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115 results for “trait dataset”
Local adaptation in trait-mediated trophic cascades dataset
<p>Predator-induced changes in prey foraging can indirectly influence community dynamics by increasing the abundance of basal resources via a trait-mediated trophic cascade. The strength of these cascades may be altered by eco-evolutionary relationships between predators and prey, but the role of basal resources has received limited attention. We hypothesized that the strength of trait-mediated trophic cascades may be shaped by selection from trophic levels above and below prey. Our field and laboratory experiments utilized snails (<em>Nucella lapillus</em>)<em> </em>from two regions in the Gulf of Maine (GoM) that vary substantially in basal resource (e.g., barnacles, mussels) availability and invasion history with the predatory green crab, <em>Carcinus maenas</em>. <em>Nucella</em> from both regions were provided with mussels as a basal resource and exposed to the presence or absence of green crab risk cues. In both experiments, <em>Nucella</em> from the northern GoM, where basal resources are scarce, displayed weaker responses to risk cues than southern <em>Nucella</em>. In the lab, however, northern <em>Nucella</em> foraged more than southern snails under risk, but foraging rates among the two regions did not differ without risk. Our results suggest that adaptation to basal resource availability may contribute to geographic variation in the strength of trait-mediated trophic cascades.</p>
SquamBase 1.0 – a dataset of squamate species traits
<p><strong>Motivation</strong>: I present a database that contains information on multiple key traits for all 11,744 recognized species of squamates worldwide. The database encompasses key traits and hopefully a reasonably comprehensive picture of available public knowledge. I present a comprehensive description of the sources and rationale leading to the assignment of each particular trait state for each species. Hopefully, the dataset can serve the scientific community and promote research and understanding of the group, comparisons with other taxa, and assessment of conservation needs. Furthermore, gaps in our knowledge of squamate traits become readily apparent and will hopefully lead to further study and even better knowledge.</p> <p><strong>Main types of variables contained</strong>: morphological, ecological, life history, geographic and conservation-related traits</p> <p><strong>Spatial</strong> <strong>location</strong>: Global</p> <p><strong>Time period</strong>: Late Holocene to recent</p> <p><strong>Major taxa and level of measurement</strong>: Squamata, species</p> <p><strong>Software format</strong>: .xlsx</p>
Minimal dataset for the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".
<p>Dataset "lin2024-sex_combined_interaction-association_signifincant_in_one_or_more_tests-summary.txt" is a minimal dataset to reproduce the figures and tables in the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank". </p> <p><br>To generate this dataset, see "https://github.com/BoxiLin/t2meta" Steps 0, 1.</p> <p>This dataset is the input for Steps 2, 3, 4, 5 to generate Figures 1-3 and Table 2-3.</p> <p> </p> <p>##### Column information ########################</p> <p>The following columns are annotations on each variant in the GWAS, calculated across the analysis subset of 361,194 samples by the Neale lab:</p> <p>code: Phenotype identifier in the form of "[UKB Data field]_raw"<br>variant: Unique variant identifier in the form "chr:pos:ref:alt", where "ref" is aligned to the forward strand.<br>chr: Chromosome of the variant.<br>pos: Position of the variant in GRCh37 coordinates.<br>rsid: rs ID<br>ref: Reference allele on the forward strand.<br>alt: Alternate allele (not necessarily minor allele).<br>p_hwe: Hardy-Weinberg p-value.<br>info: Imputation INFO score as provided by UK Biobank.</p> <p> </p> <p>The following columns are sex-stratified test statistics calculated by the Neale lab:</p> <p>minor_allele.x: Minor allele (AF < 0.5) in the female GWAS <br>minor_AF.x: Minor allele frequency in the female GWAS <br>beta.x: Estimated effect size of alt allele in the female GWAS <br>se.x: Estimated standard error of beta in the female GWAS<br>tstat.x: t-statistic of beta estimate (= beta/se) in the female GWAS <br>pval.x: p-value of beta significance test in the female GWAS </p> <p>minor_allele.y: Minor allele (AF < 0.5) in the male GWAS <br>minor_AF.y: Minor allele frequency in the male GWAS <br>beta.y: Estimated effect size of alt allele in the male GWAS <br>se.y: Estimated standard error of beta in the male GWAS <br>tstat.y: t-statistic of beta estimate (= beta/se) in the male GWAS <br>pval.y: p-value of beta significance test in the male GWAS </p> <p> </p> <p><br>The following columns are sex-combined test statistics calculated in our analysis:</p> <p>T.I: test statsitic for interaction effect-only <br>p.T.I: p-value of the interaction effect-only test <br>TSG.L: test statsitic for inverse variance weighted meta-analysis<br>p.TSG.L: p-value of the inverse variance weighted meta-analysis<br>TSG.Q: test statsitic for the omnibus meta-analysis<br>p.TSG.Q: p-value for the omnibus meta-analysis</p>
