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5,828 results for “Traits”
Data supporting the manuscript entitled: 'Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.'
<p>Data living in this data repository supports the scientific article entitled: Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.</p> <p> </p> <p> </p> <p> </p>
Data from: Trait-fitness associations via fecundity and competition in a two-million-year-long fossil record
<p>The evolution of phenotypic traits is usually studied on generational times or across species on million-year timescales. We bridge this conceptual gap by using high density sampling of a species lineage, <em>Microporella agonistes </em>(Bryozoa, Cheilostomatida), over 2 million years of its evolutionary history, to ask if trait-fitness associations are consistent with evolutionary trait models often applied to phenotypic time series. We use average fecundity and competitive outcome as two different fitness components, where competitive outcome is a proxy for partial survival. Examining three quantitative traits in multivariate analyses, we present evidence that some traits experienced substantial selective pressures, in part controlled by past environments. A complex interplay of resource competition with an altering set of competitors and past temperatures, has contributed to the changing patterns of phenotypes within the focal species. A comparison with congeneric species living in the same regional community suggests that size traits are more temporally variable and less constrained than shape traits. Our analyses also show that while controls on phenotypes are complex and varied in time, ecological and evolutionary processes that unfold on shorter time scales are not inconsistent with macroevolutionary patterns observed on longer timescales.</p>
Mate choice in the brain: Species differ in how male traits 'turn on' gene expression in female brains
<p>Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioral data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioral context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic genomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting that evolutionary tinkering with these conserved molecular processes underlies divergent mate preferences and sexual isolation. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.</p>
Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
<p>The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.</p>
Planteome/CO_340-cowpea-traits: CO_340-cowpea-traits ontology
<p>Cowpea Trait Dictionary - IITA - August 2015 - Updated Nov 2023 with the traits and variables for on farm comparative ranking of varieties</p>
Planteome/CO_370-apple-traits: Apple Trait Ontology v1.0
<p>This is the first release of the Apple Trait Ontology that is published online the <a href="https://cropontology.org/">Crop Ontology Web site</a></p>
Planteome/CO_326-coconut-traits: CO_326-coconut-traits
<p>Coconut Ontology v1.0 - Coconut trait dictionary started by CIRAD, based on the "Guidelines for collecting coconut germplasm characterisation data during prospecting missions", COGENT (to be published); the "Descriptors for Coconut, (Cocos nucifera L.)", IPGRI (1995); and the "Manual on standardized research techniques in coconut breeding", IPGRI (1996).</p>
Data from Investigating the effects of diurnal and nocturnal pollinators on male and female reproductive success and on floral trait selection in Silene dioica
<p><strong>data_all_OdEx.csv</strong>: all data about phenotypes or reproductive success at the individual scale</p> <ul> <li>ID : ID name</li> <li>nGrSemis_min : seed number needed to be sowned to get enough seedlings</li> <li>nGrGerm : seed number effectively sowned</li> <li>nGrGerm_OK : number of germinated seed</li> <li>TauxGerm : germination rate</li> <li>nFruits_MAX : maximal number of fruit that the plant could have produced</li> <li>nFruits_OK : effective number of fruits that the plant had produced</li> <li>nFruits_OK_avecPred : effective number of fruits that the plant had produced ignoring predation</li> <li>nFruits_pred : number of predated fruits</li> <li>mean_nbSeeds : mean number of seeds per fruit</li> <li>sd_nbSeeds : sd number of seeds per fruit</li> <li>mean_nbOv : mean ovule non fertilize per fruit</li> <li>sd_nbOv : sd ovule non fertilize per fruit</li> <li>mean_nbOvTOT : mean ovule number per flower</li> <li>sd_nbOvTOT : sd ovule number per flower</li> <li>prodTOT : total number of seed produced including