Diffuse supernova neutrino background with up-to-date star formation rate measurements and long-term multi-dimensional supernova simulations

The sensitivity of current and future neutrino detectors like Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to allow for the detection of the diffuse supernova neutrino background (DSNB). However, the DSNB model ingredients like the core-collapse supernova (CCSN) rate, neutrino emission spectra, and the fraction of failed supernovae are not precisely known. We quantify the uncertainty on each of these ingredients by (i) compiling a large database of recent star formation rate density measurements, (ii) combining neutrino emission from long-term axisymmetric CCSNe simulations and strategies for estimating the emission from the protoneutron star cooling phase, and (iii) assuming different models of failed supernovae. Finally, we calculate the fluxes and event rates at multiple experiments and perform a simplified statistical estimate of the time required to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO. Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$ events per year, where the errors represent our uncertainty from star formation rate density measurements, uncertainty in neutrino emission, and uncertainty in the failed-supernova scenario. In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial.Comment: 19 pages, 9 figures, 3+2 tables. Comments welcom

Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence

For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (Tympanuchus pallidicinctus). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with GPS transmitters in Kansas and Colorado, USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km2) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, ~10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for CRP enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized CRP sign up has the potential to provide 4189 km2 of additional habitat and Strategic application of tree removal has the potential to restore 1154 km2. Tree removal and CRP enrollment are Conservation tools that can align with landowner goals and are much more likely to be effective on privately owned working lands

A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data.

Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass-including uncertainty-for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability