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Figs. 11–19 in Jumping Plant Lice of the genus Calophya (Hemiptera: Calophyidae) in Mexico
Figs. 11–19. Calophya schini. (11) Female dorsal view; (12) male dorsal view; (13) female lateral view; (14) male lateral view; (15) head; (16) metaljbia; (17) an- tenna; (18) female terminalia; (19) male terminalia. Scale bars: Figs. 11–14 = 1 mm; 15–19 = 0.1 mm.
Figs. 41–45 in Jumping Plant Lice of the genus Calophya (Hemiptera: Calophyidae) in Mexico
Figs. 41–45. Calophya spondiadis sp. nov., 5th instar immature. (41) Dorsal view; (42) antenna with marginal setae; (43) forewing bud and marginal setae; (44) apical tarsal segment; (45) caudal plate and marginal setae. Scale bars: Figs. 41 = 0.5 mm; 42–45 = 0.1 mm.
Figs. 20–24. Calophya schini, 5 in Jumping Plant Lice of the genus Calophya (Hemiptera: Calophyidae) in Mexico
Figs. 20–24. Calophya schini, 5th instar immature. (20) Dorsal view; (21) antenna and marginal setae; (22) forewing bud with marginal setae; (23) apical tarsal segment; (24) caudal plate with marginal setae. Scale bars: Figs. 20 = 0.5 mm; 21, 22, 24 = 0.1 mm; 23 = 0.05 mm.
Figs. 1–10 in Jumping Plant Lice of the genus Calophya (Hemiptera: Calophyidae) in Mexico
Figs. 1–10. Calophya dicksoni. (1) Male dorsal view; (2) male lateral view; (3) forewing; (4) head; (5) paramere lateral view; (6) setae on the female procljger; (7) female terminalia; (8) male terminalia; (9) ovipositor lateral view; (10) aedeagus lateral view. Scale bars: Figs. 1–3 = 1.0 mm; 4 = 0.25 mm; 5 = 0.125 mm; 6, 9, 10 = 0.05 mm; 7, 8 = 0.1 mm.
Figs. 30–40 in Jumping Plant Lice of the genus Calophya (Hemiptera: Calophyidae) in Mexico
Figs. 30–40. Calophya spondiadis sp. nov. (30) Aedeagus, lateral view; (31) paramere, inner surface, lateral view; (32) female dorsal view; (33) male dorsal view; (34) female lateral view; (35) male lateral view; (36) head; (37) ovipositor lateral view; (38) antenna; (39) female terminalia; (40) male terminalia. Scale bars: Figs. 32–35 = 1.0 mm; 36, 38–40 = 0.1 mm; 37 = 0.05 mm.
Figure 4. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610
Figure 4. - Euphydryas maturna habitat in a commercial, thinned pine-dominated forest with ca. 30-year old trees, and in a clear-cut edge. This kind of forest habitat is probably suitable after thinning for several years, but longer than spruce-dominated forests (Fig. 3). Also, edge habitats in these relatively dry habitats overgrow somewhat slower than in moister edges (Fig. 2).
Figure 5. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610
Figure 5. - Powerline habitat of Euphydryas maturna. Vegetation under powerlines is kept open continuously, so powerline habitats may function both as breeding places and dispersal corridors.
Figure 3. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610
Figure 3. - Euphydryas maturna habitat in a commercial, thinned spruce-dominated forest. Such habitats are probably suitable after thinning for several years.
Figure 2. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610
Figure 2. - Clear-cut edge habitat of Euphydryas maturna. Clear-cut edges typically remain suitable for breeding for some years only until they become overgrown by tall grasses and tree seedlings.
Figure 1. from: Melampyrum sylvaticum as a pre-diapause host plant of the scarce fritillary (Euphydryas maturna) in Finland - Biodiversity Data Journal 3: e5610 (17 July 2015) https://doi.org/10.3897/BDJ.3.e5610
Figure 1. - Larval web of Euphydryas maturna on Melampyrum sylvaticum in Sipoo, S Finland (November 2nd, 2014).
Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)
<p>This dataset provides the raw data for the population genetic analyses for the article: "Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)" by Benoit Pujol; Juliette Archambeau; Aurore Bontemps; Mylène Lascoste; Sara Marin; and Alexandre Meunier found in the journal "Botany Letters", Vol 164 pp. 111-119 (DOI: 10.1080/23818107.2017.1310056).</p> <p>Link to journal open access article: http://www.tandfonline.com/doi/pdf/10.1080/23818107.2017.1310056</p> <p>Link to Zenodo article reporsitory: https://zenodo.org/record/801169</p> <p>The datafile includes three data sheets:</p> <p>Data, which contains for each plant : the name of the population, the name of the sampled individual, the subspecies, the latitude of the population, the longitude of the population, the altitudinal elevation of the population in meters, and the microsatellite genotype of each plant. Genotype data is recorded by locus (two columns for the two alleles at one locus). Locus name is found as the title of the column. The record for each allele is its allele size.</p> <p>valleys 1 and valleys 2, which contains the association between populations and valleys following the two scenarios that we analyzed in the paper.</p> <p>Microsatelite loci were developed during previous work: see the following paper for more details: Debout, G., E. Lhuillier, P.-J. Malé, B. Pujol, and C. Thébaud. 2012. Development and characterization of 24 polymorphic microsatellite loci in two Antirrhinum majus subspecies (Plantaginaceae) using pyrosequencing technology. Conservation Genetics Resources 4:75-79.</p>
High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome"
<p>Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome" (doi: https://doi.org/10.1101/2024.05.23.595613)</p> <p>Table A: complete list of significant anchors and associated genes from analysis of sorghum dataset</p> <p>Table B: complete list of significant anchors and associated genes from analysis of maize dataset</p> <p>Table C: complete list of significant anchors and associated genes from analysis of Arabidopsis P/Fe dataset</p> <p>Table D: complete list of significant anchors and associated genes from analysis of Arabidopsis FLOE1 dataset</p> <p>arabidopsis_floe1_ALL_anchors_satc_truncated.txt: data from the Arabidopsis FLOE1 dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>arabidopsis_pfe_ALL_anchors_satc_truncated.txt: data from the Arabidopsis P/Fe dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>maize_pollen_ALL_anchors_satc_truncated.txt: data from the maize dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>sorghum_drought_ALL_anchors_satc_truncated.txt: data from the sorghum dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample.</p> <p>cryptic_splicing_anchors.tsv: list of anchors described in Supplementary Information section of the article that are examples of cryptic splicing. Columns are dataset name, gene name/ID, anchor sequence, target 1 sequence, and target 2 sequence. </p>
Planting Date Maps (Low Resolution) and County Data for the US Corn Belt
<p>Aggregated dataset on planting dates for maize and soybean crops in the US Corn Belt from 2000-2020. This dataset was generated from Landsat satellite data and covers 12 states. The methodology is described in <a href="https://doi.org/10.1016/j.rse.2023.113551">Deines et al. 2023 </a>in Remote Sensing of Environment (open access).</p> <p>Note: Version 2 is identical to Version 1, but includes a file missing from v1 (soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv)</p> <p><strong>Preferred citation:</strong></p> <p>Deines, J.M., A. Swatantran, D. Ye, B. Myers, S. Archontoulis, & D.B. Lobell. 2023. Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020. <em><strong>Remote Sensing of Environment. </strong></em><a href="https://doi.org/10.1016/j.rse.2023.113551">https://doi.org/10.1016/j.rse.2023.113551</a></p> <p><strong>Contents</strong></p> <ol> <li>Gridded map datasets of planting dates aggregated to 10 km resolution. Map datasets are contained in zip files by crop type. Each zip file contains 21 annual geotiff rasters covering the full study region. <ol> <li>Maize_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> <li>Soybeans_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> </ol> </li> <li>Tabular county planting date statistics derived from the high-resolution (30 m) planting date maps. This data is presented in .csv format and includes the day-of-year when 10, 25, 50, 75, and 90 percent of crop area was planted for each year. There is one record for each county-year; data files are separated by crop type. <ol> <li>maize_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> <li>soybeans_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> </ol> </li> <li>Tabular planting progress by county. These .csv's present the area planted for each crop by county for each day. They can be considered the "raw" version of item #2 (county stats) and can therefore be used to derive other planting progress percentiles other than those provided in item #2. <ol> <li>maize_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> <li>soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> </ol> </li> </ol> <p><strong>Metadata</strong></p> <p>1. Planting date maps</p> <ul> <li>Value: day-of-year of planting</li> <li>No Data value = -999 (grid cells outside of the study area or lacking maize/soybeans that year)</li> <li>Map projection: EPSG:5070, CONUS Albers Equal Area</li> <li>Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</li> </ul> <p>2. Tabular datasets</p> <ul> <li>GEOID = 5 digit county FIPS, or the unique identifier assigned to each county by the US TIGER county dataset</li> <li>county_code = 3 digit county identifier. Represents places 3-5 in the 5 digit code.</li> <li>state_code = 2 digit state identifier. Represents places 1-2 in the 5 digit code.</li> <li>DOY = day-of-year</li> <li>area_daily_m2 = area of that crop planted that day in that county, in square meters</li> <li>doy_X = day-of-year when X% of area was planted that year</li> </ul> <p><br> </p>
