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112 results for “vegetation plots”
Vegetation indices calculated for ITEX flux plots in 2004-2009 at Toolik, Alaska; Abisko, Sweden; Svalbard, Norway; Zackenberg, Northeast Greenland; and Barrow, Alaska
A spectrophotometer was used to scan the canopy vegetation of ITEX flux plots. The resulting reflectance spectra were used to calculate several vegetation indices of interest (NDVI, EVI, EVI2, PRI, WBI, Chlorophyll Index). Average values of these vegetation indices for each ITEX flux plot are presented here.
Vegetation indices calculated for ITEX harvest plots in 2004-2009 at Toolik, Alaska; Abisko, Sweden; Svalbard, Norway; Zackenberg, Northeast Greenland; and Barrow, Alaska
A spectrophotometer was used to scan the canopy vegetation of ITEX harvest plots. The resulting reflectance spectra were used to calculate several vegetation indices of interest (NDVI, EVI, EVI2, PRI, WBI, Chlorophyll Index). Average values of these vegetation indices for each ITEX harvest plot are presented here. These plots also had biomass harvests performed and were analyzed for leaf area and nitrogen content (see 2003-2009gsharvest.csv, 2003-2009gsharvestLAI-N.csv).
GPS coordinates and vegetation descriptions for the ITEX circumarctic flux survey plots 2003-2009
GPS locations and vegetation descriptions for the ITEX flux survey plots. Survey plots were located in the Toolik Lake LTER fertilization experiment in Alaska; at Imnavait Creek, Alaska; at Paddus, Latnjajaure and the Stepps site near Abisko in northern Sweden; at various sites in Adventdalen, Svalbard; in the Zackenberg valley, Northeast Greenland; at BEO near Barrow, Alaska and at the Anaktuvuk River Burn in Alaska. Measurements were made during the growing seasons 2003 to 2009.
Pino Gate Prairie Dog Study: Landscape-scale Vegetation Plot Data from the Sevilleta National Wildlife Refuge, New Mexico (1999-2002)
Prairie dogs (Cynomys spp.) and banner-tailed kangaroo rats (Dipodomys spectabilis) are considered keystone species of grassland ecosystems, and co-occur in the arid grasslands of the southwestern United States and in Mexico. Their keystone status is attributed primarily to the effects of their burrowing and foraging behavior, but they differ ecologically in several important respects. We studied the comparative functional roles of these species where they co-occur at the Sevilleta National Wildlife Refuge, New Mexico, focusing on their impacts on grassland vegetation. We found that vegetation cover, structure, and species richness varied across a gradient extending out from the mound centers, and these patterns differed between prairie dog and kangaroo rat mounds. Certain species and functional groups of plants associated differentially with mounds and landscape patches occupied by prairie dogs and banner-tailed kangaroo rats. Where both species co-occurred locally there was greater soil disturbance, more organic material from their feces, and higher activity of other animals. The overall effect of these rodents was to create a mosaic of different patches across the landscape such that their combined activities increased andscape heterogeneity and plant species richness. Our results demonstrate complementary effects of two co-occurring keystone species on their associated biotic communities.
Pino Gate Prairie Dog Study: Mound-scale Vegetation Plot Data from the Sevilleta National Wildlife Refuge, New Mexico (2000-2002)
Prairie dogs (Cynomys spp.) and banner-tailed kangaroo rats (Dipodomys spectabilis) are considered keystone species of grassland ecosystems, and co-occur in the arid grasslands of the southwestern United States and in Mexico. Their keystone status is attributed primarily to the effects of their burrowing and foraging behavior, but they differ ecologically in several important respects. We studied the comparative functional roles of these species where they co-occur at the Sevilleta National Wildlife Refuge, New Mexico, focusing on their impacts on grassland vegetation. We found that vegetation cover, structure, and species richness varied across a gradient extending out from the mound centers, and these patterns differed between prairie dog and kangaroo rat mounds. Certain species and functional groups of plants associated differentially with mounds and landscape patches occupied by prairie dogs and banner-tailed kangaroo rats. Where both species co-occurred locally there was greater soil disturbance, more organic material from their feces, and higher activity of other animals. The overall effect of these rodents was to create a mosaic of different patches across the landscape such that their combined activities increased landscape heterogeneity and plant species richness. Our results demonstrate complementary effects of two co-occurring keystone species on their associated biotic communities.
