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Abstract Theoretical understanding of the upward vertical motion into the surface layer during coastal upwelling is often based on steady linear Ekman dynamics. In steady linear theory, the divergence of surface transport that leads to upwelling is associated with either overlap of the frictional boundary layers over the inner shelf or wind stress curl farther offshore. However, the alongshore current associated with a coastal upwelling front is associated with relative vorticity which modifies surface transport. A new nonlinear theory shows that, under spatially uniform wind forcing, the fraction of Ekman transport upwelled over the inner shelf tends to decrease with increasing slope Burger numberSas the baroclinic alongshore jet strengthens and cyclonic vorticity increases. Similar patterns are shown in a set of idealized numerical experiments. Unsteadiness in the alongshore flow, neglected in the theory, strongly influences the cross-shelf distribution of upwelling in the numerical model at locations offshore of the inner shelf and near the core of the upwelling jet. The theory and numerical modeling are extended to explore the effect of a large-scale alongshore pressure gradient force (PGF) that forms in response to alongshore variations in wind stress. At highS, a baroclinic PGF is associated with a shallow onshore return flow, but the fraction of modeled upwelling that occurs over the inner shelf is not strongly affected. The results emphasize that the strength and location of the alongshore jet strongly influence the cross-shelf distribution of coastal upwelling in the presence of stratification and a sloping bottom. Significance StatementWind-driven coastal upwelling is important for supplying nutrients to phytoplankton at the base of marine ecosystems. This study uses simple models to investigate factors that determine where upwelling of water into the surface layer occurs when wind blows along the coastline. With a larger difference in density between the surface and bottom layers, a steeply sloping seafloor, and at latitudes closer to the equator, the upwelling region shifts farther offshore because of the strength and location of faster ocean currents that flow along the coastline. 

The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generallycomplex. The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype–phenotype mappings. The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power. Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power. Our implementation of the approach—dubbed transcriptome-wide conditional variational autoencoder (TWAVE)—includes a variational autoencoder trained on human transcriptional data, which is incorporated into an optimization framework. Given a trait phenotype, TWAVE generates expression profiles, which we dimensionally reduce by identifying independently varying generalized pathways (eigengenes). We then conduct constrained optimization to find causal gene sets that are the gene perturbations whose measured transcriptomic responses best explain trait phenotype differences. By considering several complex traits, we show that the approach identifies causal genes that cannot be detected by the primary existing techniques. Moreover, the approach identifies complex diseases caused by distinct sets of genes, meaning that the disease is polygenicandexhibits distinct subtypes driven by different genotype–phenotype mappings. We suggest that the approach will enable the design of tailored experiments to identify multigenic targets to address complex diseases. 

ABSTRACT Fire is a common ecological disturbance that structures terrestrial ecosystems and biological communities. The ability of fires to contribute to ecosystem heterogeneity has been termed pyrodiversity and has been directly linked to biodiversity (i.e., the pyrodiversity–biodiversity hypothesis). Since climate change models predict increases in fire frequency, understanding how fire pyrodiversity influences soil microbes is important for predicting how ecosystems will respond to fire regime changes. Here we tested how fire frequency‐driven changes in burn patterns (i.e., pyrodiversity) influenced soil microbial communities and diversity. We assessed pyrodiversity effects on soil microbes by manipulating fire frequency (annual vs. biennial fires) in a tallgrass prairie restoration and evaluating how changes in burn patterns influenced microbial communities (bacteria and fungi). Annual burns produced more heterogeneous burn patterns (higher pyrodiversity) that were linked to shifts in fungal and bacterial community composition. While fire frequency did not influence microbial (bacteria and fungi) alpha diversity, beta diversity did increase with pyrodiversity. Changes in fungal community composition were not linked to burn patterns, suggesting that pyrodiversity effects on other ecosystem components (e.g., plants and soil characteristics) influenced fungal community dynamics and the greater beta diversity observed in the annually burned plots. Shifts in bacterial community composition were linked to variation in higher severity burn pattern components (grey and white ash), suggesting that thermotolerance contributed to the observed changes in bacterial community composition and lower beta diversity in the biennially burned plots. This demonstrates that fire frequency‐driven increases in pyrodiversity augment biodiversity and may influence productivity in fire‐prone ecosystems. 

