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5,828 results for “traits”
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.
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.
Categorical traits for macroalgae species of the Santa Barbara Channel
These data describe 32 categorical traits for 50 species of macroalgae found in the Santa Barbara Channel. Data are contained in one table, including a list of species, trait states for any given trait, and citations for where this information was found.
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>
Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"
<p>Methylome of the fresh water snail <em>Biomphalaria glabrata</em>. DNA was extracted from the feet of 10 individuals of <em>B. glabrata</em> originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments.<br> BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of <em>B. glabrata</em>. </p> <p>Wiggle files were generated for CpG pairs only.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</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>
Agonum sordidum, Fig_6 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum sordidum,</p> <p>Fig_6 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum rugicolle, Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum rugicolle,</p> <p>Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_nigrum, Fig_4 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_nigrum,</p> <p>Fig_4 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_mesostictum, Fig_2 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_mesostictum,</p> <p>Fig_2 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum monachum syriacum, Fig_3 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum monachum syriacum,</p> <p>Fig_3 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_marginatum, Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_marginatum,</p> <p>Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p> <p> </p>
A global database of bird nest traits
<p>The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits—site, structure, and attachment—for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research.</p>
Predicting Phenotypic Traits Using a Massive RNA-seq Dataset
<h2><strong>Abstract</strong></h2><p>The included datasets are a conglomerate of all available <i>Arabidopsis thaliana</i> RNA-seq data available from NCBI as of November 2022 processed to count data. In addition, the associated annotation files from NCBI BioProject database and processed versions of this data is included. Data has been processed according to the "Data Description Methods" in the manuscript titled "Predicting Phenotypic Traits Using a Massive RNA-seq Dataset" (in publication). The associated Methods can be found at this repository:<a href="https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics"> https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics</a>. These datasets can be used for exploring machine learning methods for predicting both continuous (Age) and categorical (Tissue) phenotypic traits using gene expression. Additionally, the gene expression data can be used on its own for the investigation of gene expression in <i>Arabidopsis thaliana.</i></p><h3><strong>Note to Researchers</strong></h3><p>This repository contains all of the datasets and information necessary to recreate the experiments in our paper. However, if may be that you are interested in our dataset for testing your own hypotheses/programs. If this is the case, we predict that you are looking for one or more of the the following 5 datasets<br> </p><h3><strong>Note on File Compression</strong></h3><p>All files in this repository are compressed using bzip2 to conserve space and allow for easier file transfer. The unzip command on linux systems is `bzip2 -d FILE_NAME`. For other computer systems (Windows and Apple) please consult your user manual.</p><h3><strong>Description All Datasets:</strong></h3><p><strong>Title:</strong> Gene Expression Count Data of all <i>Arabidopsis thaliana</i> data available from NCBI SRA as of November 2022<br><strong>Abstract: </strong>Gene Expression Count data was created using the workflow GEMmaker. The resulting Gene Expression Matrix (GEM) was then normalized and thresholded. The following 4 files are normalizations of the same data for Trimmed Mean of M values (TMM), Median Ratios Normalization (MRN), Transcripts Per kilobase Million (TPM), and No Normalization (NoNo) respectively. Additionally, Each file is included as a tsv and a python pickle. The tsv file is human readable, whereas the pickle file can be read into memory substantially faster. Format for tsv is each row represents a sample and each column represents a gene. <strong>NCBI_Nov2022_SRR_runinfo.csv</strong> is the starting file from NCBI which reports SRR information for each sample. <strong>Note 1 to Researchers: </strong>MRN normalization performed the best in our experiments and is likely what you want to use if you are doing additional expermentation with this dataset. Otherwise start with NoNo and perform your own normalizations. <strong>Note 2 to Researchers:</strong> the 54547 dataset will need to be thresholded prior to use. We include it in addition to the 32432 datasets in case you wish to try a different thresholding to the one outlined in our manuscript. <br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Count Data<br><strong>Organism:</strong> <i>Arabidopsis thaliana</i><br><strong>Files:</strong></p><p><strong>NCBI_Nov2022_SRR_runinfo.csv - </strong>Arabidopsis RNA-seq SRA RunInfo Retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>Dataset_54547_NoFilter_raw.pkl </strong>- Raw File Before thresholding (".pkl" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_54547_NoFilter_raw.tsv </strong>- Raw File Before thresholding (".tsv" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_32432_MRN.pkl</strong> - MRN normalized (".pkl" format)<br><strong>Dataset_32432_MRN.tsv - </strong>MRN normalized (".tsv" format)<br><strong>Dataset_32432_NoNo.pkl - </strong>NoNo normalized (".pkl" format)<br><strong>Dataset_32432_NoNo.tsv - </strong>NoNo normalized (".tsv" format)<br><strong>Dataset_32432_TMM.pkl - </strong>TMM normalized (".pkl" format)<br><strong>Dataset_32432_TMM.tsv - </strong>TMM normalized (".tsv" format)<br><strong>Dataset_32432_TPM.pkl - </strong>TPM normalized (".pkl" format)<br><strong>Dataset_32432_TPM.tsv - </strong>TPM normalized (".tsv" format)<br><br><br><strong>Title: </strong>Meta Data Arabidopsis Age and Tissue<br><strong>Abstract: </strong>Meta Data for Age and Tissue after processing. In our experiment this was used as response variable to gene expression. Shared columns are "bio_sample", "bioproject_name", "experiment". In addition to these processed datasets, <strong>NCBI_Nov2022_BioSample_data.tsv </strong>is the unprocessed starting material for these two data frames.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Metadata on phenotypes. ".tsv" format <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files: </strong><br><strong>NCBI_Nov2022_BioSample_data.tsv - </strong>Arabidopsis BioSample data retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>df_metadata_tissue.tsv</strong> - Tissue Annotations for 24876 samples<br><strong>df_metadata_age.tsv</strong><i><strong> - </strong></i>Age Annotations for 16078 samples. In addition to shared columns includes<i> "</i>days<i>_</i>age"(how many days old the sample is converted to days) and "annotation_age" (how the annotation was reported for this sample in the raw data file-- i.e. "days", "weeks" etc.)<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Age</strong><br><strong>Abstract: </strong>The dataset used for Machine learning on the phenotype Age that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Age_TrainTestSplits_mrn.pkl</strong> - MRN normalized<br><strong>Dataset_Age_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Age_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Age_TrainTestSplits_tpm.pkl - </strong>TPM normalized<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Tissue</strong><br><strong>Abstract: </strong>The dataset used for Machine learning on the phenotype Tissue that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits. Saved as python ".pkl" files.<br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test. Saved as python ".pkl" files.<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_mrn.pkl </strong>- MRN normalized<br><strong>Dataset_Tissue_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Tissue_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Tissue_TrainTestSplits_tpm.pkl </strong>- TPM normalized<br><strong>Dataset_Tissue_TrainTestSplits_mrn_4category.pkl </strong>- MRN for the tissue-4 dataset<br><br><br><strong>Title: </strong>BioProject Names<br><strong>Abstract:</strong> Three Column File With BioProject Name, BioSample Name, and Experiment Name<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>BioProject_Names_All.tsv</strong><br><br><br><strong>Title:</strong> Manuscript Supplemental Material<br><strong>Abstract:</strong> Supplemental tables and figures described in the manuscript (included with manuscript and here for convenience). Please see manuscript for additional information.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv", ".png".pdf"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Supplemental_Figures.zip </strong>- Supplemental figures from the manuscript. Includes description of each figure.<br><strong>Supplemental_Tables.zip </strong>- Supplemental tables from the manuscript. Includes description of each table.<br><br> </p><p><strong>Title:</strong> Splits of data for 3 experiments<br><strong>Abstract:</strong> 2 column tsv files. The first column is the experiment (sample) name, and the second column is if it is included in the train or test data. <strong>Included here to make sure pkl files are reproducible in case the pkl package breaks in the future.</strong> Not used by scripts, included to prevent future potential loss of data.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_4category_namesOnly.tsv</strong><br><strong>Dataset_Tissue_TrainTestSplits_namesOnly.tsv</strong><br><strong>Dataset_Age_TrainTestSplits_namesOnly.tsv</strong></p><p> </p><p><strong>Title:</strong> Git Code Repository<br><strong>Abstract:</strong> A tar bz2 compression of the git repository containing all of the code created for this manuscript. The same code found in this file is also avalible on GitLab at the link: https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Git Repository, python code<br><strong>Files:</strong><br><strong>modeling-with-transcriptomics-main.tar.bz2</strong> - Compressed Git repository of all code used in paper.</p>
Summary statistic of a Trans ancestry multi-trait GWAS
<p>Trans ancestry multi-trait GWAS by adapting the omnibus test to the trans ancestry setting. </p><p>Genome wide summary statistics for 19 blood count traits were retrieved from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel)</a></p><p>They curated using the JASS (Joint Analysis of Summary Statistics) pipeline https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline</p><p>See Troubat et al preprint for all details on the obtention of this dataset https://doi.org/10.1101/2023.06.23.546248</p>
