Neutrino Mass Matrix Textures: A Data-driven Approach

We analyze the neutrino mass matrix entries and their correlations in a probabilistic fashion, constructing probability distribution functions using the latest results from neutrino oscillation fits. Two cases are considered: the standard three neutrino scenario as well as the inclusion of a new sterile neutrino that potentially explains the reactor and gallium anomalies. We discuss the current limits and future perspectives on the mass matrix elements that can be useful for model building.Comment: 25 pages, 18 figure

Catchment Dissolved Organic Carbon Transport: A Modeling Approach Combining Water Travel Times and Reactivity Continuum

Quantifying the transfer of organic carbon from the terrestrial to the riverine ecosystems is of crucial importance to fully appreciate the carbon cycle at the catchment, regional and global scales. In this study, we propose a framework for modeling the flux of dissolved organic carbon (DOC) from hillslopes to stream and river networks which couples a transport model based on travel time distributions with the reactivity continuum (RC) approach to model DOC degradation. We test the model by applying it to the Plynlimon catchments (UK) exploiting both weekly and high-frequency (7-hr interval) time-series. We use information about chloride to get an independent estimate of water travel times using the framework of StorAge Selection functions. Following the RC model, the composition and the degradation of DOC along the flowpaths, and its consequent concentration in the streamflow, is described assuming that DOC is composed by a mixture of compounds that follows a continuous spectrum of reactivity. For the high-frequency data set, the model is able to reproduce DOC streamflow concentrations and to capture the complex hysteretic relation between DOC concentration and discharge. Weekly data are instead not frequent enough to properly describe DOC dynamics in this catchment. The distribution of the age of the water comprised in the streamflow proves thus a key variable to predict the quantity and the reactivity of the DOC exported from soils, and the effect of hydrologic variability on this process

A Network-Scale Modeling Framework for Stream Metabolism, Ecosystem Efficiency, and Their Response to Climate Change

Climate change and the predicted warmer temperatures and more extreme hydrological regimes could affect freshwater ecosystems and their energy pathways. To appreciate the complex spatial and temporal interactions of carbon cycling in flowing waters, ecosystem metabolism (gross primary production [GPP] and ecosystem respiration [ER]) must be resolved at the scale of an entire river network. Here, we propose a meta-ecosystem framework that couples light and temperature regimes with a reach-scale ecosystem model and integrates network structure, catchment land cover, and the hydrologic regime. The model simulates the distributed functioning of dissolved and particulate organic carbon, autotrophic biomass, and thus ecosystem metabolism, and reproduces fairly well the metabolic regimes observed in 12 reaches of the Ybbs River network, Austria. Results show that the annual network-scale metabolism was heterotrophic, yet with a clear peak of autotrophy in spring. Autochthonous energy sources contributed 43% of the total ER. We further investigated the effect of altered thermal and hydrologic regimes on metabolism and ecosystem efficiency. We predicted that an increase of 2.5? in average stream water temperature could boost ER and GPP by 31% (24%-57%) and 28% (5%-57%), respectively. The effect of flashier hydrologic regimes is more complex and depends on autotrophic biomass density. The analysis shows the complex interactions between environmental conditions and biota in shaping stream metabolism and highlights the existing knowledge gaps for reliable predictions of the effects of climate change in these ecosystems

Mapping landscape connectivity as a driver of species richness under tectonic and climatic forcing

Species distribution and richness ultimately result from complex interactions between biological, physical, and environmental factors. It has been recently shown for a static natural landscape that the elevational connectivity, which measures the proximity of a site to others with similar habitats, is a key physical driver of local species richness. Here we examine changes in elevational connectivity during mountain building using a landscape evolution model. We find that under uniform tectonic and variable climatic forcing, connectivity peaks at mid-elevations when the landscape reaches its geomorphic steady state and that the orographic effect on geomorphic evolution tends to favour lower connectivity on leeward-facing catchments. Statistical comparisons between connectivity distribution and results from a metacommunity model confirm that to the 1st order, landscape elevation connectivity explains species richness in simulated mountainous regions. Our results also predict that low-connectivity areas which favour isolation, a driver for in situ speciation, are distributed across the entire elevational range for simulated orogenic cycles. Adjustments of catchment morphology after the cessation of tectonic activity should reduce speciation by decreasing the number of isolated regions

The Metabolic Regimes at the Scale of an Entire Stream Network Unveiled Through Sensor Data and Machine Learning

Streams and rivers form dense networks that drain the terrestrial landscape and are relevant for biodiversity dynamics, ecosystem functioning, and transport and transformation of carbon. Yet, resolving in both space and time gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) at the scale of entire stream networks has been elusive so far. Here, combining Random Forest (RF) with time series of sensor data in 12 reach sites, we predicted annual regimes of GPP, ER, and NEP in 292 individual stream reaches and disclosed properties emerging from the network they form. We further predicted available light and thermal regimes for the entire network and expanded the library of stream metabolism predictors. We found that the annual network-scale metabolism was heterotrophic yet with a clear peak of autotrophy in spring. In agreement with the River Continuum Concept, small headwaters and larger downstream reaches contributed 16% and 60%, respectively, to the annual network-scale GPP. Our results suggest that ER rather than GPP drives the metabolic stability at the network scale, which is likely attributable to the buffering function of the streambed for ER, while GPP is more susceptible to flow-induced disturbance and fluctuations in light availability. Furthermore, we found large terrestrial subsidies fueling ER, pointing to an unexpectedly high network-scale level of heterotrophy, otherwise masked by simply considering reach-scale NEP estimations. Our machine learning approach sheds new light on the spatiotemporal dynamics of ecosystem metabolism at the network scale, which is a prerequisite to integrate aquatic and terrestrial carbon cycling at relevant scales

