Search for Spatial Correlations of Neutrinos with Ultra-high-energy Cosmic Rays

For several decades, the origin of ultra-high-energy cosmic rays (UHECRs) has been an unsolved question of high-energy astrophysics. One approach for solving this puzzle is to correlate UHECRs with high-energy neutrinos, since neutrinos are a direct probe of hadronic interactions of cosmic rays and are not deflected by magnetic fields. In this paper, we present three different approaches for correlating the arrival directions of neutrinos with the arrival directions of UHECRs. The neutrino data are provided by the IceCube Neutrino Observatory and ANTARES, while the UHECR data with energies above ∼50 EeV are provided by the Pierre Auger Observatory and the Telescope Array. All experiments provide increased statistics and improved reconstructions with respect to our previous results reported in 2015. The first analysis uses a high-statistics neutrino sample optimized for point-source searches to search for excesses of neutrino clustering in the vicinity of UHECR directions. The second analysis searches for an excess of UHECRs in the direction of the highest-energy neutrinos. The third analysis searches for an excess of pairs of UHECRs and highest-energy neutrinos on different angular scales. None of the analyses have found a significant excess, and previously reported overfluctuations are reduced in significance. Based on these results, we further constrain the neutrino flux spatially correlated with UHECRs

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depend on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, β = 0.23) and population synchrony (β = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (β = 0.73) to secondary consumers (β = 0.54), to primary consumers (β = 0.30) to producers (β =0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation. This article is protected by copyright. All rights reserved

Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

<p>Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x)</p> <p>The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the <strong>R_codes</strong> folder following this sequence:</p> <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> <p>and using the data available in the <strong>Input_data</strong> folder.</p> <p>The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x</p> <p>However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set.</p> <p>A protocol for requesting access to the LEPAS data sets can be found here:<br> https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas</p> <p>Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: [email protected]</p> <p>Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05.</p> <p>I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.</p>Acknowledgments. T.S. was supported by grants #19/04033-7 and #21/00619-7, São Paulo Research Foundation (FAPESP), and by grant #309496/2021-7, Brazilian National Council for Scientific and Technological Development (CNPq). Participation by CPH was supported, in part, by US National Science Foundation grant IOS-1754838. CPH thanks the PacFish/InFish Biological Opinion Monitoring Program (administered by the US Forest Service) for use of their long-term macroinvertebrate monitoring data. JDT is supported by a Rutherford Discovery Fellowship administered by the Royal Society Te Apārangi (RDF-18-UOC-007), and Bioprotection Aotearoa and Te Pūnaha Matatini, both Centres of Research Excellence funded by the Tertiary Education Commission, New Zealand. VS was supported by a FAPESP grant #2019/06291-3 during the writing of this manuscript. The FEHM (Freshwater Ecology, Hydrology and Management) research group is funded by the "Agència de Gestió d'Ajuts Universitaris i de Recerca" (AGAUR) at the "Generalitat de Catalunya" (2017SGR1643). CCB thanks PELD-PIAP/CNPq for support. M.C. was supported by a Ramón y Cajal Fellowship (RYC2020-029829-I) and the Serra Hunter programme (Generalitat de Catalunya). GAG was supported by #DEB-2025982, NTL LTER. PH received financial support from the eLTER PLUS project (Grant Agreement #871128). JMH was supported by the Federal Aid in Sport Fish Restoration Program (F-69-P, Fish Management in Ohio), administered jointly by the United States Fish and Wildlife Service and the Division of Wildlife, Ohio Department of Natural Resources (projects FADR65, FADX09, and FADB02). KLH and RP thank the Oulanka Research Station. MBF thanks over 300 students, staff, and faculty that have participated in the Kentucky Lake Long-Term Monitoring Program at Hancock Biological Station, Murray State University, Murray, KY. MJJ thanks the Northumberland Wildlife Trust for site access. IISD-ELA zooplankton samples were counted and identified primarily by Willy Findlay and Alex Salki. Field collections within IISD-ELA were overseen by Mark Lyng and Ken Sandilands. Funding for most of the IISD-ELA data was provided by Fisheries and Oceans Canada. PP and MS were supported by the Czech Science Foundation (P505-20-17305S). LCR is grateful to the Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura (Nupélia) at Universidade Estadual de Maringá for logistic support; CNPq/ PELD for financial support and CNPq for a scholarship. AR was supported by NSF CAREER #2047324 and by UC Berkeley new faculty funds. We thank countless colleagues at all partner institutes for their help with collecting the time series data