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273 results for “leaf traits”
Leaf Traits of Darlingtonia Californica in Oregon and California 2001
Scaling relationships among photosynthetic rate, foliar nutrient concentration, and leaf mass per unit area (LMA) have been observed for a broad range of plants. Leaf traits of the carnivorous pitcher plant Darlingtonia californica, endemic to southern Oregon and northern California, USA, differ substantially from the predictions of these general scaling relationships; net photosynthetic rates of Darlingtonia are much lower than predicted by general scaling relationships given observed foliar nitrogen (N) and phosphorus (P) concentrations and LMA. At five sites in the center of its range, leaf traits of Darlingtonia were strongly correlated with elevation and differed with soil calcium availability and bedrock type. The mean foliar N : P of 25.2 6 15.4 of Darlingtonia suggested that these plants were P-limited, although N concentration in the substrate also was extremely low and prey capture was uncommon. Foliar N : P stoichiometry and the observed deviation of Darlingtonia leaf traits from predictions of general scaling relationships permit an initial assessment of the "cost of carnivory" in this species. Carnivory in plants is thought to have evolved in response to N limitation, but for Darlingtonia, carnivory is an evolutionary last resort when both N and P are severely limiting and photosynthesis is greatly reduced.
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
Fern leaf traits observation at the Luquillo Experimental Forest (LEF)
Ferns are a common element of the understory of forests, yet little is known about the dynamics of leaf production. The long-term role of an individual fern in the ecosystem understory is a function of the number and size of leaves produced over time and the quality of those leaves. Selected functional plant traits (see also LUQ186 -Fern nutrients) were measured in order to supplement non-destructive measurements and detect patterns of primary productivity of ferns in the long-term studies at the Luquillo forest where ferns have been included (eg. Fern growth and demography (LUQ75), Canopy Trimming Experiment (LUQ143 and LUQ146) and the Luquillo Forest Dynamics Plot). Among the important characteristics of fern leaves in the forest understory are the area and biomass of leaves needed to calculate specific leaf area (SLA), leaf dry-matter content (LDMC) and leaf shrinkage. Therefore a large sample of whole leaves and leaf material from several species in the Luquillo Experimental Forest understory was collected, weighed and leaf area measured. The means and regression relationships among these functional traits for species, leaf type and leaf size can then be used to estimate leaf production and turnover rates in temporal studies of fern growth. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Leaf reflectance and traits of floating and emergent macrophytes
<p>This dataset includes leaf samples from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). Data were collected along three years (2016-2018) from three temperate shallow lakes surrounded by wetlands and hosting abundant macrophyte communities, located in central and southern Europe: Lake Hídvégi or Kis-Balaton (Hungary), Mantua lakes system (Italy), and Lake Varese (Italy).</p> <p>Leaf photophysiological parameters derived from chlorophyll fluorescence measured with a PAM-2500 chlorophyll fluorometer (Heinz Walz GmbH, Germany).</p> <p>Leaf pigments were derived from spectrophotometric readings of absorbance of leaf extracts in acetone 80%.</p> <p> </p>
Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"
