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583 results for “plant distributions”
Figure 2 in Temporal variation and spatial distribution of the pest insect Edessa meditabunda in cotton (Gossypium hirsutum) as an alternative host plant
Figure 2. Surface maps constructed based on Inverse Distance Weight (IDW) interpolation showing spatial distribution of nymphs in cotton between 70 (A), 77 (B), 84 (C), 91 (D) days after emergence (DAE) and Sum of all Evaluations (E). Low density is represented in green while red indicates high density of E. meditabunda.
Fig. 4 in The new polillensis group in the lanternfly genus Pyrops: Taxonomy, distribution and host plant (Hemiptera: Fulgoridae)
Fig. 4. Pyrops polillensis species group, distribution map in the Philippines.
Fig. 7. Olonia rubicunda Walker, 1851 in Revision of the Eurybrachidae (XVI). The Australian Olonia rubicunda (Walker, 1851): Description of the male, distribution and host plants (Hemiptera: Fulgoromorpha: Eurybrachidae)
Fig. 7. Olonia rubicunda Walker, 1851, distribution map.
FIGURE 1 in The endemic and range restricted vascular plants of Croatia: diversity, distribution patterns and their conservation status
FIGURE 1 Study area with main localities indicated.
FIGURE 4 in An assessment of the local endemism of flowering plants in the northern Western Ghats and Konkan regions of India: checklist, habitat characteristics, distribution, and conservation
FIGURE 4. Map showing type localities of local endemic species of NWGK
FIGURE 2 in An assessment of the local endemism of flowering plants in the northern Western Ghats and Konkan regions of India: checklist, habitat characteristics, distribution, and conservation
FIGURE 2. The occurrence of life forms across the habitats
FIGURE 3 in An assessment of the local endemism of flowering plants in the northern Western Ghats and Konkan regions of India: checklist, habitat characteristics, distribution, and conservation
FIGURE 3. The proportion of total endemics and exclusive (local) endemic species per grid in NWGK
Linked collectors and determiners for: Florabank1 - A grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region).
Natural history specimen data linked to collectors and determiners held within, "Florabank1 - A grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/271c444f-f8d8-4986-b748-e7367755c0c1">https://bionomia.net/dataset/271c444f-f8d8-4986-b748-e7367755c0c1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/271c444f-f8d8-4986-b748-e7367755c0c1">https://gbif.org/dataset/271c444f-f8d8-4986-b748-e7367755c0c1</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Distributional data on high altitude endemic taxa of vascular plants from western Balkans.
Natural history specimen data linked to collectors and determiners held within, "Distributional data on high altitude endemic taxa of vascular plants from western Balkans". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6b5003f4-5ede-4dae-becb-c3c9094cd57d">https://bionomia.net/dataset/6b5003f4-5ede-4dae-becb-c3c9094cd57d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6b5003f4-5ede-4dae-becb-c3c9094cd57d">https://gbif.org/dataset/6b5003f4-5ede-4dae-becb-c3c9094cd57d</a>. Formatted as a Frictionless Data package.
Environmental drivers of plant distributions at global and regional scales: occurrence data with associated environmental variables of plant families/genera/species
<p>How environmental factors drive plant distribution across globe is one of the most fundamental questions in ecology. Plant distributions may be shaped by various environmental factors, such as climate, topography and edaphic factors. Nevertheless, it is not clear about the relative importance of different environmental factors in driving plant distribution across spatial scales and among plant groups. This study aimed to disentangle how plant–environment relationships vary with latitude and among plant taxa including angiosperms, gymnosperms, pteridophytes and bryophytes.</p> <p><b><span>Location</span></b>: Global</p> <p><b><span>Main taxa</span></b>: Plants</p> <p><b><span>Results</span></b>: Our analyses revealed the primacy of climatic variability (temperature seasonality and isothermality) on plant distribution at the global scale. The relative contribution of temperature seasonality and isothermality peaked in tropical areas, whereas solar radiation and annual mean temperature had stronger influence at high-latitude areas. We also found wide-range plant groups tend to occur at area with higher temperature variability (isothermality and temperature seasonality) and flatter terrain (low slope). Climate extremes (low temperature and low solar radiation) determined plant distribution range and limits across latitude. Soil and topography had diverse thought less important effects (related to climate) on broad-scale plant distribution patterns.</p> <p><b><span>Main Conclusions</span></b>: Our study highlights the significance of climate variability for global plant distributions and climate extremes at higher latitude areas. Environmental effects of plant distributions vary across latitude. The findings imply that our understandings on environmental factors affecting plant distributions rely on the geographical scales that we focus on, suggesting that different geographcial and local ecological processes should be integrated to explain multi-scale distribution patterns.</p>
FIGURES 6–7 in <strong>The Eurasian species of <em>Xyela</em> (Hymenoptera, Xyelidae): taxonomy, host plants and distribution</strong>
FIGURES 6–7. Habitus of Xyela. 6, X. curva, female. 7, X. meridionalis, male.
