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157 results for “flowering time”
Figure 4 in Aquatic Diptera (Insecta) assemblages in four sympatric Eryngium (Apiaceae) phytotelmata in flowering and senescent times
Figure 4. Median number of Metriocnemus eryngiotelmatus larvae per plant in four Eryngium phytotelmata. The box indicates the 25th and 75th centiles, the line within the box indicates the median and the whiskers indicate the 90th and 10th centiles. Different letters indicate significant differences detected by Dunn's test (p <0.05).
Figure 3 in Aquatic Diptera (Insecta) assemblages in four sympatric Eryngium (Apiaceae) phytotelmata in flowering and senescent times
Figure 3. Median number of Polypedilum parthenogeneticum larvae per plant in four Eryngium phytotelmata. The box indicates the 25th and 75th centiles, the line within the box indicates the median and the whiskers indicate the 90th and 10th centiles. Different letters indicate significant differences detected by Dunn's test (p <0.05).
Figure 2 in Aquatic Diptera (Insecta) assemblages in four sympatric Eryngium (Apiaceae) phytotelmata in flowering and senescent times
Figure 2. Cumulative total of Diptera observed in successive plants (n = 40 plants for each species in autumn and in spring). (A) Eryngium horridum; (B) Eryngium stenophyllum; (C) Eryngium aff. serra; (D) Eryngium elegans. Logarithmic equations with R2 values; p <0.001 for all R2 except for E. stenophyllum in autumn (p = 0.09).
Simulated Herbarium data for testing the accuracy with which specimen data can predict the timing and duration of population-level flowering displays
<p>This dataset provides code and example data for simulating specimen collections of flowering plants across North America, and for developing phenological predictions of population-level flowering onset and termination for these data. It further presents code for assessing the accuracy of these predictions relaticve to known (simulated) population-level flowering dates at the location of each collection.</p>
Figure 2 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 2. The relationship between the number of semidominant larger M1 alleles and days to first flower (DFF), needed to model the long-day selections. Note that six smallereffect M2 genes will add another 0 to 18 days to DFF. If each M1 allele added the same amount in the long-day selections, the relationship would be linear.
Figure 1 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 1. Goodness of fit of the modeled data (A) when compared with the recorded glasshouse data (B). Graphs show cumulative days to first flowering (DFF) adaptations in Raphanus raphanistrum populations as a result of repeated early and late days to flowering selection. In both graphs, the basal population is the black solid line in the center,the darker lines are the matched generations of early flowering (EF1, EF3, FE4,and EF5; colored orange), and late flowering (LF2 and LF3; colored purple), with and late flowering (LF2 and LF3),with the two unmatched generations (EF2 and LF2) shown in lighter tones.The far-left population (early flowering EF5) displays very little phenotypic variability, whereas the far-right population (late flowering 3) is very diverse.
Figure 2 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 2. Changes in the number of seeds m−2 (A, D, G), the probability of seed capture by harvest weed seed control (HWSC) (B, E, H), and days to first flower (DFF) (C, F, I). (A–C) Varying herbicide efficiencies, in the absence of HWSC, over 30 yr. (D–F) When knockdown herbicide was less effective (-ρ-), when postemergence herbicide was less effective (-○-), and when both herbicides were effective, without HWSC (___) over 20 yr. (G–I) Model used management system P and started with either a smaller seedbank (___) or a larger seedbank (-○-) over 20 yr.
Figure 3 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 3. Changes in the number of seeds m−2 (A, D), the probability of seed capture by harvest weed seed control (HWSC) (B, E), and changes in days to first flower (DFF) (C, F) under weed management system P; HWSC efficacy (up to 20 yr) was increased from 75% (___) to 95% (-○-) (A–C); the level of fruit abscission (up to 20 yr) was changed from low (37%) (-○-) to high (74%) (—) (D–F). Note the variation between replicates was small as long as seed numbers are above 1 m−2; below that level, genetic changes in one or two plants had a more significant effect on the results.
Figure 1 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 1. Showing the number of seeds produced by weeds from each cohort (y axis), dependent on the evolved days to first flower (DFF) (x axis). The peak of each curve indicates the ideal DFF, with earlier cohorts taking longer to flower. (A) The standard farming system, before introduction of harvest weed seed control (HWSC). The range in ideal DFF across the different cohorts is approximately 15 d for Figure 1A. (B–D) How the various weed management systems affect the number of live seeds (avoiding HWSC) for weed management systems E (B), D (C), and P (D). In Figure 1B–D, the range in ideal DFF across the different cohorts is approximately 50 d.
Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
<div> <p>Understanding how plant phenotypes are shaped by their environments is crucial for addressing questions about crop adaptation to new environments. This study investigated the interplay between developmental responses to temperature fluctuations and photoperiod perception in maize that contribute to genotype-by-environment variation in flowering time. We present a physiological reaction norm for flowering time plasticity (PRN-FTP) for studying large collections of genotypes tested in multi-environment trial (MET) networks. Using a new variable for computational envirotyping of sensed photoperiod, it was found that, at high latitudes, different genotypes in the same environment can experience hours-long differences in photoperiod. This emphasizes the importance of considering genotype-specific differences in the experienced environment when investigating plasticity. A statistical framework is introduced for modeling the PRN-FTP as a non-linear response function, with parameters putatively linked to different regulatory modules for flowering time. Applying the PRN-FTP to a sample of global breeding material for maize showed that tropical and temperate maize occupy distinct territories of the trait space for PRN-FTP parameters, supporting that the geographical spread and adaptation of maize was differentially mediated by exogenous and endogenous pathways for flowering time regulation. Our results have implications for understanding crop adaptation and for future crop improvement efforts.</p> </div>
Refining Bulk Segregant Analyses: Ontology-Mediated Discovery of Flowering Time Genes in Brassica oleracea
<p>This record comprises the main supplement to study <a href="https://doi.org/10.1101/2021.08.11.455982">10.1101/2021.08.11.455982</a>. Contained in the archive identified by this record are the following files:</p> <ul> <li>SUPPLEMENTARY_METHODS.pdf contains an extended description of the methods of the study, including external links to raw, intermediate, and result data</li> <li>st[1-5]_* supplementary tables referenced in the main manuscript body of the study</li> <li>*.zip files contain intermediate data archives referenced in the supplementary methods, *.txt files with the same names list the contents of the zip files </li> <li>go-basic.obo is an unmodified copy of the Gene Ontology (in OBO format) as used in this study</li> </ul> <p><strong>NOTE 1</strong>: this record contains additional references (related identifiers) that identify all other data and source code used in this study</p> <p><strong>NOTE 2</strong>: for this record and all related records, the authors, their affiliations and their respective funding as relevant to this project are identified in the main manuscript, whose contents take precedence over equivalent metadata fields in supplementary records.</p>
Spring temperature drives phenotypic selection on plasticity of flowering time
<p>Data on field observations of flowering time and fitness of the perennial forest herb <em>Lathyrus vernus</em> and on spring temperature from weather station data. The dataset includes 22 years of data (1987–1996 and 2006–2017) from a <em>L. vernus</em> population located in a deciduous forest in Tullgarn, SE Sweden (58.9496 N, 17.6097 E). It includes records from 837 individuals (607 from 1987 to 1996, and 230 from 2006 to 2017), and from 2478 flowering events.</p> <p>The dataset includes the following variables:</p> <p>year: year of the recording</p> <p>id_nr: numeric plant id</p> <p>id: unique plant id (combination of numeric plant id and period)</p> <p>fcode: flowering code (1 if the plant flowered on that year, 0 if not)</p> <p>FFD: First Flowering Date</p> <p>n_fl: number of flowers</p> <p>n_fr: number of fruits</p> <p>totseed: total number of seeds</p> <p>intactseed: total number of intact seeds (not damaged by seed predator beetles)</p> <p>shoot_vol: shoot volume</p> <p>period: old (1987–1996) or new (2006–2017)</p> <p>n_years_fl_fitness: number of years when data on flowering and fitness is available</p> <p>n_years_study: number of years when the plant was included in the study</p> <p>mean_4: Average daily mean temperature of April (calculated from nearby meteorological station data)</p> <p>cmean_4: Mean-centred average daily mean temperature of April</p>
Spring temperature drives phenotypic selection on plasticity of flowering time
<p class="western">In seasonal environments, a high responsiveness of development to increasing temperatures in spring can infer benefits in terms of a longer growing season, but also costs in terms of an increased risk of facing unfavourable weather conditions. Still, we know little about how climatic conditions influence the optimal plastic response. Using 22 years of field observations for the perennial forest herb <em>Lathyrus vernus</em>, we assessed phenotypic selection on among-individual variation in reaction norms of flowering time to spring temperature, and examined if among-year variation in selection on plasticity was associated with spring temperature conditions. We found significant among-individual variation in mean flowering time and flowering time plasticity, and that plants that flowered earlier also had a more plastic flowering time. Selection favoured individuals with an earlier mean flowering time and a lower thermal plasticity of flowering time. Less plastic individuals were more strongly favoured in colder springs, indicating that spring temperature influenced optimal flowering time plasticity. Our results show how selection on plasticity can be linked to climatic conditions, and illustrate how we can understand and predict evolutionary responses of organisms to changing environmental conditions.<span><span> </span></span></p>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Simulated Herbarium data for testing the accuracy with which specimen data can predict the timing and duration of population-level flowering displays
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Data from: Small-scale genetic differentiation in mean flowering time, but not in plasticity, along a geothermal heating gradient
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Spring temperature drives phenotypic selection on plasticity of flowering time
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Novel major loci shape habitat-associated flowering time variation in Yellowstone monkeyflowers
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Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
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National Phenology Network tree phenology: Phenology, the timing of biological events such as bud break, plant flowering times and bird migration
Phenology is the study of recurring plant/animal phenophases. Environmental changes will likely impact phenological events at the species level and above. Most of the phenological changes are unknown at the species level and may have large impacts on natural ecosystems in the future. As part of a long term phenological experiment on forest ecosystems, since spring 2009 phenophases were observed on strategically selected forest species. This experiment is part of the National Phenology Network (USA-NPN).
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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)
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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.