial resolution of 25 m × 25 m. We found significant differences in model performance, indicating that microclimate-based SDMs outperform both their macroclimatic and aggregated counterparts. Most importantly, this study makes clear that macroclimate-based SDMs tend to introduce a systematic bias into the perceived species response curves. Additionally, macroclimatic data is unable to identify warm and cold refugia beyond the range edges of species’ distributions. We thus conclude that microclimate-based SDMs are a crucial tool to gain peculiar insights regarding biodiversity conservation, which is needed to align management actions and prioritize conservation efforts.

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.

Biodiversity monitoring is crucial for ecosystem conservation, but ground data collection is limited by cost, time, and scale. Remote sensing is a convenient approach providing frequent, near-real-time information with fine resolution over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (i.e., metrics applied, habitat type, scale, and temporal effects) and conditions under which such a relationship exists. This study investigates the applicability of the SVH for monitoring plant diversity at the landscape scale by comparing the performance of three types of SD metrics. Species richness and functional diversity were calculated for >2000 grid cells of 5′ × 3′ covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 “greenest pixel” composite by applying (i) the standard deviation of NDVI, (ii) Rao’s Q entropy index and (iii) the richness of “spectral communities”. Habitat type (i.e., land cover) was included in the models of the relationship between SD and ground biodiversity. Both species richness and functional diversity showed positive and significant relationships with each SD metric tested. However, SD alone accounted for a small fraction of the deviance explained by the models. Furthermore, the strength of the rela­ tionship depended significantly on habitat type and was highest in natural areas with transitional bushy and herbaceous vegetation. Our results underline that despite the stability of the significance of the relationship between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context- dependent and the factors mediating such a relationship must be carefully considered to avoid misleading conclusions.

The Pladias (Plant Diversity Analysis and Synthesis) Database of the Czech Flora and Vegetation was developed by the Pladias project team in 2014-2018 and has been continuously updated since then. The flora section of the database contains critically revised information on the Czech vascular flora, including 13.6 million plant occurrence records, which are dynamically displayed in maps, and data on 120 plant characteristics (traits, environmental associations and other information), divided into the sections: (1) Habitus and growth type, (2) Leaf, (3) Flower, (4) Fruit, seed and dispersal, (5) Belowground organs and clonality, (6) Trophic mode, (7) Karyology, (8) Taxon origin, (9) Ecological indicator values, (10) Habitat and sociology, (11) Distribution and frequency, and (12) Threats and protection. The vegetation section of the database contains information on Czech vegetation types extracted from the monograph Vegetation of the Czech Republic. The data are supplemented by national botanical bibliographies, electronic versions of the standard national flora and vegetation monographs, a database of more than 19,000 pictures of plant taxa and vegetation types, and digitalmaps (shapefiles) with botanical information. The data from the database are available online on a public portal www.pladias.cz, which also provides download options for various datasets and online identification keys to the species and vegetation types of the Czech Republic. In this paper, we describe the general scope, structure and content of the database, and details of the data on plant characteristics. To illustrate the data and describe the main geographic patterns in selected plant characteristics, we provide maps of mean values of numerical characteristics or proportions of categories for categorical characteristics on the map of the country in a grid of 5 longitudinal × 3 latitudinal minutes (approximately 6.0 km × 5.5 km). We also summarize the main variation patterns in the functional traits in the Czech flora using the principal component analysis.

Dust in the interstellar space is illuminated by cosmic radiation that consists of photons of different wavelengths and energetic charged particles. Whereas the photoemission is rather well understood, charging of dust grains due to interaction of with energetic charged particles was not experimentally studied in detail so far. We report the first laboratory experiment dealing with the interaction of a cosmic dust simulant with energetic charged particles emitted from a radioisotope. Measurements of the charge of micrometer silicate dust grains with an accuracy of one elementary charge revealed several processes leading to the dust charging. The observed average rate of charging events agrees well with prediction of a model based on the continuous slowing down approximation of energetic particles inside the grain. Charge steps larger than one elementary charge were attributed to emission of secondary electrons excited by the primary particle slowing down. The determined yield of secondary electron emission is approximately inversely proportional to the grain radius. The experimental results led us to the formulation of a possible scenario of interstellar dark clouds charging.