Causes of differences in the distribution of the invasive plants Ambrosia artemisiifolia and Ambrosia trifida in Yili Valley, China
<p class="1"><i>Ambrosia artemisiifolia</i> and <i>Ambrosia trifida</i> are two species of very harmful and invasive plants of the same genus. However, it remains unclear why <i>A. artemisiifolia </i>is more widely distributed than <i>A. trifida</i> worldwide.</p> <p class="1">Distribution and abundance of these two species were surveyed and measured from 2010 to 2017 in the Yili Valley, Xinjiang, China. Soil temperature and humidity, main companion species, the biological characteristics in farmland ecotone, residential area, roadside and grassland, and water demand of the two species were determined and studied from 2017 to 2018.</p> <p class="1">The area occupied by <i>A. artemisiifolia </i>in the Yili Valley was more extensive than that of <i>A. trifida</i>, while the abundance of <i>A. artemisiifolia </i>in grassland was less than that of <i>A. trifida</i> at eight years after invasion. The interspecific competitive ability of two species were stronger than those of companion species in farmland ecotone, residential, and roadside. In addition, <i>A. trifida </i>had greater interspecific competitive ability than other plant species in grassland. The seed size and seed weight of <i>A. trifida</i> were five times or eight times those of <i>A.artemisiifolia</i>. When comparing the changes under simulated annual precipitation of 840 mm versus 280 mm, the seed yield per m<sup>2</sup> of <i>A. trifida</i> decreased from 50,185 to 19, while that of <i>A. artemisiifolia </i>decreased from 15,579 to 530.</p> <p>The differences in the distribution of the two species are mainly due to differences in interspecific competitive ability, seed size, and water dependence. The two species have stronger interspecific competitive ability than that of companion species, but <i>A. artemisiifolia </i>has a smaller seed size and stronger drought tolerance, which allows <i>A. artemisiifolia </i>to spread farther than <i>A. trifida</i>. The reason for wider distribution of <i>A. trifida</i> in grassland is that <i>A. trifida</i> has stronger interspecific competitive ability than <i>A. artemisiifolia </i>under sufficient water.</p>
Supplementary material 1 from: Ramírez-Albores JE, Richardson DM, Stefenon VM, Bizama GA, Pérez-Suárez M, Badano EI (2021) A global assessment of the potential distribution of naturalized and planted populations of the ornamental alien tree Schinus molle. NeoBiota 68: 105-126. https://doi.org/10.3897/neobiota.68.68572
Table S1
Figure 3 from: Brosens D, Van Landuyt W, Vanhecke L (2012) Florabank1: a grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region). PhytoKeys 12: 59-67. https://doi.org/10.3897/phytokeys.12.2849
Figure 3 - Number of prospected 1 km? grids in each grid of 4?4 km for the period 1972-2004. A 1 km? grid cell is considered as prospected if at least 90 species have been recorded.
Figure 2 from: Brosens D, Van Landuyt W, Vanhecke L (2012) Florabank1: a grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region). PhytoKeys 12: 59-67. https://doi.org/10.3897/phytokeys.12.2849
Figure 2 - Number of prospected 1 km? grids in each grid of 4?4 km for the period 1939–1971. A 1 km? grid cell is considered as prospected if at least 90 species have been recorded.
Figure 3 from: Just A, Gourvil J, Millet J, Boullet V, Milon T, Mandon I, Dutrève B (2015) SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories. PhytoKeys 56: 47-60. https://doi.org/10.3897/phytokeys.56.5723
Figure 3 - Density of cells by richness of observed species: looking at the distribution within the dataset, it appears that cells with less than 250 distinct species recorded are over-represented.
Figure 4 from: Just A, Gourvil J, Millet J, Boullet V, Milon T, Mandon I, Dutrève B (2015) SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories. PhytoKeys 56: 47-60. https://doi.org/10.3897/phytokeys.56.5723
Figure 4 - Dataset completeness for Metropolitan France according to the Jackknife 1 estimator (data from 1990 to 2013). The number of records in each cell was used as an estimator of the sampling effort. The ratio between the observed and estimated richness of species measures the completeness of inventory in each surveyed cell (Vallet et al. 2012).
Figure 2 from: Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723
Figure 2 - Examples of the georeferencing verification process. a The georeferenced record falls within a neighbouring country (here Gabon - GAB) of its documented country (here Republic of the Congo - COG). The nearest distance between the occurrence and the border of the documented country is computed b The georeferenced record falls within a non-neighbouring country (here Equatorial Guinea - GNQ) of its documented country (here Republic of the Congo). This record is classified as 'Error' and is discarded c The georeferenced record lies beyond the coastline. The nearest distance between the occurrence and the coastline of the documented country is computed.
Figure 1 from: Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723
Figure 1 - Left map: record density in 2° × 2°cell including all georeferenced records that passed the quality checks. This map includes records that are identified or not to species level. Right map: main extent of RAINBIO geographical coverage from south of Sahel and north of Southern Africa (grey area); extent of tropical rain forest regions adapted from the land cover map published by Mayaux et al. (2004) (green area).
Figure 3 from: Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723
Figure 3 - Most represented families (sorted by number of records) for each of the three divisions of vascular plants represented in the RAINBIO database. A Magnoliophyta B Gymnosperms C Pteridophyta.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.