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22,710 results for “Plants for planting”
SGS-LTER CO2 Elevation Study: Visual estimates of plant cover on the OTC project on the Central Plains Experimental Range, Nunn, Colorado, USA 1997 - 2001
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. Every month, during the growing season, from 1997-2001, 10 small quadrats were placed in ambient and elevated CO2 open-top-chambers, and plant cover, by species, was visually estimated. In general, elevated CO2 caused an increase in one C3 grass species, Stipa comata, and a small increase in forbs.
SGS-LTER CO2 Elevation Study: Stipa comata basal size and plant density per Open Top Chamber plot on the Central Plains Experimental Range, Nunn, Colorado, USA 1997 - 2001
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. At the end of the Open Top Chamber experiment the number and basal size of Stipa comata plants in ambient and elevated (720ppm) chambered and unchambered plots was measured. There was a greater number of small plants and seedlings in the elevated CO2 plots. This research was conducted at the Central Plains Experimental Range, near Nunn, CO; lat.40degrees 40 minutes N; long. 104 degrees 45 minutes W in the shortgrass steppe region of NE Colorado, USA and as a collaboration between SGS-LTER and USDA-ARS researchers.
Stable isotopes of M. mercenaria and plant sources on the Virginia Coast
d13C, d15N, d2H stable isotope values of clams, macrophytes, macroalgae, microalgae from Cobb Island Bay vicinity
Plant Associations on the Virginia Barrier Islands: Metompkin - Smith Islands, 1974-1975.
Vegetation maps and descriptions of plant associations were compiled for 16 (15) barrier and marsh islands on the seaward margin of the Delmarva Peninsula (USA) based on 1:20,000 scale false-color infrared aerial photography taken on June 4, 1974 and subsequent extensive ground-truthing. Twenty-six mapping units based on botanical, topographic, and edaphic characteristics representative of distinct plant associations, ecotones, or non-vegetated surfaces were identified. Published maps were later digitized and georeferenced for use with GIS systems. Chimney Pole Marsh, for which only the inlet-facing shoreline was originally mapped, rather than the whole island, was never digitized and is not included in this dataset.
Species composition and plant functional traits on Hog and Metompkin Islands, VA 2016-2017
Physical characteristics (soil salinity, elevation, distance from shoreline) and biological parameters (species composition, cover, biomass, and functional traits) were measured for plants on Hog Island (Northampton Co. VA) and Metompkin Island (Accomack County, VA). Physical parameters were measured during 2016. Plant biomass, species composition, percent cover, and functional traits were measured in 2017. Data Table 1: Environmental and geographic data on Hog and Metompkin Islands, VA 2016 Data Table 2: Species composition and aboveground plant functional traits on Hog and Metompkin Islands, VA 2017 Data Table 3: Vegetative biomass on Hog and Metompkin Islands, VA 2017 Data Table 4: Plot-level root functional traits on Hog and Metompkin Islands, VA 2017 Data Table 5: Latitude-longitude coordinates of plots on Hog and Metompkin Islands, VA 2016-2017
JRC Open Power Plants Database (JRC-PPDB-OPEN)
<p>In 2017 the Joint Research Centre developed a Power Plant Database for energy systems modelling (JRC-PPDB) in order to support the unit activities in energy systems modelling and knowledge management.</p> <p>As demand for open data is increasingly sought after, an open version (JRC-PPDB-OPEN), based on exclusively open data was designed. The JRC-PPDB-OPEN is primarily based on a collection of all the information published by ENTSO-E<a href="#_ftn1">[1]</a> on the European power plants at unit level. This information was extended, improved, and where possible corrected using information contained in open datasets published by WRI Powerwatch<a href="#_ftn2">[2]</a>, Global energy observatory<a href="#_ftn3">[3]</a>, FRESNA<a href="#_ftn4">[4]</a> and the EEA<a href="#_ftn5">[5]</a>.</p> <p>The JRC-PPDB-OPEN database is a first attempt towards a more detailed and coherent, albeit still incomplete, dataset of European power plants. Further work to expand and improve the information contained therein is called for. To this extend, and in order to facilitate the future involvement of third parties in such efforts, the associations between records in the different datasets (linkage) are included.