This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries. Please reference the metadata for contact information.

The Functional Wetlands dataset is based on wetlands identified in the Cooperative Land Cover Map v3.3. Functional wetlands are defined as those in a more natural state and the prioritization is based on overlap with a Land Use Intensity index and FNAI Potential Natural Areas. For more information see the Conservation Needs Assessment Technical Report: https://www.fnai.org/conslands/florida-forever. Value 1 = Priority 1 (Highest); Value 2 = Priority 2; Value 3 = Priority 3; Value 4 = Priority 4; Value 5 = Priority 5; Value 6 = Priority 6

Wetland locations and classifications as defined by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI). These data were digitized from the 1:24,000 NWI maps. This service is for the Open Data Download application for the Southwest Florida Water Management District.

Geospatial data about Sarasota County, Florida Wetland. Export to CAD, GIS, PDF, CSV and access via API.

The AHF system has been deployed in a series of survey campaigns to collect over 60,000 points covering Everglades National Park, Loxahatchee National Wildlife Refuge, Water Conservation Areas 2 and 3, portions of Big Cypress National Preserve, as well as areas along the Lake Okeechobee littoral zone. Since the AHF System is able to penetrate Everglades vegetation and water cover, it has provided an unprecedented regional view of Everglades topographic gradients and sub-water surface structure. These data are now being used to simulate Everglades water flow with higher resolution and greater accuracy, to estimate water depths in real-time for field study planning, and as input for habitat models used to forecast the effects of water level changes on various important species. The elevation data collected through this project also formed the basic input to generate a regional topographic surface that is the basis for the Everglades Depth Estimation Network (EDEN). These high accuracy elevation data are made available to anyone through the South Florida Information Access website (http://sofia.usgs.gov) data exchange pages.

MAP Activity Accomplishment The USGS Airborne Height Finder (AHF) System was used to perform topographic surveys in Water Conservation Area 3A within the extents of the Lone Palm Head and North of Lone Palm Head 7.5-minute topographic map quadrangles as specified in the MAP/COE Interagency Agreement. The AHF system has been used throughout South Florida for elevation data collection because traditional surveying methods are too difficult, too costly, or simply impossible to use in the harsh wetland environment and broadly inaccessible terrain of the Florida Everglades. This is especially true considering the shear size of the hydrodynamic and biological modeling domains. The AHF is a helicopter-based instrument that uses a GPS receiver, a computer, and a mechanized plumb bob to make measurements. These data were post processed to the reference stations that are part of the AHF geodetic control network. For reasons of accuracy, these reference stations are located no more then 15 kilometers from the helicopter during AHF operations. The GPS data were post processed using Ashtech’s PNAV On The Fly (OTF) software to obtain the trajectory of the AHF platform. These results are then processed through an in-house software package that separates the actual survey points and results from the trajectory. The points are manually checked to ensure data accuracy and completeness. Digital elevation models (DEMs) were then generated from the elevation point data. Existing elevation data derived from LiDAR data for this area were replaced with AHF derived DEMs for reasons of vertical accuracy. The DEMs have been posted on the South Florida Information Access (SOFIA) website: http://sofia.usgs.gov/exchange/desmond/desmondelev.html.

The Comprehensive Everglades Restoration Plan (CERP - www.evergladesplan.org), authorized as part of the Water Resources and Development Act (WRDA) of 2000 (U.S. Congress 2000), is an $US8-10 billion hydrologic restoration project for south Florida. CERP includes 68 separate projects to be managed over the next 30 years by the South Florida Water Management District (SFWMD) and the U. S. Army Corps of Engineers (USACE). Restoration Coordination and Verification (RECOVER) is a system-wide program within the CERP to organize and provide scientific and technical support for design, implementation, and assessment of the restoration program. It is the role of RECOVER to develop a system-wide monitoring and assessment plan that will document how well the CERP is meeting its objectives for ecosystem restoration.

Vegetation mapping will be used to document changes in the spatial extent, pattern, and proportion of plant communities within the landscape. This map represents the 2009 baseline land-cover vegetation map of northern Everglades National Park and Big Cypress National Preserve..

A hierarchical vegetation classification model (10 m resolution) was developed for southwest Florida wetlands using a fusion of multispectral and synthetic aperture radar (SAR) remotely sensed imagery. Sentinel-1 and 2 imagery were obtained from Dec 2015-Sept 2017, split into wet and dry seasons, and processed for a range of vegetation and multi-temporal indices for a total of 26 predictor layers. Training datasets included polygons developed from field surveys and high resolution imagery collected from 2010 - 2018. The domain was first split into estuarine and interior wetlands, then an open water, forest, or grassland model (high level) was developed for each wetland type. Finally, classification model that included species and community-level classes (fine level) was created. Mean overall accuracy was 0.90 and 0.80 for the high and low level models, respectively.

