Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km²resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km²pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Ecosystems integrity and services are threatened by anthropogenic global changes. Mitigating and adapting to these changes require knowledge of ecosystem functioning in the expected novel environments, informed in large part through experimentation and modelling. This paper describes 13 advanced controlled environment facilities for experimental ecosystem studies, herein termed ecotrons, open to the international community. Ecotrons enable simulation of a wide range of natural environmental conditions in replicated and independent experimental units while measuring various ecosystem processes. This capacity to realistically control ecosystem environments is used to emulate a variety of climatic scenarios and soil conditions, in natural sunlight or through broad‐spectrum lighting. The use of large ecosystem samples, intact or reconstructed, minimizes border effects and increases biological and physical complexity. Measurements of concentrations of greenhouse trace gases as well as their net exchange between the ecosystem and the atmosphere are performed in most ecotrons, often quasi continuously. The flow of matter is often tracked with the use of stable isotope tracers of carbon and other elements. Equipment is available for measurements of soil water status as well as root and canopy growth. The experiments ran so far emphasize the diversity of the hosted research. Half of them concern global changes, often with a manipulation of more than one driver. About a quarter deal with the impact of biodiversity loss on ecosystem functioning and one quarter with ecosystem or plant physiology. We discuss how the methodology for environmental simulation and process measurements, especially in soil, can be improved and stress the need to establish stronger links with modelling in future projects. These developments will enable further improvements in mechanistic understanding and predictive capacity of ecotron research which will play, in complementarity with field experimentation and monitoring, a crucial role in exploring the ecosystem consequences of environmental changes.

A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.

Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.