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> <a href="https://transparency.entsoe.eu/">https://transparency.entsoe.eu/</a></p> <p><a href="#_ftnref2">[2]</a> <a href="http://datasets.wri.org/dataset/globalpowerplantdatabase">http://datasets.wri.org/dataset/globalpowerplantdatabase</a></p> <p><a href="#_ftnref3">[3]</a> <a href="http://globalenergyobservatory.org/">http://globalenergyobservatory.org/</a></p> <p><a href="#_ftnref4">[4]</a> <a href="https://github.com/FRESNA/powerplantmatching">https://github.com/FRESNA/powerplantmatching</a></p> <p><a href="#_ftnref5">[5]</a> <a href="https://prtr.eea.europa.eu/#/home">https://prtr.eea.europa.eu/#/home</a></p>
Studies of NH4+ and NO3- uptake ability of subalpine plants and resource-use strategy identified by their functional traits
<p>Data for the preprint "Studies of NH4+ and NO3- uptake ability of subalpine plants and resource-use strategy identified by their functional traits.”, by Legay N. , Grassein F., Arnoldi C., Segura R., Laîné P., Lavorel S., Clément J.C.</p>
Figure 3 in Notes on mating behaviour and a possible new host plant for Megacyllene angulata (Fabricius, 1775) (Cerambycidae, Coleoptera)
Figure 3. Megacyllene angulata (Fabricius, 1775). (A‑C) male. (A) dorsal habitus; (B) ventral habitus; (C) lateral habitus. (D‑F) female. (D) dorsal habitus; (E) ventral habitus; (F) lateral habitus. Scale bar = 2 mm.
Figure 1 in Notes on mating behaviour and a possible new host plant for Megacyllene angulata (Fabricius, 1775) (Cerambycidae, Coleoptera)
Figure 1. Location where the observations occurred. (A) South American with Amazonia state (Brazil) marked; (B) Amazonia state with the location of observation in Tefé municipality marked (red point); (C) area of observation.
Trichoderma atroviride P1 Colonization of Tomato Plants Enhances Both Direct and Indirect Defense Barriers Against Insects
<p><strong>FIGURE 1</strong> </p> <p>Survival rate of <em>S. littoralis</em> larvae, from 3rd instar (time 0) to pupation, reared on tomato leaves obtained from plants treated with <em>Trichoderma atroviride</em> P1 or untreated control plants. Asterisk indicates that the two survival curves are significantly different (LogRank test, <em>P</em> = 0.0027).</p> <p> </p> <p><strong>FIGURE 3</strong> </p> <p>Survival of <em>Macrosiphum euphorbiae</em> reared on tomato plants treated with <em>T. atroviride</em> P1 or untreated control plants. Asterisk indicates that the two survival curves are significantly different (LogRank test, <em>P</em> = 0.0012).</p> <p> </p> <p><strong>FIGURE 4</strong> </p> <p>Flight behavior of <em>Aphidius ervi</em> females (%) toward tomato plants inoculated with <em>T. atroviride</em> P1 and untreated controls. Asterisk indicates a significant difference, assigned by <em>G</em> test for independence (<em>P</em> < 0.001).</p> <p> </p> <p><strong>TABLE 1</strong></p> <p>GC-MS detection of VOCs released by tomato plants obtained from seeds untreated (Control) and treated with <em>Trichoderma atroviride</em> strain P1.</p> <p> </p>
Supplementary data to Clade-specific biogeographic history and climatic niche shifts of the southern Andean-southern Brazilian disjunction in plants
<p>Georeferenced locality data points used in the climatic analyses. Column "clade" indicate whether the species corresponds to the southern Andean (SA) or to the southern Brazilian (SB) clade. See the book chapter for details.</p> <p>Part of this dataset was built with information downloaded from GBIF (www.GBIF.org) for the following taxa:</p> <p><em>Araucaria</em><br> GBIF.org (06 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.2nc359<br> GBIF.org (06 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.aym9bi</p> <p><em>Butia</em><br> GBIF.org (30th May 2018) GBIF Occurrence Download https://doi.org/10.15468/dl.k42ama</p> <p><em>Colliguaja</em><br> GBIF.org (28 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.nvqo4p<br> GBIF.org (13th January 2016) GBIF Occurrence Download http://doi.org/10.15468/dl.aoovox<br> GBIF.org (15th May 2018) GBIF Occurrence Download https://doi.org/10.15468/dl.mownio</p> <p><em>Griselinia</em><br> GBIF.org (28 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.eytdse</p>
