The temporal dynamics of dissolved organic matter (DOM) are inherently linked with the functioning of aquatic ecosystems. Because DOM represents a complex mixture of millions of different compounds, the statistical analysis of DOM dynamics poses a huge challenge. Here, we present a statistical approach based on hierarchical clustering of time series that groups DOM compounds with synchronous dynamics. We applied this approach to time series of Fourier-transform ion cyclotron resonance mass spectrometry data of DOM sampled over a period of 26 months near Helgoland, an island in the Southern North Sea. We identified three DOM clusters, which represented a total of 1392 different molecular formulae and showed distinct chemical properties and noticeably compound matches within the PubChem database. Correlations of the three DOM clusters with abundance data of prokaryote and phytoplankton species and with environmental parameters provided consistent indications on the potential origin of the clustered compounds. The first cluster integrated terrestrial DOM originating from riverine discharge reaching Helgoland waters. The second cluster was attributed to DOM related to phytoplankton and microbial activity, whereas the third cluster was interpreted as representing the marine refractory DOM background. Accordingly, while further partitioning divided each of the first two clusters into five sub-clusters with distinct temporal dynamics and molecular characteristics, the third cluster persisted as a stable feature. Applying a purely mathematical approach, we thus confirmed the differential dynamics of individual DOM compounds and compound groups and showed that temporal dynamics of dissolved molecules are linked to their origin and transformation history.

Fourier ion cyclotron resonance mass spectrometry (FT-ICR MS), one of the state-of-the-art ultrahigh-resolution techniques, is widely used in dissolved organic matter (DOM) research. As research that focuses on identifying DOM molecular fingerprints increases tremendously, there is and will be an urgent need to compare among studies. Different research groups usually use various types of data processing and interpretation strategies. It is critical to explore if different interpretation strategies will impact the comparability of FT-ICR MS results and their biogeochemical interpretations. To address this question, we selected DOM samples along a freshwater-to-marine continuum to be measured by negative-ion mode electrospray ionization FT-ICR MS. We interpreted the raw MS data using different strategies, compared the results and evaluated the interpretation strategy-induced effects on biogeochemical interpretations. Our results show that a total of 3827 formulas that account for 91.6% +/- 4.1% (on average) of the total intensity are assigned in all interpretation strategies, while 6521 formulas for 8.4% +/- 4.1% (on average) of the total intensity are not commonly assigned in all three interpretation strategies. We conclude that (i) different interpretation strategies do not significantly affect the geochemical stories relied on DOM molecular composition, and (ii) comparison based on intensity results gives more reliable results than the formular number alone. Moreover, we also provide raw and interpreted data (by our different strategies) for the community to compare their interpretation. We aim to call attention to the community for improving the comparability of FT-ICR MS results and facilitating the integration of DOM molecular composition among global studies.

Key message Our results indicate that nitrogen deposition is likely to adversely affect forest bryophyte communities, having negative impacts in terms of increased dominance of nitrophilic species at the expense of N-sensitive species and a decrease in evenness. Context Elevated atmospheric deposition of nitrogen (N) has long been recognised as a threat to biodiversity and, despite declines in European emission levels, will remain a threat in the future. Aims It has proven difficult to show clear large-scale impacts of N deposition on vascular forest understorey species, and few studies have looked at impacts on forest bryophytes. Here, we assess the impact of nitrogen deposition on forest bryophyte communities. Methods We used data from 187 plots included in European monitoring schemes to analyse the relationship between levels of throughfall nitrogen deposition and bryophyte taxonomic and functional diversity and community nitrogen preference. Results We found that nitrogen deposition is significantly associated with increased bryophyte community nitrogen preference and decreases in species evenness. Conclusion Our results indicate that nitrogen deposition is likely to adversely affect forest bryophyte communities, having negative impacts in terms of increased dominance of nitrophilic species at the expense of N-sensitive species and a decrease in species evenness.

Identifying drivers of the molecular composition of dissolved organic matter (DOM) is essential to understand the global carbon cycle, but an unambiguous interpretation of observed patterns is challenging due to the presence of confounding factors that affect the DOM composition. Here, we show, by combining ultrahigh-resolution mass spectrometry and nuclear magnetic resonance spectroscopy, that the DOM molecular composition varies considerably among 43 lakes in East Antarctica that are isolated from terrestrial inputs and human influence. The DOM composition in these lakes is primarily driven by differences in the degree of photodegradation, sulfurization, and pH. Remarkable molecular beta-diversity of DOM was found that rivals the dissimilarity between DOM of rivers and the deep ocean, which was driven by environmental dissimilarity rather than the spatial distance. Our results emphasize that the extensive molecular diversity of DOM can arise even in one of the most pristine and organic matter source-limited environments on Earth, but at the same time the DOM composition is predictable by environmental variables and the lakes' ecological history.

Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (delta 13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as delta 13C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having delta 13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted delta 13C values with a mean absolute error (MAE) of 0.30%o on the training data set, surpassing traditional linear regression methods (MAE 0.85%o). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted delta 13C values in samples without known delta 13C values and in other published data sets, reflecting the delta 13C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future.

