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27 results for “leaf mass”
Data from: Herbivores alter plant-wind interactions by acting as a point mass on leaves and by removing leaf tissue
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Data from: Relationships between leaf mass per area and nutrient concentrations in 98 Mediterranean woody species are determined by phylogeny, habitat and leaf habit
Leaf structural and nutrient traits are key attributes of plant ecological strategies, as these traits are related to resource-use strategies and plant growth. However, leaf structure and nutrient composition can vary among different habitats, leaf habits or phylogenetic groups. In this study, we measured 13 leaf traits (one structural—leaf mass per area, LMA—and 12 nutrient traits) in 98 Mediterranean woody species growing over a wide range of environmental conditions, with the final aim of discerning the main causes of leaf trait variability. The variance decomposition results show that phylogeny, leaf habit and habitat type affected in several ways the structural and nutrient traits studied. Leaf nutrient concentrations are strongly positively correlated amongst themselves, and negatively correlated with LMA, in accordance with the "leaf economics spectrum". We found that leaf habit and phylogeny were important causes of variation in LMA and in a broad number of leaf nutrients (i.e., C, N, Mg, S, K), while other micronutrients seemed to be more dependent on the environment (i.e., Cu and Mn). In summary, our study reinforces the existence of the leaf economics spectrum in a broad pool of Mediterranean woody species, and demonstrates the strong influence of phylogeny, leaf habit and environmental context as the main drivers of variability in some leaf structural and nutrient traits.
Data from: The triangular seed mass-leaf area relationship holds for annual plants and is determined by habitat productivity
Plant allometries help us to understand resource allocation in plants and provide insight into how communities are structured. For woody species, a triangular allometric relationship between seed size and leaf size occurs in which all combinations are all possible, except for species with big seeds and small leaves (Cornelissen 1999). This relationship is thought to be a consequence of between habitat variation in abiotic conditions. In this study, we tested if the triangular relationship between seed mass and leaf area holds for annual species, and if variation in soil productivity and light (measured as Ellenberg indicator values: EIVs) are driving this relationship. We show that the triangular relationship also holds for annuals, which suggests that the allometric combinations between leaf area and seed mass are conserved across life-forms. The triangular relationship was driven by between-habitat variation in soil productivity. This means that as soil productivity increases, plants with bigger leaves could have either big or small seeds. However, in low soil productivity habitats, plants are constrained in their options, and plants with small leaves can only have small seeds.
Data from: Relationships between leaf mass per area and nutrient concentrations in 98 Mediterranean woody species are determined by phylogeny, habitat and leaf habit
Open the record for dataset details and reuse information.
Data from: The triangular seed mass-leaf area relationship holds for annual plants and is determined by habitat productivity
Open the record for dataset details and reuse information.
Data from: Trans-species predictors of tree leaf mass
Tree leaf mass is a small, highly variable, but critical, component of forest ecosystems. Estimating leaf mass on standing trees with models is challenging because leaf mass varies both within and between tree species and at different locations and points in time. Typically, models for estimating tree leaf mass are species-specific, empirical models that predict intra-specific variation from stem diameter at breast height (DBH). Such models are highly limited in their application because there are many other factors beyond tree girth and species that cause leaf mass to vary and because such models provide no way to predict leaf mass for species for which data are not available. We conducted destructive sampling of 17 different species in Michigan, covering multiple life history traits and sizes, to investigate the potential for using a single, 'trans-species' model for predicting leaf mass for all the trees in our study. Our results show the most important predictors of tree leaf mass are DBH, five-year basal area increment, crown class, and competition index, none of which are species specific. Species-specific variation could be captured by leaf longevity and shade tolerance and wood specific gravity was a statistically significant, but marginally-important predictor. Together, these variables describing tree size, life-history traits, and competitive environment were able to allow us to develop a generalized leaf mass model applicable to a diverse set of species, without having to develop species-specific equations.
Data from: Trans-species predictors of tree leaf mass
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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.