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22,710 results for “Plants for planting”
Biogenic secondary organic aerosol participates in plant interactions and herbivory defense
<p>Biogenic secondary organic aerosols (SOAs) can be formed from the oxidation of plant volatiles in the atmosphere. Herbivore-induced plant volatiles (HIPVs) can elicit plant defenses, but whether such ecological functions persist after they form SOAs was previously unknown. Here we show that Scots pine seedlings damaged by large pine weevils feeding on their roots release HIPVs that trigger defenses in neighboring conspecific plants. Interestingly, the biological activity persisted after HIPVs had been oxidized to form SOAs, which was indicated by receivers displaying enhanced photosynthesis, primed volatile defenses, and reduced weevil damage. The elemental composition and quantity of SOAs likely determines their biological functions. This work demonstrates that plant-derived SOAs can mediate interactions between plants, highlighting its ecological significance in ecosystems.</p>
Close to the edge: Spatial variation in plant diversity, biomass and floral resources in conventional and agri-environment cereal fields
<p>Non-crop (segetal) plants in arable systems are commonly perceived simply as "weeds", i.e. harmful at worst and undesirable at best. The increase in management intensity in European arable systems has vastly reduced the populations of all but the most disturbance-tolerant plant species, negatively impacting the whole agricultural food web. In recent years, efforts have been made to promote agricultural biodiversity through measures such as flower strips and unsprayed field margins. However, studies of their impacts on the arable flora have rarely considered their spatial variation within the crop field. We investigated the spatial distribution of vascular plant species richness and their contribution to the food web via biomass and flower units in conventional and agri-environment cereal fields in six regions of Germany. We studied two types of in-crop measures (extensive cereals without pesticides or fertiliser, and with or without intercropping with flowering species) and one adjacent measure (neighbouring flower strip), recording at 1 m intervals from the field edge to interior. These results were then extrapolated to illustrate the effects of these measures on resource provision at the field scale. Species richness and plant biomass dropped off sharply after the first metre in the conventional treatments, regardless of the adjacent habitat. The "extensive" treatments maintained a much higher level of diversity and resource provision into the field interior. At the field level, this can mean more than sixty-fold difference in provision of flowering resources between conventional management (1900 flower units/ha) and agri-environment measures (127,000 units/ha for extensive cereals).</p> <p><em>Synthesis and applications:</em></p> <p>The strong edge effects we found in conventional cultivation support the premise that reducing field sizes could play a role in promoting in-crop biodiversity. However, incorporating extensive field margins as an agri-environment measure would be more efficient at maximising diversity of generalists whilst maintaining high yields.</p>
Data from: Germination of 10 midland plant species from the eastern Mediterranean Basin: Effects of smoke, syringaldehyde, karrikinolide, and cyanohydrin
<p>The beneficial effects of smoke and its constituents, karrikinolide (KAR<sub>1</sub>) and cyanohydrin glyceronitrile, on the germination of Mediterranean lowland species are well-documented. However, very little is known about the role of these signals on the germination of plants at higher altitudes. In addition, lignin-derived chemicals, such as syringaldehyde (SAL), have recently been proposed as overlooked cues for smoke-induced germination. To address these gaps in the literature, we investigated the effects of smoke-water and SAL on the germination of 10 midland species growing on serpentine soils. We also sought to determine whether SAL interacts with KAR<sub>1</sub> and/or mandelonitrile (MAN, a cyanohydrin) to enhance seed germination. The results show that smoke-water significantly improved the germination of three species (<em>Barbarea duralii</em>, <em>Digitalis cariensis</em>, and <em>Turritis laxa</em>). SAL, on the other hand, had no positive effect on the germination of the 10 species tested. Furthermore, three smoke-sensitive species and <em>Verbascum cariense</em> responded to KAR<sub>1</sub> and/or MAN. Finally, SAL did not exert any synergistic effects on germination in interaction with KAR<sub>1</sub> and MAN. In conclusion, we provide evidence that smoke is an important germination cue also for Mediterranean midland species. Moreover, SAL did not play a role in stimulating germination in smoke-sensitive species, either independently or in combination with other smoke chemicals.</p>
Fig. 11 - Trachelium caeruleum L in New floristic data of vascular plants from central Italy
Fig. 11 - Trachelium caeruleum L. subsp. caeruleum. (Photo / Foto F. Bartolucci).