Phylogenetic trees and morphological traits dataset of Geonoma palm genus
<p>The phylogenetic trees correspond to the <em>Geonoma</em> genus and Geonomateae tribe phylogenies based on a concatenation analysis of 20 small informative genes and the csv file corresponds to the mean value of 24 morphological traits for 68 <em>Geonoma</em> species based on Henderson (2012) dataset.</p>
Datasets and scripts from: Sensory-based quantification of male colour patterns in Trinidadian guppies reveals no support for parallel phenotypic evolution in multivariate trait space
<p>Parallel evolution, in which independent populations evolve along similar phenotypic trajectories, offers insights into the repeatability of adaptive evolution. Here, we revisit a classic example of parallelism, that of repeated evolution of brighter males in the Trinidadian guppy (<em>Poecilia reticulata</em>). In guppies, colonisation of low predation habitats is associated with emergence of 'more colourful' phenotypes since predator-induced viability selection for crypsis weakens while sexual selection by female preference for conspicuousness remains strong. Our study differs from previous investigations in three respects. First, we adopt a multivariate phenotyping approach to characterise parallelism in multi-trait space. Second, we use ecologically-relevant colour traits defined by the visual systems of the two selective agents (i.e. guppy, predatory cichlid). Third, we estimate population genetic structure to test for adaptive (parallel) evolution against a model of neutral phenotypic divergence. We find strong phenotypic differentiation that is inconsistent with a neutral model but very limited support for the predicted pattern of greater conspicuousness at low predation. Effects of predation regime on each trait were in the expected direction, but weak, largely non-significant, and explained little among-population variation. In multi-trait space, phenotypic trajectories of lineages colonising low from high predation regimes were not parallel. Our results are consistent with reduced predation risk facilitating adaptive differentiation, potentially by female choice, but suggest that this proceeds in independent directions of multi-trait space across lineages. Pool-sequencing data also revealed SNPs showing greater differentiation than expected under neutrality, among which some are found in genes contributing to colour pattern variation, presenting opportunities for future genetic study.</p>
A specimen-level dataset of functional traits and DNA barcodes for Chinese bees
<p>The full potential for using DNA barcodes for profiling functional trait diversity has yet to be determined in plants and animals, thus we outline a general framework for quantifying functional trait diversity of insect community DNA and propose and assess the accuracy of three methods for achieving this. </p> <p>We built a novel dataset of traits and DNA barcodes for wild bees in China. An informatics framework was developed for phylogeny-based integration of these data and prediction of traits for any subject barcodes, and compared to two Blast-based methods of trait assignment. </p> <p>Under the specimen-level dataset, rate and accuracy of trait assigned dropped with an increase in distance between the query and its nearest reference member, though phylogenetic assignment was found to perform best under several criteria. For a wider range of compiled traits, conservative life-history traits showing the highest rates of assignment, for example sociality was predicted with confidence at 53%, parasitism at 44%, and nest location at 33%. </p> <p>Bayesian phylogenetic assignment of traits showed particular advantage over distance-based methods in the much lower rate that it would return incorrect state predictions when the query lacked relatives in the reference data. Automated, taxon-independent trait assignment might be applied at scale to either barcodes or metabarcodes in any invertebrates. With the further generation of integrated barcode and trait data, the assignment accuracy of the method is likely to increase. </p>
Dataset of "Life history traits and cancer prevalence in birds" article
<p>Data used in the "Life history traits and cancer prevalence in birds" article. This dataset provides taxonomic information about the bird species used in this article, as well as the species' neoplasia and malignancy prevalence, and life history trait data.</p>
Fig. 1 in A dataset on African bats' functional traits
Fig. 1 Graphical workflow of AfroBaT dataset construction.