How is trait diversity in a community apportioned between and within coevolving species? Disruptive selection may result in either a few species with large intraspecific trait variation (ITV) or many species with different mean traits but little ITV. Similar questions arise in spatially structured communities: heterogeneous environments could result in either a few species that exhibit local adaptation or many species with different mean traits but little local adaptation. To date, theory has been well-equipped to either include ITV or to dynamically determine the number of coexisting species, but not both. Here, we devise a theoretical framework that combines these facets and apply it to the above questions of how trait variation is apportioned within and between species in unstructured and structured populations, using two simple models of Lotka-Volterra competition. For unstructured communities, we find that as the breadth of the resource spectrum increases, ITV goes from being unimportant to crucial for characterizing the community. For spatially structured communities on two patches, we find no local adaptation, symmetric local adaptation, or asymmetric local adaptation, depending on how much the patches differ. Our framework provides a general approach to incorporate ITV in models of eco-evolutionary community assembly.

Climate warming is altering life cycles of ectotherms by advancing phenology and decreasing generation times. Theoretical models provide powerful tools to investigate these effects of climate warming on consumer-resource population dynamics. Yet, existing theory primarily considers organisms with simplified life histories in constant temperature environments, making it difficult to predict how warming will affect organisms with complex life cycles in seasonal environments. We develop a size-structured consumer-resource model with seasonal temperature dependence, parameterized for a freshwater insect consuming zooplankton. We simulate how climate warming in a seasonal environment could alter a key life-history trait of the consumer, number of generations per year, mediating responses of consumer-resource population sizes and consumer persistence. We find that, with warming, consumer population sizes increase through multiple mechanisms. First, warming decreases generation times by increasing rates of resource ingestion and growth and/or lengthening the growing season. Second, these life-history changes shorten the juvenile stage, increasing the number of emerging adults and population-level reproduction. Unstructured models with similar assumptions found that warming destabilized consumer-resource dynamics. By contrast, our size-structured model predicts stability and consumer persistence. Our study suggests that, in seasonal environments experiencing climate warming, life-history changes that lead to shorter generation times could delay population extinctions.

Despite the well known scale-dependency of ecological interactions, relatively little attention has been paid to understanding the dynamic interplay between various spatial scales. This is especially notable in metacommunity theory, where births and deaths dominate dynamics within patches (the local scale), and dispersal and environmental stochasticity dominate dynamics between patches (the regional scale). By considering the interplay of local and regional scales in metacommunities, the fundamental processes of community ecology-selection, drift, and dispersal-can be unified into a single theoretical framework. Here, we analyze three related spatial models that build on the classic two-species Lotka-Volterra competition model. Two open-system models focus on a single patch coupled to a larger fixed landscape by dispersal. The first is deterministic, while the second adds demographic stochasticity to allow ecological drift. Finally, the third model is a true metacommunity model with dispersal between a large number of local patches, which allows feedback between local and regional scales and captures the well studied metacommunity paradigms as special cases. Unlike previous simulation models, our metacommunity model allows the numerical calculation of equilibria and invasion criteria to precisely determine the outcome of competition at the regional scale. We show that both dispersal and stochasticity can lead to regional outcomes that are different than predicted by the classic Lotka-Volterra competition model. Regional exclusion can occur when the nonspatial model predicts coexistence or founder control, due to ecological drift or asymmetric stochastic switching between basins of attraction, respectively. Regional coexistence can result from local coexistence mechanisms or through competition-colonization or successional-niche trade-offs. Larger dispersal rates are typically competitively advantageous, except in the case of local founder control, which can favor intermediate dispersal rates. Broadly, our models demonstrate the importance of feedback between local and regional scales in competitive metacommunities and provide a unifying framework for understanding how selection, drift, and dispersal jointly shape ecological communities.

We investigate the evolution of superconductivity and structure with pressure for the new superconductor FeS (T-c approximate to 4.5 K), a sulfide counterpart of FeSe. A rapid suppression of T-c and vanishing of superconductivity at 4.0 GPa are observed, followed by a second superconducting dome from 5.0 to 22.3 GPa with a 30% enhancement in maximum T-c. An onsite tetragonal to hexagonal phase transition occurs around 7.0 GPa, followed by a broad pressure range of phase coexistence. The residual deformed tetragonal phase is considered as the source of second superconducting dome. The observation of two superconducting domes in iron-based superconductors poses great challenges for understanding their pairing mechanism.

Plant growth and crop harvest are impacted by both climate change and air pollution. However, their relative importance in crop yields remains elusive, especially in heavily polluted regions. Here we develop crop yield prediction models, based on a large volume of historical crop data, as well as climate and pollution records in China since 1980. A long-term surface ozone concentration data set is developed from a machine-learning model and various observations. An assessment of four climate and pollution factors reveals the critical role of particulate and ozone pollution in regulating interannual variations of crop yields in China. During 2010-2018, we find that the particulate pollution mitigation outweighs the negative impacts of concurrent climate change, resulting in 0.5%-1.9% net yield increases nationwide, despite of the ozone increases in the North China Plain. Looking to the future, the impacts of climate change, particularly from surface temperature increase, will dominate over pollution factors and profoundly reduce future maize and rice yields by 0.6 to 2.8% 10 yr(-1) by 2050. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming.