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5,828 results for “Traits”
Data from: Can avian functional traits predict cultural ecosystem services?
<ol> <li><span>The functional trait diversity of species assemblages can predict the provision of ecosystem services such as pollination and carbon sequestration, but it is unclear whether the same trait-based framework can be applied to identify the factors that underpin cultural ecosystem services and disservices. </span></li> <li><span>To explore the relationship between traits and the contribution of species to cultural ecosystem services and disservices, we conducted 404 questionnaire surveys with birdwatchers and local residents in Guanacaste, Costa Rica.</span></li> <li><span>We used an information-theoretic approach to identify which of 20 functional traits for 199 Costa Rican bird species best predicted their cultural ecosystem service scores related to birdwatching, acoustic aesthetics, education and local identity, as well as disservices (e.g., harm to crops). </span></li> <li><span>We found that diet was the most important variable explaining perceptions of cultural ecosystem service and disservice providers. Aesthetic traits such as plumage colour and pattern were important in explaining birdwatching scores. We also found people have a high affinity for forest-affiliated birds. </span></li> <li><span>The insight that functional traits can explain variation among cultural perspectives on values derived from birds offers a first step towards a trait-based system for understanding the species attributes that underpin cultural ecosystem services and disservices.</span></li> </ol>
Data from: Climate change and functional traits affect population dynamics of a long-lived seabird
1. Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete. 2. Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-lived migratory seabird breeding in the southern ocean: the black-browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: ''Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?'' 3. We develop a structured matrix population model that includes the effect of climate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and functional trait changes affect the population growth rate. 4. The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry-over effects across seasons on demographic processes. Changes in Sea Surface Temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at-sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activity during the pre-breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate. 5. These results highlight the importance of early life histories and carry-over effects of climate and functional traits on demographic rates across multiple seasons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demographic rates across the complete life cycle.
The Canopy Trait Plasticity (CANTRIP) database v1.0.0
<p>This release aligns with the version of the database published in Nature Plants.</p>
Supporting data: Prevalence of sexual dimorphism in mammalian phenotypic traits
<p>Supporting material for the manuscript <strong>“</strong><strong>Prevalence of sexual dimorphism in mammalian phenotypic traits”</strong> . This manuscript was published in Nature Communication 26th January 2017</p>
Genus Aconitum in Slovakia: a phenetic approach. S23: Genus Aconitum subgen. Aconitum sect. Cammarum full matrix of morphological traits
<p>This is full dataset of all analyzed morphological traits for genus Aconitum subgen. Aconitum sect. Cammarum in Slovakia coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S24: Genus Aconitum subgen. Aconitum sect. Cammarum matrix of selected morphological traits
<p>This is dataset of analyzed morphological traits, which were specially selected as taxonomically valuable for genus Aconitum subgen Aconitum sect. Cammarum in Slovakia and coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S21: Genus Aconitum subgen. Aconitum sect. Aconitum full matrix of morphological traits
<p>This is full dataset of all analyzed morphological traits for genus Aconitum subgenus Aconitum sect. Aconitum in Slovakia coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S27: Genus Aconitum subgen. Lycoctonum full matrix of morphological traits
<p>This is full dataset of all analyzed morphological traits for genus Aconitum subgen. Lycoctonum in Slovakia coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S22: Genus Aconitum subgen. Aconitum sect. Aconitum matrix of selected morphological traits
<p>This is dataset of analyzed morphological traits, which were specially selected as taxonomically valuable for genus Aconitum subgen Aconitum sect. Aconitum in Slovakia and coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S20: Genus Aconitum full matrix of morphological traits
<p>This is full dataset of all analyzed morphological traits for genus Aconitum in Slovakia coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S28: Genus Aconitum subgen. Lycoctonum matrix of selected morphological traits
<p>This is dataset of analyzed morphological traits, which were specially selected as taxonomically valuable for genus Aconitum subgen. Lycoctonum in Slovakia and coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S26: Genus Aconitum subgen. Anthora matrix of selected morphological traits
<p>This is dataset of analyzed morphological traits, which were specially selected as taxonomically valuable for genus Aconitum subgen. Anthora in Slovakia and coded as 0/1.</p>
Genus Aconitum in Slovakia: a phenetic approach. S25: Genus Aconitum subgen. Anthora full matrix of morphological traits
<p>This is full dataset of all analyzed morphological traits for genus Aconitum subgen. Anthora in Slovakia coded as 0/1.</p>
Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included
<p>Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included.</p>
Dataset supplementing Lichtenberg et al. (2017) Foraging traits modulate stingless bee community disassembly under forest loss. Journal of Animal Ecology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) Foraging traits modulate stingless bee community disassembly under forest loss. Journal of Animal Ecology. DOI 10.1111/1365-2656.12747.</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Ecoregional patterns of protist communities in mineral and organic soils: assembly processes, functional traits and diversity of testate amoebae in Northern Eurasia
