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Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time‐specific ML models based on 7‐day and 30‐day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non‐negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time. Plain Language Summary As a comprehensive man‐made infrastructure, reservoirs significantly influence the chemical composition, reactivity, and turnover time of dissolved organic matter (DOM) within inland waters. However, it remains elusive how DOM molecules respond to microbial processing over different time scales. Besides the well‐recognized predictive power of machine learning (ML) methodologies, we delved into the processes of tuning the ML models to acquire additional interpretability. We used an under‐sampling strategy to improve model performance and simultaneously observed the variations in model performance metrics for different biological reactivity pools over incubations with different durations. We find that shorter incubation periods result in a broader range of molecules disappearing, with a greater contribution of stochasticity, while the longer incubation allows the successive biodegradation of oxygen‐poor compounds, with a greater contribution of directed degradation. As a complement to traditional geochemical methods, we unveiled a novel perspective in understanding the DOM dynamics over time using ML. Key Points Machine learning (ML) models were built to correlate the molecular composition and biological reactivity at the world's largest reservoir Shorter incubations result in a broader range of molecules disappearing, with a greater contribution of stochasticity Tuning the ML model contributes to yield additional interpretability beyond its well‐recognized predictive power

The elemental content of organisms links cellular biochemistry to ecological processes, from physiology to nutrient dynamics. While plant stoichiometry is thought to vary with climate and nutrient availability across latitudes, the consistency of these patterns across trophic groups and realms remains unclear. Using the StoichLife database, which includes nitrogen and phosphorus content data for 5443 species across 1390 sites, we examine how solar energy (temperature, radiation) and nutrients (nitrogen and phosphorus) influence stoichiometric variation. We find that plant stoichiometry in terrestrial and freshwater ecosystems is more strongly associated with environmental gradients, particularly nitrogen deposition, than animal stoichiometry. Contrary to expectations, temperature, radiation, and labile P show limited global effects. Latitudinal patterns in stoichiometry are more closely associated with species turnover rather than intraspecific variation. Given the strong links between stoichiometry and organismal performance, these findings underscore the need to predict the ecological consequences of anthropogenic disruption to global biogeochemical cycles. Organisms vary in their nitrogen and phosphorus content, shaping ecological and evolutionary processes. This study shows that nitrogen deposition is a consistent global factor associated with plant and animal stoichiometry.

Microbial communities and dissolved organic matter (DOM) are intrinsically linked within the global carbon cycle. Demonstrating this link on a molecular level is hampered by the complexity of both counterparts. We have now investigated this connection within intertidal beach sediments, characterized by a runnel-ridge system and subterranean groundwater discharge. Using datasets generated by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and Ilumina-sequencing of 16S rRNA genes, we predicted metabolic functions and determined links between bacterial communities and DOM composition. Four bacterial clusters were defined, reflecting differences within the community compositions. Those were attributed to distinct areas, depths, or metabolic niches. Cluster I was found throughout all surface sediments, probably involved in algal-polymer degradation. In ridge and low water line samples, cluster III became prominent. Associated porewaters indicated an influence of terrestrial DOM and the release of aromatic compounds from reactive iron oxides. Cluster IV showed the highest seasonality and was associated with species previously reported from a subsurface bloom. Interestingly, Cluster II harbored several members of the candidate phyla radiation (CPR) and was related to highly degraded DOM. This may be one of the first geochemical proofs for the role of candidate phyla in the degradation of highly refractory DOM.

The elemental content of life is a key trait shaping ecology and evolution, yet organismal stoichiometry has largely been studied on a case-by-case basis. This limitation has hindered our ability to identify broad patterns and mechanisms across taxa and ecosystems. To address this, we present StoichLife, a global dataset of 28,049 records from 5,876 species spanning terrestrial, freshwater, and marine realms. Compiled from published and unpublished sources, StoichLife documents elemental content and stoichiometric ratios (%C, %N, %P, C:N, C:P, and N:P) for individual plants and animals. The dataset is standardized and, where available, includes information on taxonomy, habitat, body mass (for animals), geography, and environmental conditions such as temperature, solar radiation, and nutrient availability. By providing an unprecedented breadth of organismal stoichiometry, StoichLife enables the exploration of global patterns, ecological and evolutionary drivers, and context-dependent variations. This resource advances our understanding of the chemical makeup of life and its responses to environmental change, supporting progress in ecological stoichiometry and related fields.