DeepRVAT gene-trait association testing results on the 470k UK Biobank WES dataset
<p>Association testing results from DeepRVAT on the 470k UK Biobank WES dataset, covering all tested genes and traits. Tests were performed on the full dataset and on Caucasian individuals ("cohort" column). "Significant" indicates significance after multiple testing correction (FWER < 5%).</p>
Predicting Phenotypic Traits Using Conglomerate RNA-seq Datasets -- Datasets
<p>Phenotype Annotations and Corresponding Gene Expression Matrices created for the paper Hadish et al. [REF]. Raw Phenotypic Annotations were downloaded and processed from NCBI BioProject and raw RNA-seq data was downloaded from NCBI SRA. All annotations are contained within a single file for either Tissue or Age. Gene expression matrices are for each of the 12 species used in this project in their unproccessed form. </p> <p>Scripts used in this project are located here: </p> <p>GitLab Repository: </p> <p>https://gitlab.com/ficklin-lab/predicting-phenotypic-traits-using-conglomerate-rna-seq-datasets</p> <p>Paper Citation: </p> <p>[REF]</p>
Invader defense traits in France, Japan, and the USA: dataset and R code
<p>This record includes leaf trait data for 44 species sampled in France, Japan, and the USA. R code is associated with analyses for publication.</p>
Dataset for: Using plant traits to understand the contribution of biodiversity effects to annual crop community productivity
<p>Dataset for: Using plant traits to understand the contribution of biodiversity effects to annual crop community productivity</p>
Colorful traits in female birds relate to individual condition, reproductive performance, and male mate preferences: A meta-analytic approach dataset
<p>Colorful traits in females are suggested to have evolved and be maintained by sexual selection. Although several studies have evaluated this idea, support is still equivocal. <span><span>Evidence has been compiled in reviews, and a handful of quantitative synthesis have explored evidence of the link between condition and specific color traits in males and females. However, understanding the potential function of females' colorful traits in sexual communication has not been the primary focus of any of those previous studies</span></span><span>. </span>Here, using a meta-analytic approach, we find that evidence from empirical studies in birds supports the idea that colorful female ornaments are positively associated with residual mass and immune response, clutch size, and male-mate preferences. Hence, colorful traits in female birds likely evolved and are maintained by sexual selection.</p>
Dataset of Prediction of Self-efficacy in Recognizing Deepfake based on Personality Traits
<p>Dataset of <em>Prediction of Self-efficacy in Recognizing Deepfake based on HEXACO Personality Traits</em> </p>
Australia-wide photosynthetic trait dataset
<p><span>"Least-cost theory" posits that C<sub>3</sub> plants should balance rates of photosynthetic water loss and carboxylation in relation to the relative acquisition and maintenance costs of resources required for these activities. Here we investigated the dependency of photosynthetic traits on climate and soil properties using a new Australia-wide trait dataset spanning 528 species from 67 sites. </span></p> <p><span>We tested the hypotheses that plants on relatively cold or dry sites, or on relatively more fertile sites, would typically operate at greater CO<sub>2</sub> drawdown (lower ratio of leaf internal to ambient CO<sub>2</sub>, C<sub>i</sub>:C<sub>a</sub>) during light-saturated photosynthesis, and at higher leaf N per area (N<sub>area</sub>) and higher carboxylation capacity (V<sub>cmax 25</sub>) for a given rate of stomatal conductance to water, g<sub>sw</sub>. These results would be indicative of plants having relatively higher water costs than nutrient costs. </span></p> <p><span>In general, our hypotheses were supported. Soil total phosphorus (P) concentration and (more weakly) soil pH exerted positive effects on the N<sub>area</sub>-g<sub>sw</sub> and V<sub>cmax</sub> 25-g<sub>sw</sub> slopes, and negative effects on C<sub>i</sub>:C<sub>a</sub>. The P effect strengthened when the effect of climate was removed via partial regression. We observed similar trends with increasing soil cation exchange capacity and clay content, which affect soil nutrient availability, and found that soil properties explained similar amounts of variation in the focal traits as climate did. Although climate typically explained more trait variation than soil did, together they explained up to 52% of variation in the slope relationships and soil properties explained up to 30% of the variation in individual traits. </span></p> <p><span>Soils influenced photosynthetic traits as well as their coordination. In particular, the influence of soil P likely reflects Australia's geologically ancient low-relief landscapes with highly leached soils. Least-cost theory provides a valuable framework for understanding trade-offs between resource costs and use in plants, including limiting soil nutrients.</span></p>