germination rate</li> <li>FS : Fruit-set</li> <li>SS : Seed-set</li> <li>prodTOTsg : total number of seed produced without germination rate</li> <li>nbFlo_run0 : flower number at the beginning of the experiment</li> <li>nbFlo_run1 : flower number at the first measurement</li> <li>mean_nbFlo : mean flower number</li> <li>MeanFec : mean seed sired per males according to MEMM model</li> <li>MeanDelta : mean delta pollen dispersion according to MEMM model</li> <li>MeanMRS : mean male reproductive success (including female RS) according to MEMM model</li> <li>MedFec : same as above with the median</li> <li>MedDelta : same as above with the median</li> <li>MedMRS : same as above with the median</li> <li>VarFec : same as above with the variance</li> <li>VarDelta : same as above with the variance</li> <li>VarMRS : same as above with the variance</li> <li>ciFec : Same as above with confidence interval</li> <li>ciDelta : Same as above with confidence interval</li> <li>ciMRS : Same as above with confidence interval</li> <li>MS_Res : mating success</li> <li>mean_lFl : mean corolla width</li> <li>mean_hFl : mean calyx height</li> <li>QttTOT : pollen number per flower</li> <li>pop : which originate population</li> <li>cohort : which cohort</li> </ul> <p><strong>data_seeds_OdEx.csv</strong> : all data about seed number of weight as well as unfertilized ovule at the fruit scale for female RS</p> <ul> <li>ID : ID name</li> <li>noFruit : ID fruit</li> <li>poids : seed weight</li> <li>nbSeeds : number of seeds</li> <li>nbOv : number of unfertilized ovule</li> <li>moySeeds : mean seed size</li> <li>varSeeds : variance in seed size</li> </ul> <p><strong>data_poll_OdEx.csv</strong> : all data about pollinator observation session</p> <ul> <li>ID : ID name</li> <li>session : observation session number</li> <li>nbVis : number of independent insect attracted</li> <li>nbVisTot : number of total visit</li> <li>binVis : individual visited or not</li> </ul>
Data described in the article "Unraveling the diversity of hyphal explorative traits among Rhizophagus irregularis genotypes"
<p>The dataset includes supplementary Figures, tables and the results of two experiments published in the study titled "Unraveling the diversity of hyphal explorative traits among Rhizophagus irregularis genotypes", available here: https://doi.org/10.1007/s00572-024-01154-8</p> <p>The study compares seven homokaryotic isolates (genotypes) of Rhizophagus irregularis, aiming to characterize the range of intraspecific variability with respect to hyphal exploration of organic nitrogen (N) resources, and N supply to plants. Two experiments (one in vitro and one in open pots) were conducted, and 15N-chitin as the isotopically labeled organic N source was used.</p> <p>Experiment 1 (in vitro), mycelium of all arbuscular mycorrhizal (AM) fungal genotypes transferred a higher amount of 15N to the plants than the passive transfer of 15N measured in the non-mycorrhizal (NM) controls. Noticeably, certain genotypes (e.g., LPA9) showed higher extraradical mycelium biomass production but not necessarily greater 15N acquisition than the others. </p> <p>Experiment 2 (in pots) highlighted that some of the AM fungal genotypes (e.g., MA2, STSI) exhibited higher rates of targeted hyphal exploration of chitin-enriched zones, indicative of distinct N exploration patterns from the other genotypes. Dataset contain photos and other recorded parameters during the experiment 1 and experiment 2.</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 1 in Anatomical and histological traits of Brycon amazonicus liver cultivated in a semi-intensive system
Figure 1. Graphical representation resulting from the analysis of the weight-length ratio of B. amazonicus cultivated semi-intensively, in four stages of body growth.
Figure 4 in Anatomical and histological traits of Brycon amazonicus liver cultivated in a semi-intensive system
Figure 4. Microscopic image of Brycon amazonicus liver from semi-intensive cultivation. Note the cordonal aspect of the hepatocytes, the absence of lobulation and the presence of blood vessels without pancreatic tissue wrap (arrowhead) and with pancreatic cells surrounding them (arrow). HE, 200X. Bar = 50µm.
Figure 3 in Anatomical and histological traits of Brycon amazonicus liver cultivated in a semi-intensive system
Figure 3. Behavior of semi-intensively cultivated Brycon amazonicus hepatosomatic relationship, according to body growth. Different letters represent significant differences between groups (P <0.05).
