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691 results for “plant traits”
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
The Biomass and Plant Functional Traits of Leymus chinensis Affected by Genotypic Diversity and Soil Nitrogen Addition through a Two-year Experiment, Tianjin, China, 2021-2023
In order to investigate the effects of soil nitrogen addition on the genotypic diversity of Leymus chinensis, 12 genotypes of Leymus chinensis were used as plant material and a two-factor experimental design was carried out in this study. Factor one was genotypic diversity of L. chinensis, including three levels: mono-genotype (G1), three genotypes (G3), and six genotypes (G6). Factor two was the soil nitrogen addition level, which included four levels: no nitrogen addition (N0), 2.5 g N/(m²·a) nitrogen application (N2.5), 5 g N/(m²·a) nitrogen application (N5), and 10 g N/(m²·a) nitrogen application (N10). Each treatment had 12 combinations as replicates, and 12 genotypes of L. chinensis were used. The frequency of each genotype was standardized across all treatment levels of genotypic diversity × soil nitrogen addition. The experiment commenced in September 2021 and soil nitrogen was applied every 2 months. Plants were cultivated in the experimental field at Nankai University, but were moved to a greenhouse for overwintering from November to February each year. During the experiment, there were no stresses or disturbances such as shading, drought, or insect feeding; weeds were regularly removed.
AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,
Floral traits of animal-pollinated Sevilleta plant species
Concern about pollinator populations is widespread, with bees documented to be in decline due to factors including habitat loss, disease, and pesticides. In addition, climate change may be an important cause of bee population losses, but few studies have examined bee abundance relationships with climate variables. Importantly, bees may respond directly to climate or may exhibit indirect responses to climate via changes in plant phenology or community composition. This study collected floral trait data to complement the Sevilleta LTER pollinator monitoring, plant phenology, and plant biomass datasets, with the aim of examining whether floral resource availability mediates bee responses to climate. For 71 common, animal-pollinated flowering plant species, we measured floral traits relevant to pollination in June–October 2018 and April–August 2019 within sites representing four ecosystem types at the Sevilleta National Wildlife Refuge: Plains grassland, Chihuahuan Desert grassland, Chihuahuan Desert shrubland, and piñon-juniper woodland. On a minimum of 5 individuals per plant species, we recorded the total number of open flowers and the corolla width of flowers, along with plant height and vegetative cover. These data may be used in combination with the Sevilleta LTER pollinator monitoring, phenology, and biomass datasets to examine how bee and floral resource abundance, diversity, and phenology vary across years and whether these changes correspond with one another, as well as to consider relationships among climate, floral resource abundance/diversity, and bee abundance/diversity.
Consumer Front Plant Trait Sampling in Two Virginia Coast Salt Marshes, 2018
A consumer front forms when dense aggregations of herbivores form at the edge of a resource. The front then propagates through the ecosystem in search of additional resources. In U.S. Atlantic salt marshes, the purple marsh crab, Sesarma reticulatum, creates consumer fronts as it grazes the smooth cordgrass, Spartina alterniflora. Sesarma fronts typically form at the heads of tidal creeks and create distinct zonation between the low marsh, tall-form Spartina zones and the high marsh, short-form Spartina zones, with a denuded band of mudflat in between. Over time, Sesarma consumer fronts are moving directionally inland towards the short-form zone and away from the tall-form zone. This movement inland allows for tall-form Spartina to revegetate, preventing further marsh loss. However, it remains unknown why these consumer fronts are moving inland. To test the hypothesis that plant traits (i.e., nutritional quality, palatability) are driving the Sesarma consumer front inland, we collected Spartina from consumer fronts at 8 unique creekheads across two marsh systems on the Eastern Shore of Virginia (4 consumer fronts at Upper Phillips Creek and 4 at Upshur Creek). Spartina was collected from 15 replicate quadrats (0.0625m^2) from the tall-form low marsh zones (TSA) and from the short-form low marsh zones (SSA) at each creekhead. The short-form zone was delineated into two additional zones, an interior (SSA-I) and an exterior (SSA-E), to assess if there were any differences in plant traits between Spartina being actively grazed (SSA-E, adjacent to consumer front) and those that have not been grazed (SSA-I, 2 meters from consumer front). Collected Spartina plants were then processed for a series of plant traits that can influence herbivore preference.