<p>TA_Traits.csv file includes trait data for testate amoebae. TA_Abundance.csv file includes environmental variables (soil type and region) and relative abundance data of testate amoebae.</p>
Data and scripts for "Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"
<p>The README explains how to reproduce the analyses presented in the paper <strong>"Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"</strong> by Abrego et al.</p> <p>The input data for the script pipeline is the file “Kilpisjarvi_plant_data.csv”. This file includes the data on the plants and their traits in the long format. Hence, each row of the data matrix corresponds to measurements on one plant species in one study plot. The joint species-trait distribution modelling (JSTDM) pipeline that analyses these data consists of the following R-scripts.</p> <p>· <strong>S1_define_JSTDM_models.R</strong>. This script defines the JSDTM models (null model and environmental model) that include five response types for each species: the presence-absence, abundance conditional on presence, and the plot-level trait values of specific leaf area (SLA), leaf area (LA) and mean height (MH). The model is defined in the Hierarchical Modelling of Species Communities (HMSC) framework utilizing the R-package Hmsc. The models are saved in the file “unfitted_models.RData”.</p> <p>· <strong>S2_fit_models.R. </strong>This script loads the unfitted models and fits them using the posterior sampling methods implemented in the R-package Hmsc. The models are fitted with increasing thinning until thin=100, which value was used to generate the results of the paper. The fitted models are saved in the file "models_thin_100_samples_250_chains_4.Rdata".</p> <p>· <strong>S3_plot_Omega_matrices.R. </strong>This script loads the fitted models and plots the association matrices (Fig. 2 of the paper). The csv file containing the values used to construct Fig. 2 is also given (figure2Cdata.csv and figure2Ddata.csv).</p> <p>· <strong>S4_show_VP_Beta_Gamma.R. </strong>This script loads the fitted models and extracts information on the variance partitionings (VP; Figs. S3 and S4 of the paper), the relationships between response types and environmental predictors (beta; Fig. S2 of the paper), and the relationships between response types and species-level traits (gamma; Fig. S5 of the paper). The csv file containing the values used to construct Fig. S2 (figureS2Adata.csv and figureS2Bdata.csv), Fig. S3 (figureS3data.csv), Fig. S4 (figureS4data.csv) and Fig S5 (figureS5data.csv) are also given.</p> <p>· <strong>S5_conditional_cross_validation.R</strong>. This script performs 10-fold cross validation to the data to test the predictive power related to the modelled plant traits. The script performs both regular (unconditional) cross-validation where all data are masked for the test fold, and conditional cross-validation where only the trait data (but not the abundance data) are masked for the test fold.</p> <p>· <strong>S6_show_conditional_cross_validation_results.R. </strong>This script plots the results of cross-validation (Fig. 3 of the paper). The csv file containing the values used to construct Fig. 3 is also given (figure3Adata.csv and figure3Bdata.csv).</p> <p>· <strong>S7_scenario_predictions.R</strong>. This script performs the scenario simulations described and shown in Fig. 4 of the paper. The csv file containing the values used to construct Fig. 4 is also given (figure4Bdata.csv and figure4Cdata.csv).</p>
MAD Traits: NATDB DwCA
Part of the MAD world of packages that Make A Database from existing data. Use of MADtraits, and all MADworld packages, requires you to cite the underlying trait data it downloads - the function citations will give you this citation information for whatever data you are working with. <p></p>https://github.com/willpearse/MADtraits<p></p>Not a Trait Database. The USU Bio Nerd Herd is a programming group that meets regularly (weekly or fortnightly, depending on our schedule) in the Pearse Lab (BNR 132) on Fridays at 4pm. It__s open to undergraduates, graduate students, post-docs… anyone who__s interested in trying out biological programming and learning something new. Oh yes, we also eat pizza and go to Buffalo Wild Wings quite a lot! <p></p>https://usubionerdherd.github.io/
Assessing the long-term Stability of the Spielberger State-Trait Inventory trait scale over 3.5 Years
<p>The Spielberger State Trait Anxiety Inventory (STAI) is the most cited measure of state and trait anxiety, and is routinely employed in a variety of research and clinical contexts. Here, we investigate the temporal stability as well as the convergent and discriminant validity of the German version of the STAI-T scale across multiple time points in two independent samples (105 and 120 Caucasians). We observed temporal stabilities of .42 - .67 for intervals between 20 and 41 months and from .81 - .87 for intervals of five to 12 months, with decreasing stability as the time interval increased. Temporal stability estimates of the STAI-T were similar to those of related constructs. Additionally, examining the relationships within a nomological network support the recent conclusion that the STAI-T also shares substantial variance with questionnaires measuring negative emotionality such as depression, and hence does not measure anxiety specifically – despite its name. These results provide further psychometric information on what the STAI-T actually measures and to what extent STAI-T scores are expected to be stable across longer time intervals. This is of relevance for researchers aiming, for example, to use the STAI-T scale for predicting symptom trajectories and evaluating the effectiveness of therapeutic interventions.</p>
Data from: Within-individual leaf trait response to local light availability and biodiversity in a subtropical forest experiment
<p>This dataset contains 15 leaf traits and the relative light availability from 400 trees at sub-individual level. Leaf samples were collected in August and September 2017 in the BEF-China main experiment (site A) in different sampling heights within the individuals. Samples were taken in 66 plots across all diversity levels of the experimental site. Leaf traits were analysed using near-infrared spectroscopy. Available leaf traits are LDMC, SLA, leaf N, leaf C, CN ratio, leaf Mg, leaf Ca, leaf K, leaf P, leaf S, cellulose, lignin, phenolics and tannin. Additionally, light data is available for each height level.</p>
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.