Resampling alpine herbarium records reveals changes in plant traits over space and time - dataset
<p><strong>Data overview:</strong></p> <p>These data correspond to the analyses conducted for the article "Resampling alpine herbarium records reveals changes in plant traits over space and time" by Francesca Jaroszynska, Christian Rixen, Sarah Woodin, Jonathan Lenoir and Sonja Wipf, in Journal of Ecology</p> <p><strong>Metadata for jaroszynska_herbarium_traits_data.csv:</strong></p> <p>date = date; date of collection</p> <p>time = factor; time of collection (historical or recent)</p> <p>elevation = numerical; elevation in metres above sea level of the sample collection site</p> <p>selevation = numerical; scaled <em>elevation</em></p> <p>selevation2 = numerical; elevation in metres above sea level of sample collection site (elevation/1000).</p> <p>sSlope = numerical; scaled slope (slope/10)</p> <p>slope = numerical; computed slope based on elevation</p> <p>trait = string; name of the measured trait</p> <ul> <li> <p>crFlowerN = numerical; Cardamine resedifolia; number of flowers</p> </li> <li> <p>crHeight = numerical; Cardamine resedifolia; plant height</p> </li> <li> <p>crLeafL = numerical; Cardamine resedifolia; length of longest leaf</p> </li> <li> <p>crRosetteLeafN = numerical; Cardamine resedifolia; number of leaves in rosette</p> </li> <li> <p>paBasalLeafL = numerical; Poa alpina; basal leaf length</p> </li> <li> <p>paInflorescenceL = numerical; Poa alpina; inflorescence length</p> </li> <li> <p>paHeight = numerical; Poa alpina; plant height</p> </li> <li> <p>pvInfL = numerical; Polygonum viviparum; length of inflorescence</p> </li> <li> <p>pvLA = numerical; Polygonum viviparum; leaf area (length x width)</p> </li> <li> <p>pvLeafL = numerical; Polygonum viviparum; leaf length</p> </li> <li> <p>pvRepH= numerical; Polygonum viviparum; plant height</p> </li> <li> <p>rgFlowerStemL = numerical; Ranunculus glacialis; flowering stem length</p> </li> <li> <p>rgLeafStemL = numerical; Ranunculus glacialis; petiole length</p> </li> <li> <p>rgLeafW = numerical; Ranunculus glacialis; leaf width</p> </li> <li> <p>rgFlowerN = integer; Ranunculus glacialis; number of flowers</p> </li> </ul> <p> </p> <p>traitGroup = factor; the group to which each trait belongs (VegHeight = vegetative height, ReprHeight = reproductive height, ReprOut = reproductive output, PhotoCap = photosynthetic capacity)</p> <p>value = numerical; value of the trait measured</p> <p>species = factor; species code (car_res = Cardamine resedifolia, ran_glac = Ranunculus glacialis, pol_viv = Polygonum viviparum, poa_alp = Poa alpina)</p> <p>transect = string; transect along which the herbarium sample was taken</p> <p>confidence = factor; reliability of the metadata associated with the herbarium sample, assigned by the authors Jaroszynska and Wipf (low, medium, high)</p> <p>northness = numerical; northness</p> <p>eastness = numerical; eastness</p> <p>observer = string; botanist who conducted the collection</p> <p>sheet = string; unique identifier for the collection sheet</p> <p> </p> <p><strong>Metadata for jaroszynska_climate_traits_data.csv:</strong></p> <p>year = year; year of sample collection</p> <p>Month = integer; month of sample colection</p> <p>Temperature = numerical; monthly average temperature (ºC)</p> <p>Precipitation = numerical; monthly total precipitation (mm)</p> <p>yearMonth = string; year.month</p> <p>season = factor; season associated to the corresponding month (spring, summer, autumn, winter)</p> <p>timePeriod = factor; climate period referring to the time before, after, or during the baseline reference period (see article for further details)</p> <p>meanAnnTemp = numerical; mean annual temperature (ºC)</p> <p>sumAnnPrecip = numerical; total annual precipitation (mm)</p> <p>meanSeaTemp = numerical; mean seasonal temperature (ªC)</p> <p>sumSeaPrecip = numerical; total seasonal precipitation (mm)</p> <p>meanRefTemp = numerical; mean seasonal temperature for reference period (ªC)</p> <p>temp_anomaly = numerical; temerature anomaly from the reference period (ªC)</p> <p>lagMonths = string; used in seasonal calculation</p> <p>seasonal_precip = numerical; seasonal precipitation (mm)</p> <p>precip_anomaly = numerical; seasonal precipitation anomaly (mm)</p>
Dataset related to article: Differential effect of climate of origin and cultivation climate on structural and biochemical plant traits