JSTOR plant type specimens linked to GBIF occurrences
<p>A mapping between URLs for type specimens in JSTOR Global Plants and the corresponding occurrence in the Global Biodiversity Information Facility (GBIF).</p><p>Guide to fields:</p><ul><li><strong>doi</strong>: JSTOR identifier</li><li><strong>code</strong>: Barcode:</li><li><strong>gbif</strong>: GBIF occurrence id</li><li><strong>occurrenceUrl</strong>: URL to specimen in original herbarium database</li><li><strong>occurrenceID</strong>: occurrenceID stored in GBIF</li><li><strong>title</strong>: Title of specimen in JSTOR</li><li><strong>resource_type</strong>: Type of resource</li><li><strong>canonical</strong>: Canonical taxonomic name</li><li><strong>stored_under_name</strong>: Taxonomic name specimen is stored under</li><li><strong>type_status</strong>: What kind of type</li><li><strong>family</strong>: Family plant species belongs to</li><li><strong>collector</strong>: Collector</li><li><strong>date</strong>: Date of collection</li><li><strong>country</strong>: Country of collection</li><li><strong>herbarium</strong>: Herbarium where specimen is stored</li><li><strong>names</strong>: All taxonomic names associated with specimen as JSON array</li><li><strong>url</strong>: JSTOR URL</li><li><strong>thumbnailUrl</strong>: URL to thumbnail of image in JSTOR</li></ul>
Phylogenomic analysis of cell-surface receptors and downstream signaling components in the plant lineage
<p>Here we identified cell-surface receptors and downstream signaling components from the genomes of 350 plant species. </p><p>Zip file contains:</p><p>Folder 'seqeunces for downstream signaling components' - FASTA and TREE files of the identified downstream signaling components.</p><p>Folder 'sequences for cell-surface receptors' - FASTA and TREE files of the identified cell-surface receptors.</p><p>Folder 'Specific analysis' - Contains specific analysis for the identified cell-surface receptors.</p><p>Subfolder 'ID analysis' - Contains information on ID clusters and motifs analysis in IDs.</p><p>Subfolder 'LRR motif gap analysis' - Contains information on small (10-29 aa) and large (30-90) gaps between LRR motifs in RLPs and RLKs.</p><p>Subfolder 'LRR-RLK & LRR-RLP phylogenetic analysis' - Contains FASTA and TREE files of the specific domain/regions (C3, C3-F, eJM-TM-cJM, and all) in LRR-RLPs and LRR-RLKs. This subfolder also contains the specific amino acid, charge and motif analysis in this region (see C3F-end features.xlsx).</p><p>Protein counts per species file - Contains the total number of each protein family/subfamily in each of the 350 species.</p><p>simpleToFullNames (translator file)- Translator file for the original ID of each gene. </p>
Plant life history data as evidence of an historical mixed-severity fire regime in Banksia woodlands
<p><i><strong>Context:</strong></i> The concept of the fire regime serves as an agreed upon template by which to inform understanding and management of fire-prone ecosystems globally. While observations from satellite imagery or palaeoecological proxy data can provide direct evidence of past fire regimes, they may be limited in temporal and/or spatial scale and are not available for all ecosystems. However, fire-related plant trait and demographic data offers an alternative approach to understand species-fire regime associations at the ecosystem scale. </p><p><i><strong>Aims:</strong></i> We aimed to quantify the life history strategies and associated fire regimes for six co-occurring shrub and tree species from fire-prone, Mediterranean climate Banksia woodlands in southwestern Australia. </p><p><i><strong>Methods:</strong></i> We collected static demographic data on size structure, seedling recruitment, and plant mortality across sites of varying time since last fire. We combined demographic data with key fire-related species traits to define plant life history strategies. We then compared observed life histories with <i>a priori</i> expectations for surface, stand-replacing, and mixed-severity fire regime types to infer historical fire regime associations.