TagSeq for gene expression in non-model plants: a pilot study at the Santa Rita Experimental Range NEON core site
<p>TagSeq analysis scripts and assembled transcriptomes for four vascular plant species from the Santa Rita Experimental Range, AZ. Transcriptomes for each species were sequenced and assembled as described below. Additional details available in the associated manuscript: MS LINK. Raw reads for each available at NCBI BioProject #PRJNA599443.</p> <p> </p> <p><strong>Taxon selection and sampling </strong></p> <p>This study focused on four commonly-occurring species at the Santa Rita Experimental Range Long Term Research and Core NEON site (SRER). These include the native species <em>Tidestromia</em> <em>lanuginosa</em> (Nutt.) Standl. (Amaranthaceae; ‘woolly tidestromia’), <em>Parkinsonia</em> <em>florida</em> (Benth. ex A. Gray) S. Watson. (Fabaceae; ‘blue palo verde’), and <em>Bouteloua</em> <em>aristidoides</em> (Kunth) Griseb. (Poaceae; ‘needle grama’), as well as the introduced species <em>Eragrostis</em> <em>lehmanniana</em> Nees (Poaceae; ‘Lehmann lovegrass’; native to southern Africa). All species were identified using a combination of the historical flora of the Santa Rita Experimental Range (Medina, 2003), the Arizona Flora (Kearney et al., 1960), and the Flora of North America (Flora of North America Editorial Committee, eds. 1993). Vouchers were deposited in the University of Arizona herbarium (ARIZ). Tissue from mature plants was collected from an apparently healthy individual representing each target species during the 2017 growing season. An entire stem was sampled for <em>B. aristidoides</em> (with flowers and fruits) and <em>E. lehmanniana</em> (without flowers or fruits). Leaves and leaflets only were sampled for <em>P. florida</em> and <em>T. lanuginosa</em>.</p> <p> </p> <p><strong>RNA extraction and RNA-seq</strong></p> <p>Total RNA was extracted from tissue using the Spectrum Plant Total RNA Kit (Sigma-Aldrich Co., St. Louis, MO, USA) following Protocol A. RNA was used to prepare cDNA using Nugen’s Ovation RNA-Seq System via single primer isothermal amplification (Catalogue # 7102-A01) and automated on the Apollo 324 liquid handler (Wafergen). cDNA was quantified on the Nanodrop (Thermo Fisher Scientific) and was sheared to approximately 300 bp fragments using the Covaris M220 ultrasonicator. Libraries were generated using Kapa Biosystem’s library preparation kit (KK8201). Fragments were end repaired and A-tailed, and individual indexes and adapters (Bioo, catalogue #520999) were ligated on each separate sample. The adapter ligated molecules were cleaned using AMPure beads (Agencourt Bioscience/Beckman Coulter, A63883), and amplified with Kapa’s HIFI enzyme (KK2502). Each library was then analyzed for fragment size on an Agilent’s Tapestation, and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on Thermo Fisher Scientific’s Quantstudio 5 before multiplex pooling (13-16 samples per lane) and paired-end sequencing at 2x150 bp on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using fastQC (Andrews, 2010).</p> <p> </p> <p><strong><em>De novo</em> transcriptome assembly</strong></p> <p>Raw sequence reads were processed using the SnoWhite pipeline (Barker et al., 2010a; Dlugosch et al., 2013), which included trimming adapter sequences and bases with a quality score below 20 from the 3' ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust (Lassmann et al., 2009), and removing polyA/T tails with SeqClean (https://sourceforge.net/projects/seqclean/). All transcriptomes were assembled with SOAPdenovo-Trans v1.03 (Xie et al., 2014) using a k-mer of 57. Assembled sequences for each species are in the files ending ".scafSeq".</p> <p> </p> <p><strong>Protein Translations</strong></p> <p>We used TransPipe (Barker et al., 2010) to identify plant proteins within the assembled transcripts for each reference transcriptome and provide protein and in-frame nucleic acid sequences for each species. The reading frame and protein translation for each sequence was identified by comparison to protein sequences from 25 sequenced and annotated plant genomes from Phytozome (Goodstein et al., 2012). Using BLASTX (Wheeler et al., 2008), best hit proteins were paired with each gene at a minimum cutoff of 30% sequence similarity over at least 150 sites. Genes that did not have a best hit protein at this level were removed. To determine the reading frame and generate estimated amino acid sequences, each gene was aligned against its best hit protein by Genewise 2.2.2 (Birney et al., 2004). Based on the highest scoring Genewise DNA-protein alignments, stop and 'N' containing codons were removed to produce estimated amino acid sequences for each gene. Output included paired DNA and protein sequences with the DNA sequence reading frame corresponding to each protein sequence. Nucleic acid sequence files end in “.fna”, whereas amino acid sequence files end in “.faa”. Numbers of sequences in each of these files correspond to the position of the sequence in the associated assembly file.