Fig. 9 in New floristic data of vascular plants from central Italy
Fig. 9 - Iris pallida Lam. (Photo / Foto F. Falcinelli).
Fig. 10 - Opuntia scheerii F.A.C in New floristic data of vascular plants from central Italy
Fig. 10 - Opuntia scheerii F.A.C.Weber. (Photo / Foto F. Conti).
Fig. 8 - Cotoneaster lacteus W.W in New floristic data of vascular plants from central Italy
Fig. 8 - Cotoneaster lacteus W.W.Sm. (Photo / Foto F. Falcinelli).
Fig. 1 - Astragalus exscapus L in New floristic data of vascular plants from central Italy
Fig. 1 - Astragalus exscapus L. subsp. exscapus. (Photo / Foto F. Falcinelli).
Fig. 6 - Oxytropis ocrensis F in New floristic data of vascular plants from central Italy
Fig. 6 - Oxytropis ocrensis F.Conti & Bartolucci. (Photo / Foto F. Conti).
Fig. 2 in New floristic data of vascular plants from central Italy
Fig. 2 - Calendula tripterocarpa Rupr. (Photo / Foto F. Conti).
Plant diversity darkspots for global collection priorities: time-to-event datasets per botanical country as defined by the World Geographical Scheme for Recording Plant Distributions (WGSRPD).
<p>Datasets used to predict the number of plant species remaining to be described and/or geolocated within a botanical country, which represents the third level of subdivision (generally equating to a political country) used by WGSRPD for recording plant distributions. The folder is composed of two subfolders <em>has_coords</em> and <em>has_no_coords</em> containing the time-to-event data for species with valid and no (invalidated) occurrence records within a given botanical country respectively<em>.</em></p> <ul> <li>Each folder contains a<strong> </strong>list of 361 botanical countries with the following 16 fields:</li> </ul> <pre><strong>species:</strong> species name<br><strong>time_ofdescription:</strong> year of the (first) description<br><strong>time_ofcollection:</strong> year of the collection of the earliest record<br><strong>family:</strong> species family name<br><strong>lifeform_description: </strong>the life form categorised into 4 classes <br><strong>CHELSA_bio_1: </strong>annual mean temperature (°C)<br><strong>CHELSA_bio_12:</strong> annual precipiation (mm)<br><strong>CHELSA_bio_15</strong>: temperature seasonality (-)<br><strong>CHELSA_bio_4</strong>: precipitation seasonality (-)<br><strong>elevation</strong>: elevation (m)<br><strong>range_size_area:</strong> total area of the botanical countries encompassing the species' native range <br>according to the World Checklist of Vascular Plants (WCVP) (km^2) <br><strong>taxo_activity</strong>: taxonomic activity calculated as the number of named authors in the World Checklist of Vascular Plants<br>describing species from the same family during the year of description of the species,<br>divided by the number of species described within the given family that year. <br><strong>num_records_per_year:</strong> geographic activity calculated as the number of occurrence records<br>collected within the native range of the species, divided by the number of years between <br>the earliest and the lastest (first) record collected within this range.<br><strong>num_uses</strong>: number of human uses<br><strong>time_todescription:</strong> number of years between the (first) description and 1753<br><strong>time_tocollection:</strong> number of years between the (first) description and the collection of the first record of the species<br><br></pre> <p> </p>
Fig. 2. Four Ficus benjamina plants inside a in Host specificity studies on Gynaikothrips (Thysanoptera: Phlaeothripidae) associated with leaf galls of cultivated Ficus (Rosales: Moraceae) trees
Fig. 2. Four Ficus benjamina plants inside a collapsible cage.
