Environmental identities of college students reveal potential conflicts and common ground for wildlife conservation - Richard Von Furstenberg; Lincoln R. Larson; M. Nils Peterson; Kangjae Jerry Lee; Victoria R. Vayer; kathryn stevenson; Stacy A.C. Nelson; Jeremy Bruskotter; Adam A. Ahlers; Christine Anhalt-Depies; Taniya Bethke; Chris Chizinski; et al. (Biological Conservation, 2025)

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.

Residual correlation and ensemble modelling to improve crop and grassland models - Renáta Sándor; Fiona Ehrhardt; Peter Grace; Sylvie Recous; Pete Smith; Val Snow; Jean-François Soussana; Bruno Basso; Arti Bhatia; Lorenzo Brilli; Jordi Doltra; Christopher D. Dorich; et al. (Environmental Modelling & Software, 2023)

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N 2 O emissions - Fiona Ehrhardt; Jean‐François Soussana; Gianni Bellocchi; Peter Grace; Russel McAuliffe; Sylvie Recous; Renáta Sándor; Pete Smith; Val Snow; Massimiliano de Antoni Migliorati; Bruno Basso; Arti Bhatia; et al. (Global Change Biology, 2018)
Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations ( SD ) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield‐scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed.

Complex carbon cycle responses to multi‐level warming and supplemental summer rain in the high A rctic - Elizabeth D. Sharp; Patrick F. Sullivan; Heidi Steltzer; Adam Z. Csank; Jeffrey M. Welker (Global Change Biology, 2013)
The Arctic has experienced rapid warming and, although there are uncertainties, increases in precipitation are projected to accompany future warming. Climate changes are expected to affect magnitudes of gross ecosystem photosynthesis ( GEP ), ecosystem respiration ( ER ) and the net ecosystem exchange of CO 2 ( NEE ). Furthermore, ecosystem responses to climate change are likely to be characterized by nonlinearities, thresholds and interactions among system components and the driving variables. These complex interactions increase the difficulty of predicting responses to climate change and necessitate the use of manipulative experiments. In 2003, we established a long‐term, multi‐level and multi‐factor climate change experiment in a polar semidesert in northwest G reenland. Two levels of heating (30 and 60 W m −2 ) were applied and the higher level was combined with supplemental summer rain. We made plot‐level measurements of CO 2 exchange, plant community composition, foliar nitrogen concentrations, leaf δ 13 C and NDVI to examine responses to our treatments at ecosystem‐ and leaf‐levels. We confronted simple models of GEP and ER with our data to test hypotheses regarding key drivers of CO 2 exchange and to estimate growing season CO 2 ‐C budgets. Low‐level warming increased the magnitude of the ecosystem C sink. Meanwhile, high‐level warming made the ecosystem a source of C to the atmosphere. When high‐level warming was combined with increased summer rain, the ecosystem became a C sink of magnitude similar to that observed under low‐level warming. Competition among our ER models revealed the importance of soil moisture as a driving variable, likely through its effects on microbial activity and nutrient cycling. Measurements of community composition and proxies for leaf‐level physiology suggest GEP responses largely reflect changes in leaf area of S alix arctica , rather than changes in leaf‐level physiology. Our findings indicate that the sign and magnitude of the future High A rctic C budget may depend upon changes in summer rain.

Transfection of MDA-MB-231 human breast carcinoma cells with bone sialoprotein (BSP) stimulates migration and invasion in vitro and growth of primary and secondary tumors in nude mice - Julie A. Sharp; Mark Waltham; Elizabeth D. Williams; Michael A. Henderson; Erik W. Thompson (Clinical & Experimental Metastasis, 2004)