</p> <p> </p> <p><strong>Custom scripts</strong></p> <p>“removePCRdups57.pl” is a Perl script that takes an input FASTQ file and removes exact duplicates identified over a supplied length at the beginning (3’ end) of the read. </p> <p>Run: perl removePCRdups57.pl <inputFASTQ> <length></p> <p> </p> <p>“create_GTF.pl” is a Perl script that takes an input FASTA file and creates a GTF file suitable for input into HtSeq-count v.0.5.4 (Anders et al., 2015).</p> <p>Run: perl create_GTF.pl <inputFASTA></p> <p> </p> <p>“combine_HtSeq.pl” is a Perl script that takes a set of htseq output files and makes a tab delim table of counts with header of sample names and first col of row names. The input file list file should be a text file with lists of Htseq files to combine on each line, where lines are tab delimited of the following form:</p> <p> <NameForOutputFile> <firstHtseqFile> <NextHtseqFile> <...etc...></p> <p>Run: perl combine_HtSeq.pl <inputFileList></p> <p> </p>
Figure 1 in First record of Petrobia (Petrobia) pseudotetranychina (Trombidiformes: Tetranychidae) in Asia, with two new host plants for Tetranychidae from Iran
Figure 1. Petrobia (Petrobia) pseudotetranychina (female) – Dorsal view of idiosoma.
Replication code and data for: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity."
<p>Replication code and data for the paper: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity." The following files are included in this repository:</p> <p>1) R scripts (numbered 0 through 9) include replication code for data analysis</p> <p>2) Datasets (6 zip folders) contain the data analyzed in the R scripts</p> <p> </p> <p> </p>
Plant invasion has limited impact on soil microbial alpha-diversity : a meta-analysis
<p>Plant invasion has proved to be a significant driver of ecosystem change, and with increased probability of invasion due to globalization, agricultural practices and other anthropogenic causes, it is crucial to understand its impact across multiple trophic levels. With the strong linkages between above and belowground processes, the response of soil microorganisms to plant invasion is the next logical step in developing our conceptual understanding of this complex system. In our study, we utilized a meta-analytical approach to better understand the impacts of plant invasion on soil microbial diversity. We synthesized 70 independent studies with 23 unique invaders across multiple ecosystem types to search for generalizable trends in soil microbial a-diversity following invasion. When possible, soil nutrient metrics were also collected in an attempt to understand the contribution of nutrient status shifts on microbial a-diversity. Our results show plant invasion to have highly heterogenous and limited impacts on microbial a-diversity. When taken together, our study indicates soil microbial a-diversity to remain constant following invasion, contrary to the aboveground counter parts. As our results suggest a decoupling in patterns of below and aboveground diversity, future work is needed to examine the drivers of microbial diversity patterns following invasion.</p>
Assemblies and annotations from the paper "Genome compartmentalization predates species divergence in the plant pathogen genus Zymoseptoria"
<p>These files are the assemblies and annotations produced and analyzed in the revised version of the manuscript entitled "Genome compartmentalization predates species divergence in the plant pathogen genus Zymoseptoria".</p>
The transcription regulatory code of a plant leaf
