Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

description: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.; abstract: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Please email GIS.Librarian@FloridaDEP.gov for additional information.

description: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale. This update of the Collier County soils map originally released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.; abstract: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale. This update of the Collier County soils map originally released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

Geospatial data about Escambia County, Florida Soils. Export to CAD, GIS, PDF, CSV and access via API.

In 2007, the U.S. Geological Survey began a low-density (1 site per 1,600 sq. km., 4857 sites) geochemical and mineralogical survey of soils of the conterminous United States. Sampling and analytical protocols were developed at a workshop in 2003, and pilot studies were conducted from 2004-2007 to test and refine these recommended protocols. The final sampling protocol for the national-scale survey included, at each site, a composite sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, from a depth of approximately 80 to 100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total, or near-total, elemental content. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling in the conterminous U.S. was completed in 2010 with chemical and mineralogical analyses completed in May, 2013. The resulting data set provides an estimate of the abundance and spatial distribution of chemical elements and minerals in soils of the conterminous U.S. and represents a baseline for soil geochemistry and mineralogy against which future changes may be recognized and quantified. This report (1) describes the sampling, sample preparation, and analytical methods used; (2) gives details of the quality control protocols used to monitor the quality of chemical and mineralogical analyses over approximately six years, and (3) makes available the soil geochemical and mineralogical data in downloadable tables.

This data layer is a GIS representation of soil quality information which has been submitted to the Department and deemed acceptable to be considered background. The data was originally obtained through publications or as a part of site assessment activities. The data has not been altered in any way. Please reference the metadata for contact information.

A soil-water balance model (SWB) was developed to estimate potential recharge and irrigation water demand from the groundwater flow system in Florida and parts of Georgia, Alabama, and South Carolina for the period 1895 through 2010. This SWB model executable code detailed in the report SWB—A Modified Thornthwaite-Mather Soil-Water-Balance Code for Estimating Groundwater Recharge; Chapter 31 of Section A, Groundwater, of Book 6, Modeling Techniques By S.M. Westenbroek, V.A. Kelson,W.R. Dripps,R.J. Hunt, and K.R. Bradbury (https://pubs.usgs.gov/tm/tm6-a31/) The SWB model was not calibrated; however, various water budget components from the model output compared reasonably well with other estimates including irrigation pumpage quantities reported by the States of Florida and Georgia. Irrigation water demand for 6 crop types (citrus, field crops, hay, sod, sugar cane, and vegetables) were calculated for the period 1950-2010. This USGS data release contains all of the input ...

This dataset contains record of soil type and soil depth across Everglades National Park. This data was obtained through published literature, unpublished field data, and inter-agency collaboration. Data come from either soil cores to bedrock or refusal (as specified by data source), cores that did not reach bedrock, a soil probe to determine depth, or the depth of installed surface elevation tables. Soil type was classified either along a gradient of organic matter content when data was available: mineral (<40%), intermediate (40-70%), or organic (<70%). When data on organic matter content wasn’t available but qualitative descriptions of soil type were, the type unit of soil was classified as either organic or mineral based on the descriptive lithology. When lithologic descriptions were provided and indicated the soil profile was not entirely one soil type (e.g. peat), depth of peat within core was recorded. When data source had specific coordinates, those coordinates were used. For older studies that only provided a figure showing the location of each estimate at a broad scale, location was estimated by georeferencing the figure in ArcMap, and dropping a point at each location. Records were cross-referenced with a recent vegetation map developed by the National Park Service in order to obtained vegetation community at each location (Ruiz et al. 2017).

Soil water and groundwater monitoring support the development of soil water retention curves by the University of Florida Institute of Food and Agricultural Sciences (IFAS) [Contractor] and the correlation of soil moisture data to groundwater levels and canal stages. As part of adaptive water management practices associated with this Project, water level increases are planned to occur by modifying stage at the S-18C structure. The increases are to be incremental in nature by 0.1 ft per step with a total increase of potentially 0.4 ft. It is anticipated that this rise in canal water elevation will influence groundwater levels in adjacent areas. This phenomenon has been reported in research conducted by the University of Florida (Ritter and Muñoz-Carpena, 2006, Journal of Hydrology).

One of the areas affected by project operations is an active agricultural area with a unique geographic location at the foot of the coastal bench. It is considerably lower in land elevation than surrounding farm areas just to the north. Changes in the water table can impact roots of local crops causing reductions in crop yields or complete loss of crops. There is concern on how the planned rise in water elevation in the canal (due to change in operation of S-18C) will influence the usability of adjacent land for agricultural purposes. Currently, agriculture located in the adjacent land is primarily composed of vegetable and ornamental nursery crops. Vegetables are predominantly grown during the dry season while ornamental nursery crops are produced year round. Nursery crops may be containerized or may be planted in the field.

