Find research datasets worth reusing

Fig. 5 in Immediate Allelopathic Effect Of Two Invasive Heracleum Species On Acceptor-Germination

Fig. 5. CCU content accordingly rapeseed germination in leachates of H. sosnovskyi and H. mantegazzianum (p<0,05; mean±SE).

Fig. 3 in Immediate Allelopathic Effect Of Two Invasive Heracleum Species On Acceptor-Germination

Fig. 3. Inhibition level of acceptor-rapeseed germination in leachates of H. sosnovskyi and H. mantegazzianum (p<0,05; mean±SE).

Fig. 1 in Immediate Allelopathic Effect Of Two Invasive Heracleum Species On Acceptor-Germination

Fig. 1. Pathways scheme of direct and indirect impacts of invasive species on ecosystem functioning (Jones & Gutiérrez 2007).

Fig. 4 in Immediate Allelopathic Effect Of Two Invasive Heracleum Species On Acceptor-Germination

Fig. 4. Inhibition of acceptor-ryegrass germination in leachates of H. sosnovskyi and H. mantegazzianum (p<0,05; mean±SE).

opencc-zeroJan 2022View details →

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Variable seed bed microsite conditions and light influence germination in Australian winter annuals

Environmentally cued germination may play an important role in promoting coexistence in Mediterranean annual plant systems if it causes niche differentiation across heterogeneous microsite conditions. In this study, we tested how microsite conditions experienced by seeds in the field and light conditions in the laboratory influenced germination in 12 common annual plant species occurring in the understorey of the York gum-jam woodlands in southwest Western Australia. Specifically, we hypothesized that if germination promotes spatial niche differentiation, then we should observe species-specific germination responses to light. In addition, we hypothesized that species' laboratory germination response may depend on the microsite conditions experienced by seeds while buried. We tested the laboratory germination response of seeds under diurnally fluctuating light and complete darkness, which were collected from microsites spanning local-scale environmental gradients known to influence community structure in this system. We found that seeds of 6 out of the 12 focal species exhibited significant positive germination responses to light, but that the magnitude of these responses varied greatly with the relative light requirement for germination ranging from 0.51 to 0.86 for these species. In addition, germination increased significantly across a gradient of canopy cover for two species, but we found little evidence to suggest that species' relative light requirement for germination varied depending on seed bank microsite conditions. Our results suggest that variability in light availability may promote coexistence in this system and that the microsite conditions seeds experience in the intra-growing season period can further nuance species germination behaviour.

opencc-by-4.0Jan 2022View details →

Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential

This dataset contains all the raw source data and MATLAB analysis functions that comprise the study: Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273. Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)* 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland. 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland  4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland *Corresponding author: Mikko J. Nissi Department of Applied Physics, University of Eastern Finland POB 1627 FI-70211, Kuopio, Finland mikko.nissi@uef.fi +358-50-5955517 Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping   Study and data description Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed. The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds.  The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).   Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are: <ul> <li>additional_info_scotspine: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li>manuscript_figures: Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li>matlab_scripts: Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li>mri_scotspine: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li>radiography_scotspine: Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, Tucson, Az, USA) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, Tucson, Az, USA).</li> <li>readme.txt: More information on the file and folder structure and datatypes.</li> </ul>   Please see the included readme.txt for further details.   (Teemu Tuomainen, Jan 25, 2022)

opencc-by-4.0Jan 2022View details →

Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination

These are the processed BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a>. The raw sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).   Code Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.   Metadata File: WU368_kim_et_al_nature_2022_meta.tsv Notes: <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> Abbreviations: <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul>   Information on the 2099 recombinant mAbs generated in this study File: WU368_kim_et_al_nature_2022_mabs.tsv Notes on columns: <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul>   Processed BCR data - heavy chains File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv Analysis was based on heavy chain-based clonal inference. Notes on columns: The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login">iReceptor</a> use these. Non-standard columns are noted below. <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul>   Processed BCR data - light chains File: WU368_kim_et_al_nature_2022_bcr_light.tsv Light chains were not used for heavy chain-based clonal inference or analysis.   Processed transcriptomics data Files: <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> Notes: <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.

