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Background and aims: Nutrient addition increases plant aboveground production but causes species richness decline in many herbaceous communities. Asymmetric competition for light and detrimental effects of nitrogen have been shown to cause species richness decline in mesic ecosystems. However, it remains unclear whether and how other limiting factors may also play a role in the decline of species richness, especially in ecosystems where soil water could be more limiting. Methods: We conducted a meta-analysis of > 1600 experiments on nutrient and water addition across grasslands worldwide. Results: We find that nitrogen addition, alone or combined with other nutrients, significantly increases aboveground production but decreases species richness. However, water addition can avoid species loss when nutrients were added, indicating that water is a crucial limiting resource in driving species richness decline under nutrient addition. Overall, water limitation may be the primary driver of species richness decline under nutrient addition in approximately 70% of global grassland areas where mean annual soil water content is ≤ 30%. Therefore, as nutrient availability increases in global grasslands, soil moisture limitation may be responsible for the decline of species richness in regions. Conclusion: Our study quantifies the soil water threshold below which plant species is mainly driven by water limitation, and highlights a novel and widespread mechanism driving species richness decline in global grasslands under nutrient addition. 

Abstract Meta‐analysis (MA), a powerful tool for synthesizing reported results, is influential in ecology. While ecologists have long been well‐informed on the potential problems associated with nonindependence in experimental work (e.g., pseudoreplication), they have, until recently, largely neglected this issue in MA. However, results used in MAs are likely much more similar when they come from the same locality, system, or laboratory. A simple and common form of nonindependence in MA arises when multiple data points, that is, observed effect sizes, come from the same paper. We obtained original data from 20 published MAs, reconstructed the published analyses, and then, for 14 that had not accounted for a paper effect, used three approaches to evaluate whether within‐paper nonindependence was a problem. First, we found that “nonsense” explanatory variables added to the original analyses were statistically significant (p < 0.05) far more often than the expected 5% (25%–50% for four nonsense variables). For example, the number of vowels in the first author's name had a significant effect 50% of the time. Second, we found that an added dummy variable, which was randomly assigned at one of two levels, was statistically significant an average of 38% of the time, far exceeding the expected 5%. Even after including a random paper effect in the analyses, there was still an excess of significant results, suggesting that the within‐paper nonindependence was more complex than modeled with the random paper effect. Third, we repeated the original MAs that did not include random paper effects (n = 14 MAs) but added a random paper effect to each revised analysis. In 12 out of the 14 MAs, an added random effect was statistically significant (indicating group nonindependence that was not accounted for in the original analyses), and often the original inferences were substantially altered. Further, incorporating random paper effects was not a sufficient solution to nonindependence. Thus, problems resulting from nonindependence are often substantial, and accounting for the problem will likely require careful consideration of the details of the potential dependence among observed effect sizes. MAs that do not properly account for this problem may reach unwarranted conclusions. 

Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. [1] to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O’Dea et al. [2] can improve the quality of ecological meta-analyses. 

Abstract Climate change is rapidly altering hydrological processes and consequently the structure and functioning of Arctic ecosystems. Predicting how these alterations will shape biogeochemical responses in rivers remains a major challenge. We measured [C]arbon and [N]itrogen concentrations continuously from two Arctic watersheds capturing a wide range of flow conditions to assess understudied event‐scale C and N concentration‐discharge (C‐Q) behavior and post‐event recovery of stoichiometric conditions. The watersheds represent low‐gradient, tundra landscapes typical of the eastern Brooks Range on the North Slope of Alaska and are part of the Arctic Long‐Term Ecological Research sites: the Kuparuk River and Oksrukuyik Creek. In both watersheds, we deployed high‐frequency optical sensors to measure dissolved organic carbon (DOC), nitrate (), and total dissolved nitrogen (TDN) for five consecutive thaw seasons (2017–2021). Our analyses revealed a lag in DOC: stoichiometric recovery after a hydrologic perturbation: while DOC was consistently elevated after high flows, diluted during rainfall events and consequently, recovery in post‐event concentration was delayed. Conversely, the co‐enrichment of TDN at high flows, even in watersheds with relatively high N‐demand, represents a potential “leak” of hydrologically available organic N to downstream ecosystems. Our use of high‐frequency, long‐term optical sensors provides an improved method to estimate carbon and nutrient budgets and stoichiometric recovery behavior across event and seasonal timescales, enabling new insights and conceptualizations of a changing Arctic, such as assessing ecosystem disturbance and recovery across multiple timescales. 

Abstract Topologically ordered phases of matter elude Landau’s symmetry-breaking theory, featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise to exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation of signatures of such a phenomenon—a prethermal topologically ordered time crystal—with programmable superconducting qubits arranged on a square lattice. By periodically driving the superconducting qubits with a surface code Hamiltonian, we observe discrete time-translation symmetry breaking dynamics that is only manifested in the subharmonic temporal response of nonlocal logical operators. We further connect the observed dynamics to the underlying topological order by measuring a nonzero topological entanglement entropy and studying its subsequent dynamics. Our results demonstrate the potential to explore exotic topologically ordered nonequilibrium phases of matter with noisy intermediate-scale quantum processors. 

Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise to exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation of signatures of such a phenomenon -- a prethermal topologically ordered time crystal -- with programmable superconducting qubits arranged on a square lattice. By periodically driving the superconducting qubits with a surface-code Hamiltonian, we observe discrete time-translation symmetry breaking dynamics that is only manifested in the subharmonic temporal response of nonlocal logical operators. We further connect the observed dynamics to the underlying topological order by measuring a nonzero topological entanglement entropy and studying its subsequent dynamics. Our results demonstrate the potential to explore exotic topologically ordered nonequilibrium phases of matter with noisy intermediate-scale quantum processors.