There are three (3) main concerns to address regarding the change in canal elevation: 1) Will the rise in canal water level change soil moisture so that the land is not suitable for current uses? 2) Will the rise in canal water level continue to provide adequate flood protection? 3) How much time should be allowed to lower the water table if a large rainfall event is expected? Soil properties vary within this region, with three (3) dominant soil types: Biscayne gravelly marl, Krome very gravelly loam, and Chekika very gravelly loam. Determination of the soil water retention curve for each of these soils is critical to understanding their soil water holding capacity and drainage characteristics. The soil water retention curve provides a relationship between the soil suction or tension and the soil water volumetric content. The soil water retention curve is developed in the laboratory. This relationship is used to understand soil water dynamics in the field, where real-time soil moisture data at specific sites is collected using soil moisture sensors. The soil moisture data collected can then be correlated with measured groundwater level using hydrostatic assumptions and groundwater level data (Barquin et al., 2009; UF MS thesis). It is also possible to develop a relationship between groundwater level and canal level (Ritter and Muñoz-Carpena, 2006, Journal of Hydrology). The development of these hydrologic relationships depends on the collection and evaluation of quality, georeferenced soil moisture, groundwater level, and canal stage data.

The objectives of this Project are:

1) To collect soil water content and groundwater level data in three (3) dominant soil types in the affected agricultural area near the C-111 canal. 2) To evaluate collected data to determine if relationships can be developed between groundwater level, canal level and soil water content

This study evaluated CO2 flux from soils and pneumatophores of Avicennia germinans mangrove trees subjected to nitrogen and phosphorus fertilization versus an unfertilized control with a basin mangrove ecosystem. Data were collected twice, once in the summer (June 2020) and once in the winter (November 2020) and will be used to help develop a carbon budget for basin mangroves on Sanibel Island, Florida. These data were presented as part of an M.S. Thesis (Florida Atlantic University, Natalie T. Faron, 2021), entitled “The impact of nutrient loading on the soil and root respiration rates of Florida mangroves”.

These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Florida Site Files" data publication.

Site description.

 This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens.

 Field design and sample collection.

 We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI).

 Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C

The spatial datasets within this archive support the Tampa Bay Estuary Program (TBEP) funded Alafia River Historically Mined Lands Watershed Restoration Plan (TBEP Tech Report #08-25, https://drive.google.com/file/d/1dcwlIrubfy43lja8Ovou6P2YOM1cVRV7/view). The purpose of this project was to develop a basin-wide assessment and comprehensive restoration plan to address the enhancement and restoration potential of historically mined lands in the Alafia River watershed. The identified objectives to meet that purpose include: • Creating and facilitating a stakeholder forum (Working Group) that coordinates technical expertise, partnerships and financial resources to address restoration needs in the Alafia River watershed. • Building on recent work by Tampa Bay Water and TBEP to identify and address Alafia River watershed hydrologic, water quality, and habitat needs. • Assessing restoration potential, approaches, and benefits for water quality improvement, flows, and habitat creation/enhancement on historically mined lands. • Developing a plan for restoration of publicly owned historically mined land areas in the Alafia River watershed. This dataset includes all relevant shapefiles and raster files used during the project including information on the extent of historically mined lands and reclamation areas, native habitats and stream channels, soil conditions, hydrology, and restoration and conservation opportunities.

This project was initiated 1998 to examine the variation in percent soil moisture in relation to rainfall, vegetation type, gaps, and time-since-fire in upland habitats at Archbold Biological Station, in south-central Florida. Data were collected from 78 sampling points across four vegetation types (rosemary scrub, scrubby flatwoods, oak-hickory scrub and sandy roadsides) with different time-since-fires (2-3 years or >20 years post-fire). In January 2006, 30 additional sampling points were added to include a fourth vegetation type (southern ridge sandhill). Data were collected at three depths below the soil surface (10, 50 and 90 cm) weekly (1 October 1998 – 9 June 1999), then bi-weekly (23 June 1999 – 3 October 2001), monthly (17 October 2001 – 15 June 2011), and every other month thereafter until the project ended on 16 July 2018.

