The Everglades Vulnerability Analysis (EVA) is a series of connected, modular Bayesian networks that predict the response of several Everglades indicators of ecosystem health to changes in hydrology, salinity, and the landscape. This release provides the code to update the vegetation module of EVA, validate the updated module, and provides the process and outputs of a sensitivity analysis of the module. Key updates include expanding the number of vegetation classes predicted from 6 to 11 classes, simplifying the inputs to the module, and increasing the number of vegetation observations used to parameterize the network. The validation of the module includes the process to calculate receiver operating characteristic curves and their associated area under the curve values, multi-class Brier scores, and classification error loss from a 10-fold cross-validation on the network. The sensitivity analyses explore the period of record under scenarios of altered hydrology or salinity and determine the most likely vegetation outcome given the proportion of states within the period of record being explored.

The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models, web applications, and decision support tools that generates daily water-level data and derived hydrologic data across the freshwater part of south Florida's Greater Everglades. EDEN provides continuous daily water-level and depth surfaces on a 400-meter grid using an interpolation algorithm, a network of over 200 gaging stations, and a digital elevation model (DEM). The water-level surfaces cover an area of 9,132 square kilometers and the water depth surfaces cover an area of 7,491 square kilometers. For a subset of gaging stations, ground elevation measurements were taken to better understand the elevation in the area surrounding the gaging station. The mean, maximum, and minimum ground elevation measurements are provided for the area within a 10-meter radius of the water level gaging station. The major vegetation community type was also recorded. Within a 400-meter radius, a secondary vegetation community type was recorded when possible, along with mean, maximum, and minimum ground elevation measurements.

The Everglades National Park vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service Vegetation Mapping Inventory Program (NPS VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park (EVER) prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within EVER as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in resource management, research, and monitoring. The vegetation map of EVER covers an area of 4,482.2 square kilometers (1.108 million acres [ac]) and consists of four mapping regions: Region 1 – Shark River Slough/Long Pine Key; Region 2 – The Southeast Saline Everglades; Region 3 – The Southwest Coastal Everglades; and Region 4 – The Northwest Coastal Everglades. Region 1 was mapped by the SFWMD and USACE while Regions 2-4 were mapped by the South Florida Caribbean Network (SFCN). Photo-interpretation on the map was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery, acquired by the SFWMD in 2009, on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed for this project. Additionally, photo-interpreters categorized the absolute cover of invasive species and cattails (Typha sp.) detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%).

This dataset consists of a file geodatabase containing the digitized ecotone boundaries between mangroves and freshwater marsh in 2013 and 2019 at 14 systematically selected segments in Big Cypress National Preserve and Everglades National Park (southwest Florida, USA).

The Comprehensive Everglades Restoration Plan (CERP) requires the use of ecological indicators to measure the success of restoration efforts. The Everglades amphibian community is ideal because amphibians are present in all habitats and under all hydrologic regimes. During Everglades restoration, hydrologic patterns will change and the response of ecological indicators will determine success. Fourteen amphibian species were detected through visual encounter surveys, vocalization surveys and trapping methods throughout the study and the occurrence information collected in this project database.

The Comprehensive Everglades Restoration Plan (CERP) requires the use of ecological indicators to measure the success of restoration efforts. The Everglades amphibian community is ideal because amphibians are present in all habitats and under all hydrologic regimes. During Everglades restoration, hydrologic patterns will change and the response of ecological indicators will determine success. Fourteen amphibian species were detected through visual encounter surveys, vocalization surveys and trapping methods throughout the study and the occurrence information collected in this project database.

The Comprehensive Everglades Restoration Plan (CERP) requires the use of ecological indicators to measure the success of restoration efforts. The Everglades amphibian community is ideal because amphibians are present in all habitats and under all hydrologic regimes. During Everglades restoration, hydrologic patterns will change and the response of ecological indicators will determine success. Fourteen amphibian species were detected through visual encounter surveys, vocalization surveys and trapping methods throughout the study and the occurrence information collected in this project database.

The Comprehensive Everglades Restoration Plan (CERP) requires the use of ecological indicators to measure the success of restoration efforts. The Everglades amphibian community is ideal because amphibians are present in all habitats and under all hydrologic regimes. During Everglades restoration, hydrologic patterns will change and the response of ecological indicators will determine success. Fourteen amphibian species were detected through visual encounter surveys, vocalization surveys and trapping methods throughout the study and the occurrence information collected in this project database.

The Everglades Depth Estimation Network (EDEN) produces daily depth estimates for the Greater Everglades. This data release includes geospatial data to produce depth estimates for the EDEN from updated digital elevation models. The data release includes three main types of data: 1) 10-m digital elevation models (DEMs) with elevation uncertainty treatment; 2) 50-m DEMs with elevation uncertainty treatment; and 3) spatial metadata for the DEMs used. These data address elevation error by using Monte Carlo simulations with 1,000 iterations with observations of elevation error in vegetated wetlands and assumptions error in vegetated non-wetland areas and non-vegetated areas. On a per-pixel basis, we created raster surfaces that represented the minimum elevation, maximum elevation, and percentiles (1 to 99). We determined the “best” elevation percentiles for each EDEN zone (Haider and others, 2020) based on the mean bias error, which was calculated for the difference between the USGS high-accuracy elevation dataset (HAED; Jones and Price, 2007) and the DEM. In this case, the percentile DEM with the mean bias error closest to zero for each zone was selected. All zones were combined to create a seamless mosaic. For each zone, upper and lower elevation estimates were determined based on a general rule that selected the percentile that was the farthest from the “best” percentile but had a mean bias error that was within (+/-) 5 cm. Areas in lower and upper estimate DEMs that have “NoData” values indicate that there was no percentile that could be used to satisfy this rule. For example, a zone may not have a lower estimate if the “best” estimate was the minimum raster. A zone may not have an upper estimate if the next percentile had a mean bias error that was greater than 5 cm. In version 2.0, we resolved issues with overestimation of ground elevation, which led to underestimated water depths, in parts of Water Conservation Areas by using a land cover map and estimated depth at the time of light detection and ranging (lidar) data. For more information, see the processing steps.

This data was collected as part of a sampling protocol to monitor ecotonal movement of mangroves and upslope freshwater marshes in Everglades National Park and Big Cypress National Preserve. Remote sensing approaches are used to delineate the physical location of the ecotone (boundaries) between mangroves and freshwater marsh. Fourteen 3 kilometer (1.9 mile [mi]) segments are systematically placed along the ecotone within the two parks. Aerial imagery is used to digitize the mangrove-marsh ecotone in each segment and must be 1 meter (3.3 feet [ft]) or better resolution and have positional accuracy of ± 3 meters (9.8 feet [ft]) or better. Four ground-truth field point locations (marker points) are established approximately 1 kilometer (0.62 miles [mi]) apart along the ecotone of each segment to provide field-based measurements and ground-truthing of the digitized line. Vegetation data is collected at each sampling point, with this vegetation coverage determining where the placement of the ecotone should be. generally, this is where mangrove cover drops below 50% cover. The vegetation data also verifies if it is mangroves (typical Red) that is the dominant tree on the tress side of the ecotonal edge. The vegetation data also indicated the dominant graminoid on the more inland side of the ecotone. We have found 3 major groups for the marsh dominant vegetation type. This could be important mitigating factor or a indicator of future conditions. NOTE - Data for this data release was pulled from the mangrove marsh ecotone database and preliminarily cleaned and organized in May 2025. The data should undergo further QAQC before being used in analysis.