Minimal flavor violation in the see-saw portal

We consider an extension of the Standard Model with two singlet leptons, with masses in the electroweak range, that induce neutrino masses via the see-saw mechanism, plus a generic new physics sector at a higher scale, Λ. We apply the minimal flavor violation (MFV) principle to the corresponding Effective Field Theory (νSMEFT) valid at energy scales E ≪ Λ. We identify the irreducible sources of lepton flavor and lepton number violation at the renormalizable level, and apply the MFV ansätz to derive the scaling of the Wilson coefficients of the νSMEFT operators up to dimension six. We highlight the most important phenomenological consequences of this hypothesis in the rates for exotic Higgs decays, the decay length of the heavy neutrinos, and their production modes at present and future colliders. We also comment on possible astrophysical implications

Modeling the coupled dynamics of stream metabolism and microbial biomass

Estimating and interpreting ecosystem metabolism remains an important challenge in stream ecology. Here, we propose a novel approach to model, estimate, and predict multiseasonal patterns of stream metabolism (gross primary production [GPP] and ecosystem respiration [ER]) at the reach scale leveraging on increasingly available long-term, high-frequency measurements of dissolved oxygen (DO). The model uses DO measurements to estimate the parameters of a simple ecosystem model describing the underlying dynamics of stream autotrophic and heterotrophic microbial biomass. The model has been applied to four reaches within the Ybbs river network, Austria. Even if microbial biomasses are not observed, that is, they are treated as latent variables, results show that by accounting for the temporal dynamics of biomass, the model reproduces variability in metabolic fluxes that is not explained by fluctuations of light, temperature, and resources. The model is particularly data-demanding: to estimate the 11 parameters used in this formulation, it requires sufficiently long, for example, annual, time series, and significant scouring events. On the other hand, the approach has the potential to separate ER into its autotrophic and heterotrophic components, estimate a richer set of ecosystem carbon fluxes (i.e., carbon uptake, loss, and scouring), extrapolate metabolism estimates for periods when DO measurements are unavailable, and predict how ecosystem metabolism would respond to variations of the driving forces. The model is seen as a building block to develop tools to fully appreciate multiseasonal patterns of metabolic activity in river networks and to provide reliable estimations of carbon fluxes from land to ocean

Permafrost dynamics and the risk of anthrax transmission: a modelling study

A recent outbreak of anthrax disease, severely affecting reindeer herds in Siberia, has been reportedly associated to the presence of infected carcasses or spores released from the active layer over permafrost, which is thawing and thickening at increasing rates, thus underlying the re-emerging nature of this pathogen in the Arctic region because of warming temperatures. Anthrax is a global zoonotic and epizootic disease, with a high case-fatality ratio in infected animals. Its transmission is mediated by environmental contamination through highly resistant spores which can persist in the soil for several decades. Here we develop and analyze a new epidemiological model for anthrax transmission that is specifically tailored to the Arctic environmental conditions. The model describes transmission dynamics including also herding practices (e.g. seasonal grazing) and the role of the active layer over permafrost acting as a long-term storage of spores that could be viable for disease transmission during thawing periods. Model dynamics are investigated through linear stability analysis, Floquet theory for periodically forced systems, and a series of simulations with realistic forcings. Results show how the temporal variability of grazing and active layer thawing may influence the dynamics of anthrax disease and, specifically, favor sustained pathogen transmission. Particularly warm years, favoring deep active layers, are shown to be associated with an increase risk of anthrax outbreaks, and may also foster infections in the following years. Our results enable preliminary insights into measures (e.g. changes in herding practice) that may be adopted to decrease the risk of infection and lay the basis to possibly establish optimal procedures for preventing transmission; furthermore, they elicit the need of further investigations and observation campaigns focused on anthrax dynamics in the Arctic environment

Dark photon bounds in the dark EFT

Dark photons are massive abelian gauge bosons that interact with ordinary photons via a kinetic mixing with the hypercharge field strength tensor. This theory is probed by a variety of different experiments and limits are set on a combination of the dark photon mass and kinetic mixing parameter. These limits can however be strongly modified by the presence of additional heavy degrees of freedom. Using the framework of dark effective field theory, we study how robust are the current experimental bounds when these new states are present. We focus in particular on the possible existence of a dark dipole interaction between the Standard Model leptons and the dark photon. We show that, under certain assumptions, the presence of a dark dipole modifies existing supernovae bounds for cut-off scales up to O(10-100 TeV). On the other hand, terrestrial experiments, such as LSND and E137, can probe cut-off scales up to O(3 TeV). For the latter experiment we highlight that the bound may extend down to vanishing kinetic mixing