<p><strong>Description</strong></p> <p>In this work, we use few-shot learning to segment the body and vein architecture of <em>P. trichocarpa</em> leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy and retain biologically realistic features. The leaf and vein segmentations are compared against a U-Net baseline model, and subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes.</p> <p><strong>Directories:</strong></p> <pre><code>Few-shot learning for p. trichocarpa leaf traits ├── data │ ├── genomes │ │ ├── Ptri_V4_Nisq1.[...].bed │ │ ├── Ptri_V4_Nisq1.[...].bim │ │ └── Ptri_V4_Nisq1.[...].fam │ ├── images │ │ └── *.jpeg │ ├── leaf_masks │ │ └── *.png │ ├── leaf_preds │ │ └── *.png │ ├── leaf_unet_preds │ │ └── *.png │ ├── results │ │ ├── digital_traits.tsv │ │ ├── gwas_results.csv │ │ ├── manual_traits.tsv │ │ ├── vein_density_blups.tsv │ │ └── vein_density_tps_adj.tsv │ ├── vein_bce_preds │ │ └── *.png │ ├── vein_bce_probs │ │ └── *.png │ ├── vein_fl_preds │ │ └── *.png │ ├── vein_fl_probs │ │ └── *.png │ ├── vein_masks │ │ └── *.png │ ├── vein_unet_bce_preds │ │ └── *.png │ ├── vein_unet_bce_probs │ │ └── *.png │ ├── vein_unet_fl_preds │ │ └── *.png │ ├── vein_unet_fl_probs │ │ └── *.png ├── figures │ └── *.png ├── logs │ ├── leaf_tracer_256.txt │ ├── leaf_unet_256.txt │ ├── vein_grower_bce_128.txt │ ├── vein_grower_fl_128.txt │ ├── vein_unet_bce_128.txt │ └── vein_unet_fl_128.txt ├── models │ ├── BuildCNN.py │ ├── BuildUNet.py │ ├── LeafTracer.py │ └── VeinGrower.py ├── notebooks │ ├── Figures.ipynb │ ├── GrowerInference.ipynb │ ├── GrowerTraining.ipynb │ ├── TracerInference.ipynb │ ├── TracerTraining.ipynb │ ├── UNetLeafSegmentation.ipynb │ └── UNetVeinSegmentation.ipynb ├── utils │ ├── GetLowestGPU.py │ ├── ImageLoader.py │ ├── LeafGenerator.py │ ├── ModelWrapperGenerator.py │ ├── TimeRemaining.py │ ├── TraceInitializer.py │ ├── UNetTileGenerator.py │ └── VeinGenerator.py └── weights ├── leaf_tracer_256_best_val_model.save ├── leaf_unet_256_best_val_model.save ├── vein_grower_bce_128_best_val_model.save ├── vein_grower_fl_128_best_val_model.save ├── vein_unet_bce_128_best_val_model.save └── vein_unet_fl_128_best_val_model.save </code></pre> <p><strong>Data:</strong></p> <p>The <code>data</code> folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, <code>C_1_1_2_bot.jpeg</code>, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs.</p> <ul> <li>The <code>images</code> folder includes the 2,906 high-resolution leaf scans taken in the field.</li> <li>The <code>leaf_masks</code> folder includes 50 ground truth segmentations used for training the leaf tracing algorithm.</li> <li>The <code>leaf_preds</code> folder includes the 2,906 predicted segmentations from the leaf tracing algorithm.</li> <li>The <code>leaf_unet_preds</code> folder includes the 2,906 predicted segmentations from the U-Net model for leaf segmentation.</li> <li>The <code>vein_masks</code> folder includes 8 ground truth segmentations used for training the vein growing algorithm.</li> <li>The <code>vein_*_preds</code> folder includes the 1,453 predicted segmentations from the vein growing algorithm, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_*_probs</code> folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_preds</code> folder includes the 1,453 predicted segmentations from the U-Net model for vein segmentation, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_probs</code> folder includes the 1,453 predicted probability maps from the U-Net model for vein segmentation before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>genomes</code> folder includes the set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes with a README file detailing the steps taken.</li> <li>The <code>results</code> folder includes <ul> <li>Raw values of the 68 predicted leaf traits in <code>digital_traits.tsv</code></li> <li>Manually measured values of petiole length and width in <code>manual_traits.tsv</code></li> <li>Thin plate spline (TPS) adjusted values of the vein density trait in <code>vein_density_tps_adj.tsv</code></li> <li>Best linear unbiased prediction (BLUP) adjusted values of the vein density trait in <code>vein_density_blups.tsv</code></li> <li>GWAS results for the vein density trait, including chromosome positions and corresponding P values, in <code>gwas_results.csv</code></li> </ul> </li> </ul> <p><strong>Figures:</strong></p> <p>The <code>figures</code> folder includes all figures and videos used in the manuscript. See <code>notebooks/Figures.ipynb</code> for the methods used to generate these figures.</p> <p><strong>Logs:</strong></p> <p>The <code>logs</code> folder includes logs of CNN convergence for the training and validation sets during model training for the leaf tracing CNN vein growing CNN, and U-Net models. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN).</p> <p><strong>Models:</strong></p> <p>The <code>models</code> folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time.</p> <ul> <li><code>BuildCNN.py</code> defines the CNN architecture for leaf tracing or vein growing, with user-specified input shape, output shape, layers, and output activation functions.</li> <li><code>BuildUNet.py</code> defines the U-Net architecture for leaf and vein segmentation, with user-specified input/output shape, layers, and output activation functions.</li> <li><code>LeafTracer.py</code> defines the leaf tracing algorithm at inference time.</li> <li><code>VeinGrower.py</code> defines the vein growing algorithm at inference time.</li> </ul> <p><strong>Notebooks:</strong></p> <p>The <code>notebooks</code> folder includes Jupyter notebooks used for model training, model inference, and figure generation.</p> <ul> <li><code>Figures.ipynb</code> is used to generate all of the manuscript figures.</li> <li><code>GrowerTraining.ipynb</code> is used to train the vein growing CNN.</li> <li><code>GrowerInference.ipynb</code> is used to apply the vein growing algorithm to the 1,453 leaf bottom images.</li> <li><code>TracerTraining.ipynb</code> is used to train the leaf tracing CNN.</li> <li><code>TracerInference.ipynb</code> is used to apply the leaf tracing algorithm to the 2,906 leaf top and bottom images.</li> <li><code>UNetLeafSegmentation.ipynb</code> is used to train and apply U-Net for leaf segmentation.</li> <li><code>UNetVeinSegmentation.ipynb</code> is used to train and apply U-Net for vein segmentation.</li> </ul> <p><strong>Utils:</strong></p> <p>The <code>utils</code> folder includes utility scripts implemented in Python that assist in model training and inference.</p> <ul> <li><code>ImageLoader.py</code> loads image/mask pairs for sampling training/validation tiles.</li> <li><code>LeafGenerator.py</code> generates inputs/outputs for the leaf tracing CNN.</li> <li><code>VeinGenerator.py</code> generates inputs/outputs for the vein growing CNN.</li> <li><code>UNetTileGenerator.py</code> generates inputs/outputs for the U-Net model.</li> <li><code>GetLowestGPU.py</code> identifies available GPUs using the <code>nvidia-smi</code> command and selects the one with lowest memory usage, if none available the device is set to CPU.</li> <li><code>ModelWrapperGenerator.py</code> wraps the PyTorch CNN and data loaders with similar functionality to the Keras Model class in TensorFlow (e.g., model.fit(...)).</li> <li><code>TimeRemaining.py</code> is used by the model wrapper to estimate remaining time left per epoch.</li> <li><code>TraceInitializer.py</code> is used by the tracing algorithm at inference time to initialize the leaf trace using automatic thresholding.</li> </ul> <p><strong>Weights:</strong></p> <p>The <code>weights</code> folder includes the CNN parameters from the epoch resulting in the best validation error. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN). The weights are loaded into the CNN models for inference.</p> <p><strong>Citation:</strong></p> <pre><code>@article{ doi:10.34133/plantphenomics.0072, author = {John Lagergren and Mirko Pavicic and Hari B. Chhetri and Larry M. York and Doug Hyatt and David Kainer and Erica M. Rutter and Kevin Flores and Jack Bailey-Bale and Marie Klein and Gail Taylor and Daniel Jacobson and Jared Streich }, title = {Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa}, journal = {Plant Phenomics}, volume = {0}, number = {ja}, pages = {}, year = {}, doi = {10.34133/plantphenomics.0072}, URL = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0072}, eprint = {https://spj.science.org/doi/pdf/10.34133/plantphenomics.0072}, }</code></pre>
Seasonal trends in leaf level physiological parameters, obtained through gas exchange, reflectance spectroscopy and, functional trait analysis