A real-time and accurate characterization of the X-ray beam size is essential to enable a large variety of different experiments at free-electron laser facilities. Typically, ablative imprints are employed to determine shape and size of µm-focused X-ray beams. The high accuracy of this state-of-the-art method comes at the expense of the time required to perform an ex-situ image analysis. In contrast, diffraction at a curved grating with suitably varying period and orientation forms a magnified image of the X-ray beam, which can be recorded by a 2D pixelated detector providing beam size and pointing jitter in real time. In this manuscript, we compare results obtained with both techniques, address their advantages and limitations, and demonstrate their excellent agreement. We present an extensive characterization of the FEL beam focused to ≈1 µm by two Kirkpatrick-Baez (KB) mirrors, along with optical metrology slope profiles demonstrating their exceptionally high quality. This work provides a systematic and comprehensive study of the accuracy provided by curved gratings in real-time imaging of X-ray beams at a free-electron laser facility. It is applied here to soft X-rays and can be extended to the hard X-ray range. Furthermore, curved gratings, in combination with a suitable detector, can provide spatial properties of µm-focused X-ray beams at MHz repetition rate.

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.

Forest microclimates differ from regional macroclimates because forest canopies affect energy fluxes near the ground. However, little is known about the environmental drivers of understorey temperature heterogeneity and its effects on species assemblages, especially at landscape scales. We aimed to identify which temperature variables best explain the landscape-scale distribution of forest vegetation and to disentangle the effects of elevation, terrain attributes and canopy cover on understorey temperatures. We measured growing season air temperature, canopy cover and plant community composition within 46 plots established across a 400-km2 area in Czech Republic. We linked growing season maximum, mean and minimum temperatures with elevation, canopy cover and topographic proxies for heat load, topographic position, soil moisture and cold air drainage, and created fine-scale topoclimatic maps of the region. We compared the biological relevance of in situ measured temperatures and temperatures derived from fine-scaled topoclimatic maps and global WorldClim 2 maps. Maximum temperature was the best predictor of understorey plant species composition. Landscape-scale variation in maximum temperature was jointly driven by elevation and terrain topography ( $$R_{{{\text{adj}}.}}^{2}$$ = 0.79) but not by canopy cover. Modelled maximum temperature derived from our topoclimatic maps explained significantly more variation in plant community composition than WorldClim 2 grids. Terrain topography creates landscape-scale variation in maximum temperature, which in turn controls plant species assembly within the forest understorey. Maximum temperature is therefore an important but neglected microclimatic driver of species distribution across landscapes.

Climate measurements are needed at a scale at which organisms live and die. Currently available climate sensors, however, are not well suited for long-term field measurements at such a scale. We have therefore developed a new temperature and moisture logger, the Temperature-Moisture-Sensor (TMS), which we designed for a wide range of ecological applications. The device mimics a small herbaceous plant. Its belowground part houses a patented, proprietary soil moisture sensor working on the time-domain transmission principle. Air, surface and soil temperatures are measured simultaneously by three independent sensors. The TMS data logger has a large memory and long battery life, so it is suitable for taking long-term microclimate measurements in the field. With a data acquisition interval of 15 min, it has sufficient memory to last for almost 15 years. We have thoroughly tested the TMS logger both in the laboratory and in demanding field conditions ranging from tropical rain forests of Africa to high-elevation cold deserts of the Himalayas. The device has provided microclimate measurements in a wide range of environmental conditions and has also performed well in controlled laboratory settings. The key added value of the TMS logger is that it concurrently measures soil moisture as well as soil, surface and air temperature at a biologically relevant scale. It is also able to continuously measure the microclimate for several years even in the most extreme conditions. The device can therefore be used to build extensive tailored field measurement networks providing crucial data about microclimate conditions shaping biological processes in the face of climate change.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately. ispartof: GLOBAL CHANGE BIOLOGY vol:27 issue:23 pages:6307-6319 ispartof: location:England status: published

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes. ispartof: GLOBAL CHANGE BIOLOGY vol:26 issue:11 pages:6616-6629 ispartof: location:England status: published