This data release includes geospatial data for irregularly flooded wetlands and high marsh and salt pannes/flats along the northern Gulf of Mexico coast from Texas to Florida. Specifically, this release includes seven products: (1) a map highlighting the continuous probability that an area is an irregularly flooded wetland; (2) a map of irregularly flooded wetland probability reclassified into four bins; (3) a map delineating high marsh and salt pannes/flats; (4) a map from Lake Pontchartrain, Louisiana to the Florida Big Bend delineating the coverage of irregularly flooded wetlands that have Juncus roemerianus (Black needlerush) as the dominant vegetation species; (5) a spatial metadata file showing what elevation data were used for specific locations; (6) a supplemental version of the high marsh and salt pannes/flats map that has a second class for high marsh for parts of Texas where succulents and Distichlis spicata were dominant species; and (7) a dataset of supplemental project-specific field reference data collected throughout the northern Gulf of Mexico coast. Collectively, the products in this data release were developed using a two-step approach that utilized the best-available elevation data and satellite data from 2018 to 2020. The first step in the process was to create a probabilistic map of irregularly flooded wetlands using light detection and ranging (lidar)-derived digital elevation models (DEMs), tidal datums, and nuisance flooding levels. Monte Carlo simulations were used to propagate uncertainty in elevation-based data, and existing land cover data were used to restrict the output to coastal wetland areas. Due to the focus of this study on high marsh, these coastal wetland areas did not include tidal forested fresh wetlands. The second step was to delineate high marsh and salt pannes/flats using reference data which included project-specific data collection in collaboration with land managers and other ancillary datasets across the northern Gulf of Mexico coast. These data were combined with Sentinel-1 synthetic aperture radar imagery, multispectral optical satellite imagery from Sentinel-2, DEMs, and the irregularly flooded wetland probability layer to generate a contemporary map of high marsh and salt pannes/flats along the northern Gulf of Mexico coast. This product is the first regional map of these wetland systems across the northern Gulf of Mexico coast.

Tidal flats are non-vegetated areas of sand or mud protected from wave action and composed primarily of mud transported by tidal channels. An important characteristic of the tidal flat environment is its alternating tidal cycle of submergence and exposure to the atmosphere. This GIS data set was created to show a statewide representation of unvegetated tidal flats, compiled from the best available sources. The sources included individual seagrass mapping studies and National Wetlands Inventory (NWI) data for Florida. The NWI was ERASEd using more recent data sources that showed some areas were indeed vegetated. See Source Information for more details on how each source was used.

Vegetation mapping will monitor the spatial extent, pattern, and proportion of plant communities within major landscape regions of the Greater Everglades Wetlands. Specific landscape changes to be monitored that pertain to the CERP include the following: · Changes in the extent and orientation of sloughs, tree islands, and sawgrass ridges as flow patterns, flow volumes, hydroperiods, and water quality are modified in the ridge and slough landscape · Changes in the extent and distribution of cattail as flow patterns, flow volumes, hydroperiods, and water quality are modified in the ridge and slough landscape · Changes in the extent and distribution of exotic plant communities · Changes in the distribution and configuration of tidal creeks, salt marshes, and mangrove forests as changing flow patterns and volumes interact with sea level and salinity in the mangrove estuaries of Florida Bay and the Gulf of Mexico · Changes in the distribution of plant communities in calcitic wetlands, including tussock-forming Muhlenbergia and sawgrass communities in the major breeding locations of the Cape Sable seaside sparrow, as hydrologic gradients change · Changes in the distribution of plant communities of eastern Big Cypress with the removal of L-28 and hydroperiod restoration in the Kissimmee Billy Strand Regional landscape patterns will be monitored using a combination of a transect and sentinel site sampling design (Section 3.1.3.1) and a stratified random sampling design (Section 3.1.3.10). Aerial photo-interpretation is currently the best tool available to produce dependable and accurate maps of the Everglades (Welch et al. 1995, Doren et al. 1999, Rutchey and Vilchek 1999, Richardson and Harris 1995). Aerial photography of the greater Everglades wetland system at a scale of 1/24,000 will be purchased at three-year intervals. Photography will be interpreted and ground-truthed to produce vegetation maps at three-year intervals for the randomly selected cells. Additional cells will be mapped to supplement the stratified random cells along the alignments of the coastal, marl prairie -slough, and WCA gradients that are described above. The vegetation classification scheme of Jones et al. (unpublished report) will be used to identify major plant communities that are defined by typical dominant species.

