SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Benjamin R. Goldstein; Christopher Pate; Paula Castiblanco; Maggie E. Abell; Jessie Adley; Briana Agenbroad; Adam A. Ahlers; Peter D. Alexander; et al. (Global Ecology and Biogeography, 2025)
Motivation SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Pro- and anti-inflammatory macrophages express a sub-type specific purinergic receptor profile - J. Merz; A. Nettesheim; S. von Garlen; P. Albrecht; B. S. Saller; J. Engelmann; L. Hertle; I. Schäfer; D. Dimanski; S. König; L. Karnbrock; K. Bulatova; et al. (Purinergic Signalling, 2021)
Extracellular nucleotides act as danger signals that orchestrate inflammation by purinergic receptor activation. The expression pattern of different purinergic receptors may correlate with a pro- or anti-inflammatory phenotype. Macrophages function as pro-inflammatory M1 macrophages (M1) or anti-inflammatory M2 macrophages (M2). The present study found that murine bone marrow-derived macrophages express a unique purinergic receptor profile during in vitro polarization. As assessed by real-time polymerase chain reaction (PCR), Gαs-coupled P1 receptors A2A and A2B are upregulated in M1 and M2 compared to M0, but A2A 15 times higher in M1. The ionotropic P2 receptor P2X 5 is selectively upregulated in M1- and M2-polarized macrophages. P2X 7 is temporarily expressed in M1 macrophages. Metabotropic P2Y receptors showed a distinct expression profile in M1 and M2-polarized macrophages: Gαq coupled P2Y 1 and P2Y 6 are exclusively upregulated in M2, whereas Gαi P2Y 13 and P2Y 14 are overexpressed in M1. This consequently leads to functional differences between M1 and M2 in response to adenosine di-phosphate stimulation (ADP): In contrast to M1, M2 showed increased cytoplasmatic calcium after ADP stimulation. In the present study we show that bone marrow-derived macrophages express a unique repertoire of purinergic receptors. We show for the first time that the repertoire of purinergic receptors is highly flexible and quickly adapts upon pro- and anti-inflammatory macrophage differentiation with functional consequences to nucleotide stimulation.

Comparative analysis of scalar upper tail indicators - L. M. Wietzke; B. Merz; L. Gerlitz; H. Kreibich; B. Guse; A. Castellarin; S. Vorogushyn (Hydrological Sciences Journal, 2020)

A continuous modelling approach for design flood estimation on sub-daily time scale - B. Winter; K. Schneeberger; N.V. Dung; M. Huttenlau; S. Achleitner; J. Stötter; B. Merz; S. Vorogushyn (Hydrological Sciences Journal, 2019)

Tissue Contaminant Burdens in San Francisco Estuary White Sturgeon (Acipenser transmontanus): Implications for Population Recovery - Deke T. Gundersen; Steven C. Zeug; Robert B. Bringolf; Joseph Merz; Zachary Jackson; Molly A. H. Webb (Archives of Environmental Contamination and Toxicology, 2017)

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude - A. Garde; B. F. Giraldo; R. Jané; T. D. Latshang; A. J. Turk; T. Hess; M. M. Bosch; D. Barthelmes; T. M. Merz; J. Pichler Hefti; O. D. Schoch; K. E. Bloch (Medical & Biological Engineering & Computing, 2015)

Flood risk assessments at different spatial scales - H. de Moel; B. Jongman; H. Kreibich; B. Merz; E. Penning-Rowsell; P. J. Ward (Mitigation and Adaptation Strategies for Global Change, 2015)

Spatial distribution and turnover of root-derived carbon in alfalfa rhizosphere depending on top- and subsoil properties and mycorrhization - Silke Hafner; Guido L. B. Wiesenberg; Ekaterina Stolnikova; Klara Merz; Yakov Kuzyakov (Plant and Soil, 2014)

Comparative Risk Assessments for the City of Cologne – Storms, Floods, Earthquakes - G. Grünthal; A. H. Thieken; J. Schwarz; K. S. Radtke; A. Smolka; B. Merz (Natural Hazards, 2006)

Variation for neutral markers is correlated with variation for quantitative traits in the plant pathogenic fungus Mycosphaerella graminicola - J. ZHAN; C. C. LINDE; T. JÜRGENS; U. MERZ; F. STEINEBRUNNER; B. A. McDONALD (Molecular Ecology, 2005)
We compared genetic variation and population differentiation at RFLP marker loci with seven quantitative characters including fungicide resistance, temperature sensitivity, pycnidial size, pycnidial density, colony size, percentage of leaves covered by pycnidia (PLACP) and percentage of leaves covered by lesions (PLACL) in Mycosphaerella graminicola populations sampled from four regions. Wide variation in population differentiation was found across the quantitative traits assayed. Fungicide resistance, temperature sensitivity, and PLACP displayed a significantly higher Q ST than G ST , consistent with selection for local adaptation, while pycnidial size, pycnidial density and colony size displayed a lower or significantly lower Q ST than G ST , consistent with constraining selection. There was not a statistical difference between Q ST and G ST in PLACL. We also found a positive and significant correlation between genetic variation in molecular marker loci and quantitative traits at the multitrait scale, suggesting that estimates of overall genetic variation for quantitative traits in M. graminicola could be derived from analysis of the molecular genetic markers.

Abstracts of the 6th FECS Conference 1998 Lectures - F. Sherwood Rowland; Donald R. Blake; B. R. Larsen; Anne Lindskog; Peter J. Peterson; W. Peter Williams; T. J. Wallington; M. J. Pilling; N. Carslaw; D. J. Creasey; D. E. Heard; P. Jacobs; et al. (Environmental Science and Pollution Research, 1998)