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 Bayesian networks that models the landscape-scale response of indicators of Everglades ecosystem health to changes in hydrology and salinity on the landscape. Using the uncertainty built into each network, it also produces surfaces of vulnerability in relation to user-defined ‘ideal’ outcomes. This dataset includes the code used to build the modules and generate outputs of module outcome probabilities and landscape vulnerability.

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0-1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

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.

Ecological models facilitate evaluation 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. 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 with days since drydown (DSD) metric, and (6) Alligator Habitat Suitability Index (HSI). This is round 5 of ecological modeling for the WERP.

The thematic accuracy of the EVER vegetation map was assessed using randomly selected accuracy assessment (AA) points/grid cells across all four mapping regions. These AA points were collected between 2010 and 2013 to be as close as possible to the 2009 flight dates of the imagery used in mapping - specifically to reduce the impacts of natural community succession or perturbation events, like fires and windstorms. These data were collected from helicopter by a trained botanist who could identify South Florida plants and plant communities. The overall vegetation code was derived from the species level percent cover information as well as plot level information on tree, shrub, and herb cover and canopy height. These points were not available to the vegetation project manager, lead photo-interpreter, and the photo-interpreters working on the project until after the map was finished and the final map accuracy had been computed.

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

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. The models of particular interest to the South Florida Water Management District for planning for the Everglades Agricultural Area (EAA) Reservoir were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator, (2) Florida apple snail (native) population model (EverSnail), (3) Wader Distribution Evaluation Modeling (WADEM), (4) Small-sized freshwater fish density, and (5) American alligator production probability (i.e., habitat suitability index (HSI)). We ran these models using hydrologic conditions (provided by the South Florida Water Management District, see Process Steps section below) for baseline and future conditions for the EAR.

Geodesign Technologies conducted an initial assessment of the development likelihood and conservation priority for the Everglades Headwaters National Wildlife Refuge and Conservation Area study region in central Florida. Geodesign used two prior analyses as the basis for this assessment, both of which are at a statewide Florida scale. The University of Florida's CLIP3 (Critical Lands and Waters Identification Project 3.0; Oetting et. al 2014) was the basis for the biodiversity assessment, and their prior statewide scenario simulations (Vargas et al. 2014) were used as an indicator of likelihood of development under a suite of divergent statewide policies. References: 1. Oetting, J., T. Hoctor, and M. Volk. 2014. Critical Lands and Waters Identification Project (CLIP): Version 3.0. Technical Report - February 2014. 110 pp. 2. Vargas, J.C., Flaxman, and B. Fradkin. 2014. Landscape Conservation and Climate Change Scenarios for the State of Florida: A Decision Support System for Strategic Conservation. Summary for Decision Makers. GeoAdaptive LLC, Boston, MA and Geodesign Technologies Inc., San Francisco CA. 22 pp.