This group of layers was developed by the Balmoral Group and contains the natural, cultural, and historical resources critical assets layers for Florida as defined in 380.093(2)(a) Florida Statutes. The layers were sourced from various public State of Florida land use, Florida State Parks, State of Florida inventory of historical structures, and Federal Sources. Natural, cultural, and historical resources critical assets include conservation lands, parks, shorelines, surface waters, wetlands, and historical and cultural assets. Typically, the data are utilized in various vulnerability assessments in evaluating the exposure and sensitivity from combined events of sea level rise, precipitation, major storms, and flooding. The data will also be used in efforts to complete a comprehensive statewide assessment for the State of Florida.

For file geodatabase download, Click Here. The South Florida Water Management District (District or SFWMD) and the U.S. Army Corps of Engineers have built six large treatment wetlands, referred to as Stormwater Treatment Wetlands (STAs), in the Everglades Agricultural Area (EAA) as part of a State and Federal initiative to protect the Everglades (Chimney and Goforth, 2001; Sklar et al., 2005). These treatment wetlands are intended to reduce high phosphorus concentrations in surface runoff coming from the EAA before this water reaches the northern portion of the present-day Everglades, i.e., the Water Conservations Areas. Each STA is subdivided into a number of treatment cells by interior levees. Treatment wetlands reduce the concentration of water-borne pollutants through natural bio-geochemical processes (Kadlec and Wallace, 2009). Wetland biogeochemistry, in turn, is intimately associated with the extent and condition of the wetland’s vegetation community (Reddy and DeLaune, 2009). Because of the important relationship between wetland treatment performance and vegetation, the vegetation communities in the STAs have been monitored throughout their operational histories. This effort was mandated as a condition of STA operating permits and by the Process Development and Engineering section of the District’s Long Term Plan (Burns & McDonnell, 2003). The vegetation communities in the STAs have been monitored using two different approaches: (1) vegetation maps were prepared for each STA based on the spatial distribution of different vegetation types interpreted from aerial photographs and (2) field surveys were conducted at a network of sites within each wetland to catalog plant taxa and assess vegetation areal coverage of the dominant taxa. The field-survey program was initiated as a cost-effective alternative to mapping for characterizing the plant community.For information about the imagery collection access this file: 2011 Imagery Collection in STAsThe Survey Report can be accessed from here: 2011 Survey ReportFor details how the data was processed see the Lineage section.

The EnviroAtlas Tampa, FL Meter-Scale Urban Land Cover (MULC) data was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from April-May 2010 at 1 m spatial resolution. Eight land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, water, agriculture, woody wetland, and emergent wetland. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Tampa, and includes the cities of Clearwater and St. Petersburg, as well as additional out-lying areas. An accuracy assessment using a stratified random sampling of 600 samples (100 per class) yielded an overall accuracy of 70.67 percent and an area weighted accuracy of 81.87 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This project consists of 5 files, a ReadMe (text) and four geospatial shapefiles generated in ArcPro 3.0.4. (ESRI). Two shapefiles depict the distribution and spatial extent of artificial water features (AWFs, e.g., reservoirs, stormwater retention ponds) in the 1950s and in 2007 in the Tampa Bay Watershed. The other two depict the distribution and spatial extent of wetlands in the 1950s and in 2007 in the Tampa Bay Watershed. We used a combination of heads-up digitizing (while observing 1950s aerial black and white aerial imagery) and reference to ancillary datasets to map wetlands and AWFs in the 1950s. We based the wetlands 2007 map off the land use land cover dataset published by SWFWMD (2008). We similarly based the 2007 AWF dataset on the SWFWMD LULC dataset but additionally digitized AWF features while referencing aerial imagery and products supplied by the Mosaic Company.Additional method descriptions can be found in Rains et al. 2013 (wetland datasets, https://link.springer.com/article/10.1007/s13157-013-0455-4) and in Rains et al. (2023) titled Reorganizing the waterscape: asymmetric loss of wetlands and gain of artificial water features in a mixed-use watershed (AWF and change datasets). We initially developed the two wetland datasets, in collaboration with the Balmoral Group, to support a wetland area change analysis in the Tampa Bay Watershed. We subsequently used these datasets in our present work (Rains et al. 2023) to analyze change in wetland distribution, configuration, and geometry (e.g., perimeter length). The wetland datasets are not meant as a map of jurisdictional wetlands.