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Data from: Selection on early survival does not explain germination rate clines in Mimulus cardinalis

Premise Many traits covary with environmental gradients to form phenotypic clines. While local adaptation to the environment can generate phenotypic clines, other nonadaptive processes may also. If local adaptation causes phenotypic clines, then the direction of genotypic selection on traits should shift from one end of the cline to the other. Traditionally genotypic selection on non-Gaussian traits like germination rate have been hampered because it is challenging to measure their genetic variance. Methods Here we used quantitative genetics and reciprocal transplants to test whether a previously discovered cline in germination rate showed additional signatures of adaptation in the scarlet monkeyflower (Mimulus cardinalis). We measured genotypic and population level covariation between germination rate and early survival, a component of fitness. We developed a novel discrete log-normal model to estimate genetic variance in germination rate. Results Contrary to our adaptive hypothesis, we found no evidence that genetic variation in germination rate contributed to variation in early survival. Across populations, southern populations in both gardens germinated earlier and survived more. Conclusions Southern populations have higher early survival but this is not caused by faster germination. This pattern is consistent with nonadaptive forces driving the phenotypic cline in germination rate, but future work will need to assess whether there is selection at other life stages. This statistical framework should help expand quantitative genetic analyses for other waiting-time traits.

opencc-zeroJul 2022View details →

opencc-by-4.0Jul 2022View details →

Phytotoxicity level of sewage sludge from different cleaning stages on germination and initial growth of Adenanthera pavonina L. (False pau-brasil)

Database referring to the variables, data and equations of the manuscript: Stabilized sewage sludge presents suitable phyto-toxic conditions for germination of false-pau-brasil   Alves, G. de O. ., Cardoso, P. H. S. ., Gonçalves, P. W. B. ., Moreira, C. D. D. ., & Sampaio, R. A. . (2021). LODO DE ESGOTO ESTABILIZADO APRESENTA CONDIÇÕES FITOTÓXICAS ADEQUADAS PARA A GERMINAÇÃO DO FALSO PAU-BRASIL. ENERGIA NA AGRICULTURA, 36(3), 348–361. https://doi.org/10.17224/EnergAgric.2021v36n3p348-361

opencc-by-4.0Aug 2022View details →

Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm. in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm.

opencc-by-4.0Aug 2022View details →

Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows). in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows).

opencc-by-4.0Dec 2012View details →

Supplementary material 1 from: Hirsch H, Wypior C, von Wehrden H, Wesche K, Renison D, Hensen I (2012) Germination performance of native and non-native Ulmus pumila populations. NeoBiota 15: 53-68. https://doi.org/10.3897/neobiota.15.4057

Location and climate information of the sampled Ulmus pumila populations in China and the U.S. Maximum (max.) temperatures for the months May, June and July are provided to show the temperature range during the main germination period (lowest and highest values are italicized). Climatic information was extracted from the WORLDCLIM database (Hijmans et al. 2005).

opencc-by-4.0Dec 2012View details →

Supplementary material 2 from: Hirsch H, Wypior C, von Wehrden H, Wesche K, Renison D, Hensen I (2012) Germination performance of native and non-native Ulmus pumila populations. NeoBiota 15: 53-68. https://doi.org/10.3897/neobiota.15.4057

Comparison of climatic conditions (a: mean annual temperature; b: annual precipitation) between the Chinese and North American locations of Ulmus pumila. Wilcoxon rank sum tests were used to test for differences between both ranges. Mean annual temperatures are significantly higher for locations from the U.S. (W = 7, p < 0.05). Annual precipitation is marginal higher in the invasive populations compared to the native populations (W = 9, p = 0.05). Significant differences are symbolized by different lowercases above the boxes.