3D vegetation scans of DivResource experiment plots (Bad Lauchstädt, Germany)
<p>Meta data:</p> <ul> <li>Place and time of recording: Germany, Bad Lauchstädt, UFZ research station, DivResource experiment, 11.09.2023</li> <li>Specification of the type of smartphone used: iPhone 13</li> <li>App used: Scaniverse</li> <li>Brief information on the project context: Pilot test to create 3-demensional animations of vegetation plots with smartphone and 3D scanning app</li> </ul>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression
<p><strong>Questions.</strong> Plant cover values in vegetation-plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is if we can detect species trends in vegetation-plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.</p> <p><strong>Location</strong>. The Netherlands, Western Europe.</p> <p><strong>Methods. </strong>We used vegetation-plot data collected from 10.000 fixed plots which were surveyed once every four years during 1999-2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.</p> <p><strong>Results.</strong> We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.</p> <p><strong>Conclusions. </strong>Our model enables researchers to detect trends in cover using longitudinal vegetation-plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring datasets.</p>
Distribution of vegetation sampling plots in four ecosystem types
<p>The table contains a number of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as a number of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Distribution of vegetation sampling plots in various ecosystem types
<p>The table contains no. of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as no. of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Botanic records from the forest reserves of south west Ghana: Plant species distribution data with checklist and conservation assessments from 114 vegetation plots
<p>South west Ghana is a biodiversity hotspot within the western African lowland tropical rainforest region, supporting many endemic and restricted range plant species. This dataset comprises botanic records from five forest reserves of south west Ghana (Ankasa, Boi Tano, Tano Nimri, Jema Assemkron, Nini Suhein). Vascular plant species distribution data (12,232 records) from 114 vegetation plot samples are presented, surveyed between 1981 and 2015. A plant species checklist including conservation assessments for each species is included. Nomenclature is current as of 2016. The dataset is linked to the publication Marshall et al, 2023, Implications for conservation assessment from flux in the botanical record over 20 years in south west Ghana, Ecology and Evolution <a href="https://doi.org/10.1002/ece3.9775">https://doi.org/10.1002/ece3.9775</a>. The dataset is also used in Marshall et al, 2022, Predictors of plant endemism in two west African forest hotspots, Frontiers in Ecology and Evolution 10:980660 <a href="https://doi.org/10.3389/fevo.2022.980660">https://doi.org/10.3389/fevo.2022.980660</a>.</p>
Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression
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Botanic records from the forest reserves of south west Ghana: Plant species distribution data with checklist and conservation assessments from 114 vegetation plots
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BESLTER Permanent Plot vegetation data combined for the survey years of 1998, 2003, and 2015
BESLTER Permanent Plot vegetation data combined for the survey years of 1998, 2003, and 2015. Introduction: Urban forests are often highly fragmented with many exotic species. Altered disturbance regimes and environmental pollutants influence urban forest vegetation. One of the best ways to understand the impacts of urban land-use on forest composition is through long-term research. In 1998, the Baltimore Ecosystem Study (BES) established eight forest plots to investigate the impacts of urbanization on natural ecosystems (Groffman et al. 2006). Four plots were established in urban forest patches and four in rural forests. All eight plots are located within the Baltimore Metropolitan Area. Purpose: Vegetation in the BES long-term plots were sampled in 1998, 2003, and 2015 to understand the influence of urbanization on species abundances and to quantify change in forest composition, diversity, and structure (Groffman et al. 2006 and Templeton 2016). Plot Structure: Six of the plots are 40�40m (1600m2). The Hillsdale 1 and 2 plots are 30�30m (900m2). The Hillsdale plots are smaller to fit within the boundaries of the forest patch. Sites were selected with the following criteria in mind: 1) to represent urban and non-urban forests, 2) away from obvious habitat boundaries or edges, 3) with consistent drainage lines within the plot, and 4) with at least 80% continuous tree canopy. All vegetation layers were sampled in order to characterize the structure and composition of the plant community. Each plot was permanently outlined with metal markers buried at or below the soil line. Between each of the plot corners, metal markers were placed at 10m intervals. The 10m markers divided the plot into 16 10x10m subplots (nine 10x10m subplots at Hillsdale). Each 10x10m subplot was then further divided into four 5x5m subplots. Only one of the four 5x5m subplots in each 10x10m subplot was used for all vegetation sampling below the tree layer. Shrubs and vines were measured along two
Vegetation Cover Permanent Plot Data : Biomass at Upper Phillips Creek marsh at the Virginia Coast Reserve 1990
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Vegetation Cover Permanent Plot Data: Bulk Density at Upper Phillips Creek marsh at the Virginia Coast Reserve 1990-1992
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Vegetation Cover Permanent Plot Data : Macroorganic Material at Upper Phillips Creek at the Virginia Coast Reserve 1990-1992
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Data from: Informative plot sizes in presence-absence sampling of forest floor vegetation
1. Plant communities are attracting increased interest in connection with forest and landscape inventories due to society's interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here we investigate the use of presence-absence sampling with the aim to monitor state and change of plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible. 2. Plant occurrences were modeled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new. 3. We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square meter the optimum plot size is 1.6 square meters. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory. 4. Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rare plants using presence-absence sampling in monitoring programmes.
Vegetation samples at the permanent plots of LTER site "Appennino Centrale: Velino-Duchessa" (1993-2023)
<p>The present dataset comprises observations spanning from 1993 to 2023, focusing on biotic parameters collected from six permanent plots included into the LTER site “Appennino Centrale: Velino-Duchessa” (<a href="https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29">https://deims.org/12c79ecb-7890-4b75-9655-0883dacd8a29</a>), located in Central Italy above the timberline (2125-2225 m a.s.l.). Specifically, it includes a synthesis of phytosociological relevés collected, according the Braun-Blanquet approach (coverage scale as modified by Pignatti) in 6 permanent plots of 10x10 m (https://deims.org/dataset/0c540fe8-1984-11e5-a766-005056ab003f), representative of two high-elevation plant communities (alpine tundra, <em>Saxifrago speciosae-Silenetum ceniasiae,</em> and snow-bed grassland, <em>Trifolio thalii-Festucetum microphyllae).</em> Species are named according to Flora d'Italia (Pignatti, 1982). The site, managed by Carabinieri, Forest and Environmental Protection Department, is part of the site “IT01- Apennines - High elevation Ecosystems – Italy” (<a href="https://deims.org/70b5c2bd-b0c3-4132-8988-f527893bfa42">https://deims.org/70b5c2bd-b0c3-4132-8988-f527893bfa42</a>) which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER). The data collection started in 1993 and it is continuously updated every year.</p>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
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