The availability of growth data in N. norvegicus is important for management purposes due to a lack of aging criteria and the commercial importance of fisheries in this species. Growth varies as a function of stock density, hence comparisons of growth rates between stocks at known density is particularly valuable. Growth is also related to starting size in males, making raw data on size-specific growth rates more valuable. Internally injected passive tags allowed us to track the growth of male and female individuals over one or two years. The spatial position of tagged recaptures was recorded to measure site fidelity of tagged releases. A total of 3300 pots were fished and their spatial positions were recorded to enable Catch Per Unit Effort calculations. Similarly, spatially geo-referenced v-notching and notched recovery enables spatially gridded densities to be calculated. Finally, acoustic mapping was carried out both on and off the fishing ground and was ground-truthed with sedimentology from grabs at 22 stations. These data are useful for fisheries and macroecological studies.Design Type(s)observation design • organism development design • longitudinal study designMeasurement Type(s)growth • habitatTechnology Type(s)observational methodFactor Type(s)geographic location • biological sex • notchedSample Characteristic(s)Nephrops norvegicus • Ireland • ocean biomeMachine-accessible metadata file describing the reported data (ISA-Tab format)

The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here we use multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km 3 per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors—including human-induced warming and water-management practices—rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability. This study quantifies and attributes the changes of surface water storage in global dryland basins over 1985–2020, indicating that long-term changes are mainly linked to anthropogenic factors rather than precipitation.

To understand ecosystem responses to anthropogenic global change, a prevailing framework is the definition of threshold levels of pressure, above which response magnitudes and their variances increase disproportionately. However, we lack systematic quantitative evidence as to whether empirical data allow definition of such thresholds. Here, we summarize 36 meta-analyses measuring more than 4,600 global change impacts on natural communities. We find that threshold transgressions were rarely detectable, either within or across meta-analyses. Instead, ecological responses were characterized mostly by progressively increasing magnitude and variance when pressure increased. Sensitivity analyses with modelled data revealed that minor variances in the response are sufficient to preclude the detection of thresholds from data, even if they are present. The simulations reinforced our contention that global change biology needs to abandon the general expectation that system properties allow defining thresholds as a way to manage nature under global change. Rather, highly variable responses, even under weak pressures, suggest that ‘safe-operating spaces’ are unlikely to be quantifiable. The utility of the threshold paradigm, such that relatively small perturbations drive abrupt ecosystem changes, is challenged by a synthesis of 36 meta-analyses, which detected few signatures of thresholds from over 4,600 global change impacts on natural ecological communities.

Dissolved black carbon (DBC) plays a key role in global carbon cycle and pollutant transport. However, the time‐consuming and labor‐intensive chemical analysis limits its spatiotemporal resolution. Here, we developed models to predict DBC from chromophoric dissolved organic matter (CDOM) measurements across the land‐to‐ocean continuum. We found that the mean ratio of DBC to light absorbance at 254 nm (a254) changed <20% among different environments. However, a single‐wavelength model is inadequate for precise prediction due to microbial production of CDOM. Incorporating longer wavelengths using multiple linear regression improves model performance. Random Forest Regression using the full spectral range performed even better at all environments, including the open ocean, achieving a root mean square logarithmic error of <0.15, median symmetric accuracy of <10%, and R2 of >0.85. This study demonstrates the feasibility of using CDOM to predict DBC concentrations and highlights the potential for in situ monitoring and remote sensing applications.

High concentrations of cyanobacterial toxins such as microcystin represent a global challenge to water quality in lakes, threatening health, economies and ecosystem stability. Lakes are sentinels of climate change but how warming will affect microcystin concentrations is still unclear. Here we examine how warming impacts the probability of exceeding microcystin water quality thresholds across 2,804 lakes in the United States and show how future warming will alter these probabilities. We find that higher temperatures consistently increase the likelihood of microcystin occurrence but that the probability of microcystin concentrations above water quality thresholds is highest for water temperatures between 20 and 25 °C. Regions with temperatures that promote microcystin will shift to higher latitudes in the coming decades, leading to relative changes in exceedance probabilities of more than 50% in many basins of the United States. High nitrogen concentrations amplify the impact of rising temperatures, calling for increased awareness of a substantial hazard to ecosystems and human health under global warming.