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10 results for “rock cover”
Percent cover of under- and mid-story vegetation and seedling counts in the Future of Oak Forests project at Black Rock Forest, Cornwall, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. In 2006 and 2008, before exclosures were erected, pre-treatment surveys were conducted in all unexclosed (n=120) quadrats. Surveys of all 240 understory quadrats were conducted annually in late summer (August to September) from 2009 to 2018 and then again in 2021. At each quadrat, trained observers identified all vascular plants to species and assigned each species a percent cover value. The percent cover of moss was also recorded but moss species were not identified. Counts of tree seedlings and some woody shrubs were also recorded in addition to percent cover values. Seedlings were considered saplings, and therefore not counted, once they reached 1.3 m tall (breast height).
Tree species, diameter, regeneration, and herbaceous cover from 218 plots in 1985 in Black Rock Forest, NY.
A stand inventory was completed in 1985 in Black Rock Forest, Cornwall, NY across 3112 acres. Trees greater than 2" in diameter at breast height (DBH) were tallied using a 10 basal area factor prism in 218 plots across 71 stands. For each tree, species, DBH, number of eight foot pieces, overall form, crown class, and any special notes were recorded. Regeneration was measured at each location by tallying all trees less than 2" DBH in a 2-m radius plot. Shrub and herbaceous cover at each location were also tallied in a 2-m radius plot.
Predictive high-resolution mapping of sea floor rock cover for the UK and Ireland
<p>Predicted seabed rock cover using the machine learning algorithm Catboost and marine environmental predictors.</p> <p>Supporting data for T4.1 of Horizon 2020 project FutureMARES.</p>
Sevilleta site, station Deep Well, study of cover of rock in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Sevilleta (SEV) contains cover of rock measurements in percent units and were aggregated to a yearly timescale.
Sevilleta site, station Five Points, study of cover of rock in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Sevilleta (SEV) contains cover of rock measurements in percent units and were aggregated to a yearly timescale.
Data from: Mortality versus survival in drought‐affected Aleppo pine forest depends on the extent of rock cover and soil stoniness
Open the record for dataset details and reuse information.
How surface rock cover affects water and nutrient availability of Sonoran Desert annual plants -- Honors Thesis
Water and nutrient availability are the primary and secondary drivers of net primary productivity (NPP) in arid ecosystems. Although precipitation regulates water inputs, soil properties influence water availability for plant growth. Aridland soils are often covered with surface rocks, which can increase or decrease water availability by modifying evaporation, infiltration, light levels, and temperature. Due to the complexity of these direct and indirect mechanisms, the relationship between rock cover and NPP is not well understood. In this research we explore the relationship between rock cover, soil nutrient availability, and aboveground growth of desert annual plants over four years across a long-term nutrient enrichment experiment in the Sonoran Desert. We surveyed surface rock cover at fifteen sites in central Arizona that have been fertilized with nitrogen (N) and phosphorus (P), alone and in combination, for seven years. Using ANCOVA, we then explored the relative importance of rock cover, precipitation, and nutrient treatment on peak aboveground biomass of spring herbaceous annual plants that were collected in 2008, 2009, 2010, and 2013. We expected surface rocks to strengthen the positive relationship between precipitation, nutrient additions, and annual plant growth. Precipitation, nutrient additions, and surface rock cover together significantly influence growth of Sonoran Desert annual plants. As expected, nutrients and precipitation were the strongest drivers of annual plant biomass. Plant growth was positively related to N additions across all four years (ANCOVA, p <0.01); P in 2008 and 2010 years (p = 0.01 and 0.005, respectively);and precipitation in three of the four years (p < 0.05). Precipitation was the primary driver of plant biomass in the two driest years, 2009 and 2013 (partial eta2 = 0.51 and 0.52, respectively). Gravel (2-64 mm diameter) was only rock size class that was significantly related to annual plant biomass. Contrary to our expecta
Supplementary material 1 from: Knüsel S, Conedera M, Bugmann H, Wunder J (2019) Low litter cover, high light availability and rock cover favour the establishment of Ailanthus altissima in forests in southern Switzerland. NeoBiota 46: 91-116. https://doi.org/10.3897/neobiota.46.35722
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Text–fig. 1. Map of the Anti-Atlas area (Morocco) with the sampled locality in the Zagora region (marked with a black star) (after Gutiérrez-Marco et al. 2003, and Sumrall and Zamora 2011). Key: a, Precambrian and Palaeozoic rocks; b, Ordovician rocks; c, post-Palaeozoic cover. in Pauxillites Thaddei A New Lower Ordovician Hyolith From Morocco
Text–fig. 1. Map of the Anti-Atlas area (Morocco) with the sampled locality in the Zagora region (marked with a black star) (after Gutiérrez-Marco et al. 2003, and Sumrall and Zamora 2011). Key: a, Precambrian and Palaeozoic rocks; b, Ordovician rocks; c, post-Palaeozoic cover.
Surface rock cover soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Version 2: Unfortunately, errors were found in the original training data preparation in version 1. This version corrects those errors and has resulted in cross validation accuracy increases (R<sup>2</sup>) from ~0.4 to ~0.55 for both rock cover and representative rock size.</p> <p>Repository includes maps of surface rock cover (sfragcov) and dominant surface rock size (sfragsize) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Press. A hybrid approach for predictive soil property mapping using conventional soil survey data. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_model_additional_elements.extension</p> <p>Example: sfragsize_r_2D_QRF.tif</p> <p>Indicates dominant surface fragment size (sfragsize) using a 2D model employing a quantile regression forest (QRF). This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. If the first name is sfragcov, it indicates that the layer is for surface fragment cover.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (2019) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C.,2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p>
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