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 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 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. We created a soil depth layer for use in the sawgrass peat module by using universal kriging to estimate soil depth at a 400-meter resolution for individual management areas across the Greater Everglades footprint. We compiled soil depth data from various sources totaling 687 coordinate point locations across all sampling years which ranged from 1964 to 2018. We calculated mean soil depth when multiple depths were documented at the same location. We examined soil depth within each management area for normality and used a cube root transformation on the data to normalize the distribution where needed. We also observed spatial trends in soil depth, and therefore we examined easting and northing as covariates when kriging. We used the ‘autoKrige’ function from the R package automap to fit variograms with easting and northing and to perform kriging. We selected the best fitting model, back-transformed, and bias corrected interpolated depths and used the kriged depths to create the soil depth layer.

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. Specifically, this dataset includes accuracy information by zone. 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 the 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.

One of the largest and most expensive restoration efforts in the world is occurring in the Everglades, a sub-tropical freshwater wetland system located in southern Florida. This unique ecosystem supports several endemic and endangered species, provides flood control for Florida's large urban population, and provides water for both agriculture and drinking supply within the state. The Comprehensive Everglades Restoration Plan (CERP), authorized by Congress in 2000, guides federal, state, and local efforts to build the infrastructure necessary to bring more water into the Everglades and restore its ecological integrity. The Everglades encompasses the southern coast of Florida and restoration efforts are likely to be impacted by climate-induced sea-level rise. However, currently, many project planning studies do not formally incorporate the potential impacts of sea-level rise when evaluating restoration plan outcomes. Resource managers and project planners require methods and tools to confidently incorporate scenarios of sea-level rise into their evaluations. This effort demonstrates how incorporating sea-level rise scenarios into Everglades restoration project planning can help managers decide whether projects will maintain or improve the ecological integrity of this critical system and ensure water availability for wildlife and humans. The following model outputs were generated to explore how sea-level rise may impact ecological models: two sea-level rise scenarios (an intermediate scenario of 53 cm and a high scenario of 152 cm) using the BISECT hydrodynamic model, and the Everglades Vulnerability Analysis vegetation sub-model outputs generated using the baseline, intermediate, and high sea-level rise hydrologic scenarios. We also provide R code used to visualize the outputs.

These data are summaries and comparisons of the EverForecast outputs from May 2021. EverForecast is a near-term hydrologic forecasting application that provides daily water depth forecasts across the freshwater Everglades (Pearlstine et al. 2020); water depth forecasts are then used to run species models. Here, we examine the EverForecast outputs of five species models: (1) American alligator production probability (i.e., habitat suitability index (HSI)), (2) Florida apple snail (native) population model (EverSnail), (3) Cape Sable Seaside Sparrow probability of presence model (EverSparrow), (4) small fish density model, and (5) wading bird probability of presence model (EverWaders). These species model outputs are summarized on a biweekly (14 day) time step for each EverForecast region into three hydrologic categories relative to the full forecast: (1) low depth, (2) medium depth, (3) high depth. The outputs show tradeoffs among species when selecting hydrologic conditions to prioritize the ecological conditions for one species over others.

Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities, and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Apple Snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) American alligator habitat suitability index (HSI). This is round 1 (of 4) for the WERP.

Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Florida apple snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) Alligator Habitat Suitability Index (HSI; includes Breeding Potential metric). This is round 4 (of 4) in the WERP.

Operational ecological forecasting is an emerging field that leverages ecological models in a new, cross-disciplinary way, using a real-time or nearly real-time climate forecast to project near-term ecosystem states. These applications give decision-makers lead time to anticipate and manage state changes that degrade ecosystem functions or directly impact humans. The Everglades Forecasting model (EverForecast) is an operational water stage forecast providing 6-month forecasts of daily projected, spatially continuous stage values across the Water Conservation Areas, Big Cypress National Preserve, Everglades National Park, Big Cypress Seminole Indian Reservation, and Miccosukee Federal Indian Reservation and Leased Lands. The forecast provided here starts on April 13, 2020 and ends on October 12, 2020. It includes the central tendency from the spatial position analysis and the Monte Carlo simulation outputs and processes to generate these data.

Due to their position at the land-sea interface, coastal wetlands are sensitive to sea-level rise and many other aspects of global change. Small changes in coastal wetland surface elevation can lead to comparatively large changes in coastal wetland ecosystem structure and function, and in some cases wetland loss. The surface elevation table (SET)-marker horizon (MH) approach (SET-MH, together) is a method for quantifying net wetland surface elevation change while accounting for the relative contributions of various biological, geological, and hydrological processes that can occur within different segments of the soil profile (e.g., deep, shallow subsurface, and surface soil depths). This data release includes long-term and high temporal resolution surface elevation table (SET) and marker horizon (MH) data from nine study sites in Everglades National Park. All data files and their associated metadata documentation can be found within "Everglades NP SET-MH Data (ver 2.0, July 2019).zip". First posted August 10th, 2017 Revised July 1st, 2019, ver 2.0