Moth trends and traits in Flanders (northern Belgium)
<p>This code is related to the investigation of species traits as a guidance for moth conservation in the highly anthropogenic European region of Flanders (northern part of Belgium) based on Multi-Species Change Indices (MSCIs).</p> <p><strong>Abstract</strong></p> <ol> <li>Insects appear to decline rapidly in recent decades. This so-called sixth mass extinction garnered significant media attention, raising public awareness.</li> <li>Macro-moths—a species-rich and ecologically diverse insect group—face severe declines, particularly in urbanised and intensively farmed areas.</li> <li>Flanders is a highly anthropogenic region, serving as a case study where the impact on macro-moths of stressors like intensive agriculture, industrialization and urbanization has been quantified through a recently compiled Red List. Here, for 717 macro-moth species, we calculated relative changes in distribution area between a reference period (1980-2012) and the subsequent period (2013-2022). By correlating these species-specific trends with ten key ecological and life-history traits, we calculated more general Multi-Species Change Indices (MSCIs).</li> <li>These MSCIs showed that species associated with wet biotopes and heathlands declined on average by 20-25%, while (sub)urban species increased by more than 60%. Species feeding on lichens or mosses increased by 31%, while grass-feeding species decreased by 20%. Both very small (+34%) and very large species (+15%) increased, whereas medium-sized species decreased by 5%. Monophagous (+17%), migrant (+88%), and colour-invariable species (+5%) increased, while colour-variable species decreased (-8%). Finally, Holarctic (-21%) and Palearctic species (-5%) decreased, while Mediterranean (+27%) and Western-Palearctic species (+9%) increased.</li> <li>Our trait-based approach identifies key threats and mitigation strategies for moths in anthropogenic regions, offering evidence-based insights for crafting efficient management recommendations and informed conservation policies to safeguard moth communities.</li> </ol>
How is tree growth rate linked to root functional traits in phylogenetically related poplar hybrids?
<p>Fine roots play a crucial role in soil nutrient and water acquisition, significantly contributing to tree growth. Fine roots with a high specific root length (SRL) and small diameter are often considered to help trees grow fast. However, inconsistencies in the literature do not provide a clear basis on the effect of root functional traits, such as SRL or root mass density (RMD), on tree growth rate in phylogenetically related trees. Our aim was to examine relationships between tree growth rate and root functional traits, using clones displaying different growth rates in a hybrid poplar plantation located in New Liskeard, ON, Canada. Fine roots (diameter < 2 mm) samples were collected using soil cores at depths of 0–20, 20–40 and 40–60 cm, and analyzed for morphological, chemical and architectural traits. High SRL and thin fine roots were associated with the least productive clones, which is not consistent with the root economics spectrum (RES) theory. However, the most productive clone had larger fine root diameter and higher root lignin concentrations, probably reducing root construction and maintenance costs and C losses. Therefore, at the 0–20 and 20–40 cm depths, tree growth rates showed positive correlations with root diameter and root lignin concentrations, but negative correlations with SRL and root soluble compounds concentration. Increasing RMD at the 0–20 cm depth promoted tree growth rates, showing the importance of soil exploration in the topsoil for tree growth. We conclude that fine root variation does not always follow the RES hypothesis and argue that the rapid growth rate of trees may also be driven by fine root growth in diameter and mass in phylogenetically related trees.</p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry
<p>Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry. </p> <p>Dataset includes 1) model and analysis, and 2) supplemental data. </p> <p>In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file. </p> <p><strong>Acknowledgments</strong></p> <p>We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. </p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). </p> <p><strong>Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations</strong></p> <p>Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9<br> 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA.<br>2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA.<br>3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106<br>4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA<br>5Department of Natural Resources and the Environment, University of New Hampshire;<br>Durham, NH, USA 03824<br>6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ,<br>85721, USA<br>7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France<br>8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210<br>9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA.<br>10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA.<br>11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA.<br>12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University<br>of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia<br>13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA</p>
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