</p><p><i><strong>Key results:</strong></i> Fire-killed shrubs and weakly serotinous trees had abundant post-fire seedling recruitment, but also developed multi-cohort populations during fire-free periods via inter-fire seedling recruitment. Resprouting shrubs had little seedling recruitment at any time, even following fire, and showed no signs of decline in the long absence of fire likely due to their very long lifespans. </p><p><i><strong>Conclusions:</strong></i> The variation in life history strategies for these six co-occurring species is consistent with known ecological strategies to cope with high variation in fire intervals in a mixed-severity fire regime. While resprouting and strong post-fire seedling recruitment indicate a tolerance of frequent fire, inter-fire recruitment and weak serotiny is interpreted as a bet-hedging strategy to cope with occasional long fire-free periods that may otherwise exceed adult and seed bank lifespans. </p><p><i><strong>Implications:</strong></i> Our findings suggest that Banksia woodlands have evolved with highly variable fire intervals in a mixed-severity fire regime. Further investigations of species adaptations to varying fire size and patchiness can help extend our understanding of fire regime tolerances.</p>
Plant community compositional stability over 40 years in a Fraser River Estuary tidal freshwater marsh
<p class="MsoNormal"><span>Long-term data sets documenting temporal changes in vegetation communities are uncommon, yet imperative for understanding trends and triggering potential conservation management interventions. For example, decreasing species diversity and increasing non-native species abundance may be indicative of decreasing community stability. We explored long-term plant community change over a 40-year period through the contribution of data collected in 2019 to two historical datasets collected in 1979 and 1999 to evaluate decadal changes in plant community biodiversity in a tidal freshwater marsh in the Fraser River Estuary in British Columbia, Canada. We found that plant assemblages were characterized by similar indicator species, but most other indicator species changed, and that overall </span><span>α-diversity</span><span> decreased while </span><span>β</span><span>-diversity increased. Further, we found evidence for plant assemblage homogenization through the increased abundance of invasive species such as yellow flag iris (<em>Iris pseudacorus</em>), and reed canary grass (<em>Phalaris arundinacea</em>). These observations may inform concepts of habitat stability in the absence of direct anthropogenic disturbance and corroborate globally observed trends of native species loss and non-native species encroachment. Our results indicate that within the Fraser River Estuary, active threat management may be necessary in areas of conservation concern in order to prevent further native species biodiversity loss. </span></p>
Data from: What goes in must come out? The metabolic profile of plants and caterpillars, frass, and adults of Asota (Erebidae: Aganainae) feeding on Ficus (Moraceae) in New Guinea
<p>Insect herbivores have evolved a broad spectrum of adaptations in response to the diversity of chemical defences employed by plants. Here we focus on two species of New Guinean Asota and determine how these specialist moths deal with the leaf alkaloids of their fig (Ficus) hosts. As each focal Asota species is restricted to one of three chemically distinct species of Ficus, we also test whether these specialized interactions lead to similar alkaloid profiles in both Asota species. We reared Asota caterpillars on their respective Ficus hosts in natural conditions and analyzed the alkaloid profiles of leaf, frass, caterpillar, and adult moth samples using UHPLC–MS/MS analyses. We identified 43 alkaloids in our samples. Leaf alkaloids showed various fates. Some were excreted in frass or found in caterpillars and adult moths. We also found two apparently novel indole alkaloids likely synthesized de novo by the moths or their microbiota—in both caterpillar and adult tissue but not in leaves or frass. Overall, alkaloids unique or largely restricted to insect tissue were shared across moth species despite feeding on different hosts. This indicates that a limited number of plant compounds have a direct ecological function that is conserved among the studied species. Our results provide evidence for the importance of phytochemistry and metabolic strategies in the formation of plant–insect interactions and food webs in general. Furthermore, we provide a new potential example of insects acquiring chemicals for their benefit in an ecologically relevant insect genus.</p>
Invasive plant dataset
<p>This dataset pertains to three types of plants: the apple tree (<i>Malus pumila</i> Mill.), and the invasive species<i> Erigeron annuus</i> (L.) Pers. and <i>Erigeron canadensis</i> L. Images of all three species were collected on-site in an apple orchard using a drone. The drone was equipped with a DJI DL 35mm F2.8 LS ASPH lens, with a sensor size of 35.9 mm by 24 mm. To preserve greater image detail and minimize background blurring, the aperture was set at f/16 during image capture. The images were taken from heights of 5m, 10m, 15m, 20m, and 25m, with corresponding Ground Sample Distances (GSD) of 0.0625 cm/pixel, 0.125 cm/pixel, 0.1875 cm/pixel, 0.25 cm/pixel, and 0.3125 cm/pixel, respectively. The dataset includes both original and data-augmented images. Data augmentation techniques employed include flipping, rotation, elastic deformation, contrast adjustment, brightness modification, sharpening changes, noise addition, blurring, and mosaicking. The images are 512×512 pixels in size.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.