Morpho-anatomical traits explain the effects of bacterial-feeding nematodes on soil bacterial community composition and plant growth and nutrition
<p>Soil Bacterial populations</p> <p>V3-V4, of the 16S rRNA gene using the primers 341F CCTAYGGGRBGCASCAG and 806R GGACTACNNGGGTATCTAAT.</p>
Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)
Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.
Species trait tissue chemistry: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Niwot plant functional traits, 2008 - 2018.
Globally, human activities are altering environmental conditions through changes to disturbance regimes, climate, and primary productivity. Attempts to generalize changes in biodiversity in response to altered environmental conditions have had mixed success and the mechanisms that causes differences in responses are poorly understood. These contingent responses represent one of the largest obstacles to synthesis in community ecology and global change biology. Over the past decade, much progress has been made in resolving these ecological contingencies by shifting from a taxonomic perspective on community assembly and global change to a focus on functional traits. Functional traits are any morphological, physiological, or phenological characteristic of a species linked to fitness and have proven to be effective for understanding species interactions, biogeographic processes, and environmental change. Between 2008 and 2018, we measured plant height, specific leaf area, leaf size, chlorophyll content, stomatal conductance, leaf dry matter content, and leaf chemical composition (nitrogen, carbon, δ13C, δ15N) on 2689 individual plants from 141 species (not all traits on all individuals or species). These measurements were collected to capture intraspecific trait variation among the key habitat types on Niwot Ridge and to examine how species traits respond to global change in the ITEX experiment.
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Data from: Consistent trait-environment relationships within and across tundra plant communities
<p>A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.</p> <p>Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (2021). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution</p> <p>These are the data and codes from Kemppinen et al. (2021).</p>
Potential effects of invasive plants on mosquito life-history traits.
<p>Invasive plants offer suitable oviposition sites for some vector species (a); invasive plant litter increases proliferation of immature vectors (b); dense canopy cover or thickets of invasive plants provide suitable micro-habitats for adult mosquitoes (c); nectariferous flowers (d) and extra-floral glands (e) of invasive plants are important sugar sources for adult vectors; invasive plants can influence the pathogen transmission ability of the vector (f).</p> <p>A grey-scaled version was published as Figure 1 in <a href="https://doi.org/10.3390/v13010032">Agha et al. (2020)</a>.</p> <p>Required software: <a href="https://krita.org/">Krita</a> and <a href="https://www.gimp.org/">Gimp</a>.</p>
Taxonomy, occurrences, phylogeny, traits and uses of the entire plant genus Scleria (Cyperaceae)
<p>This resource includes several datasets:</p> <p>(1) Taxonomy (261 species): Updated taxonomy of the genus Scleria at the species level based on Bauters et al. 2016 and 2019.</p> <p>(2) Occurrences (latitude/longitude): data was compiled using observations from the Global Biodiversity Information Facility, Red List, research grade identifications from iNaturalist (accessed 13/08/2023) and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. The dataset includes 22,759 observations from 248 species. Methodology follows Larridon et al. (2021).</p> <p>(3) Phylogeny of the genus based on three markers (ITS, ndhF, rps16) from Larridon et al. (2021) (136 species).</p> <p>(4) Traits. (i) We measured maximum height, maximum blade length, maximum blade width, stem width, nutlet length and nutlet width from 1,254 specimens of 209 species housed at Royal Botanic Gardens, Kew and the Muséum National d'Histoire Naturelle in Paris. (ii) We also compiled another dataset of 16 continuous and categorical traits for all 261 Scleria species derived from protologues and descriptions from regional floras. (iii) We measured nutlet weight for 141 species.</p> <p>(5) Uses & ecology: ethnobotany (mostly medicinal uses) and references to its ecology in several ecosystems (e.g., pollination, dispersal, ecological role). This data was gathered from several bibliographical sources, also provided.</p> <p>(6) Pictures of nutlets from 141 species.</p>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Contemporary phenotypic change in plant quantitative traits
