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1. Planktic and benthic foraminiferal isotope and abundance records from the subpolar North Atlantic during the last 1,200 years

   https://doi.org/10.5281/zenodo.8429224  

   Lu, Wanyi; Oppo, Delia W; Gebbie, Geoffrey; Thornalley, David_J R (January 2023, Zenodo)

   Data published in Lu et al. 2023 and R script repository to reproduce published figures. Lu W., D. W. Oppo, G. Gebbie, D. J. R. Thornalley, Surface climate signals transmitted rapidly to deep North Atlantic throughout last millennium. Science, 382, 834-839 https://doi.org/10.1126/science.adf1646. 

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2. Plant neighborhood shapes diversity and reduces interspecific variation of the phyllosphere microbiome. Meyer et. al 2022. ISME-J.

   https://doi.org/10.6084/m9.figshare.16575194.v1  

   Meyer, Kyle (January 2022, figshare)

   This is the data archive for: Meyer et al. 2022. Plant neighborhood shapes diversity and reduces interspecific variation of the phyllosphere microbiome. ISME-J. Please cite this article when using these archived data.</div>DOI: 10.1038/s41396-021-01184-6</div></div>Included are raw genetic sequences of the V5-V7 region of the 16S rRNA gene derived from experimental leaf surfaces of tomato, pepper, and bean plants.</div></div>Included in this archive are:</div>Raw sequence data (RawFASTQ.zip)</div>Reproducible R scripts (MeyerEtAl2021_RScript.R, VarPartSupplement.R)</div>R objects corresponding to archived scripts (.RDS)</div>Data for generating certain plots (PermanovaRValues.txt, PermanovaValuesByHost.txt, NeutralModelRValuesByHarvest.txt, VarPartHostEffects.txt)</div>Sample metadata (NeighborhoodMetaData.txt)</div>Phylogenetic Tree file for sample ASVs (PhyloTree.tre)</div>Geographic distance matrix for distances between plots (GeodistNeighborhood.txt)</div>ddPCR (microbial abundance) data (ddPCR_Neighborhood.csv)</div>R script for rarefication function (Rarefy_mean.R)</div>Taxonomic assignments for all ASVs in study (Taxonomy_Neighborhood.txt)</div>R image files to load R environment instead of running script (MeyerEtAl2021_RScript.RData, VarPartSupplement.RData)</div></div></div></div> 

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3. GIRF Pulse Experiment

   https://doi.org/10.5281/zenodo.11406309  

   Hastings, Yvette D; Smith, Rose Marie; Mann, Kyra; Brewer, Simon C; Goel, Ramesh; Hinners, Sarah Jack; Follstad_Shah, Jennifer (January 2024, Zenodo)

   R code for Hastings, Y. D. (2022). Green Infrastructure Microbial Community Response to Simulated Pulse Precipitation Events in the Semi-Arid Western United States (Master's thesis, The University of Utah). This study was supported by a grant from the US National Science Foundation (DEB 2006308). R code for and Hastings, Y. D., et al. Green Infrastructure Microbial Community Response to Simulated Pulse Precipitation Events in the Semi-Arid Western United States. In review. Abstract: Nutrient retention in urban stormwater green infrastructure (SGI) of water-limited biomes is not well quantified, especially when stormwater inputs are scarce. We examined the role of plant diversity and physiochemistry as drivers of microbial community physiology and soil N pools and fluxes in bioswales subjected to simulated precipitation and a montane meadow experiencing natural rainfall within a semi-arid region during drought. Precipitation generally elevated soil moisture and pH, stimulated ecoenzyme activity, and increased the concentration of organic matter, proteins, and N pools in both bioswale and meadow soils; but the magnitude of change differed between events. Microbial community growth was static and N assimilation into biomass was limited across precipitation events. Unvegetated SGI plots had greater soil moisture, yet effects of plant diversity treatments on microbial C:N ratios, organic matter content, and N pools were inconsistent. Differences in soil N concentrations in bioswales and the meadow were most directly correlated to changes in organic matter content mediated by ecoenzyme expression and the balance of C, N, and P resources available to microbial communities. Our results add to growing evidence that ecological function of SGI is comparable to neighboring natural vegetated systems, particularly when soil media and water availability are similar. The file and R code structure is as follows: Data - Contains all data used for the analysis Results - Contains all figures, RMANOVA, and Piecewise Structural Equation Modeling results. renv - R environment used for project EEA_Vector_Analysis.R - R code used to analyze coenzyme (EEA) responses, including RMANOVA to look for significant differences in EEA response to simulated pulse events and Vector Analysis to determine the nutrient resource acquisition. Gravimetric_soil_moisture_pH.R - R code used for RMANOVA of gravimetric soil moisture and pH responses to simulated pulse events. MicrobialBiomass_EEA.Rproj - Downloaded R project Microbial_biomass.R - R code used for RMANOVA of microbial biomass carbon, nitrogen, and C:N responses to simulated pulse events. OM_protien_N_pools_fluxes.R - R code used for RMANOVA of organic matter content, proteins, and N pools and fluxes responses to simulated pulse events. PSEM_final.R - R code used for Pearson Correlation and Piecewise Structural Equation Modeling. Rclimate.R - R code used to obtain summary statistics of climate data from GIRF and TM climate and soil sensors. 

