Data archive for Tier 1 of the ForceSMIP project, which is described in "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)" by Wills et al. Please cite that paper for any usage of this data (preprint citation information below; cite final published version once available). Wills, R.C.J., C. Deser, K.A. McKinnon, A. Phillips, S. Po-Chedley, S. Sippel, A.L. Merrifield, C. Bône, C. Bonfils, G. Camps-Valls, S. Cropper, C. Connolly, S. Duan, H. Durand, A. Feigin, M.A. Fernandez, G. Gastineau, A. Gavrilov, E. Gordon, M. Günther, M. Höver, S. Kravtsov, Y.-N. Kuo, J. Lien, G.D. Madakumbra, N. Mankovich, M. Newman, J. Rader, J.-R. Shi, S.-I. Shin, G. Varando: Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), ESS Open Archive, https://doi.org/10.22541/essoar.175003371.14843115/v1.Types of data included here are:Evaluation-Tier1: Raw data for 10 evaluation members, including reanalysis/observations (member "1I")ensmeans-Tier1: The "true forced response", from the corresponding large ensemble mean, for the 9 evaluation members that are from models (all except "1I")ForceSMIP-estimates-Tier1: ForcesSMIP method estimates of the forced response in each evaluation memberEach of these types of data is provided at monthly temporal resolution over 1950-2022, for each of 8 variables: tos (sea-surface temperature), tas (surface air temperature), pr (precipitation), psl (sea-level pressure), monmaxtasmax (monthly maximum daily maximum temperature), monmintasmin (monthly minimum daily minimum temperature), monmaxpr (monthly maximum daily precipitation), and zmta (zonal-mean atmospheric temperature). Annual maximums of monmaxtasmax and monmaxpr give the annual maximums TXx and Rx1day following standard notational conventions in the study of extreme events (Zhang et al. 2011, https://doi.org/10.1002/wcc.147). Similarly, the annual minimum of monmintasmin gives TNn.For further details about the dataset and how it was generated, see Wills et al., "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)".Correspondence: Robert Jnglin Wills ([email protected])

taxonomy_silva.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the SILVA database v132.taxonomy_greengenes.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the Greengenes database.16S_rep_seqs.fasta contains all representative 16S rRNA gene amplicon sequence variants.16S_reads_table.tsv contains the read counts (abundances) of 16S rRNA gene sequences assigned to each representative sequence per sample.16S_master.table.clean.csv contains comprehensive information on 16S rRNA gene relative abundances, taxonomy, and representative sequences.metagenome_gene_database-annotated.xlsx contains annotated genes, gene abundances per sample, and gene descriptions based on KEGG database gene IDs and descriptions.heatmap_metagenome_gene_database-annotated.xlsx includes annotated gene IDs and descriptions, normalized gene abundances displayed as both values and heatmaps, and statistical abundance analyses performed using MaAsLin2.metadata.txt contains relevant metadata associated with each collected sample.Maaslin2_output_treatment_gene_ID.zip statistical abundance analyses at the gene level performed using MaAsLin2 - output files (all timepoints n=8).Maaslin2_output_module_trt_end.zip tatistical abundance analyses at the module level performed using MaAsLin2 - output files (two last timepoints n=4).Data_Acids_gL.csv - Fatty acid production data in g/L.

taxonomy_silva.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the SILVA database v132.taxonomy_greengenes.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the Greengenes database.16S_rep_seqs.fasta contains all representative 16S rRNA gene amplicon sequence variants.16S_reads_table.tsv contains the read counts (abundances) of 16S rRNA gene sequences assigned to each representative sequence per sample.16S_master.table.clean.csv contains comprehensive information on 16S rRNA gene relative abundances, taxonomy, and representative sequences.metagenome_gene_database-annotated.xlsx contains annotated genes, gene abundances per sample, and gene descriptions based on KEGG database gene IDs and descriptions.heatmap_metagenome_gene_database-annotated.xlsx includes annotated gene IDs and descriptions, normalized gene abundances displayed as both values and heatmaps, and statistical abundance analyses performed using MaAsLin2.metadata.txt contains relevant metadata associated with each collected sample.Maaslin2_output_treatment_gene_ID.zip statistical abundance analyses at the gene level performed using MaAsLin2 - output files (all timepoints n=8).Maaslin2_output_module_trt_end.zip tatistical abundance analyses at the module level performed using MaAsLin2 - output files (two last timepoints n=4).Data_Acids_gL.csv - Fatty acid production data in g/L.

taxonomy_silva.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the SILVA database v132.taxonomy_greengenes.tsv contains taxonomy assignments for all representative 16S rRNA gene amplicon sequence variants using the Greengenes database.1_16S_rep_seqs.fasta contains all representative 16S rRNA gene amplicon sequence variants.2_16S_reads_table.tsv contains the read counts (abundances) of 16S rRNA gene sequences assigned to each representative sequence per sample.16S_master.table.clean.csv contains comprehensive information on 16S rRNA gene relative abundances, taxonomy, and representative sequences.4_metagenome_gene_database-annotated.xlsx contains annotated genes, gene abundances per sample, and gene descriptions based on KEGG database gene IDs and descriptions.4_heatmap_metagenome_gene_database-annotated.xlsx includes annotated gene IDs and descriptions, normalized gene abundances displayed as both values and heatmaps, and statistical abundance analyses performed using MaAsLin2.metadata.txt contains relevant metadata associated with each collected sample.Maaslin2_output_treatment_gene_ID.zip statistical abundance analyses at the gene level performed using MaAsLin2 - output files (all timepoints n=8).Maaslin2_output_module_trt_end.zip tatistical abundance analyses at the module level performed using MaAsLin2 - output files (two last timepoints n=4). Data_Acids_gL.csv - Fatty acid production data in g/L.

