Scintillator ageing of the T2K near detectors from 2010 to 2021

The T2K experiment widely uses plastic scintillator as a target for neutrino interactions and an active medium for the measurement of charged particles produced in neutrino interactions at its near detector complex. Over 10 years of operation the measured light yield recorded by the scintillator based subsystems has been observed to degrade by 0.9–2.2% per year. Extrapolation of the degradation rate through to 2040 indicates the recorded light yield should remain above the lower threshold used by the current reconstruction algorithms for all subsystems. This will allow the near detectors to continue contributing to important physics measurements during the T2K-II and Hyper-Kamiokande eras. Additionally, work to disentangle the degradation of the plastic scintillator and wavelength shifting fibres shows that the reduction in light yield can be attributed to the ageing of the plastic scintillator. The long component of the attenuation length of the wavelength shifting fibres was observed to degrade by 1.3–5.4% per year, while the short component of the attenuation length did not show any conclusive degradation

Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models

1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided

Data from: Early- and late-flowering guilds respond differently to landscape spatial structure

1. Species with unique phenologies have distinct trait syndromes and environmental affinities, yet there has been little exploration of whether community assembly processes differ for plants with different phenologies. In this study, we ask whether early- and late-blooming species differ in the ways that dispersal, persistence, and resource-acquisition traits shape plant occurrence patterns in patchy habitats. 2. We sampled plant communities in 51 Ozark dolomite glade grasslands, which range in size from 100 ha. We modelled the occurrence of 71 spring- and 43 summer-blooming grassland species these patches, using as predictors both environmental variables (landscape structure, soil resources) and plant traits related to dispersal, longevity, and resource acquisition. We were especially interested in how the environmental variables and plant traits interacted to determine the occurrence of phenological strategies in habitats that vary in size and isolation. 3. Summer-blooming species with better persistence and dispersal abilities had higher relative frequencies in smaller, more isolated habitat patches, and summer-blooming species with higher specific leaf area—suggesting fast growth and low stress tolerance—were more likely to occur in patches with greater soil organic matter and clay content. However, spring-blooming species showed much weaker interactions between functional traits and environmental gradients, perhaps because environmental conditions are less harsh in spring than in summer. 4. Synthesis: Several axes of plant life history variation may simultaneously influence community responses to habitat connectivity. In this case, explicitly considering plant phenology helped identify generalizable relationships between functional traits and landscape spatial structure

The T2K experiment

The T2K experiment is a long baseline neutrino oscillation experiment. Its main goal is to measure the last unknown lepton sector mixing angle θ13 by observing νe appearance in a νμ beam. It also aims to make a precision measurement of the known oscillation parameters, and sin22θ23, via νμ disappearance studies. Other goals of the experiment include various neutrino cross-section measurements and sterile neutrino searches. The experiment uses an intense proton beam generated by the J-PARC accelerator in Tokai, Japan, and is composed of a neutrino beamline, a near detector complex (ND280), and a far detector (Super-Kamiokande) located 295 km away from J-PARC. This paper provides a comprehensive review of the instrumentation aspect of the T2K experiment and a summary of the vital information for each subsystem