<p>The transcription regulatory network underlying essential and complex functionalities inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general and comparative picture of this complex regulatory network. Here, we used ChIP-seq to determine the binding profiles of 104 TF expressed in the maize leaf (Data can be downloaded from NCBI SRA under accession number PRJNA518749) </p> <p>With this large dataset, we trained machine-learning models to identify TF sequence preferences. A contrast between Maize and Arabidopsis TF sequence preferences revealed that DNA binding follows the conservation of TF protein families. Finally, the trained models were used to predict and compare the regulatory networks in other grasses species (Sorghum and Rice), which revealed that the edges between TF and TF coding genes are more likely to be maintained (<em>i.e., </em>evolutionarily conserved). </p> <p>On a practical level, we expect the presented TF binding models to be integrated into pipelines to predict effects of non-coding variants, both common and rare, on TF binding, to pinpoint causal sites. As the possibility of being able to predict and generate novel variation not seen in nature could fundamentally change future plant breeding.</p> <p>Detail: Each *tar.gz file is a <strong><a href="https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-019-1693-2">bag-of-k-mer model</a></strong> fitted for a single ZmTF, which can be used for predictions. Information about each ZmTF is included in the table tfids.tsv</p> <p>For more information about the project: <br> <a href="https://www.biorxiv.org/content/10.1101/2020.01.07.898056v3"><strong>The transcription regulatory code of a plant leaf</strong></a></p>
Belowground competition alters attractiveness of an insect-pollinated plant to pollinators
<p>Dataset of paper "Belowground competition alters attractiveness of an insect-pollinated plant to pollinators", AOB Plants</p>
Within and cross species predictions of plant specialized metabolism genes using transfer learning
<p>Datasets for <em>Within and cross species predictions of plant specialized metabolism genes using transfer learning.</em> </p> <ol> <li>Dataset 1: All gene features used in the full feature machine learning models including expression, co-expression, evolutionary, duplication, and protein domain features.</li> <li>Dataset 2: All gene features used in shared feature machine learning models including expression, evolutionary, duplication, and protein domain features.</li> <li>Table S1: All gene annotations from TomatoCyc or manual annotation.</li> <li>Table S2: All model scores.</li> <li>Table S3: All gene scores and predictions from each model.</li> <li>Table S4: Feature importance for 5 models.</li> <li>Table S5: Statistical analysis between classes for binary and continuous feature data.</li> <li>Table S6: RNAseq datasets used in analysis.</li> </ol>
Floral density and co-occurring congeners alter patterns of selection in annual plant communities
While the evolution and diversification of flowers is often attributed to pollinator-mediated selection, interactions between co-occurring plant species can alter patterns of selection mediated by pollinators and other agents. The extent to which both floral density and congeneric species richness affect patterns of net and pollinator-mediated selection on multiple co-occurring species in a community is unknown and is likely to depend on whether co-occurring plants experience competition or facilitation for reproduction. We conducted an observational study of selection on four species of <i>Clarkia</i> (Onagraceae) and tested for pollinator-mediated selection on two <i>Clarkia</i> species in communities differing in congeneric species richness and local floral density. When selection varied with community context, selection was generally stronger in communities with fewer species, where local conspecific floral density was higher, and where local heterospecific floral density was lower. These patterns suggest that intraspecific competition at high densities and interspecific competition at low densities may affect the evolution of floral traits. However, selection on floral traits was not pollinator-mediated in <i>C. cylindrica</i> or <i>C. xantiana</i>, despite variation in pollinator visitation and the extent of pollen limitation across communities for <i>C. cylindrica</i>. As such, interactions between co-occurring species may alter patterns of selection mediated by abiotic agents of selection.
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Allen Brain Atlas
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OpenNeuro
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