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157 results for “Leaf analysis”
Data for a leaf litter decomposition study and soil density fractionation analysis at a whole-watershed fertilization experiment in a temperate forest
To assess how elevated N deposition influences leaf litter decomposition dynamics and soil organic matter formation in a temperate deciduous forest, we coupled a reciprocal transplant leaf litter decomposition study with an analysis of the distribution of soil organic matter in mineral associated and particulate organic matter fractions at a long-term, whole-watershed, N fertilization experiment. We found that nearly 30 years of N additions slowed decay rates by about 11% for leaf litter decomposed in the fertilized watershed, regardless of the watershed from which the initial litter was collected. An apparent consequence of the altered rates of decomposition was that the soil in the fertilized watershed had about a 40% greater fraction of SOM in light particulate organic matter compared to the reference watershed, which was positively correlated with the bulk soil carbon to nitrogen ratio. Collectively, our results suggest that under conditions of N saturation, the physical transfer pathway of SOM formation is favored, which can have important implications for the future of the soil organic matter stock and nutrient cycling.
Don't put all your eggs in one leaf-roll: a network analysis of candy-striped spider leaf preferences for egg deposition
<p>"Enoplognatha leaf choice.R" - R script for all analyses and figures for the manuscript.</p> <p>"Enop_LeafENNR_EnopChoice.csv" - Interaction matrix showing the leaves selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_LeafTSENNR_EnopChoice.csv" - Interaction matrix showing the tropho-species selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant tropho-species community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_SNRENNR_EnopChoice.csv" - Interaction matrix showing the semi-natural habitats selected by each individual candy-striped spider.</p> <p>"Enop_SNRENNR_SNRs.csv" - Matrix equally representing the semi-natural habitats as the number of quadrats surveyed in each.</p> <p>"Leaf traits.csv" - The leaf trait data used to cluster leaves into tropho-species.</p> <p>"leaf.incidence.csv" - The number of candy-striped spiders that interacted with each plant species for assessment of interaction diversity and completeness.</p> <p>"Site Map.csv" - Latitude and longitude of the ten sampling sites used in this study.</p> <p>"TS plotting order.csv" - A file used to set the plotting order of tropho-species in a bipartite plot.</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.
Data and analysis script to support "Multi-site analysis of sequence in leaf-out and flowering reveals evidence of local adaptation"
<p>Data files and R script used to download and analyze plant leaf-out and flowering observations maintained by the USA National Phenology Network to evaluate the consistency of leaf-out and flowering among species pairs over multiple years.</p>
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.
Data for the analysis from "Evidence for positive priming of leaf litter decomposition by contact with eutrophic pond sediments"
<p>These are the data files used in the analysis of the results of the experiments that are reported in the manuscript "Evidence for positive priming of leaf litter decomposition by contact with eutrophic pond sediments". More details on the analysis can be found in at: https://github.com/KennyPeanuts/sediment_priming</p>
Data from: Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance
<p>Data and code associated with the submitted manuscript "Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance". Detailed descriptions of each file can be found in the README.txt . The raw sequence data associated with this work can be downloaded from the NCBI Sequence Read Archive, listed under BioProject #PRJNA565009<strong>.</strong></p>
Fig. 3 in Molecular characterization and phylogenetic analysis of Trypanosoma spp. detected from striped leaf-nosed bats (Hipposideros vittatus) in Zambia
Fig. 3. Species delimitation of Trypanosoma cruzi clade. Maximum likelihood phylogeny with outgroup (Trypanosoma lewisi) and with Baysian support values presented 17 linages recognized as species for the PTP analysis. Monophyletic groups in red indicated single putative species as well as terminal branches in blue.. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Molecular characterization and phylogenetic analysis of Trypanosoma spp. detected from striped leaf-nosed bats (Hipposideros vittatus) in Zambia
Fig. 1. Giemsa staining of Trypanosoma sp. from ZB17–105 in BSK-M medium Representative images of ZB17-105 in the BSK-M medium are displayed at the same magnification (x1000). (a,b) flagellates resembling promastigote forms. (c) possibly epimastigote forms under division. K: kinetoplast, N: nucleus, F: flagellum.
Linked collectors and determiners for: Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae).
Natural history specimen data linked to collectors and determiners held within, "Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0d2332a5-1968-49d7-a570-f31d386eccb3">https://bionomia.net/dataset/0d2332a5-1968-49d7-a570-f31d386eccb3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0d2332a5-1968-49d7-a570-f31d386eccb3">https://gbif.org/dataset/0d2332a5-1968-49d7-a570-f31d386eccb3</a>. Formatted as a Frictionless Data package.
Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"
<p>Raw VCF file for <em>Populus trichocarpa</em> V4 SNPs - Biallelic SNP variants were called for 1492 Populus trichocarpa genotypes using GATK Version 4.0 software.</p>
Thesis DRIS leaf nutrient analysis results
<p>The data file contains results from leaf nutrient analysis that have been analyzed with the Florida citrus DRIS program, created by Arnold Schumann (UF IFAS Citrus Research and Education Center, Soil and Precision Agricultura Lab). This data was used to train an AI model to recognize nutrient deficiencies of citrus. </p>
Code for data analysis - intra-community variability of leaf-out in temperate tree canopies
<p>The code and data were used for producing the results of "Phenology across scales: an intercontinental analysis of leaf-out dates in temperate deciduous tree communities", by Delpierre et al.</p>
Wang et al Narrow-leafed lupin selective sweep analysis results
<p>Narrow-leafed lupin selective sweep analysis results, supplementary datasets. </p>
FIGURE 282 in Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)
FIGURE 282. Known distribution of the Calapnita phyllicola group, part 2 (cf. Fig. 281). Note that all western specimens of C. phyllicola are assigned tentatively.
FIGURE 281 in Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)
FIGURE 281. Known distribution of the Calapnita phyllicola group, part 1 (cf. Fig. 282). The western Java record is based on a single female of uncertain identity. For details on the undescribed species in RMNH see C. phasmoides.
FIGURE 283 in Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)
FIGURE 283. Known distribution of the Calapnita vermiformis group, part 1 (cf. Fig. 284). The Java record of C. lawangan is based on specimens assigned tentatively.
FIGURES 285–288 in Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)
FIGURES 285–288. Interspecific variation in proportions (285–286) and absolute sizes (287–288) of selected characters in the Calapnita vermiformis group. These graphs are based on measurements of holotypes and ignore intraspecific variation. The graphs show the conspicuous but largely continuous interspecific variation in some male genital characters.
FIGURES 254–271 in Revision and cladistic analysis of the Southeast Asian leaf-dwelling spider genus Calapnita Simon (Araneae, Pholcidae)
FIGURES 254–271. Calapnita nunezae sp. nov. (254–262; ZFMK, Ar 16064–65) and C. maragusan sp. nov. (263–271; ZFMK, Ar 16098). 254–256, 263–265. Left procursi, prolateral, dorsal, and retrolateral views. 257, 269. Left palpal trochanters, retrolateral views. 258, 266. Left palpal femora, prolateral views. 259–260, 267–268. Right male chelicerae, frontal and lateral views. 261, 270. Epigyna, ventral views. 262, 271. Cleared female genitalia, dorsal views. Scale lines: 0.3 mm.
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