The role of non-native plant species in modulating riverbank erosion: a systematic review - extracted coded articles dataset
<p>Data extraction sheet of code typologies for the review titled: The role of non-native plant species in modulating riverbank erosion: a systematic review. The uploaded csv file contains all the extracted values for each typology. </p>
Fig. 1 in Frugivorous flies (Diptera: Tephritidae, Lonchaeidae), their host plants, and associated parasitoids in the extreme north of Amapá State, Brazil
Fig. 1. Fruit sampling sites in extreme north of Amapá State, Brazil (May 2011–Jul 2013).
Fig. 7 - Salix pentandra L in New floristic data of vascular plants from central Italy
Fig. 7 - Salix pentandra L. (Photo / Foto F. Conti).
Fig. 3 in New floristic data of vascular plants from central Italy
Fig. 3 - Cytisus villosus Pourr. (Photo / Foto F. Falcinelli).
dataset for: Fire activity and drought increases ozone-plant damage to the Amazon rainforest.
<h3>Abstract</h3> <div> <p><strong>This dataset relates to work undertaken for publication 'Fire activity and drought increases ozone-plant damage to the Amazon rainforest.'</strong></p> <p><strong>Datasets and jupyter notebooks allow the figures and data to be reproduced.</strong></p> <p> </p> <p><strong>ABSTRACT: </strong>Human activity is exposing the Amazon rainforest to increasing stressors, including plant damage due to elevated ozone (O<sub>3</sub>) pollution. O<sub>3</sub>-plant damage reduces plant photosynthesis and the land carbon sink thus increasing atmospheric CO<sub>2</sub>. Factors that control O<sub>3</sub>-plant damage in the Amazon have not been characterised so damage may be exacerbated by recurrent extreme drought and fires. We identify drivers of interannual variability in O<sub>3</sub>-plant damage using a land surface model validated against satellite products. We find that O<sub>3</sub>-plant damage increases with fire activity, with additional interactive effects during major droughts. The indirect increase in atmospheric CO<sub>2</sub> from fire-driven O<sub>3</sub>-plant damage is ~50% the magnitude of direct CO<sub>2</sub> emissions from fires in non-drought years, suggesting the negative impact of fires on the Amazon carbon budget is severely underestimated. During droughts, leaves close their stomata, which reduces O<sub>3</sub> uptake and should protect against O<sub>3</sub> damage. However, due to higher fire activity, and elevated O<sub>3</sub> concentrations, O<sub>3</sub>-plant damage during droughts is up to 77 Tg-C (or over 2 times) greater than average. As droughts are set to become more frequent, our results demonstrate that high fire activity associated with drought increases the O<sub>3</sub>-plant damage risk to the rainforest, especially in remote areas.</p> </div>
Plant Growth Regulators in Barley in Northern Grains Region Australia
<p><strong>Project title: </strong>NGN Validating the use of plant growth regulators to manage excessive growth in barley in Northern Region warm growing environments (2022-24, AMP2205-004RTX). </p> <p><strong>Methodology: </strong>Seven barley PGR trials were run in 2022-23 at a range of locations in northern NSW. In 2022, three trials assessed PGRs Moddus Evo<sup>®</sup> (250 g/L Trinexapac-Ethyl) and ethephon (Promote<sup>®</sup> Plus 900, 900 g/L Ethephon) and a range of use patterns across 4 barley varieties. Following high levels of lodging in 2022, four trials in 2023 quantifed the impact of PGR treatments on different varieties, times of sowing and nitrogen levels. </p> <p>Four varieties – Leabrook, Laperouse, Planet, Maximus CL – were selected to represent the range of lodging susceptibilities in commercial barley varieties. Moddus Evo<sup>®</sup> and ethephon use patterns were tested to allow for early and late control of barley biomass production as well as bounce back, where compensatory growth occurs under favourable conditions following a PGR application (Table 1).