<p>This is a new version of the Gorné & Díaz 2017 database (doi:10.5281/zenodo.580095). We cheked the categorization of each case, fixed of some mistakes. Also, we disambiguated the trait type moderator and add a new (mean based) measure of change.</p> <p>This database included studies that provide data of changes in quantitative traits of angiosperms within a known temporal framework (<300 years). The search was performed by Scopus (www.scopus.com), up to 22 December 2015 (search strings in Gorné and Díaz 2017). The database includes studies that measured intraspecific change in a quantitative trait and which report the elapsed time when the phenotypic change occurred. The studies recorded a single population before and after a change in the environment or compared two (or more) populations by measuring a quantitative trait across two situations, where one of them was a new condition of known age. Both, by measuring change directly in the field or by performing common condition experiments (e.g. common garden experiments or reciprocal transplants). Studies reporting results from artificial selection or interspecific hybridization were excluded. The environmental changes included expansions of distributional range, soil or air pollution, exposure to herbicides, changes in salinity, pH, climate, disturbance or irrigation regime, and addition or loss of species in the local community. All data available in each study were recorded, including several observations of the same species. These procedures resulted in a database containing 1716 observations from 128 studies, with changes in populations of 152 species from 34 families, in elapsed times of < 260 years, and covering a wide range of traits, lifespan, growth forms and environmental situations.</p> <p>All data points were categorized according to biological properties of the study system (lifespan, growth form, trait type) and methodological ones. The amount and rate of phenotypic change is expresed as the standardized mean difference Hedges <em>g</em> (Hedges 1981, 1982), a rate of change which is the Hedges <em>g</em> over the elapsed time in years, and the log-transformation of both of them. The standardized mean difference is equal to the <em>haldane</em> numerator, which is a standard rate of evolution (Haldane 1949; Gingerich 1993). In addition, we upgraded the Díaz and Gorné (2017) database, computing the response ratio effect size (<em>logRR</em>) (Hedges et al. 1999) whenever possible. The response ratio is a mean-scaled metric equal to the <em>darwins</em> numerator (Haldane 1949). So that we compute a rate of change similar to <em>darwins</em> (time expressed as years instead of million years).</p> <p> </p> <p>contact email address: gorneld@gmail.com</p>
Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction
<p>This is the data set supporting the analyses performed in the article entitled "Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction", by Natasha de Manincor, Alessandro Fisogni, and Nicole E. Rafferty, published in Ecology Letters (2023, 26:323-334, <a href="https://doi.org/10.1111/ele.14158">https://doi.org/10.1111/ele.14158</a>).</p> <p>The experiment has been performed in the greenhouse facilities at the University of California, Riverside, in 2021.</p> <p>The two treatments analyzed are ambient vs warmed (+ 4 °C), the focal pollinator species is <em>Osmia lignaria</em>, and the three focal plant species are <em>Collinsia heterophylla</em>, <em>Nemophila menziesii</em>, and <em>Phacelia campanularia</em>.</p> <p>Data are tab separated .txt files.</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</p>
Plant functional traits in the black sand extended growing season experiment, 2023.
Climate change is predicted to affect the alpine at a more rapid rate than other ecosystems. One predicted consequence is earlier snowmelt, which the Niwot Ridge LTER site Black Sand experiment aimed to replicate. Black sand was hand-spread across the early snowmelt treatment to cause a more rapid snowmelt than in the control plots. Sand was still spread across the control plots after snowmelt to control for the effects of the sand on vegetation. Each treatment also contained ITEX chambers which raised the temperature of individual plots. Within this larger experiment, we measured plant functional traits to see if they varied across the four treatments: control, control + ITEX, early snowmelt, and early snowmelt + ITEX.
SEV-LTER Plant Traits Database
This dataset contains measurements of morphological (leaf, stem, root, and seed), nutrient, and isotopic traits for plant species growing in the Sevilleta National Wildlife Refuge. Approximately 104 species were sampled in or near four core sites of the SEV-LTER (core_blue, core_black, core_creosote, and core_PJ) plus the Sevilleta Field Station between 2017 and 2021. In addition, seed masses were measured from a 2016-era seed collection provided by Jenny Noble and added to the dataset; for these, site = NA.
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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.