<p><span>Exploring patterns and causes of intraspecific trait variation is crucial for a better understanding of the effects of climate change on plant populations and ecosystems. However, our current understanding of the intraspecific trait variation is mainly based on structural (morphological) traits, and we have limited knowledge on patterns and causes of variation in biochemical traits (e.g., leaf pigments), which are also crucial for plant adaptation. As a result, we also do not know how similar the climatic effects on structural versus biochemical traits are. </span></p> <p><span>Using plant traits from 110 genotypes representing 11 <em>Festuca</em> <em>rubra</em> populations grown in 4 different climates, we studied trait covariation among structural traits (linked to fitness, resource use, gas exchange, and reproduction) and biochemical traits (linked to photosynthesis, photoprotection, and oxidative stress). We also disentangled the relative role of the climate of origin and the climate of cultivation in the structural versus biochemical traits and tested for adaptive plasticity in the traits. </span></p> <p><span>We found that 1) biochemical traits did not covary with structural traits and represent independent '<em>photoharvesting</em> – <em>photoprotection</em>' strategy dimension of functional variation; 2) interactive effects of climate of origin and cultivation were more pronounced for biochemical than structural traits</span>. 3) T<span>rait plasticity was affected by the climate of origin (</span>precipitation and temperature as well as their interaction<span>); 4) </span><em>F. rubra</em> showed both adaptive and mal-adaptive plasticity, and adaptiveness depended upon trait type, cultivation climate, and climate of origin.</p> <p><span>Overall, our results suggest that structural and biochemical plant traits respond differentially to climate and thus the response of one group of traits cannot be predicted from the other. Responses are also strongly determined by interactions between the climate of origin and cultivation. Thus, more studies on variation in biochemical traits, their correspondence to other traits, and their variation with climate are needed. </span></p>
Dataset for Trans-generational effects on diapause and life-history-traits of an aphid parasitoid
<p>Dataset for the following article: Transgenerational effects act on a wide range of insects’ life-history traits and can be involved in the control of developmental plasticity, such as diapause expression. Decrease in or total loss of winter diapause expression recently observed in some species could arise from inhibiting maternal effects. In this study, we explored transgenerational effects on diapause expression and traits in one commercial and one Canadian field strain of the aphid parasitoid <em>Aphidius ervi</em>. These strains were reared under short photoperiod (8:16 h LD) and low temperature (14 °C) conditions over two generations. <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/diapause">Diapause</a> levels, developmental times, physiological and morphological traits were measured. Diapause levels increased after one generation in the Canadian field but not in the commercial strain. For both strains, the second generation took longer to develop than the first one. Tibia length and wing surface decreased over generations while fat content increased. A crossed-generations experiment focusing on the industrial parasitoid strain showed that offspring from mothers reared at 14 °C took longer to develop, were heavier, taller with wider wings and with more fat reserves than those from mothers reared at 20 °C (8:16 h LD). No effect of the mother rearing conditions was shown on diapause expression. Additionally to direct plasticity of the offspring, results suggest transgenerational plasticity effects on diapause expression, development time, and on the values of life-history traits. We demonstrated that populations showing low diapause levels may recover higher levels through transgenerational plasticity in response to diapause-induction cues, provided that environmental conditions are reaching the induction-thresholds specific to each population. Transgenerational plasticity is thus important to consider when evaluating how insects adapt to changing environments.</p>
Datasets: Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide
<p>This repository contains the datasets used in the main article:</p> <ul> <li>There is one Excel file per taxonomic group (amphibians, bats, bees, birds, ground beetles, and reptiles).</li> <li>Each file consists of three Excel spreadsheets: "Species" = matrix of species by sites; "Sites" = ID and coordinates of sites + urban and forest land cover variables; "Traits" = matrix of species by traits.</li> <li>Each spreadsheet contains the raw data used for the analyses in the article. For more information on how to handle the data, see the "Method" section.</li> </ul>
Earthworm's traits dataset from the Amazon region
<p>The dataset comprises information on earthworm species, their functional traits, associated soil properties, and geographic details such as locality and altitude. The data is sourced from the Soil Invertebrates Collection, hosted within the Soil Biology Laboratory at Maranhão State University (UEMA), situated in the Eastern Amazon region.</p>
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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)
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.