Research on the Czech flora has a long tradition and yielded a large number of records on the occurrence of plants. Several independent electronic databases were established during the last three decades in order to collect and manage these records. However, this fragmentation and the different characteristics of each database strongly limit the utilization and analyses of plant distribution data. Solving these problems was one of the aims of the Centre of Excellence PLADIAS (Plant Diversity Analysis and Synthesis. 2014-2018). which is also the source of the name of the central database of the project: Pladias - Database of the Czech Flora and Vegetation (www.pladias.cz). We developed an occurrence module as a part of the Pladias database in order to integrate species occurrence data on vascular plants in the Czech Republic for use in pure and applied research. In this paper, we present a description of the structure of this database, data handling and validation, creation of distribution maps based on critically evaluated records as well as descriptions of the original databases and explorative analyses of spatiotemporal and taxonomic coverage of the integrated occurrence data. So far we have integrated more than 13 million records of almost 5 thousand taxa (species, subspecies, varieties and hybrids). which came from five large national databases. seven regional projects and records collected within the PLADIAS project. The Pladias database is now the largest set of data on vascular plant occurrence in the Czech Republic, which is subject to continuous quality control. Analyses of this database pointed to differences in spatial and taxonomic coverage of the source datasets. However, it also showed that the targeted effort of experts focused on validating existing records, as well as the collection of new data is still necessary in order to obtain reliable distribution data for individual species.

Increasingly available high-resolution digital elevation models (DEMs) facilitate the use of fine-scale topographic variables as proxies for microclimatic effects not captured by the coarse-grained macroclimate datasets. Species distributions and community assembly rules are, however directly shaped by microclimate and not by topography. DEM-derived topography, sometimes combined with vegetation structure, is thus widely used as a proxy for microclimatic effects in ecological research and conservation applications. However, the suitability of such a strategy has not been evaluated against in situ measured microclimate and species composition. Because bryophytes are highly sensitive to microclimate, they are ideal model organisms for such evaluation. To provide this much needed evaluation, we simultaneously recorded bryophyte species composition, microclimate, and forest vegetation structure at 218 sampling sites distributed across topographically complex sandstone landscape. Using a LiDAR-based DEM with a 1 m resolution, we calculated eleven topographic variables serving as a topographic proxy for microclimate. To characterize vegetation structure, we used hemispherical photographs and LiDAR canopy height models. Finally, we calculated eleven microclimatic variables from a continuous two-year time- series of air and soil temperature and soil moisture. To evaluate topography and vegetation structure as substitutes for the ecological effect of measured microclimate, we partitioned the variation in bryophyte species composition and richness explained by microclimate, topography, and vegetation structure. In situ measured microclimate was clearly the most important driver of bryophyte assemblages in temperate coniferous forests. The most bryophyte-relevant variables were growing degree days, maximum air temperature, and mean soil moisture. Our results thus showed that topographic variables, even when derived from high-resolution LiDAR data and combined with in situ sampled vegetation structure, cannot fully substitute effects of in situ measured microclimate on forest bryophytes.

Wildfires in temperate Central Europe have traditionally been perceived as a mere consequence of human activity without any relevance to natural forest development, despite their documented frequent occurrence. As a result, knowledge about local fire ecology and patterns of wildfire occurrence in the landscape is lacking. We aimed to reveal the factors influencing the spatial distribution of forest fires in the Czech Republic as a model area for the broader region. Specifically, we aimed to (1) find out which factors influence the occurrence and frequency of the forest fires at the country scale and in a selected fire-prone region; (2) examine the relationship of lightning strikes and their polarity with wildfire incidence; (3) identify the conditions determining areas with naturally driven fire-prone conditions. We took data on 15,985 wildfire records and explored their spatial distribution using GIS layers of human, topographic, climatic and vegetation composition factors. We analysed the data using GLM and hierarchical partitioning methods. Wildfire occurrence was controlled mostly by environmental factors whereas wildfire frequency was strongly driven by human factors. In the selected fire-prone region, the effect of environmental factors was even more pronounced and wildfire frequency was also driven, albeit marginally, by lightning strikes of positive polarity. The pattern of wildfire occurrence in the Czech Republic was similar also to those from regions where wildfire is considered a natural part of local ecosystems. We identified the areas with natural fire-prone conditions which probably led to the development of local fire-adapted ecosystems.

It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs’ vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.

Several aspects of wind farms’ environmental impacts have been thoroughly studied. Their effect on surface temperature, however, has not been sufficiently explored. We analysed variations in land surface temperature observed over 5 months on a large wind farm (42 000 kW maximum output). To describe the near-surface microclimate variability, we measured air temperature at 15 cm above ground using 14 autonomous microclimatic stations arranged in the vicinity of 4 turbines. The observation covered various weather conditions from summer to winter. In contrast to some other recent studies, we confirmed no clear long-term, stable effect of wind turbines on near-ground temperatures. The only effect we found was a daytime warming effect at one of the four turbines. Our results suggest that in mountainous conditions the effect of turbulence caused by wind turbines can be overridden by natural wind turbulence.