This data package contains leaf level gas exchange, reflectance spectroscopy, and functional trait measurements collected in six common deciduous tree species across the full 2021 growth season (May -October) at the Black Rock Forest in Cornwall, New York, USA. Branches were sampled predawn using the shotgun method of branch retrieval, and re-cut under water to preserve hydraulic function before transport to the lab. Gas exchange data included in this package are stomatal response curves (irradiance response) which can be used to estimate stomatal slope and intercept. Spectroscopic data are full-range (350 – 2500 nm) leaf reflectance spectra collected on all leaves sampled for gas exchange and traits. Leaf level trait measurements include leaf mass per area (LMA), leaf dry matter content (LDMC), elemental nitrogen and carbon expressed on a per mass basis, and fitted values of Asat, Vcmax, and Rdark scaled to a reference temperature of 25C. Data from these three data tables (stomatal responce, spectra, leaf traits) can be cross referenced using the unique SampleID. Additional data tables include stomatal anatomy (stomatal density, length, and width of the guard cells), hydraulic properties estimated from pressure volume curves (relative water deficit at the turgor loss point), and predawn water potential for all sampled branches. Site level data includes the dGPS location of each sampled tree, its species, and DBH. Each tabular data file (*.csv) is accompanied by a data description (*_dd.csv) which includes relevant metadata (unit, definition, data type). Copies of all raw instrument output (spectroradiometer, LICOR, pressure chamber) and included as .zip files.
canopy herbivory and leaf traits of tree communities in tropical montane rainforests of southern Ecuador
This dataset contains herbivory data estimated as leaf area loss [cm²] and [%] and several leaf traits measured either conventionally or via spectral sensing-based techniques from canopies of tree communities in tropical montane rainforests of the Andes in southern Ecuador between February and March in 2019. The data were used by Schön et al. (in prep) to evaluate whether leaf traits are valuable indicators of canopy herbivory mainly caused by arthropods and further, whether chemical leaf traits estimated via spectral sensing-based techniques have similar strong relations to herbivory as leaf traits measured conventionally. Herbivory was estimated with the software WinFOLIA ™ 2019a from scanned mature and sun-exposed leaves of tree canopies. Spectral sensing-based leaf traits comprising secondary plant metabolites and both structural and nutritional cell components were estimated from leaves with an OceanOptics spectrometer HDX. Conventionally measured leaf traits comprising morphological and nutritional traits were derived by applying both elemental and morphometrical analyses (e.g., ICP analysis, a digital micrometer and penetrometer). For detailed descriptions of the methodology see Schön et al. (in prep), Homeier et al. (2021), and Limberger et al. (2021). Research was conducted by the subprojects A1, B1, and B4 within the framework of the RESPECT project (Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs) funded by the DFG with the grant numbers: BE1780/51-1, BE1780/51-2, Ho3296/6-1, FA 925/11-1, FA 925/11-2, FA 925/16-1, BR1293/17-1.
Leaf traits plus growth and dieback data for cloud forest epiphytes in a cloud exclusion treatment at Wayqecha Biological Station, Peru
Morphological traits [specific leaf area, stomatal length and density, leaf thickness, cuticle thickness and hydrenchymal layer thickness] and physiological traits [stomatal conductance, minimum leaf conductance and foliar water uptake capacity] along with pressure volume curves were measured at the Wayqecha Biological Station, Peru. Vascular epiphytes were sampled from a control plot and fog reduction treatment plot constructed from two 30 m tall aluminum towers, 40 m apart with panels of polyethylene mesh strung between the towers. Measurements of leaf morphological traits were taken in June-July 2022 in the control and treatment plots to assess plasticity to fog reductionin the fifth year of the experiment. Samples of branches and leaves for each individual approximately 1-2 meters off the ground were collected in the plot, close enough to the curtain to maximize any effects of fog interception. Growth and dieback data were also collected species of vascular and non-vascular epiphytes attached to wooden transplant boards in the control and treatment plot in 2017. Rates of growth and dieback were recorded for vascular epiphytes from 2017-2019 and for non-vascular epiphytes from 2018-2019. The 2017 data collection took place in November while data for 2018 and 2019 were collected in June.
