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ABSTRACT Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, N₂O emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health. IMPORTANCEOur research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system. 

Abstract AimTheoretical, experimental and observational studies have shown that biodiversity–ecosystem functioning (BEF) relationships are influenced by functional community structure through two mutually non‐exclusive mechanisms: (1) the dominance effect (which relates to the traits of the dominant species); and (2) the niche partitioning effect [which relates to functional diversity (FD)]. Although both mechanisms have been studied in plant communities and experiments at small spatial extents, it remains unclear whether evidence from small‐extent case studies translates into a generalizable macroecological pattern. Here, we evaluate dominance and niche partitioning effects simultaneously in grassland systems world‐wide. LocationTwo thousand nine hundred and forty‐one grassland plots globally. Time period2000–2014. Major taxa studiedVascular plants. MethodsWe obtained plot‐based data on functional community structure from the global vegetation plot database “sPlot”, which combines species composition with plant trait data from the “TRY” database. We used data on the community‐weighted mean (CWM) and FD for 18 ecologically relevant plant traits. As an indicator of primary productivity, we extracted the satellite‐derived normalized difference vegetation index (NDVI) from MODIS. Using generalized additive models and deviation partitioning, we estimated the contributions of trait CWM and FD to the variation in annual maximum NDVI, while controlling for climatic variables and spatial structure. ResultsGrassland communities dominated by relatively tall species with acquisitive traits had higher NDVI values, suggesting the prevalence of dominance effects for BEF relationships. We found no support for niche partitioning for the functional traits analysed, because NDVI remained unaffected by FD. Most of the predictive power of traits was shared by climatic predictors and spatial coordinates. This highlights the importance of community assembly processes for BEF relationships in natural communities. Main conclusionsOur analysis provides empirical evidence that plant functional community structure and global patterns in primary productivity are linked through the resource economics and size traits of the dominant species. This is an important test of the hypotheses underlying BEF relationships at the global scale. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL. 

Climate change is increasing the frequency and severity of short-term (~1 y) drought events—the most common duration of drought—globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function—aboveground net primary production (ANPP)—was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought. 

Non-technical summary We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding of Earth's sensitivity to carbon dioxide, finds that permafrost thaw could release more carbon emissions than expected and that the uptake of carbon in tropical ecosystems is weakening. Adverse impacts on human society include increasing water shortages and impacts on mental health. Options for solutions emerge from rethinking economic models, rights-based litigation, strengthened governance systems and a new social contract. The disruption caused by COVID-19 could be seized as an opportunity for positive change, directing economic stimulus towards sustainable investments. Technical summary A synthesis is made of ten fields within climate science where there have been significant advances since mid-2019, through an expert elicitation process with broad disciplinary scope. Findings include: (1) a better understanding of equilibrium climate sensitivity; (2) abrupt thaw as an accelerator of carbon release from permafrost; (3) changes to global and regional land carbon sinks; (4) impacts of climate change on water crises, including equity perspectives; (5) adverse effects on mental health from climate change; (6) immediate effects on climate of the COVID-19 pandemic and requirements for recovery packages to deliver on the Paris Agreement; (7) suggested long-term changes to governance and a social contract to address climate change, learning from the current pandemic, (8) updated positive cost–benefit ratio and new perspectives on the potential for green growth in the short- and long-term perspective; (9) urban electrification as a strategy to move towards low-carbon energy systems and (10) rights-based litigation as an increasingly important method to address climate change, with recent clarifications on the legal standing and representation of future generations. Social media summary Stronger permafrost thaw, COVID-19 effects and growing mental health impacts among highlights of latest climate science. 

A globally distributed field experiment shows that wood decay, particularly by termites, depends on temperature. 

Abstract The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 . 

Abstract Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.