Disturbances caused by the European spruce bark beetle (Ips typographus L.) infestations are amongst the main drivers of forest ecosystem dynamics in stands dominated by Norway spruce (Picea abies ...

We used the newly formed database of phytoplankton samples from the Czech Republic, containing 696 taxa from 662 samples of various types of stagnant waters (fishponds, alluvial backwaters, flooded sand- and gravel pits, lakes in abandoned quarries in former coal mines, reservoirs and others) to test for the relationships between phytoplankton composition and productivity, geographical and climatic variables (elevation, temperature, precipitation) and season of the year. As a surrogate of productivity, the number of cells/ml and the total biovolume[μm3]/ml, both spanning over more than six orders of magnitude (from 101 to 107 for the number of cells and from 102 to 109 for the biovolume) were used. The phytoplankton was characterized by its species composition and also by the composition of large taxonomic groups. The data were analysed by constrained ordination (Canonical Correspondence Analysis), including variation partitioning, and by ANOVA of the estimates of species optima based on weighted averages of environmental characteristics. All the explanatory variable groups, i.e. productivity, geography/climate, and season have significant effects on both the species composition and the composition of large taxonomic groups. Productivity is the best predictor of both species and large taxonomic group composition, followed by climatic variables and finally season. The relative effectiveness of productivity as a predictor was considerably greater for large taxonomic groups. The productivity characterized by the number of cells was always a better predictor than when characterized by biovolume. The species optima estimated as weighted averages of corresponding environmental variables show consistent patterns according to large taxonomic groups, but also according to the genera within the groups: in particular, the cyanobacteria and Chrysophyceae preferred on average the most and the least productive environments respectively, however, there were large differences in species preferences also within groups and even within genera. The optima of species on the trophic gradient are suitable characteristics for ecological indication and are presented for more than 400 taxa in the appendix together with estimates of species tolerance.

Forest disturbances are sensitive to climate. However, our understanding of disturbance dynamics in response to climatic changes remains incomplete, particularly regarding large-scale patterns, interaction effects and dampening feedbacks. Here we provide a global synthesis of climate change effects on important abiotic (fire, drought, wind, snow and ice) and biotic (insects and pathogens) disturbance agents. Warmer and drier conditions particularly facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens. Widespread interactions between agents are likely to amplify disturbances, while indirect climate effects such as vegetation changes can dampen long-term disturbance sensitivities to climate. Future changes in disturbance are likely to be most pronounced in coniferous forests and the boreal biome. We conclude that both ecosystems and society should be prepared for an increasingly disturbed future of forests. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Aim Species distributions along an environmental gradient are often not symmetric but skewed towards one end of the gradient. Various explanations for this skewness have been proposed but the patterns of niche asymmetry along extensive environmental gradients have been rarely explored. In this study, we tested three predictions of asymmetric abiotic stress limitation (AASL) hypothesis that predicts a steeper decrease in the probability of occurrence towards the more stressful end of a plant distributional range. Location Ladakh, arid Himalayas, where drought stress dominates in the lower elevation, whilst the cold stress dominates in the upper elevations. Methods Using data from 4062 plots (2640–6150 m a.s.l.), we explored the shapes of response curves of 395 vascular plant species through Huisman-Olff-Fresco models. We compared the observed patterns of niche asymmetry along the elevational gradient with null models. Results Species with symmetric response curves (61.5%) prevailed at lower elevations, whilst species with left-skewed responses (36.2%) were significantly underrepresented up to 3750 m a.s.l. and occurred significantly more frequently at 5150–5450 m a.s.l. Right-skewed responses were rare (2.3%) along the whole gradient. The steepness of the response increased with elevation. Response types were found in similar proportions across different habitats and functional groups. Main conclusions Our results support the predictions of AASL hypothesis for cold limits, but not for dry limits. The low proportion of right-skewed responses over the entire gradient suggests an effective adaptation of the local flora to arid conditions, or sufficient opportunity to avoid drought stress through the presence of favourable habitat patches. The accumulation of skewed responses at high elevations likely reflects shared physiological limits of many steppe species, whose distribution abruptly ends at the transition between steppe and alpine zones. Cold, therefore, represents a stronger barrier to species distribution than drought.