This data set serves as documentation of land cover and land use (LCLU) within the South Florida Water Management District as it existed in 2017-19. Land Cover Land Use data was updated from 2014-16 LCLU by photo-interpretation from 2017-19 aerial photography and classified using the SFWMD modified FLUCCS classification system. Features were interpreted from the county-based aerial photography (4 in - 2 ft pixel), see imagery year in the "AERIAL DATE" field. The features were updated on screen from the 2014-16 vector data. Horizontal accuracy of the data corresponds to the positional accuracy of the county aerial photography. The minimum mapping unit for classification was 0.5 acres for wetlands and 5 acres for uplands. This data is partial and is not considered complete until the entire SFWMD has been completed.Photointerpretation Key: https://geoext.geoapps.sfwmd.gov/TPubs/2014_SFWMD_LULC_Photointerpretation_Key.pdf

This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, wetland, and agriculture. Impervious is a combination of dark and light impervious. Forest is a combination of trees and forest and woody wetlands. Green space is a combination of trees and forest, grass and herbaceous, agriculture, woody wetlands, and emergent wetlands. Wetlands includes both Woody and Emergent Wetlands.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Freshwater wetland features located within the Tampa Bay Estuary watershed and Manatee County during the time period of 1948-1958.This dataset was compiled from a number of different sources. The first is a 1952 National Wetlands Inventory dataset that was created by the USGS's National Wetlands Research Center. The second is 1950 land use/land cover that was created by the Southwest Florida Water Management District. The third dataset was the focus of a recent effort that followed LULC photointerpretation methods to digitize wetlands from 1948-58 imagery. Georeferenced imagery was obtained from 1948 from the SWFWMD and from 1948-52 from the Florida Fish & Wildlife Research Institute. For all remaining areas ungeoreferenced imagery was obtained from the University of Florida's Map and Digital Imagery Library and georeferenced by Water Institute staff.The methods used to develop this data are documented in the following publications:a) Rains, Mark, Shawn Landry, Valerie Seidel and Tom Crisman. (2012). "Prioritizing Habitat Restoration Goals in the Tampa Bay Watershed." Final report to the Tampa Bay Estuary Program, April 2012, St. Petersburg, FL, 69 p. URL: https://www.tbeptech.org/TBEP_TECH_PUBS/2012/TBEP_10_12_USF_Prioritizing_Habitat_Restoration_Goals_2012_04.pdfb) Rains, Mark, Shawn Landry, Kai Rains, Valerie Seidel, Tom Crisman (2013). “Using net wetland loss, current wetland condition, and planned future watershed condition for wetland conservation planning and prioritization, Tampa Bay Watershed, Florida.” Wetlands 33(5): 949-963. DOI: 10.1007/s13157-013-0455-4. URL: http://link.springer.com/article/10.1007/s13157-013-0455-4

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer. The DEMs created for this project were developed using the NOAA National Weather Service's Weather Forecast Office (WFO) boundaries. Because the WFO boundaries can cover large areas, the WFO DEM was divided into smaller DEMs to ensure more manageable file sizes. The Tampa (FL) WFO DEM was split into two smaller DEMs. They are divided along county lines and are: 1. Tampa (FL) WFO - Citrus, Hernando, Pasco, Pinellas, and Hillsborough Counties 2. Tampa (FL) WFO - Manatee, Sarasota, Charlotte, and Lee CountiesThis is Tampa Bay file 2 of 2. This metadata record describes the DEM for Tampa (FL) WFO - Manatee, Sarasota, Charlotte, and Lee Counties. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Manatee, Sarasota, Charlotte, and Lee counties, that met project specifications.The DEM is derived from LiDAR datasets collected for the Florida Department of Emergency Management (FDEM) and the Southwest Florida Water Management District (SWFWMD). The FDEM LiDAR data for Manatee, Sarasota, Charlotte and Lee Counties was collected in 2007 and 2008. Small portions of Manatee and Charlotte Counties were collected in 2005. Hydrographic breaklines used in the creation of the DEM were obtained from FDEM and SWFWMD. In some cases, the National Wetlands Inventory and National Hydrography Dataset were used to supplement breaklines from FDEM and SWFWMD. The DEMs are hydro flattened such that water elevations are less than or equal to 0 meters.The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters.The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise And Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

A central prediction of the current Everglades restoration plan is that the return to natural flows and hydropatterns will result in large, sustainable breeding wading bird populations; a return to natural timing of nesting; and restoration of nesting in the coastal zone. The timing, location, size, and productivity of wading bird nesting will be monitored over the geographic range of the Everglades ecosystem. Monitoring methods will allow for comparison of historical and current information. The geographic regions monitored will include Florida Bay; mangrove estuaries and ecotone; freshwater marshes of ENP; WCAs 1, 2, and 3; Rotenberger and Holey Land; and BCNP.

