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112 results for “vegetation plots”
Soil salinity and organic content at GCE-LTER vegetation monitoring plots in October 2020
Soil samples were collected in conjunction with Fall 2020 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water. Organic content was measured gravimetrically by comparing ash-free dry weight and total weight of soil samples.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2021
Soil samples were collected in conjunction with Fall 2021 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2022
Soil samples were collected in conjunction with Fall 2022 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2023
Soil samples were collected in conjunction with Fall 2023 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Long-term growth, mortality and regeneration of trees in permanent vegetation plots in the Pacific Northwest, 1910 to present
A network of more than 130 permanent vegetation plots provides long-term information on patterns and rates of forest succession in most of the major forest zones of the Pacific Northwest. The plot network extends from the coast to the Cascades in western Oregon and Washington and east to ponderosa pine forests in the Oregon Cascades. Most of the permanent plots were established during two intervals: from 1910 to 1948, and from 1970 to 1989. The earlier plots were established by U.S. Forest Service researchers to quantify timber growth in young stands of important commercial species and to help answer other applied forestry questions. The more recent period of plot establishment began under the Coniferous Forest Biome program of the International Biological Program during the 1970s, and continued under the Long-term Ecological Research program. A broader set of objectives motivated plot establishment since 1970, especially quantification of composition, structure, and population and ecosystem dynamics of natural forests. Plots have one of three spatial arrangements: (1) contiguous rectangles subjectively placed within an area of homogeneous forest; (2) circular plots subjectively placed within an area of homogeneous forest; and (3) circular plots systematically located on long transects to sample an entire watershed, ridge, or reserve. Rectangular study areas are mostly 1.0 ha or 0.4 ha (1.0 ac) in size (slope-corrected). Circular plots are 0.1 ha (0.247 ac), not corrected for slope. The tree stratum is the focus of work in closed-forest study areas. All trees larger than a minimum diameter (5 cm for most areas) are permanently tagged. Plots are censused every 5 or 6 years. Attributes measured or assessed at each census include tree diameter, tree vigor, and the condition of the crown and stem. The same attributes are recorded for trees (ingrowth) that have exceeded the minimum diameter since the previous census. In many plots tree locations are surveyed to provide a
Wildlands and Woodlands Stewardship Science Vegetation Plots in New England 2009-2015
The Wildlands and Woodlands (W&W) initiative is a broad, collaborative effort to protect 70% of New England in forest over the next 50 years. At the heart of this initiative is the awareness that our wooded landscapes provide immeasurable economic, environmental, and cultural benefits and the conviction that we should understand these systems better, manage them wisely, and conserve them for the future. As part of W&W, Stewardship Science seeks to encourage widespread application of an accessible approach to monitoring forests that interested landowners or conservation-minded individuals can use to track changes in their woods over time. Whether the motivation is active management for timber, understanding how forests are being shaped by factors ranging from climate change and ice storms to insect pests, or simple pleasure in observing nature’s dynamics, anyone equipped with a notebook, tape measure, pencil, and the willingness to puzzle through a book of tree identification can readily develop a robust and valuable set of observations. This idea is not new. For over 150 years, leading conservationists and ecological thinkers beginning with Henry David Thoreau have argued that there is much to be learned through simple, long-term measurements of forest growth and change. Yet there are still remarkably few examples of private landowners, land trusts, timber companies, or conservation organizations that base their understanding and management practices on a regular system of observations and measurements. Because the vast majority of forestland in New England is privately owned, most of these lands remain unmonitored, and management plans are often drawn up from casual rather than systematic observation. For more background information on the project, please see the Wildlands & Woodlands Stewardship Science manual. This data package contains vegetation and environmental data on 64 20x20m plots set up in four areas across New England by staff and summer field crews fro
Hemlock Understory Vegetation Plots at Harvard Forest since 2002
Hemlock (Tsuga canadensis) forests in New England are changing rapidly with the invasion of the hemlock woolly adelgid (HWA, Adelges tsugae), a non-native insect pest that kills hemlock trees. As the adelgid invades hemlock stands at Harvard Forest, we are monitoring how forest structure and function changes. We surveyed community vegetation structure and composition prior to adelgid infestation, and are repeating this survey as infestation progresses. These measurements will allow us to link the long-term history of these hemlock stands to other current studies of hemlock response to adelgid infestation.
