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1. Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs

   https://doi.org/10.15485/2561510  

   Dyer, Lee; Chambers, Jeff; Pastorello, Gilberto; Weber, Anna (January 2025, Next-Generation Ecosystem Experiments (NGEE) Tropics)

   {"Abstract":["This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package."]} 

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2. Replicability of representational similarity and its role in successful memory retrieval - Stage 2

   https://doi.org/10.17605/osf.io/zuyna  

   Diana, Rachel A; Yuksel, Ece; Shafer, Erica; Netto, Madeline (December 2025, OSF)

   {"Abstract":["See Manuscript and ReadMe in "Files" section for information"]} 

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3. Dissolved CH4 concentrations and fluxes to the atmosphere from a tropical floodplain lake

   https://doi.org/doi.org/10.1007/s10533-020-00650-1  

   Barbosa, PM; Melack, JM; Amaral, JHF; MacIntyre, S; Kasper, D; Cortes, AC; Farjalla, FV; Forsberg, BR (January 2020, Biogeochemistry)

   Large uncertainties in estimates of methane (CH4) emissions from tropical inland waters reflect the paucity of information at appropriate temporal and spatial scales. CH4 concentrations, diffusive and ebullitive fluxes, and environmental parameters in contrasting aquatic habitats of Lake Janauaca´, an Amazon floodplain lake, measured for two years revealed patterns in temporal and spatial variability related to different aquatic habitats and environmental conditions. CH4 concentrations ranged from below detection to 96 lM, CH4 diffusive fluxes from below detection to 2342 lmol m-2 h-1, and CH4 ebullitive fluxes from 0 to 190 mmol m-2 d-1. Vegetated aquatic habitats had higher surface CH4 concentrations than open water habitats, and no significant differences in diffusive CH4 fluxes, likely due to higher k values measured in open water habitats. CH4 emissions were enhanced after a prolonged low water period, when the exposed sediments were colonized by herbaceous plants that decomposed after water levels rose, possibly fueling CH4 production. Statistical models indicated the importance of variables related to CH4 production (temperature, dissolved organic carbon) and consumption (dissolved nitrogen, oxygenated water column), as well as maximum depth. 

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4. GRiMeDB: the Global River Methane Database of concentrations and fluxes

   https://doi.org/10.5194/essd-15-2879-2023  

   Stanley, Emily H.; Loken, Luke C.; Casson, Nora J.; Oliver, Samantha K.; Sponseller, Ryan A.; Wallin, Marcus B.; Zhang, Liwei; Rocher-Ros, Gerard (January 2023, Earth System Science Data)

   Abstract. Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) into the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere across broad spatial scales. However, progress toward these ends has been slow because of pronounced spatial and temporal variability of lotic CH4 concentrations and fluxes and by limited data availability across diverse habitats and physicochemical conditions. To address these challenges, we present a comprehensive database of CH4 concentrations and fluxes for fluvial ecosystems along with broadly relevant and concurrent physical and chemical data. The Global River Methane Database (GriMeDB; https://doi.org/10.6073/pasta/f48cdb77282598052349e969920356ef, Stanley et al., 2023) includes 24 024 records of CH4 concentration and 8205 flux measurements from 5029 unique sites derived from publications, reports, data repositories, unpublished data sets, and other outlets that became available between 1973 and 2021. Flux observations are reported as diffusive, ebullitive, and total CH4 fluxes, and GriMeDB also includes 17 655 and 8409 concurrent measurements of concentrations and 4444 and 1521 fluxes for carbon dioxide (CO2) and nitrous oxide (N2O), respectively. Most observations are date-specific (i.e., not site averages), and many are supported by data for 1 or more of 12 physicochemical variables and 6 site variables. Site variables include codes to characterize marginal channel types (e.g., springs, ditches) and/or the presence of human disturbance (e.g., point source inputs, upstream dams). Overall, observations in GRiMeDB encompass the broad range of the climatic, biological, and physical conditions that occur among world river basins, although some geographic gaps remain (arid regions, tropical regions, high-latitude and high-altitude systems). The global median CH4 concentration (0.20 µmol L−1) and diffusive flux (0.44 mmolm-2d-1) in GRiMeDB are lower than estimates from prior site-averaged compilations, although ranges (0 to 456 µmol L−1 and −136 to 4057 mmolm-2d-1) and standard deviations (10.69 and 86.4) are greater for this larger and more temporally resolved database. Available flux data are dominated by diffusive measurements despite the recognized importance of ebullitive and plant-mediated CH4 fluxes. Nonetheless, GriMeDB provides a comprehensive and cohesive resource for examining relationships between CH4 and environmental drivers, estimating the contribution of fluvial ecosystems to CH4 emissions, and contextualizing site-based investigations. 