This repository includes multiple sequence alignments and phylogenetic trees of RMES1-like genes in the sorghum pangenome and across Poaceae. The recent adaptation of the cereal crop sorghum to a global aphid outbreak was a fortuitous case of evolutionary rescue, but the pangenomic and molecular basis is not known. The sorghum pangenome contains extensive copy number variation at the locus and a segmental duplication on Chr10 (Fig. S9). The causative NLRs (RMES1A and RMES1B) lack signaling domains and have ATPase mutations expected to abrogate function (Fig. 4f), suggesting RMES1 NLRs regulate immunity via a noncanonical mechanism. The RMES1 NLR family is ancient, orthologous to phloem-feeding resistance genes in rice (Fig 5b) and syntenic across the grass super-pangenome (Fig. 5b, 5c). Thus, gene birth-and-death processes at an ancient gene cluster created rare standing variation and provided the adaptive allele for evolutionary rescue.

Understanding how riverscape features influence gene flow is crucial for managing population connectivity in freshwater species. We examined the spatial genetic structure of non-native brook trout (Salvelinus fontinalis) in a headwater stream network proposed for the reintroduction of federally threatened greenback cutthroat trout (Oncorhynchus virginalis stomias). Brook trout were used as a surrogate species to evaluate the suitability of this habitat for supporting a native trout metapopulation. Using 12 microsatellite loci, we genotyped 757 individual brook trout from 22 sampling sites and modelled the effects of physical riverscape features on gene flow. Genetic clustering analysis identified four distinct tributary groups, while pairwise genetic differentiation (mean FST = 0.04; mean Jost’s D = 0.06) indicated some genetic connectivity across the network. Vertical barriers and steep stream gradients impeded gene flow, whereas high-order mainstem streams facilitated trout movement. Gene flow was stronger in the downstream direction, and streams with barriers and steeper gradients showed increased asymmetry between upstream and downstream movement. Results suggest that this stream network provides sufficient genetic connectivity to support a metapopulation of native trout. Managers should prioritize habitats with gradual stream gradients and fewer barriers to promote genetic connectivity in reintroduced native trout.

Large- to global-domain short-term prediction of clouds (0-3 hours), or cloud nowcasting, remains relevant to civilian and military applications ranging from solar energy production to intelligence gathering.  Despite the capabilities of contemporary numerical weather prediction models, nowcasting methods based on near real-time observations (i.e. satellite imagery) hold operational value due to their relative computational efficiency and accuracy for short-term applications.  A commonly used nowcasting approach involves using two or more images to retrieve the apparent motions of features, or optical flow, which can be used to extrapolate the future location of those features.  However, such approaches generally assume that the optical flow field remains unchanged with respect to time which is challenging to apply to piecewise cloud fields from satellite imagery.  Here, we propose a method to nowcast clouds that adapts a computer vision technique for image interpolation, commonly referred to as warping, to account for temporal changes to optical flow fields derived from infrared satellite imagery. We evaluate the proposed method for 991 randomly selected regional cases from 2024 and perform a detailed analysis on three specific cases. Applying a dense (every image pixel) optical flow retrieval technique to full-disk GOES infrared imagery, we demonstrate that forward warping of the optical flow field when coupled with simple occlusion reasoning, improves skill in cloud nowcasting.

Background: While computed tomographic (CT) myelography is increasingly available and has been evaluated in alive horses, objective criteria for diagnosing cervical vertebral compressive myelopathy (CVCM) are lacking. Objectives: Establish morphometric dimensions of cervical vertebral canal and spinal cords from horses with CVCM and compare those to unaffected horses with the use of cone beam CT (CBCT). Study design: Prospective observational study. Methods: Four control horses and ten horses with CVCM underwent diagnostic imaging and histopathology. Morphometric measurements were obtained from cervical radiographs and radiographic and CBCT myelography. Receiver operating characteristic curves were generated to establish thresholds of measurements. Results: Intravertebral sagittal ratios were significantly different between CVCM and control horses. Dorsal myelographic column reduction was significantly different between compressed site and non-compressed sites. Full myelographic area (FMA), dural area (DA), and spinal cord area (SCA) were significantly smaller in the CVCM horses, and were significantly smaller at compressed sites when compared to non-compressed sites. Reductions of FMA and DA and ratios of spinal cord area to full myelographic and dural area were significantly larger at compressed sites when compared to non-compressed sites. Diagnostic thresholds to consider for CVCM are FMA<294mm2, DA<188mm2, and SCA <104mm2. Compressed sites were associated with thresholds of FMA<274mm2, DA<188mm2, or reduction of FMA>9%, reduction of DA>14%, SCA:FMA>36.7%, and SCA:DA>57.9%. Main limitations: Small number of horses. CVCM horses were younger than control horses. Conclusions: CBCT myelography provides quantitative parameters that can support a diagnosis of CVCM and should be used alongside radiographic myelography.

Annual aboveground net primary production (ANPP) records from two dunes (N3 and W4) at two topographic positions: ridge (DT) and swale (ID).

Annual aboveground net primary production (ANPP) records from two dunes (N3 and W4) at two topographic positions: ridge (DT) and swale (ID).