Primary lakes in north and central Florida developed from GNIS, USGS 1:24k Hydrography data, 1994 DOQQs, and USGS DRGs and reviewed by DEP and WMD personnel.

Lake Okeechobee is located in south Florida and is bounded by the Kissimmee River Basin to the north and Everglades National Park to the south. Lake Okeechobee is the largest lake (1890 km2) in Florida and encompasses a drainage area of over 14,200 km2. The lake provides agricultural water supply, back-up water supply for urban areas, flood protection to adjacent communities, critical bird and fisheries habitats, is part of the Okeechobee Waterway navigation canal, and boating recreation. Over the past 100 years, land use change and population increases have adversely impacted the health of the lake mostly by extreme water level fluctuations and excessive nutrient loading mostly from agricultural activities. High-resolution bathymetric mapping was conducted in 2001 in Lake Okeechobee by the USGS, in cooperation with SFWMD. High-resolution, acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Survey tracklines were spaced 1000 meters apart and orientated in a north-south direction. Tracklines collected in an east-west orientation (intersecting tracklines) functioned to serve as a cross-check and to assess the relative vertical accuracy of the survey. Ideally, vertical data values at the crossing should be exactly the same. In reality, this is not always the case due to random errors of survey system. Several perimeter survey lines were also collected. Soundings were collected along each trackline at 3-meter spacing. Approximately 1,550 kilometers of survey lines were collected. In shallow areas, data was collected in a minimum of 0.6 meters water depth except where there is potential damage to the bottom environment or the boat/motors was a significant possibility. This report serves as an archive of processed single-beam bathymetry data that were collected in Lake Okeechobee, Florida in 2001. Geographic information system data products include XYZ data, bathymetric contours, USGS quadrangle maps, and associated formal Federal Geographic Data Committee (FGDC) metadata.

A point feature class representing the random site selections within small lakes comprising the small lake sample frames from the cycle 1 to the most recent Status Network cycle. Refer to https://floridadep.gov/DEAR/Watershed-Monitoring-Section for more information on the Status monitoring network.

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A point feature class representing the random site selections within large lakes comprising the large lake sample frames from Cycle 2 to the current Status Network cycle. Refer to https://floridadep.gov/dear/watershed-monitoring-section/content/status-monitoring-network for more information on the Status monitoring network.

This coverage describes the spatial extent of the North and South Lake Vegetation Index (LVI) biological regions, as described in Fore et al. 2007, Assessing the Biological Condition of Florida Lakes: Development of the Lake Vegetation Index (LVI) (ftp://ftp.dep.state.fl.us/pub/labs/lds/reports/15094.pdf). Some component metrics of the LVI are scored differently for each region. The dividing line between the regions also divides the North Central and South Central Climatological Divisions of the United States Department of Agriculture (USDA).

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

Hydrology polygon features in Lee County, Florida digitized from aerial photography.