Towers Forestry Plot, Long-term Vegetation Monitoring in a 1-ha old-growth Rainforest, La Selva Research Station, OTS, Sarapiquí, Heredia, Costa Rica, 2010–2020
The Towers Plot is a 1-hectare permanent vegetation plot established in 2010 under the canopy towers at La Selva Research Station, Sarapiquí, Heredia, Costa Rica. The plot was created by the Organization for Tropical Studies (OTS) to monitor long-term changes in forest structure, composition, and dynamics in an old-growth tropical rainforest. All woody stems with a diameter at breast height (DBH) of 10 cm or greater—including trees, palms, and lianas—were tagged, mapped, and measured following standardized procedures. Censuses were conducted between 2010 and 2020 to document growth, mortality, and recruitment. The dataset includes taxonomic identifications, stem diameter measurements, spatial coordinates within the plot, and metadata describing field methods and species composition. The plot was established beneath three canopy towers that had been previously constructed through the NSF-funded Major Research Instrumentation (MRI) project, NSF 0722741, which provided key infrastructure for canopy and environmental research at La Selva. This proximity created a valuable opportunity to integrate vegetation monitoring with existing environmental instrumentation. Johana Hurtado, coordinator of the Tropical Ecology, Assessment and Monitoring (TEAM) project at La Selva, collaborated with OTS staff in the establishment of the plot, ensuring methodological consistency with other tropical forest monitoring sites. This dataset provides a comprehensive record of woody plant diversity and forest structure in a lowland old-growth Neotropical rainforest. It supports research on forest dynamics, carbon storage, and ecosystem change. The overall monitoring project is ongoing; this data package contains observations from 2010 through 2020.
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Riparian Woody Vegetation Composition and Structure in Long-Term Monitoring Plots Along the Sabie River, Kruger National Park (2011-2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2009 and 2010 from 15 long-term riparian monitoring plots located along the Sabie River in the southern region of the Kruger National Park, South Africa. The Sabie River is the park’s most perennial river system and supports diverse riparian plant communities influenced by hydrological variability, flooding dynamics, sediment deposition, herbivory, and climate-driven disturbance. Within each monitoring plot, field teams recorded woody species identity, stem density, plant height, and stem diameter. Additional structural and condition indicators were collected, including canopy breakage, evidence of bark stripping, resprouting status, and whether individuals were toppled or alive at the time of sampling. These structural attributes provide detailed assessments of disturbance impacts and vegetation condition within riparian zones. Data collection followed the same standardized protocols as the Southern Granites long-term vegetation monitoring program, allowing for cross-site comparisons between upland savanna and riparian systems. This dataset provides a baseline for evaluating long-term ecological change in riparian woody plant communities and supports ongoing research on the ecological functioning and resilience of river corridors in Kruger National Park.
Bonanza Creek LTER: Point Bar Vegetation Survey of Bonanza Creek LTER Research Plots (2007-Present)
Beginning in 2007 ocular vegetation estimates were replaced by the point bar system. The point bar is meant to be a more objective way of carrying out annual vegetation surveys. In particular because you are placing the point bar in the same location every time a site is visited, a better understanding of vegetation change over time is possible. This method replaces the old system of estimating percent cover visually (<a href="https://www.lter.uaf.edu/data/data-detail/id/174"> Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Percent Cover (1975 - 2009) </a>), which is often subject to personal bias and small shifts in species composition are often overlooked. Data from both methods were collected during the 2007, 2008, and 2009 field seasons, and regression analysis shows unique and statistically significant relationships between the two methods depending on growth form. At each site, growth forms were evaluated separately, and at each site there is a specific regression model for each growth form. In this way a user can correlate the two methods and data collected before 2007 can be compared to data collected after the new protocol was established.
Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities
<p>The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here.</p> <p>Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class). Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. <em>Acacia aneura</em>, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid). </p> <p>The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104. </p> <p> </p> <p> </p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
Pre- and post-fire vegetation and fuel loading data from mixed conifer plots in Arizona and New Mexico: 2010-2023
A permanent plot network was installed in mixed conifer stands across the U.S. Southwest (Arizona and New Mexico) between 2010-2013, primarily to monitor the spread and severity of white pine blister rust (WPBR), a disease caused by the fungal pathogen Cronartium ribicola on southwestern white pine (Pinus strobiformis). Study sites were mid-to-high elevation mixed conifer stands composed of southwestern white pine, Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), quaking aspen (Populus tremuloides), Gambel oak (Quercus gambelii), blue spruce (Picea pungens), Engelmann spruce (Picea engelmannii), corkbark fir (Abies latifolia var. arizonica), Rocky Mountain bristlecone pine (Pinus aristata), and New Mexico locust (Robinia neomexicana). After plot installation, 6 fires occurred in the study area, burning an estimated total of 489,390 acres and 30 plots. We remeasured plots at 1-, 5- and 10-year intervals post-fire, quantifying burn severity via a composite burn index (CBI) at the first year post-fire. We also assessed regeneration, overstory mortality, and fuel loading. Overstory variables collected included tree species, status, diameter at breast height (DBH), and mortality, as well as height, height to live crown base, strata, and crown class on a subset of trees. Understory trees were tallied by species. Fuel load was measured via transect and calculated in megagrams per hectare categorically based on fuel type. Other variables such as basal area and trees per hectare were derived and calculated. This dataset was utilized in the manuscript "Climate, fire, and the future of mixed conifer ecosystems in the U.S. Southwest" (currently in review), and R code used for analyses is included in the dataset.
Landscape Ecosystem Classification Soils and Vegetation Plots Data at the University of Michigan Biological Station, Pellston, Michigan from 1987 to 2015 remeasurements
Landscape ecosystems are a means of understanding the spatial patterns of and the functional interrelationships in forest ecosystems. Landscape ecosystem research is a multifactor, holistic approach to identifying, classifying, describing, and mapping terrain ecosystems. Abiotic and biotic factors are integrated in the field to distinguish repeating units similar in ecological structure and function. Landscape ecosystems are identified by simultaneous integration of physiographic, soil, and vegetation information. The more stable components--physiography and soil--largely determine local climate, and water and nutrient relations, and thus the interrelationships of physiography and soil form the foundation of a landscape ecosystem classification. Vegetation is seen as a phytometer that integrates the many abiotic factors and their interactions, and therefore reflects differences in ecosystem structure and function. When the three main ecosystem factors are analyzed simultaneously, one can perceive interrelationships that result in ecologically meaningful differences among segments of the ecosphere. Landscape ecosystems are spatial; they are volumetric, multi-dimensional segments of earth, whose components include soil, water, atmosphere, solar radiation, and biota. These segments can be identified, classified, described, and mapped at various scales. From the years of 1988 to 2001, various graduate students of Burton V. Barnes completed their masters thesis and dissertations in this pursuit. The attached data set is a culmination of these individual work. Each plot has measurements at various scales within the 10 by 30 meet plot. A stratified random design was used to locate plot locations. The random design was stratified by major and minor landforms in the region. All trees within the plot where identified and dbh was measured. All individual shrubs where identified and abundance was counted within the entire plot. Soils pits locations for each plot where selected
Vegetation indices calculated from reflectance spectra collected at LTER plots at Toolik Lake, Alaska during the 2007-2019 growing seasons.
Vegetation indices calculated from reflectance spectra collected at Arctic LTER experimental plots at Toolik Lake, Alaska during the 2007-2019 growing seasons. Long term experimental plots span several different vegetation types: Heath (HTH89), Moist Acidic Tussock (MAT89 and Low Fert), Moist Non-Acidic Tussock (MNAT), Non-Acidic Non-Tussock (NANT), Shrub (SHB), and Wet Sedge (WSG). Plots are differentiated by their experimental treatment and are located in replicate blocks.Canopy reflectance is measured by hand-held spectrophotometer and several indices of interest (NDVI, EVI, EVI2, PRI, WBI, and Chlorophyll index) are calculated.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2015
Soil samples were collected in conjunction with Fall 2015 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2016
Soil samples were collected in conjunction with Fall 2016 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2017
Soil samples were collected in conjunction with Fall 2017 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Soil salinity at GCE-LTER vegetation monitoring plots in October 2018
Soil samples were collected in conjunction with Fall 2018 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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