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5. RefAHL: A curated quorum sensing reference linking diverse LuxI-type signal synthases with their acyl-homoserine lactone products

   https://doi.org/10.5061/dryad.866t1g21s  

   Schaefer, Amy; Murdock, Ethan; Pelletier, Dale; Harwood, Caroline; Greenberg, Peter E; Puri, Aaron (January 2025, Dryad)

   {"Abstract":["Some bacteria use acyl-homoserine lactone (AHL) signals in quorum sensing,\n a type of cell-cell communication. Here we present “RefAHL”, a curated\n collection of LuxI-type AHL synthases with their AHL structures and\n associated metadata. RefAHL is publicly available as a community resource\n to help catalog LuxI-type diversity encoded in (meta)genomic data."],"Methods":["The RefAHL dataset was collected, assembled and curated by humans\n from the public scientific literature."],"TechnicalInfo":["# RefAHL: A curated quorum sensing reference linking diverse LuxI-type\n signal synthases with their acyl-homoserine lactone products Dataset DOI:\n [10.5061/dryad.866t1g21s](10.5061/dryad.866t1g21s) ## Description of the\n data and file structure Here we curate a list of previously published LuxI\n homologs, which have been experimentally demonstrated to synthesize an\n acyl homoserine lactone (AHL) signal of well-supported structure. We refer\n to this collection as “RefAHL” and will update the collection when new\n LuxI-AHL signals are defined. Contact Amy Schaefer\n ([amyschae@uw.edu](mailto:amyschae@uw.edu)) or Aaron Puri\n ([a.puri@utah.edu](mailto:a.puri@utah.edu)) with any questions or to\n submit newly defined LuxI-AHL signal pairs. There are two associated files\n (note that date suffixes in YYYYMMDD format will change in future\n versions). ### Files and variables #### File:\n RefAHL\\_complete\\_revYYYYMMDD.xlsx **Description:** The\n RefAHL_complete_revYYYYMMDD.xlsx file has Tab 1 ("RefAHL\n LuxI-AHL"), Tab 2 (“AHL compounds”), and Tab 3 ("RefAHL\n confidence ranking"), which contain the following variables (columns)\n and their descriptions: ##### Variables * (1) RefAHL identifier: Unique\n RefAHL name, comprised from the beginning of the genera/clone name\n (letters) and a number; if a LuxI homolog has >65% amino acid identity\n and synthesizes the same AHL product with an existing RefAHL LuxI, they\n are given the same RefAHL ID with a subcategory letter (e.g. Mes_001a,\n Mes_001b) (1) Taxonomy or clone: Indicates either bacterial Class taxonomy\n or clone if derived from metagenomic sequence (1) Published LuxI homolog\n name: Most, but not all, LuxI homologs have an associated gene/protein\n name; for those homologs in the same RefAHL identifier grouping the shared\n percent amino acid identity is indicated in parentheses (1) Major AHL\n (defined in Tab 2): The abbreviation of the major AHL signal synthesized\n by the LuxI homolog; in cases where cognate LuxR activity data is not\n available, the major AHL is defined as the most abundant AHL produced; see\n Tab 2 for full compound names and additional chemical information; in\n cases where the major AHL depends on the growth condition or strain\n background, this is listed in parentheses [e.g. in minimal medium (M9) vs.\n rich medium (K9) for Burk_007] (1) AHL confidence category (defined in Tab\n 3):  Confidence category ranking for the AHL product synthesized by the\n LuxI homolog depending upon its associated data; see Tab 3 for specific\n criteria (1) Strain or metagenome:  Name of the bacterial strain or\n metagenomic library clone encoding the LuxI homolog gene (1) Reference(s)\n (PMID): Reference(s) for the data summarized in RefAHL; in most cases this\n is the PubMed identifier (PMID) number for published manuscript(s); one\n entry references unpublished 14C-methionine feeding data, which supports\n the published relaxed-specificity bioassay data (1) LuxI-homolog protein\n sequence: Amino acid sequence of the LuxI homolog (1) IMG gene\n identifier: Unique object identifier of the LuxI homolog gene in the\n JGI-IMG database; ‘none’ indicates the genome sequence is not hosted by\n IMG  (1) GenBank identifier: Unique object identifier of the LuxI homolog\n gene in the GenBank database; only used when there was no IMG identifier\n available * (2) AHL abbreviation: Abbreviation of the acyl homoserine\n lactone (AHL) structure used in Tab 1 (2) AHL common name: Common chemical\n name of the AHL structure (2) PubChem compound: Unique object identifier\n of the AHL structure in the NIH PubChem database; ‘not available’\n indicates the AHL has an undefined double bond so a PubChem number cannot\n be assigned; 'none' indicates the compound has not been\n deposited in PubChem (2) AHL isomeric SMILES: The isomeric Simplified\n Molecular Input Line Entry System sstructure line notation, which\n describes the AHL structures; we assume a stereochemistry of L for the\n homoserine lactone and R for any 3-hydroxy acyl groups; ‘not available’\n indicates the AHL has an undefined double bond so an isomeric SMILES\n cannot be assigned (2) AHL formula: Chemical formula for the AHL structure\n (2) AHL monoisotopic mass: Monoisotopic mass for the AHL structure * (3)\n Category: Confidence ranking of data used to assign the AHL structure;\n lower values denote better confidence (3) Criteria: Criteria used to\n assign the category confidence rank; the rationale for omitting data\n (superscript note a) that utilize only relaxed-specificity bioassays is\n discussed in the manuscript text #### File: RefAHL\\_rev20250421.fasta\n **Description:** The RefAHL_revYYYYMMDD.fasta file contains the following:\n Header: Contains the RefAHL identifier and Major AHL separated by an\n underscore for each LuxI homolog entry listed in .xls spreadsheet in fasta\n format; the text ‘RefAHL’ is included for each header for easy filtering\n Sequence: One letter coded amino acid sequence for the LuxI homolog ##\n Code/software .xlsx files can be opened with any version of Microsoft\n Excel, as well as Google Sheets, WPS Office or OpenOffice Calc .fasta\n files can be opened with any text editor  ## Access information Other\n publicly accessible locations of the data: * none Data was derived from\n the following sources: * data was assembled from the original references\n as cited in RefAHL."]} 

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