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.

Peatlands store a significant amount of terrestrial organic carbon in plant biomass and soils. The Spruce and Peatland Responses Under Changing Environments (SPRUCE) project is a warming and elevated carbon dioxide (eCO₂) experiment designed to test how the carbon sequestration and storage capacity of peatland ecosystems will respond to climate change. Here, we report changes in the vascular plant community that have occurred during the first five years of SPRUCE. We tracked species composition, diversity, and aboveground net primary production (ANPP) in chambers warmed at a wide range of temperatures (+0, +2.25, +4.5, +6.75, +9 °C), and two CO₂ levels (~400 [ambient] and 900 parts per million). We observed an increase in aboveground vascular plant biomass accumulation, due primarily to an increase in shrub abundance. Overall species diversity decreased substantially, likely due in part to shading by increases in shrub density. The main driver of change in the vascular plant community was temperature, with minimal effects of CO₂ evident. These results indicate an overall increase in ANPP with warming, but highlight the importance of interactions between direct (warming) and indirect (competition) effects in determining how boreal peatlands will respond to climate change.

Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO₂. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems.

Tree rings are the most widely‐used proxy records for reconstructing Common Era temperatures. Tree‐ring records correlate strongly with temperature on an interannual basis, but studies have found discrepancies between tree rings and climate models on longer timescales, indicating that low‐frequency noise could be prevalent in these archives. Using a large network of temperature‐sensitive tree‐ring records, we partition timeseries variance into a common (i.e., “signal”) and non‐climatic (i.e., “noise”) component using a frequency‐resolved signal‐to‐noise ratio (SNR) analysis. We find that the availability of stored resources from prior years (i.e., biological “memory”) dampens the climate signal at high‐frequencies, and that independent noise reduces the SNR on long timescales. We also find that well‐replicated, millennial‐length records had the strongest common signal across centuries. Our work suggests that low‐frequency noise models are appropriate for use in pseudoproxy experiments, and speaks to the continued value of high‐quality data development as a top priority in dendroclimatology.

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.