To address the global challenge of reducing greenhouse gas emissions contributing to climate change, it is essential to explore innovative, renewable, and sustainable energy solutions. Bioenergy, derived from biological sources, plays a vital role by providing renewable options for heat, electricity, and vehicle fuel. Biofuels from food crops like sugarcane and cassava demonstrate the potential of agricultural products for energy generation, while jatropha is cultivated primarily for oil. This learning activity focuses on land suitability mapping for these selected crops in Florida, incorporating criteria such as temperature, rainfall, soil type, soil pH, and topography. The analysis evaluates the land requirements of food and energy crops within the Food-Energy-Water (FEW) nexus framework, addressing potential land-use conflicts. Geographic Information Systems (GIS) are employed to identify optimal regions for energy crop cultivation, promoting sustainable practices that balance food security, water conservation, and renewable energy production. The modules are developed and designed for undergraduate students, particularly those enrolled in any of courses such as environmental science, GIS, natural resource management, agricultural science and remote sensing. Students will apply GIS and remote sensing techniques to analyze interactions among food, energy, and water resources, focusing on resilient crops. The activity incorporates the 4DEE framework – Core Ecological Concepts, Ecological Practices, Human-Environment Interactions, and Cross-Cutting Themes to enhance understanding of the FEW nexus. Through hands-on projects addressing real-world ecological challenges, students will develop critical skills in geospatial data analysis, data interpretation, and ethical considerations, preparing them for sustainable resource management. Likewise on part of the instructors, the activity is designed for those with intermediate to advanced GIS expertise, particularly in ArcGIS Pro and Google Earth Engine for spatial analysis and a basic understanding and application of the Food-Energy-Water (FEW) Nexus to guide students in making informed land-use decisions that support sustainable development goals.

Mapping soil quality (nutrients, carbon quality, process dynamics) has recently been undertaken as part of an effort to characterize baseline conditions for performance assessment of Everglades recovery activities. While large scale maps are useful for characterization of spatial pattern, several layers of uncertainty limit use as measures of performance and restoration progress. In particular, large scale maps (Greater Everglades) make specific assumptions about short range variability that are not well quantified. Nugget variance (variability in space over short separation distances) directly confounds use of baseline map products because future spatial sampling will not, in practicality, be at identical locations. If nugget variability is high, then significant uncertainty about ecosystem change arises from not knowing if observed differences arise from intrinsic ecosystem processes or from responses to human management. Our primary objective is to determine the extent to which spatial variability and sampling uncertainty confound ecological change detection. We will use hierarchically nested sampling of soils to establish nugget variability so that change through time can be assigned as observational uncertainty or management response. A related issue for mapping soil nutrients is greatly improving our understanding of the role of ecosystems in regulating nutrient conditions. Previous mapping efforts regarded space as the primary co-variate with soil nutrients, when there are numerous reasons to expect that ecological type and status are more important descriptors (with spatial autocorrelation playing a secondary, though still significant role). Our second objective is to partition variability in soil nutrient conditions in space and time, by ecosystem type, ecological status, and proximity to canals. We anticipate sampling of soils on a fine scale which traverse ecological transitions will help develop predictive models of the role of ecosystem type, geographic setting and, eventually, spatial structuring in regulating soil processes.

This dataset links to the Long Term Ecological Research (LTER) Florida Coastal Everglades (FCE) Core Research Data Table of Contents (DTOC). The DTOC contains links to 173 individual datasets, which may be queried from the DTOC page. FCE Core Research Data are long-term data sets that address FCE LTER objectives and hypotheses, and that are supported primarily by LTER funds. All data are provided with accompanying metadata. Metadata includes details about the data including how, when and by whom a particular set of data was collected, and information regarding the data format. The FCE practice of dataset versioning has been discontinued as of March 2013. All long-term data will have new data appended to the file and the accompanying metadata will be updated. FCE data may be freely downloaded with as few restrictions as possible. Consultation or collaboration with the original investigators is strongly encouraged. Please keep the dataset originator informed of any plans to use the dataset, and include the dataset's proper citation and Digital Object Identifier (DOI) found under the 'How to cite these data' on the dataset's summary table. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/FlCoastalEverglades_eaa_2015_March_19_1527

This study monitored soil surface elevation change from mangrove forests fertilized with nitrogen and phosphorus from 2018-2021. The mangroves selected at Ding Darling National Wildlife Refuge (NWR) have been previously exposed to high nutrient loading from agricultural discharge into the Caloosahatchee River, which elevated soil phosphorus levels to 3-4 times ambient before treatments were impose. Sea-level rise vulnerability with additional nitrogen and phosphorus is a concern for these mangrove ecosystems.