LTM01 Herbarium and grassland sites leaf trait measurements at Konza Prairie
Using herbarium specimens spanning 133 years and field-collected measurements, we assessed intraspecific trait (leaf structure and stomata) variability from grass species in the Great Plains of North America. We focused on two widespread, closely-related grasses from tribe Paniceae: Dichanthelium oligosanthes subsp. scribnerianum (C3) and Panicum virgatum (C4). Thirty-one specimens per taxon were sampled from local herbaria from the years 1887 – 2013 to assess trait responses across time to changes in atmospheric [CO2] and growing season precipitation and temperature. In 2021 and 2022, the species were measured from eight grasslands sites to explore how traits vary spatially across natural continental precipitation and temperature gradients. For temporal trends, we predicted Δ13C would decrease in D. oligosanthes and exhibit no change in P. virgatum. Dichanthelium oligosanthes is a C3 species, which we predict will respond to increased [CO2] concentrations by increasing its WUE to either conserve water while maintaining the same rates of photosynthesis or increase photosynthesis and maintain the same rates of water loss, thus decreasing Δ13C. We did not expect Δ13C of P. virgatum to respond over time because discrimination in C4 species is minimally affected by [CO2]. We also predicted both grasses will increase tissue C:N ratios and decrease stomatal density and stomatal lengths on both sides of the leaves in response to increased [CO2] over time. Lastly, we hypothesized %N and δ15N would decrease for both taxa. Because both taxa are widely distributed across North America and are known to exhibit variation in leaf morphology, we expected specific leaf area (SLA) to be greater in areas with warmer temperatures but not be correlated with differences in precipitation. We expect leaf dry matter content (LDMC) to increase with greater precipitation and decrease with higher temperatures.
LPT01 Leaf physiological and structural traits of encroaching shrub species at Konza Prairie
A variety of leaf-level physiological and structural traits were collected on seven species of encroaching shrubs at Konza Prairie Biological Station during the summer of 2022. Shrub species spanned an order of magnitude in abundance. These data were used to assess if the most abundant encroaching species at Konza Prairie have common growth forms and physiology or unique traits that differentiate their carbon- and water-use strategies. Measurements included A-Ci response curves, light response curves, pressure-volume curves, specific leaf area, leaf dry matter content, leaf carbon and nitrogen content, leaf 13C and wood density. All measurements were collected on the same shrub individuals.
leaf anatomy, vascular traits and nanomechanical cell-wall properties in European beech provenances
<p>The file contains leaf anatomical data (thickness of individual leaf parenchyma layers), vascular traits of leaf midrib (vessel area and density and derived parameters), and nanomechanical properties of xylem cell walls (modulus of elasticity, adhesion, energy dissipation and deformation), which were studied in 15 provenances of European beech, originating from sites distributed across the whole range of the species. The trial plot (locality Tale in central Slovakia) was established in 1998 with 2-years-old seedling within the international provenance experiment with beech coordinated by the Institute of Forest Genetics of Thuenen Institute Grosshansdorf. Leaf anatomy was studied using light microscopy, while fluorescent microscopy was used to acquire vascular traits and atomic-force microscopy for nanomechanical cell-wall traits. Sun leaves were collected from 4 trees per provenances, 1 leaf per tree was analyzed. AFM was done in a subset of 8 provenances. The aim of the study was assessing geographical trends of the studied traits and their association with climate at the sites of origin to reveal potential adaptive variation patterns.</p>
Leaf growth response to mild drought: natural variation sheds light on trait architecture
