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The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2021 water year (1 Oct 2020 to 30 Sep 2021) from samples collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. 

Abstract Measurements of riverine dissolved inorganic carbon, total alkalinity (A_T), pH, and the partial pressure of carbon dioxide (pCO₂) can provide insights into the biogeochemical function of rivers, including the processes that control biological production, chemical speciation, and air‐water CO₂fluxes. The complexity created by these combined processes dictates that studies of inorganic carbon be made over broad spatial and temporal scales. Time‐series data like these are relatively rare, however, because sampling and measurements are labor intensive and, for some variables, good measurement quality is difficult to achieve (e.g., pH). In this study, spectrophotometric pH and A_Twere quantified with high precision and accuracy at biweekly to monthly intervals over a four‐year period (2018–2021) along 216 km of the Upper Clark Fork River (UCFR) in the northern Rocky Mountains, USA. We use these and other time‐series data to provide insights into the processes that control river inorganic carbon, with a focus onpCO₂and air‐water CO₂fluxes. We found that seasonal snowmelt runoff increasedpCO₂and that expected increase and decrease ofpCO₂due to seasonal heating and cooling were likely offset by an increase and loss of algal biomass, respectively. Overall, the UCFR was a small net source (0.08 ± 0.14 mol m⁻² d⁻¹) of CO₂to the atmosphere over the four‐year study period with highly variable annual averages (0.0–0.10 mol m⁻² d⁻¹). The seasonally correlated, offsetting mechanisms highlight the challenges in predictingpCO₂and air‐water CO₂fluxes in rivers. 

Abstract Rivers efficiently collect, process, and transport terrestrial‐derived carbon. River ecosystem metabolism is the primary mechanism for processing carbon. Diel cycles of dissolved oxygen (DO) have been used for decades to infer river ecosystem metabolic rates, which are routinely used to predict metabolism of carbon dioxide (CO₂) with uncertainties of the assumed stoichiometry ranging by a factor of 4. Dissolved inorganic carbon (DIC) has been less used to directly infer metabolism because it is more difficult to quantify, involves the complexity of inorganic carbon speciation, and as shown in this study, likely requires a two‐station approach. Here, we developed DIC metabolism models using single‐ and two‐station approaches. We compared metabolism estimates based on simultaneous DO and DIC monitoring in the Upper Clark Fork River (USA), which also allowed us to estimate ecosystem‐level photosynthetic and respiratory quotients (PQ_Eand RQ_E). We observed that metabolism estimates from DIC varied more between single‐ and two‐station approaches than estimates from DO. Due to carbonate buffering, CO₂is slower to equilibrate with the atmosphere compared to DO, likely incorporating a longer distance of upstream heterogeneity. Reach‐averaged PQ_Eranged from 1.5 to 2.0, while RQ_Eranged from 0.8 to 1.5. Gross primary production from DO was larger than that from DIC, as was net ecosystem production by . The river was autotrophic based on DO but heterotrophic based on DIC, complicating our understanding of how metabolism regulated CO₂production. We suggest future studies simultaneously model metabolism from DO and DIC to understand carbon processing in rivers. 

Abstract Global pollinator declines threaten food production and natural ecosystems. The drivers of declines are complicated and driven by numerous factors such as pesticide use, loss of habitat, rising pathogens due to commercial bee keeping and climate change. Halting and reversing pollinator declines will require a multidisciplinary approach and international cooperation. Here, we summarize 20 presentations given in the symposium ‘Protecting pollinators and our food supply: Understanding and managing threats to pollinator health’ at the 19th Congress of the International Union for the Study of Social Insects in San Diego, 2022. We then synthesize the key findings and discuss future research areas such as better understanding the impact of anthropogenic stressors on wild bees. 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

The IceCube Upgrade is an extension of the existing IceCube Neutrino Observatory and will be deployed in the 2025–2026 austral summer. It will significantly improve the sensitivity of the detector to atmospheric neutrino oscillations. The existing 86-string IceCube array contains a dense in-fill known as DeepCore which is optimized to measure neutrinos with energies down to a few GeV. The IceCube Upgrade will consist of seven new densely instrumented strings placed within the DeepCore volume to further enhance the performance in the GeV energy range. The additional strings will feature new optical modules, each containing multiple photomultiplier tubes (PMTs), in contrast to the existing modules that each contain a single PMT. This will more than triple the number of PMT channels with respect to the current IceCube configuration, allowing for improved detection efficiency and reconstruction performance at GeV energies. We describe necessary updates to simulation, event selection, and reconstruction to accommodate the higher data rates observed by the upgraded detector and the addition of multi-PMT modules. We determine the expected sensitivity of the IceCube Upgrade to the atmospheric neutrino oscillation parameters ${\sin }^2{\theta }_{23}$and $\mathrm{\Delta }{m}_{32}^2$, the appearance of tau neutrinos and the neutrino mass ordering. The IceCube Upgrade will provide neutrino oscillation measurements that are of similar precision to those from accelerator experiments, while providing complementarity by probing higher energies and longer baselines, and with different sources of systematic uncertainties. 

Abstract Recently, IceCube reported neutrino emission from the Seyfert galaxy NGC 1068. Using 13.1 yr of IceCube data, we present a follow-up search for neutrino sources in the northern sky. NGC 1068 remains the most significant neutrino source among 110 preselected gamma-ray emitters while also being spatially compatible with the most significant location in the northern sky. Its energy spectrum is characterized by an unbroken power-law with spectral indexγ = 3.4 ± 0.2. Consistent with previous results, the observed neutrino flux exceeds its gamma-ray counterpart by at least 2 orders of magnitude. Motivated by this disparity and the high X-ray luminosity of the source, we selected 47 X-ray-bright Seyfert galaxies from the Swift/BAT spectroscopic survey that were not included in the list of gamma-ray emitters. When testing this collection for neutrino emission, we observe a 3.3σexcess from an ensemble of 11 sources, with NGC 1068 excluded from the sample. Our results strengthen the evidence that X-ray-bright cores of active galactic nuclei are neutrino emitters.