</p> <p><a name="_Ref165987766"></a>Table 1. PGR use patterns tested on dryland barley in 2022 and 2023</p> <table> <tbody> <tr> <td> <p><strong> </strong></p> </td> <td> <p><strong>Application 1</strong></p> </td> <td> <p><strong>Application 2</strong></p> </td> </tr> </tbody> <tbody> <tr> <td> <p><strong>PGR Treatment Name</strong></p> </td> <td> <p><strong>PGR</strong></p> </td> <td> <p><strong>Rate (ml)</strong></p> </td> <td> <p><strong>GS</strong></p> </td> <td> <p><strong>PGR</strong></p> </td> <td> <p><strong>Rate (ml)</strong></p> </td> <td> <p><strong>GS</strong></p> </td> </tr> <tr> <td> <p>Moddus 31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>31</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 300 @ 31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>300</p> </td> <td> <p>31</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 37</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>37</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 31 + 37</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>37</p> </td> </tr> <tr> <td> <p>Ethephon 41</p> </td> <td> <p>Ethephon*</p> </td> <td> <p>400</p> </td> <td> <p>41</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Ethephon 45</p> </td> <td> <p>Ethephon*</p> </td> <td> <p>400</p> </td> <td> <p>45</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>GS=Growth Stage; 900 g/L Ethephon</p> <p>In 2022, the impact of PGR treatments (n=6) on four barley varieties were tested at three locations in northern NSW (Table 2 and Table 3).</p> <p>1. At Boomi and Gurley a variety (n=4) by PGR strategy (n=5) full factorial plot trial was established. Due to high rainfall and subsequent waterlogging the trial planted at Boomi failed to establish.</p> <p>2. At Tulloona a trial was established to replace the failed Boomi trial. PGR treatments (n=5) were applied over a commercial crop of Planet barley in a randomised complete block design.</p> <p>3. At Spring Ridge, a partial factorial experiment assessing varieties (n=4) was established with 6 PGR treatments.</p> <p>In 2023 trial sites were established at Boomi, Gurley, Pallamallawa and Breeza.</p> <p><span><span>4. </span></span>At Boomi full replicated randomised incomplete block design trials assessed the impact of time of sowing (n=2, early and mid time of sowing) and PGRs (n=6) for four barley cultivars. For the first time of sowing, PGRs (n=6) were applied to four varieties; Leabrook, Laperouse, Planet and Maximus CL, at the second time of sowing PGRs were only applied to lodging susceptible variety Leabrook.</p> <p>5. At Gurley, the same trial was repeated as describe above for Boom</p> <p>6. At Breeza, the four barley cultivars were tested with six PGR treatments with one time of sowing only.</p> <p>7. At Pallamallawa, Leabrook was planted with four nitrogen rates (0, 75, 150 and 250 kg N/ha) applied as urea-N and six PGR treatments were applied. This trial failed due to residual herbicide damage and thus the results from this trial are not presented in this report. Consequently, yield responses to PGR treatment under a range of nitrogen levels was unable to be captured in this project.</p> <p>Seasonal conditions in 2022 and 2023 differed markedly for both in-crop temperatures and rainfall. In 2022, cooler than average spring maximum temperatures were experienced at Tulloona and Gurley while at Spring Ridge maximum spring temperatures were warmer than average. In contrast, in 2023 maximum winter and spring temperatures were 1.5<span>⁰</span>C higher than monthly averages between June and October (Appendix A).</p> <p>There were large differences in annual and growing season rainfall (May-October, GSR) between 2022 and 2023 (Table <span>4</span><span></span>). In 2022, annual rainfall was 62-241mm above the long term average while in 2023 annual rainfall was 163-195mm below the long-term average. More importantly, were the differences in growing season rainfall. In 2022 growing season rainfall was 202-242mm above average (Decile 10) while in 2023 it was 136-175mm below average (Decile 1). However, high levels of soil water in 2023 compensated for the lack of in-crop rain.