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. 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.

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.

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.

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.

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.

A biophysical approach to modeling overland flow in the Everglades can help predict future outcomes for ecological habitat, water storage during droughts, and water conveyance during floods. The data provided include measurements of vegetation stem architecture, microtopography, and landscape pattern metrics. Stem architecture measurements present the opportunity to estimate flow roughness of distinct vegetation communities based on hydraulic principles. At a larger scale, the microtopography and the connectivity of the sloughs between ridges offer a way to quantify the effects of flow blockage and tortuous flow paths on overland flow. Combined with theory, these data provide the capacity to simulate overland flow in both the historic, pre-drainage Everglades as well as in the present-day managed Everglades. Also provided are the hydrologic data, e.g., water slopes, water depths and overland flow velocities, that can be used to verify a biophysical model. Ultimately, the purpose is to anticipate how changing flow and water depth will interact with evolving vegetation and landscape conditions to influence future water availability for society and for the ecosystem, both in the Everglades and in other low-gradient floodplains.

Data Release from the High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2018. Data were obtained from field sites located in the Everglades between two canals (L-67A and L-67C) from 2010 to 2018. During this time, five major controlled flow releases occurred by opening the culvert S152 on canal L-67A. Data consist of water velocity (continuous and discrete), water levels (continuous and discrete), suspended sediment concentration, load and flux (discrete), suspended phosphorus concentration, load and flux (discrete), grainsize distribution (continuous and discrete), biogeochemistry (discrete), water quality (continuous), temperature (continuous) and vegetation (discrete).