Abstract Graphene is a privileged 2D platform for hosting confined light-matter excitations known as surface plasmon polaritons (SPPs), as it possesses low intrinsic losses and a high degree of optical confinement. However, the isotropic nature of graphene limits its ability to guide and focus SPPs, making it less suitable than anisotropic elliptical and hyperbolic materials for polaritonic lensing and canalization. Here, we present graphene/CrSBr as an engineered 2D interface that hosts highly anisotropic SPP propagation across mid-infrared and terahertz energies. Using scanning tunneling microscopy, scattering-type scanning near-field optical microscopy, and first-principles calculations, we demonstrate mutual doping in excess of 10¹³cm^–2holes/electrons between the interfacial layers of graphene/CrSBr. SPPs in graphene activated by charge transfer interact with charge-induced electronic anisotropy in the interfacial doped CrSBr, leading to preferential SPP propagation along the quasi-1D chains that compose each CrSBr layer. This multifaceted proximity effect both creates SPPs and endows them with anisotropic propagation lengths that differ by an order-of-magnitude between the in-plane crystallographic axes of CrSBr. 

Massive scalar fields are promising candidates for addressing many unresolved problems in fundamental physics. We report the first model-agnostic Bayesian search of massive scalar fields that are nonminimally coupled to gravity in LIGO/Virgo/KAGRA gravitational-wave data. We find no evidence for such fields and place the most stringent upper limits on their coupling for scalar masses $\lesssim 2\times {10}^{-12}\mathrm{eV}$. We exemplify the strength of these bounds by applying them to massive scalar-Gauss-Bonnet gravity, finding the tightest constraints on the coupling constant to date, $\sqrt{{\alpha }_{\mathrm{GB}}}\lesssim 1\mathrm{km}$for scalar masses $\lesssim {10}^{-13}\mathrm{eV}$to 90% credible level. Published by the American Physical Society2025 

Societal Impact StatementAgricultural practices have had a negative impact on the physical, chemical, and biological components of soil. Perennial cropping systems that facilitate positive soil microbial interactions could not only rebuild soils but also sustain productivity through expected variations in environmental conditions. Here, we show the presence of arbuscular mycorrhizal (AM) fungi, soil symbionts that can improve host performance and soil health, increased the growth of intermediate wheatgrass, a novel perennial grain crop, in populations that have been increasingly bred for desirable agricultural characteristics. The right pairing of intermediate wheatgrass and a beneficial AM fungal community could lead to more sustainable agroecosystems. SummaryIntermediate wheatgrass (IWG) is a novel perennial grain that can provide many soil health benefits in agroecosystems; however, little is known about how selection for agronomic traits has impacted interactions with soil biota. Here, we assess how the selection for agronomic traits in IWG has impacted its relationship with arbuscular mycorrhizal (AM) fungi.First, growth response to AM fungi was compared across five generations of IWG with varying degrees of selection. Second, variation in AM fungal responsiveness was compared among genets of IWG individuals within a more advanced generation. Finally, a meta‐analysis was performed on all published studies exploring AM fungal inocula effects on IWG performance to increase understanding of selection effects.AM fungal responsiveness increased with selection for agronomic traits, responsiveness varied among genets in the advanced generation, and a majority of genets performed better in the presence of AM fungi. The meta‐analysis supported the findings that AM fungal responsiveness has increased with selection in IWG.Further studies are needed to realize the combined potential soil health and sustainability benefits of IWG and AM fungi, including assessment of symbiotic benefits beyond biomass production, identification of IWG traits correlated with responsiveness, and characterization of AM fungal community response to IWG. 

Abstract Specialist insects are especially susceptible to loss of genetic diversity in the face of habitat destruction and fragmentation. Implementing effective conservation practices for specialist insects will benefit from knowledge of population structure and genetic diversity. Because insects are hyper-diverse, characterizing the population structure of all species within the insect community is untenable, even if focused within a particular habitat type. Thus, concentrating on a single species specialized to a particular habitat type is needed to infer general trends. Here, we investigate the range-wide population genetics ofTetraopes texanusHorn 1878 (Coleoptera: Cerambycidae), which provides a useful model of grassland insects due to its’ habitat specificity and unique biology.Tetraopes texanusoccurs primarily in Texas and Oklahoma, into Northern Mexico, and possibly into eastern New Mexico but also occurs in Black Belt prairies of Mississippi and Alabama. Mitochondrial and nuclear DNA (RAD-seq) analysis identified two distinct population clusters ofT. texanuscorresponding to the Texas and Oklahoma population and the Mississippi and Alabama population. Demographic models indicate ongoing, though incomplete, isolation of the two populations, with estimated dates of divergence in the mid-Pleistocene, coinciding with the end of a glacial period and a shift in glacial interval. These results can inform conservation of grassland adapted insects and offers insight to the biogeography of the Gulf Coastal Plain.