Figure 2 in Anatomical and histological traits of Brycon amazonicus liver cultivated in a semi-intensive system
Figure 2. Brycon amazonicus liver after removal of the celomatic cavity and dissection of an individual belonging to the PIII. Note the hepatic lobation and gallbladder (G) located next to the right hepatic lobe, and the red-brown color of de liver.
Functional traits and habitat use: investigating community assembly in a montane community (Carabidae: Nebria)
<p>The processes that influence community assembly, such as competition for resources and environmental filtering, are often scale-dependent and vary across ecotones. Trait-based ecology provides a useful framework for testing which ecological processes most strongly influence local community composition, especially across environmental gradients where species diversity varies. Where environmental filtering dominates, species distributions are expected to be defined by strong turnover along environmental gradients, with more similar species occupying more similar habitats. Where interspecific competition dominates, species are expected to diverge in relative abundance and resource utilization at sites, so species can co-occur. Here, we integrate measurements of functional traits, microhabitat usage, isotopic composition (δ<sup>15</sup>N and δ<sup>13</sup>C), and abundance to test the importance of environmental filtering and resource/habitat partitioning in shaping a montane ground beetle species assemblage (Carabidae: Nebriini: <em>Nebria</em>) in the isolated, volcanic peaks of the northern Cascades Range, U.S.A. Across species of <em>Nebria</em>, body size, pronotal shape, temperature preference, and isotopic enrichment varied across habitats [gravel, rocks 10 cm – 50 cm in diameter), large rocks (>50 cm in diameter), vegetation-covered rocks, and alpine (snowfields and talus)], and habitat/microhabitat features were reliable predictors of species presence. Resource consumption among mid-elevation species on Mt. Rainier – the peak with the greatest species diversity – is highly overlapping. Species turnover and nestedness varied significantly across habitat gradients and peaks throughout this region and varied nearly significantly across sites. Across habitat types and sites, more similar species are more likely to coexist. These results suggest that environmental filtering is the primary process structuring this species assemblage, although we find detailed evidence for microhabitat niche partitioning among species of <em>Nebria</em> at the site-scale.</p>
Fig. 2 in Fishing bait worm supplies in Japan in relation to their physiological traits
Fig. 2. Schematic representation of inhabitable salinity range of imported worms. The inhabitable range of salinity was evaluated from the 100 % survival of individuals after 5 days.
Fig. 3 in Sexual Size Dimorphism in Ground Beetle Carabus cumanus Fischer von Waldheim, 1823 (Coleoptera, Carabidae) and its Variation in Different Traits
Fig. 3. Results of RMA regression in C. cumanus traits: a - elytra length, b - elytra width, c - pronotum length, d - pronotum width, e - head length, f - distance between eyes (1 - steppe biotope, 2 - forest biotope. Circles and triangles denote individuals measured in forest and steppe biotopes respectively. Black dotted line denotes isometry)
Fig. 4 in Sexual Size Dimorphism in Ground Beetle Carabus cumanus Fischer von Waldheim, 1823 (Coleoptera, Carabidae) and its Variation in Different Traits
Fig. 4. Values of SSD in different traits in C. cumanus. Significant values of SSD (due to Fig. 3, where deviation from isometric curve were significant) are marked by asterisks (A - elytra length, B - elytra width, V - pronotum length, G - pronotum width, D - head length, E - distance between eyes)
Figure 2 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 2. Sustainability of the programs with stacked HT traits or residual herbicides, as influenced by application time (PRE and POST) and number of herbicide SOAs on (A) weed density and (B) resistance evolution. Resistance evolution is presented as % individuals that are resistant to at least one of the herbicides excluding H, either in the form of single or multiple resistance.The populations consist of 80% individuals resistant to H initially. Herbicide scenarios are detailed in Table 2. The simulations were set to stop when weed density exceeded 1 plant m−2, hence the incomplete lines of scenario EWM(i).
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.