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.

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.

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.

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.

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).

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).

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.

Data were collected between 2010 and 2022 in a research area of the Everglades known as the Decompartmentalization Physical Model (DPM), a wetland area in the central Everglades that includes canals and levees bordering Water Conservation Area 3A (WCA-3A) to the northwest and Water Conservation Area 3B (WCA-3B) to the southeast. During the twelve-year study period more than ten major controlled flow releases occurred by opening the S-152 culverts on canal L-67A that released experimental high flows through the wetland. Data consist of water levels (continuous and discrete), water velocity (continuous and discrete), bed shear stress (discrete), suspended sediment concentration (discrete), dissolved phosphorus concentration, load, and flux (discrete), suspended phosphorus concentration, load and flux (discrete), grainsize distribution (continuous and discrete), related biogeochemistry (discrete), water quality parameters (continuous), temperature profiles (continuous), microtopography (discrete), and vegetation community type and stem density (discrete).