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3,105 results for “Vegetation =”
FIGURE 10. Miconia paucartambensis staminate plant A. Habit. B. Vegetative node with sessile leaves. C. Inflorescence axis D in Six new species of Miconia (Miconieae, Melastomataceae) from the Andes
FIGURE 10. Miconia paucartambensis staminate plant A. Habit. B. Vegetative node with sessile leaves. C. Inflorescence axis D. Inflorescence cyme at anthesis. E. Bracts subtending bracteoles. F. Flower at anthesis (R) and longitudinal section (L). G. Inner surface of hypanthium with calyx teeth and lobes. H. Petal. I. Stamens from left to right: dorsal, ventral and side view. Drawn from Michelangeli 1676, 1677, NY.
FIGURES 3–8. Specimen LAF 6956A, vegetative anatomy. FIG. 3. Thallus habit with numerous protuberances. Scale bar 9 in Sporolithon sinusmexicanum sp. nov. (Sporolithales, Rhodophyta): a new rhodolith-forming species from deepwater rhodolith beds in the Gulf of Mexico
FIGURES 3–8. Specimen LAF 6956A, vegetative anatomy. FIG. 3. Thallus habit with numerous protuberances. Scale bar 9 mm. FIG. 4. Surface view (upper bracket) and vertical fracture showing new vegetative layer (middle bracket) over older layer (lower bracket). Scale bar 176 μm. FIG. 5. Secondary hypothallium (upper bracket) over older vegetative layer (lower bracket). Scale bar 52 μm. FIG. 6. Perithallium showing cell fusions (arrows). Scale bar 34 μm. FIG. 7. Perithallium (lower bracket), epithallium (upper bracket) and intercalary meristem (arrow). Scale bar 24 μm. FIG. 8. Meristematic cells (M) and armored epithallial cells (arrows) with intact cell roofs (circle pointers). Scale bar 11 μm.
FIGURE 6. Serjania perulacea var. perulacea. A. Vegetative branch. B. Inflorescence. C. Pistillate flower. D. Anterior petal with adnate appendage. E. Posterior petal with adnate appendage. F in Geographic variation of Serjania perulacea (Paullinieae, Sapindaceae) and description of two new varieties
FIGURE 6. Serjania perulacea var. perulacea. A. Vegetative branch. B. Inflorescence. C. Pistillate flower. D. Anterior petal with adnate appendage. E. Posterior petal with adnate appendage. F. Stamen of a pistillate flower. G. Cross section of the stem. H. Mericarp. I. Seed in longitudinal section. [A–G. Gottsberger, I. & Gottsberger, G.23-23771 (ULM); H–I. Hatschbach et al. 63833 (CTES)].
FIGURE 2. Eremocaulon triramis, vegetative morphology. A–D in Eremocaulon triramis (Poaceae: Bambusoideae: Bambuseae: Guaduinae): a new species from the Atlantic rainforest of the State of Espírito Santo, Brazil
FIGURE 2. Eremocaulon triramis, vegetative morphology. A–D (Jesus-Costa et al. 108). A. Scandent habit. B. The summit of the foliar leaf sheath showing the white to stramineous, erect, laminar structure consisting of fused fimbriae borne on the summit of the underlapping margin. C. Branch complement of three subequal branches. D. Culm leaf with reflexed blade. Photographs by C. Jesus-Costa.
FIGURE 3. Eremocaulon triramis, vegetative morphology. A–D in Eremocaulon triramis (Poaceae: Bambusoideae: Bambuseae: Guaduinae): a new species from the Atlantic rainforest of the State of Espírito Santo, Brazil
FIGURE 3. Eremocaulon triramis, vegetative morphology. A–D (Jesus-Costa et al. 108) A. Culm leaf showing reflexed blade. B. Detail of culm leaf showing oral setae (B1) and fimbriae (B2). C. Branch complement and foliage leaves, showing the strongly excentric midrib. D. Ligular area of the foliar leaf: right, summit of the overlapping margin bearing free, basally straight to apically curly fimbriae and left, summit of the underlapping margin bearing the laminar structure consisting of fused fimbriae and the lower portion of the overlapping margin bearing fused fimbriae. (Illustration by Reinaldo Pinto).
