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50 results for “Forest Structure and Composition”
Figure 3 from: Silva JL, Silva RJ, Fernandes IM, Sousa WO, Vaz-de-Mello FZ (2020) Species composition and community structure of dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) compared among savanna and forest formations in the southwestern Brazilian Cerrado. Zoologia 37: 1-12. https://doi.org/10.3897/zoologia.37.e58960
Figure 3 Proportions of the three guilds of dung beetle by abundance and richness compared among the four plant formations in the Serra Azul State Park (SASP), summing both trap types (pitfall, FIT).
Figure 6 from: Silva JL, Silva RJ, Fernandes IM, Sousa WO, Vaz-de-Mello FZ (2020) Species composition and community structure of dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) compared among savanna and forest formations in the southwestern Brazilian Cerrado. Zoologia 37: 1-12. https://doi.org/10.3897/zoologia.37.e58960
Figure 6 Ordination (PCoA) of the beetle assemblages in each plant formation in Serra Azul State Park (SASP), state of Mato Grosso, Brazil, with the vectors of spatial attributes (dbMEM).
Figure 6 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 6. Analysis of CCA plot illustrating correlation between calciumcarbonate and plant communities along axis-1 and 2.
Figure 1 in Fire effects on Atlantic Forest sites from a composition, structure and functional perspective
Figure 1. Paraíba do Sul river basin study site, Southeast Atlantic Forest biome, inside São Paulo state, Brazil and South America. Red and blue pins represent burned and unburned forest sites where field inventory was performed, with satellite images of each.
Figure 5 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 5 - Relative abundance across the forest age gradient for a representative species from each of the indicator classes (see text for definitions of indicator classes).
Figure 4 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 4 - Proportions of carabid populations brachypterous and macropterous for five forest age classes. Significant differences occurred for all forest age classes except the zero age class (χ2 < 0.05).
Figure 3 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 3 - Results of Non-Metric Multidimensional Scaling (NMDS) analysis for 33 study sites. The analysis was based on the 17 most common carabid beetle species from five forest age classes (0, 10, 50, 85, and 150 years). Each the five polygons represent different forest age classes, as indicated by different symbols.
Figure 2 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 2 - Carabid beetle species accumulation curves for five forest age classes. Vertical line indicates species richness of each curve at n = 233 individuals.
Figure 1 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 1 - Map of the study area in Piedmont, North Carolina, with 33 sample sites indicated.
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
ScienceDex guides
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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.