<p>Plant growth and crop yield are negatively affected by a reduction in water availability. However, a clear understanding of how growth is regulated under non-lethal drought conditions is lacking. Recent advances in genomics, phenomics and transcriptomics allow in-depth analysis of natural variation. In this study, we conducted a detailed screening of leaf growth responses to mild drought in a worldwide collection of <em>Arabidopsis thaliana</em> accessions. </p> <p>The raw phenotyping can be found in:<br> - cellularData.txt -> mature (23 days after stratification; DAS) leaf epidermis (third leaf) analysed for cell area, cell number, pavement cell area, pavement cell number, stomatal index and leaf area of the analysed leaf.</p> <p>- leaf3AreaMaturity.txt -> area of the third leaf at maturity (23DAs) in mm<sup>2.</sup></p> <p>- leaf3AreaProliferation.txt -> area of the third leaf at proliferation (last day of full cell proliferation; 8-10 DAS) in mm<sup>2</sup>.</p> <p>- rosetteArea Maturity.txt -> projected rosette area at maturity (22DAS)</p> <p>The phenotyping results have been normalised for batch effects ('experiment' in raw data)</p> <p>- allPhenotypesNormalised.txt -> contains the normalised data for all the measured phenotypes</p> <p>All datafiles indicate the accession name ('Accession'), the unique identifier for each accessions ('Ecotype_ID') as used in the 1001genomes project (www.1001genomes.org) and the treatment ('C' indicate well-watered plants, 'S' the mild-drought treated plants).</p> <p>These results and methodological results are described in Clauw et al. (2016, The Plant Cell).</p> <p>Citation:</p> <p><strong>Clauw, Pieter, Frederik Coppens, Arthur Korte, Dorota Herman, Bram Slabbinck, Stijn Dhondt, Twiggy Van Daele, et al. 2016. “Leaf Growth Response to Mild Drought: Natural Variation in Arabidopsis Sheds Light on Trait Architecture.” The Plant Cell, October. doi:10.1105/tpc.16.00483.</strong></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
FIGURE 4 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 4. Site-specific data for lamina length and lamina width plotted for each taxon. 4A: Lamina length for Platanus neptuni. 4B: Lamina width for P. neptuni. 4C: Lamina length for Eotrigonobalanus furcinervis. 4D: Lamina width for E. furcinervis. 4E: Lamina length for Daphnogene cinnamomifolia. 4F: Lamina width for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 1 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 1. Map showing the locations of the considered sites, which are numbered according to Table 1.
FIGURE 3 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 3. Site-specific data for lamina area and lamina perimeter plotted for each taxon. 3A: Lamina area for Platanus neptuni. 3B: Lamina perimeter for P. neptuni. 3C: Lamina area for Eotrigonobalanus furcinervis. 3D: Lamina perimeter for E. furcinervis. 3E: Lamina area for Daphnogene cinnamomifolia. 3F: Lamina perimeter for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 6 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 6. Site-specific data for lamina centroid and leaf length-to-width ratio (LWR) plotted for each taxon. 6A: Lamina centroid for Platanus neptuni. 6B: LWR for P. neptuni. 6C: Lamina centroid for Eotrigonobalanus furcinervis. 6D: LWR for E. furcinervis. 6E: Lamina centroid for Daphnogene cinnamomifolia. 6F: LWR for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 2 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 2. Plasticity index (PI) of various leaf traits, for the considered sites and taxa. 2A: PI for lamina area. 2B: PI for lamina length. 2C: PI for lamina perimeter. 2D: PI for lamina width. 2E: PI for lamina circularity. 2F: PI for lamina centroid. Squares: Platanus neptuni. Circles: Daphnogene cinnamomifolia. Triangles: Eotrigonobalanus furcinervis. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 5 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 5. Site-specific data for lamina circularity and lamina roundness plotted for each taxon. 5A: Lamina circularity for Platanus neptuni. 5B: Lamina roundness for P. neptuni. 5C: Lamina circularity for Eotrigonobalanus furcinervis. 5D: Lamina roundness for E. furcinervis. 5E: Lamina circularity for Daphnogene cinnamomifolia. 5F: Lamina roundness for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A: Eocene. 7B: Oligocene. Triangles: Platanus neptuni. Squares: Eotrigonobalanus furcinervis. Circles: Daphnogene cinnamomifolia.
ScienceDex guides
Understand access before you commit
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.