</p> <p><a name="_Ref167779013"></a>Table <span><span>4</span></span>. Plant available water and rainfall data for barley PGR trials in 2022 and 2023</p> <table> <tbody><tr> <td> <p><strong><span>Site</span></strong></p> </td> <td> <p><strong><span>Trial</span></strong></p> </td> <td> <p><strong><span>Planting PAW (mm)</span></strong></p> </td> <td> <p><strong><span>Annual rainfall (mm) </span></strong></p> </td> <td> <p><strong><span>Annual rainfall (mm)</span></strong></p> </td> <td> <p><strong><span>Growing season rainfall (mm)*</span></strong></p> </td> <td> <p><strong><span>Growing season rainfall (mm)*</span></strong></p> </td> </tr> </tbody><tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>Year of trial</span></strong></p> </td> <td> <p><strong><span>Long Term Average</span></strong></p> </td> <td> <p><strong><span>Year of trial</span></strong></p> </td> <td> <p><strong><span>Long Term Average</span></strong></p> </td> </tr> <tr> <td> <p><span>Tulloona</span></p> </td> <td> <p><span>PGR over </span><span>commercial crop</span></p> </td> <td> <p><span>165</span></p> </td> <td> <p><span>754</span></p> </td> <td> <p><span>548</span></p> </td> <td> <p><span>456</span></p> </td> <td> <p><span>214</span></p> </td> </tr> <tr> <td> <p><span>Gurley</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>165</span></p> </td> <td> <p><span>622</span></p> </td> <td> <p><span>560</span></p> </td> <td> <p><span>406</span></p> </td> <td> <p><span>204</span></p> </td> </tr> <tr> <td> <p><span>Spring Ridge</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>202</span></p> </td> <td> <p><span>870</span></p> </td> <td> <p><span>629</span></p> </td> <td> <p><span>465</span></p> </td> <td> <p><span>254</span></p> </td> </tr> <tr> <td> <p><span>Boomi</span></p> </td> <td> <p><span>PGR x Variety </span><span>x 2 Times of Sowing</span></p> </td> <td> <p><span>201</span></p> </td> <td> <p><span>367</span></p> </td> <td> <p><span>543</span></p> </td> <td> <p><span>50</span></p> </td> <td> <p><span>201</span></p> </td> </tr> <tr> <td> <p><span>Gurley</span></p> </td> <td> <p><span>PGR x Variety </span><span>x 2 Times of Sowing</span></p> </td> <td> <p><span>198</span></p> </td> <td> <p><span>365</span></p> </td> <td> <p><span>560</span></p> </td> <td> <p><span>68</span></p> </td> <td> <p><span>204</span></p> </td> </tr> <tr> <td> <p><span>Breeza</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>253</span></p> </td> <td> <p><span>484</span></p> </td> <td> <p><span>647</span></p> </td> <td> <p><span>122</span></p> </td> <td> <p><span>265</span></p> </td> </tr> <tr> <td> <p><span>Pallamallawa#</span></p> </td> <td> <p><span>PGR x Nitrogen</span></p> </td> <td> <p><span>171</span></p> </td> <td> <p><span>459</span></p> </td> <td> <p><span>641</span></p> </td> <td> <p><span>58</span></p> </td> <td> <p><span>233</span></p> </td> </tr> </tbody> </table> <p><span>*Measured as rainfall from May to October, #Trial failed due to residual herbicide damage </span></p> <p>Plant height was measured during early grain fill in each replicate from ground level to the top of the spike excluding awns of the main tiller. Lodging was scored visually with a 0 for no lodging and 10 for 100% of the crop completely lodged and flat against the ground. In 2023 no lodging was evident at Boomi, Gurley or Pallamallawa thus scores were not recorded. Biomass cuts were taken at GS 30, GS 55 and GS 99 at 30mm above ground level with 1 x 0.5m quadrat. <span> </span></p>
Fig. 3 in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 3. Variations of percentage of habitat affinity types in the Go- gunsanArchipelago.
Fig. 1. A in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 1. A map of investigated area in the Gogunsan Archipelago.
ScienceDex guides
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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.