Nesting of six wading bird species will be monitored: wood stork, white ibis, roseate spoonbill, snowy egret, great egret, and great white heron. These are the species for which the best historical comparisons exist for one or more of the parameters of interest: range of trophic levels, prey sizes, and foraging techniques used (Ogden, 1994; Frederick et al., 1996). Nesting will be monitored between January and late June of each year, with the exception of Florida Bay (November through June). However, there is the possibility that monitoring in the mainland areas will need to be expanded if wood storks begin nesting earlier than January. Evidence of early nesting (eggs or young) is likely to be discovered on January surveys, and timing of surveys will be adjusted accordingly.

The timing, location, and size of nesting events will be monitored using systematic aerial surveys followed by ground counts. Established techniques used in the freshwater marsh sections of the study area (Frederick et al., 2001) will be adapted to specific habitats in Big Cypress and the mainland mangrove estuary. Ground counts will focus on the largest colonies of each species based on the analysis of past years, which suggests that 90% of nesting birds are found on average in 3 to 33 colonies depending on the species (Frederick, personal communication). Accuracy in aerial counts of large colonies will be improved through the use of aerial photography followed by later counts of those photos (Frederick et al., in prep.).

Florida Bay. Roseate spoonbill and ibis nests in Florida Bay are generally located in dense red mangrove stands and are not generally visible from outside the colony. All islands that were previously reported to have had nesting colonies (Lorenz et al., 2001) will be surveyed monthly during the nesting season, and the number of nests will be counted. While traversing Florida Bay by boat, locations of roseate spoonbill and white ibis activity will be investigated for new nesting sites. The timing of colony surveys late in the incubation period and during mild climatic conditions and the limitation of time in an individual colony to less than one hour whenever possible will minimize impacts of surveys on colonies (Lorenz et al., 2001).

Roseate Spoonbill Foraging Location. In order to use nesting effort and nesting success as criteria for ecosystem evaluation, the location of primary foraging grounds must be monitored for each colony group (Lorenz et al., 2001). In order to identify the direction of foraging grounds from nesting colonies, flight line counts similar to those described by Dusi and Dusi (1978) will be made at the two largest colonies in each colony group. Flight line counts will yield an estimate of the proportion of birds using general areas (e.g., eastern, middle, or western mainland sites; mainline keys; etc.). To get more specific foraging locations, individual birds will be followed using a fixed-wing aircraft from their nesting colonies to the first foraging location. Flight line observation and following flights will also greatly aid in identifying new colony sights locations throughout the bay.

Refinement of Nest Survey and Counting Methods. Any periodic surveys are likely to lead to underestimates due to asynchronous nesting and the possibility that nests may start and fail in between survey dates. Comparing typical monthly survey schedules with a large sample of known nesting histories of individual nests shows that the monthly survey schedule that has been followed in the central Everglades since 1986 has been associated with a known correction factor, with annual variation in that correction factor of 26% above and below any annual estimate (Frederick et al., in prep.) Therefore, the resulting nesting population estimates are likely to be associated with this level of error. However, estimation of this error rate is based on only 2 – 4 years of information on marked nests, depending on species. The database of individual nest histories will be expanded in order to refine the estimation of error associated with monthly surveys. This involves close monitoring of individual nests at one or more colonies throughout the nesting season in order to measure both duration and seasonal timing of nesting attempts.

This polygon feature class identifies the extent and type of llittoral vegetation for Lake Istokpoga mapped by SFWMD scientists from May 2015 aerial photography. The Vegetation communities were mapped using digital aerial imagery acquired from an Intergraph DMC Sensor. Mapping was accomplished through the use of Esri software supplemented with fieldwork. Each distinct community of emergent and floating vegetation was mapped according to the Florida Land Use, Cover and Forms Classification System (FLUCCS) as modified by FWC for the purposes of mapping lake vegetation.