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4. Laminar specificity and coverage of viral-mediated gene expression restricted to GABAergic interneurons and their parvalbumin subclass in marmoset primary visual cortex

   https://doi.org/10.7554/eLife.97673  

   Federer, Frederick; Balsor, Justin; Ingold, Alexander; Babcock, David P; Dimidschstein, Jordane; Angelucci, Alessandra (September 2024, eLife)

   In the mammalian neocortex, inhibition is important for dynamically balancing excitation and shaping the response properties of cells and circuits. The various computational functions of inhibition are thought to be mediated by different inhibitory neuron types, of which a large diversity exists in several species. Current understanding of the function and connectivity of distinct inhibitory neuron types has mainly derived from studies in transgenic mice. However, it is unknown whether knowledge gained from mouse studies applies to the non-human primate, the model system closest to humans. The lack of viral tools to selectively access inhibitory neuron types has been a major impediment to studying their function in the primate. Here, we have thoroughly validated and characterized several recently developed viral vectors designed to restrict transgene expression to GABAergic cells or their parvalbumin (PV) subtype, and identified two types that show high specificity and efficiency in marmoset V1. We show that in marmoset V1, AAV-h56D induces transgene expression in GABAergic cells with up to 91–94% specificity and 79% efficiency, but this depends on viral serotype and cortical layer. AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86–90% efficiency, depending on layer. Thus, these viral vectors are promising tools for studying GABA and PV cell function and connectivity in the primate cortex. 

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5. Evolutionary effects of nitrogen are not easily predicted from ecological responses

   https://doi.org/10.5061/dryad.59zw3r2bg  

   Waterton, Joseph; Hammond, Mark; Lau, Jennifer (January 2022, Dryad)

   Anthropogenic nitrogen (N) addition might alter the evolutionary trajectories of plant populations, in part because it alters the abiotic and biotic environment by increasing aboveground primary productivity, light asymmetry, and herbivory intensity, and reducing plant species diversity. Such evolutionary impacts could be caused by N altering patterns of natural selection (i.e., trait-fitness relationships) and the opportunity for selection (i.e., variance in relative fitness). Because at the community level N addition favors species with light acquisition strategies (e.g., tall species), we predict that N would also increase selection favoring those same traits. We also hypothesize that N could alter the opportunity for selection via its effects on mean fitness and/or competitive asymmetries.To investigate these evolutionary consequences of N, we quantified the strength of selection and the opportunity for selection in replicated populations of the annual grass Setaria faberi Herrm. (giant foxtail) growing in a long-term N addition experiment. We also correlated our measures of selection and opportunity for selection with light asymmetry, diversity, and herbivory intensity to identify the proximate causes of any N effects on evolutionary processes. N addition increased aboveground productivity, light asymmetry, and reduced species diversity. Contrary to expectations, N addition did not strengthen selection for trait values associated with higher light acquisition such as greater height and specific leaf area (SLA); rather, it strengthened selection favoring lower SLA. Increased light asymmetry was associated with stronger selection for lower SLA and lower species diversity was associated with stronger selection for greater height and lower SLA, suggesting a role for these factors in driving N-mediated selection. The opportunity for selection was not influenced by N addition (despite increased mean fitness) but was negatively associated with species diversity. Our results indicate that anthropogenic N enrichment can affect evolutionary processes, but that evolutionary changes in plant traits within populations are unlikely to parallel the shifts in plant traits observed at the community level. Data was collected in 2020 from a field experiment in a long-term ecological research site (Kellogg Biological Station LTER site in Michigan, USA). The Data folder contains 3 separate datasets as CSV files, each with accompanying .txt metadata files: 1) a dataset of individual-level data (Waterton2022_NitrogenEvolution_Individual_Data.csv); 2) a dataset of annual net primary productivity (ANPP; Waterton2022_NitrogenEvolution_ANPP_Data.csv); 3) a dataset of light measurements (Waterton2022_NitrogenEvolution_Light_Data.csv). An R script for reproducing the analyses and figures is available at https://doi.org/10.5281/zenodo.7121361. R statistical software is required to run the R script. 

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