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Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided.Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills.Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. 

Abstract Cryptic species (evolutionarily distinct lineages that do not align with morphologically defined species) are being increasingly discovered but are poorly integrated into ecological theory. In particular, we still lack a useful understanding of if and how cryptic species differ in ways that affect community recovery from disturbances and responses to anthropogenic stressors, such as the removal of consumers and pollution from nutrients. On coral reefs, nutrient pollution increases the growth of macroalgae that displace corals. Reductions in herbivorous fishes reduce the suppression of macroalgae, while reductions in coralivorous fishes reduce predation on corals. An unresolved question is if and how cryptic coral species respond differently to these impacts, thereby differing in their ability to influence coral community dynamics and maintain coral dominance. Therefore, we assessed how the response of crypticPocilloporaspecies over a period of three years following a simulated disturbance from a cyclone depended on the experimental reduction of fish consumer pressure and nutrient addition. After three years, five morphologically cryptic, but genetically distinct,Pocilloporaspecies recruited to the reef. However, recruitment was dominated by two species:Pocillopora tuahiniensis(46%) andPocillopora meandrina(43%). Under ambient conditions, recruitment ofP. tuahiniensisandP. meandrinawas similar, but experimentally reducing consumer pressure increased recruitment ofP. tuahiniensisby up to 73% and reduced recruitment ofP. meandrinaby up to 49%. In both species, nutrient enrichment increased recruitment and colony growth rates equally, but colonies ofP. tuahiniensisgrew faster and were up to 25% larger after three years than those ofP. meandrina,and growth was unaffected by reduced consumer pressure. Predation by excavating corallivorous fish was higher forP. meandrinathan forP. tuahiniensis, especially under nutrient enrichment. In contrast, polyp extension (an indicator of elevated heterotrophic feeding as well as susceptibility and attractiveness to corallivores) was lower forP. meandrinathan forP. tuahiniensis, especially under low to medium consumer pressure. Overall, we uncovered ecological differences in the response of morphologically cryptic foundation species to two pervasive stressors on coral reefs. Our results demonstrate how cryptic species respond differently to key anthropogenic stressors, which may contribute to response diversity that can support ecological resilience or increase extinction risk. 

ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials. 

Abstract Anaerobes thrive in the absence of oxygen and are an untapped reservoir of biotechnological potential. Therefore, bioprospecting efforts focused on anaerobic microbial diversity could rapidly uncover new enzymes, pathways, and chassis organisms to drive biotechnology innovation. Despite their potential utility, anaerobic fermenters are viewed as inefficient from a biochemical perspective because their metabolisms produce fewer ATP (~2) per molecule of glucose processed than heterotrophic respirers (~32–38 ATP). While aerobes excel at ATP generation, they are often less efficient than anaerobes at processes that compete with ATP generation for cellular resources. This perspective highlights how anaerobic adaptations are advantageous for synthetic biology and biomanufacturing applications through the engineering of microbial cell factories. We further highlight emerging applications of anaerobic bioprocessing, including the use of anaerobic metabolisms for lignocellulosic bioprocessing, human and environmental health, and value‐added bioproduction. 

ABSTRACT The introduction of degradable units into the backbone of commodity vinyl polymers represents a major opportunity to address the societal challenge of plastic waste and polymer recycling. Previously, we reported the facile copolymerization ofα‐lipoic acid derivatives containing 1,2‐dithiolane rings with vinyl monomers leading to the incorporation of degradable S–S disulfide bonds along the backbone at relatively high dithiolane monomer feed ratios. To further enhance the recyclability of these systems, here we describe a facile and user‐friendly strategy for backbone degradation at significantly lower dithiolane loading levels through cleavage of both SS and SC backbone units. Copolymers ofn‐butyl acrylate (nBA) or styrene (St) with small amounts of eitherα‐lipoic acid (LA) or ethyl lipoate (ELp) dissolved in DMF were observed to undergo efficient degradation when heated at 100°C under air. For example, at only 5 mol% ELp, a high molecular weight poly(ELp‐co‐nBA) (M_n = 62 kg mol⁻¹) degraded to low molecular weight oligomers (M_n = 3.2 kg mol⁻¹) by simple heating in DMF. In contrast, extended heating of either poly(nBA) or poly(St) homopolymers under the same conditions did not lead to any change in molecular weight or cleavage of the C–C backbone. This novel approach allows for the effective degradation of vinyl‐based polymers with negligible impact on properties and performance due to the low levels of dithiolane incorporation. 

ABSTRACT Quantifying ecosystem services provided by mobile species like insectivorous bats remains a challenge, particularly in understanding where and how these services vary over space and time. Bats are known to offer valuable ecosystem services, such as mitigating insect pest damage to crops, reducing pesticide use, and reducing nuisance pest populations. However, determining where bats forage is difficult to monitor. In this study, we use a weather‐radar‐based bat‐monitoring algorithm to estimate bat foraging distributions during the peak season of 2019 in California's Northern Central Valley. This region is characterized by valuable agricultural crops and significant populations of both crop and nuisance pests, including midges, moths, mosquitos, and flies. Our results show that bat activity is high but unevenly distributed, with rice fields experiencing significantly elevated activity compared to other land cover types. Specifically, bat activity over rice fields is 1.5 times higher than over any other land cover class and nearly double that of any other agricultural land cover. While irrigated rice fields may provide abundant prey, wetland and water areas showed less than half the bat activity per hectare compared to rice fields. Controlling for land cover type, we found bat activity significantly associated with higher flying insect abundance, indicating that bats forage in areas where crop and nuisance pests are likely to be found. This study demonstrates the effectiveness of radar‐based bat monitoring in identifying where and when bats provide ecosystem services.