FIGURE 4. Magnolia montebelloensis. A. Flower after male phase. B. Flower manually forced open with stamens removed. C. Flower bud with densely hirsute spathaceous bract and early vegetative buds. D in Magnolia montebelloensis, a new species in section Magnolia from Lagunas de Montebello National Park, Chiapas, México, with a key to Magnoliaceae of Chiapas
FIGURE 4. Magnolia montebelloensis. A. Flower after male phase. B. Flower manually forced open with stamens removed. C. Flower bud with densely hirsute spathaceous bract and early vegetative buds. D. Leaf stipule in late vegetative bud. E. Gynoecium with early axillary leaf buds and terminal internode; sepals, petals and stamens removed. F. Developing fruit. G–H. Fruits during dehiscence. Illustration by Esaú Vázquez-Verdejo. A–C & E from the holotype. D, F–G from Vázquez-García et al. 10107; H from Vázquez-García et al. 10108.
FIGURE 1. Homalolepis planaltina. A. Habit, vegetative stage. B. Stem thickened with underground development. C in Two new dwarf species of Homalolepis (Simaroubaceae) from the Brazilian Cerrado (Neotropical savanna)
FIGURE 1. Homalolepis planaltina. A. Habit, vegetative stage. B. Stem thickened with underground development. C. Portion of a leaf showing the opposite and subopposite leaflets. D. Habit, reproductive stage; the inflorescence grows near ground level. E. Flower F. and G. Stamens with anther, filament and filament appendage. H. Flower with petals removed showing the gynoecium on top of a gynophore and style (A–H. Devecchi & Bordon 320).
Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex
<p># Data and srcipts used to perform analyses presented in the manuscript: "Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex"</p>
Mid-Holocene ENSO Variability reduced by northern African vegetation changes: a model intercomparison study
<p>This dataset contains model outputs from four climate models: EC-Earth, iCESM, UofT-CCSM4 and GISS. For each model, data is provided for three simulations: pre Industrial (PI), reference mid-Holocene without Green Sahara (MH_PMIP), and mid-Holocene with Green Sahara (MH_GS). For each simulation, data is provided for the variables: surface temperature, precipitation and zonal wind strength.</p>
Development of Indoor Vegetable Plant Rack Design Based on IOTs to Prevent Stunts
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Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
FIGURE 2. Dendrobium jinghuanum B.Q.Zheng & Y.Wang. A. Ramets with vegetative and flowering stems. B. Flower, front view. C. Flower, dorsal view. D in Dendrobium jinghuanum, a new orchid species from Yunnan, China: evidence from both morphology and DNA
FIGURE 2. Dendrobium jinghuanum B.Q.Zheng & Y.Wang. A. Ramets with vegetative and flowering stems. B. Flower, front view. C. Flower, dorsal view. D. Decomposition of a flower. E. Floral buds. F. Column with an anther cap. G. Pollinarium. Illustration based on the holotype, Zheng 001, by Jialin Shan.
Vedioes of artificial rainfall experiment of wide-grade colluvium slope and vegetated soil slope
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Determinants of Agricultural Technology Adoption in Urban Vegetable Production: A Case Study of Chelencko and Kullubi Cities
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Figure 3 in Importance of riparian vegetation and wood-pastures in the maintenance of bat assemblages in a highly fragmented landscape in Veracruz, Mexico
Figure 3: Rank abundance curves of bats captured in wood-pastures (A) and riparian vegetation (B) in Jamapa, Veracruz, Mexico. Numbers indicate species, 1: Artibeus jamaicensis, 2: Sturnira parvidens, 3: A. lituratus, 4: S. hondurensis, 5: Glossophaga soricina, 6: G. commissarissi, 7: Rhogeesa tumida, 8: Desmodus rotundus, 9: Phyllostomus discolor, 10: Eptesicus furinalis, 11: Carollia sowelli, 12: A. phaeotis, 13: Uroderma bilobatum, 14: Molossus rufus, 15: C. perspicillata, 16: Chiroderma salvini, 17: A. watsoni, 18: C. villosum, 19: Pteronotus parnelli, 20: Platyrrhinus helleri, 21: Micronycteris microtis, 22: A. toltecus, 23: Centurio senex, 24: P. davyi, 25: Mormoops megallophylla, 26: Eumops bonariensis, 27: Promops centralis, 28: Myotis californicus, 29: M. keaysi.
Figure 2 in Importance of riparian vegetation and wood-pastures in the maintenance of bat assemblages in a highly fragmented landscape in Veracruz, Mexico
Figure 2: Species accumulation curves for wood-pastures and riparian vegetation in the locality of Jamapa, Veracruz, Mexico. Hill's numbers (effective numbers of species) are in brackets. According to non-overlapping confidence intervals only q1 ("typical" species) and q2 (dominant species) were significantly higher in riparian vegetation compared to pastures.