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Physical seed damage, not rodent's saliva, accelerates seed germination of trees in a subtropical forest

Many tree species adopt fast seed germination to escape the predation risk by rodents. Physical seed damage and the saliva of rodents on partially consumed seeds may also act as cues for the seed to accelerate the germination process. However, the impacts of these factors on seed germination rate and speed remain unclear. In this study, we investigated such impacts on the germination rate and speed (reversal of germination time) of four tree species (Quercus variabilis, Q. serrata, Q. acutissima, and Q. glauca) after partial consumption by four rodent species, through a series of experiments. We also examined how seed traits may affect the damage degree by rodents by analyzing the relationship between the germination rate and time of rodent-damaged seeds and the traits. We found that artificially and rodent-damaged seeds exhibited a significantly higher seed germination rate and speed, compared to intact seeds. Also, the rodent saliva on seeds showed no significant effect on seed germination rate and speed. Furthermore, We observed significant positive correlations between several seed traits (including seed mass, coat thickness, and protein content) and seed germination rate, but these seed traits had a positive correlation with the germination rate and speed. These correlations are likely due to the beneficial traits countering seed damage by rodents. Overall, our results highlight the significant role of physical seed damage by rodents (rather than their saliva) in facilitating seed germination of tree species and potential mutualism between rodents and trees. Additionally, our results may have some implications in forest restoration, such that intentionally sowing or dispersing slightly damaged seeds by humans or drones may increase the likelihood of successful seed regeneration.

opencc-zeroJun 2024View details →

Figure 7 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 7. Results from the sensitivity analysis depicting variations in the overall effect size estimates (mean ± 95% confidence intervals [CIs]) of water-stress effects on (A) weed germination/emergence, (B) seedling radicle/root length, (C) plant height, and (D) leaf area when a particular study is omitted from the analysis. The vertical black solid and dashed lines represent overall effect sizes (mean ± 95% CIs) with all studies included.

Figure 3 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 3. Overall water-stress effects on germination/emergence of grass and broadleaf weeds (top) and six weed families—Asteraceae, Fabaceae, Convolvulaceae, Amaranthaceae, Rubiaceae, and Poaceae (bottom). The vertical black dashed line represents zero effect. The black dots are overall mean effect sizes, and the black lines are 99% confidence intervals (CIs).The values in parentheses are the number of observations followed by the number of studies for each pair-wise comparison. The mean effect sizes were considered significantly different when their 99% CIs did not include zero.

Figure 4 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 4. The log response ratio for germination and seedling radicle length of broadleaf (green dots/line) and grass (red dots/line) weed species as a function of water-stress intensity. Water stress increased as solution osmotic potential (ψsolution) decreased and vice versa.The subgroups for germination are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −0.8, −0.8 to −1.0, −1.0 to −1.4, and <−1.4 MPa, while the subgroups for radicle length are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −1.0, and <−1.0 MPa. Only ψsolution-based studies were used in this analysis. For each subgroup, the solid dots and lines represent mean effect sizes and their corresponding 99% confidence intervals (CIs).The mean effect sizes were considered significantly different when their 99% CIs did not include zero. Similarly, the water-stress effects were significantly different for each subgroup and among weed types only when their 99% CIs did not overlap with one another. The fitted lines represent a four-parameter logistic regression model, and the coefficients of the models are presented in Table 2.

Figure 1 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Page and McKenzie 2021) flow diagram highlighting the selection procedure of 86 scientific published papers included in the meta-analysis.

Figure 8 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 8. Results from the sensitivity analysis depicting variations in the overall effect size estimates (mean ± 95% confidence intervals [CIs]) of water-stress effects on (A) branches/tillers per plant, (B) leaves per plant, (C) inflorescences per plant, (D) seeds per plant, (E) total biomass, (F) root biomass, (G) shoot biomass, and (H) root:shoot ratio, when a particular study is omitted from the analysis. The vertical black solid and dashed lines represent overall effect sizes (mean ± 95% CIs) with all studies included.

Figure 6 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis

Figure 6. Density plots depicting the distribution of the individual effect sizes for all 12 response variables considered in this meta-analysis: (A) weed seed germination/emergence; (B) radicle/root length, plant height, and leaf area; (C) branches/tillers per plant, leaves per plant, inflorescences per plant, and seeds per plant; and (D) total biomass, root biomass, shoot biomass, and root:shoot ratio.