Dataset for "Insect frass from upcycling vegetable by-products with cereals: effects on the soil properties, plant development and soil invertebrate fitness"
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Decline in coupling between vegetation photosynthesis and greening in northern ecosystems during the photosynthesis-up period
<p>The maximum seasonal vegetation photosynthesis (Pho<sub>max</sub>) is crucial to regulating the global carbon dynamics. Among them, the seasonal increments in vegetation photosynthesis (ΔPho) have major implications for understanding Pho<sub>max</sub>. However, the interannual variability of ΔPho within the photosynthesis-up period (PUP) and its influencing factors remain unclear. To address this knowledge gap, we identified PUP and quantified the multi-year characteristics of ΔPho using satellite-derived solar-induced chlorophyll fluorescence. We further investigated the response of ΔPho in northern ecosystems to climate change and vegetation greening by integrating climate data and the normalized difference vegetation index. In the northern ecosystems, longer PUP often spatially correlated with a higher ΔPho. An increasing trend was evident regarding the multi-year variations in ΔPho, suggesting enhanced vegetation photosynthesis within the PUP. This phenomenon is primarily driven by increased solar radiation and intensified vegetation greening. Additionally, based on the results derived from satellite data, we found three pieces of evidence for the decoupling trend between vegetation photosynthesis and greening under the influence of climate change: first, the inconsistent trends between ΔPho and greening; second, the declining moving trend in the correlation coefficient between ΔPho and greening, approximately 9.17 × 10<sup>−</sup><sup>4</sup>; and third, the weakened dominant role of greening on ΔPho. The findings are also partially validated by the results from ecosystem model simulations. This study provides insights into the interannual variability of ΔPho and its influencing factors, and indicates that vegetation dynamics and terrestrial carbon cycle are likely to become more complex under future climate change scenarios.</p>
Urban Vegetation Carbon Sink Response to Urbanization Depends on Urban Expansion Rate
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Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars"
<p>This dataset contains the data used in the burned area classificaiton and statistical analysis in:</p> <p><em>Rietze et al. (<strong>in prep.</strong>) - Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars</em></p> <p>All code to preprocess, analyze and visualize this data can be found under <a href="https://github.com/nrietze/SiberiaFires" target="_blank" rel="noopener">https://github.com/nrietze/SiberiaFires</a>.</p> <p><strong>Folder structure:</strong></p> <p>This dataset contains three major components:</p> <ol> <li>The feature layers like training polygons used for the burned area classification and areas of interest.</li> <li>The burned area maps produced from 3 m PlanetScope imagery.</li> <li>The raster layers used in the statistical analysis, e.g., pre-processed digital elevation models and burned area products.</li> </ol> <pre><code>└───geodata ├───feature_layers │ ├───aoi_wv │ ├───burn_polygons │ │ └───planet │ └───training_polygons └───raster ├───arcticDEM ├───burned_area │ └───planet ├───landsat ├───predictors └───water_area └───planet</code></pre> <p><strong>Detailed description of content:</strong></p> <p> </p> <p><em><strong>Feature layers</strong></em></p> <table> <tbody> <tr> <td>planet_masks.shp</td> <td>Manually delineated polygons to mask out undetected clouds in the Berelech and Lapcha sites.</td> </tr> <tr> <td>aoi_wv/aois_analysis.geojson</td> <td>Square areas of interest for data cropping and selection.</td> </tr> <tr> <td>burn_polygons/planet/rough_burn_perimeter_{<em>fire scar name</em>}.shp</td> <td>6 shapefiles (1 per fire scar) that contain the fire perimeter generated in ArcGIS.</td> </tr> <tr> <td>training_polygons/training_polygons_burn_area.shp</td> <td>Training polygons for all fire scars used for the burned area classification.</td> </tr> </tbody> </table> <p> </p> <p><strong><em>Raster data</em><br></strong></p> <table> <tbody> <tr> <td>arcticDEM/aoi_{<em>fire scar name</em>}_dem_v3_utm.tif</td> <td>Elevation data (<a href="https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics/v3.0/2m.json">ArcticDEM v3</a>) reprojected to UTM 55N and cropped to the square areas of interest of each fire scar. </td> </tr> <tr> <td>burned_area/ba_descals_landsat_2020_utm_shifted.tif</td> <td>Landsat-based burned area from <a href="https://doi.org/10.1126/science.abn9768" target="_blank" rel="noopener">Descals et al. (2022)</a>, contains data from "Tile 17" only. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class (2019) = 29, burned class (2020) = 30, unburned = 0)</td> </tr> <tr> <td>burned_area/N75E145_burn_class_UTM_55N.tif</td> <td>Landsat-based burned area from <a href="https://vapd.gitlab.io/post/gabam/" target="_blank" rel="noopener">Wei et al. (2022)</a>. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class = 255, unburned = 0)</td> </tr> <tr> <td>burned_area/planet/{<em>fire scar name</em>}_burned_area_top5TD.tif</td> <td>Binary PlanetScope-based burned area (this study) for each fire scar based on random forest classifiers using the top 5 predictors ranked by transformed divergence (TD). (burned class = 2, unburned = 1)</td> </tr> <tr> <td>landsat/LC08_L2SP_115010_20200608_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 8 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>landsat/LC08_L2SP_116010_20200615_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 15 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>predictors/{<em>fire scar name</em>}_predictors_30m.tif</td> <td>Raster maps of model predictors & the response <em>burned_fraction</em> for the ZOIB model (exported in "ZOIB_model.R" before running the model). Raster bands are named.</td> </tr> <tr> <td>water_area/{<em>fire scar name</em>}_Landsat_mask.tif</td> <td>Landsat-8 binary (water = 2, clear pixels = 1) water mask for each fire scar based on the Quality assessment rasters.</td> </tr> <tr> <td>water_area/planet/{<em>fire scar name</em>}__water_area_top5TD.tif</td> <td>[deprecated] 3 m resolution water areas classified from PlanetScope imagery. Not used for the analysis.</td> </tr> </tbody> </table> <p> </p> <p><strong>Using this data:</strong></p> <p>Clone the Github repository before downloading this data and insert the contents of this dataset into the empty "data" folder from the Github repo. </p> <p>Important: Please move the entire "geodata" folder into the "data" folder from the code repo.</p> <p>If you use this data, please cite as follows:</p> <p>Rietze, N., Heim, R. J., Troeva, E., Schaepman-Strub, G. & Assmann, J. J. (in prep.). Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12650945" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12650945</a></p> <p><strong>Abstract (from manuscript):</strong></p> <p>The fire season of 2020 in Siberia set a precedent for extreme wildfires in the Arctic tundra. Large fires burned in the carbon-rich permafrost landscape, releasing vast amounts of carbon, and changing land surface processes by burning vegetation and organic soils. However, little is known about the mosaics of burned and unburned patches formed by tundra fires and the underlying processes that generate them. In this study, we investigated six fire scars in the northeastern Siberian tundra using high-resolution PlanetScope imagery (3 m) to map burned fraction within the scars. We then used Bayesian mixed models to identify which biotic and abiotic predictors influenced the burned fraction. We observed high spatial variation in burned fraction across all tundra landforms common to the region. Current medium-resolution fire products could not capture this heterogeneity, thereby underestimating the burned area of fire scars by a factor of 1.1 to 4.4. The heterogeneity of the burn mosaic indicates a mix of burned and unburned patches, with median unburned patch sizes being smaller than 180 to 324 m². Pre-fire land surface temperature, vegetation heterogeneity and topography predicted burn fraction in our analysis, matching factors previously shown to influence large-scale fire occurrence in the Arctic. Future studies need to consider the fine-scale heterogeneity within tundra landscapes to improve our understanding and predictions of fire spread, carbon emissions, post-fire recovery and ecosystem functioning.</p> <p><strong>Acknowledgements (from manuscript):</strong></p> <p>N.R. was supported through the TRISHNA Science and Electronics Contribution (T-SEC), ESA PRODEX Trishna T-SEC project (PEA C4000133711). Field work and vegetation sample processing were conducted in the scope of State Assignment of the Ministry of Science and Higher Education of the Russian Federation (Project AAAA-A21-121012190038-0), using the equipment of the Centre for collective use of Federal Research Centre "Yakut Scientific Centre" (grant no. 13.TsKP.21.0016). We would like to thank Planet Labs fo free access to PlanetScope imagery. We would like to thank Tim Gyger for helpful discussions regarding our statistical analysis. The authors declare no competing interests.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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