The Florida Fish and Wildlife Conservation Commission (FWC) collected annual trawl data at 27 open-water sites from 1987 to 1991 (Bull et al. 1995). Nearly 37,000 fish were recorded in 438 10-minute open-water trawls (Bull et al. 1995). Seven species accounted for 98% of the total number and total fish biomass. Clustering of sites based on mean catch of the primary species expressed as number and weight produced four distinct groups. The groups were labeled as the northeast shore, northwest shore, south-southwest shore and open water area. Areal fish distribution patterns also were compared using analysis of variance (ANOVA) and Tukey’s HSD post hoc test. Within the four groups there were significant differences in the distribution of certain fish species. In addition to the open-water trawl sites, the FWC has utilized electrofishing techniques to collect annual largemouth bass (Micropterous salmodies) (LMB) data from 22 near-shore and interior marsh locations since 1999 (Havens et al. 2004). Although the trawl and electrofishing data provide some baseline information, still there is limited data regarding temporal changes in the community structure, density and condition of the primary sport fish LMB, black crappie (Pomoxis nigromaculatus), bluegill (Lepomis macrochirus) and redear (Lepomis microlophus) sunfish) and other fish species in Lake Okeechobee. During this study, fish species will be collected from 49 historic sampling locations. Fish assemblages in the 27 open water regions of the lake will be sampled with an Otter Trawl net. The 22 near-shore and interior marsh sites will be sampled utilizing electrofishing gear. Ancillary data, including water temperature, dissolved oxygen, pH, turbidity, conductivity, and sediment/aquatic plant type will be recorded at the 49 sampling locations.
The two historic sets of non-MAP data will be used to help establish baseline conditions for the near-shore, interior marsh and open-water fishery. It is appropriate to include the non-MAP data in our analysis as current sampling will occur at the historical locations and sampling methods will be similar. We anticipate significant spatial differences in fish abundance and biomass will exist at the near-shore, interior marsh and open water sites. Therefore, similar statistical tests including cluster analysis and analysis of variance should be used to evaluate temporal changes in the near-shore and open water fishery. Detailed statistical analysis should be conducted at a minimum of every three years to evaluate long-term trends and establish relationships between fish distribution, condition, and community structure and environmental conditions including habitat and water depth.
The objectives of this project are to evaluate temporal changes in Lake Okeechobee’s fishery by determining annual changes in the areal distribution, condition, density and community structure (year classes) of all major fish species found in the near-shore, interior marsh and open-water regions of the lake. Ancillary data including water temperature, dissolved oxygen, pH, turbidity, conductivity, and sediment type also will be recorded.

Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system were developed to define an updated hydrogeologic framework as part of the U.S. Geological Survey Groundwater Resources Program. This map layer shows areal and linear water features of Florida, Georgia, South Carolina, and Alabama. The original file was produced by joining the individual State hydrography layers from the 1:2,000,000- scale Digital Line Graph (DLG) data produced by the USGS. This map layer was formerly distributed as Hydrography Features of the United States. This is a revised version of the January 2003 map layer.

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Restaurants in Lauderdale Lakes, Florida. name, maps, price Range, types, city, continent, Country, Website, email, administrative división, address, telephone

These data were automated to provide an accurate high-resolution historical shoreline of Caloosahatchee River - Ft. Myers Shores to Lake Hicpochee, Florida suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation...

Benthic macroinvertebrates play an important role in the Lake Okeechobee ecosystem. They provide food resources for fish, contribute to sediment nutrient recycling, and can serve as sensitive indicators of water quality (Jonasson 1969, Brinkhurst 1965, Warren et al. 1995). Given these overall MAP goals, the evaluation of benthic invertebrate communities of the Lake Okeechobee pelagic region was implemented with the following objectives: 1. Sample Lake Okeechobee sublittoral zone benthic invertebrate communities in three areally dominant habitat zones (mud, sand, peat) twice annually for a three year period, duplicating the timing, locations, and methods of Warren et al. (1995).

1. Use results from sampling to evaluate the pelagic region benthic invertebrate community structure, thereby establishing a baseline for future evaluation and comparison. Elements of community structure to be documented include: taxonomic composition, taxa richness, absolute abundance, relative abundance, diversity (Shannon’s equation, as per Krebs 1999), and evenness (as per Pielou 1977).

2. Compare current (2005-08) pelagic region benthic invertebrate community structure with corresponding structural elements of the 1987-96 study period. Based upon these comparisons, identify changes in invertebrate community structure and speculate on the implications to future invertebrate community health as well as the overall health of the Lake Okeechobee ecosystem.

3. Relate results from the study to CERP hypotheses and apply conclusions to the adaptive management process.

The intent of maintaining continuity with the methods and sampling sites utilized during the 1987-1996 FWC sublittoral zone evaluation (Warren 1991, Warren et al. 1995) was to supplement the pre-CERP implementation baseline (2005-2008 study) with additional data collected over a longer term and over a broader range of environmental conditions.

Benthic macroinvertebrate communities of the Lake Okeechobee pelagic zone were sampled by the Florida Fish and Wildlife Conservation Commission (FWC) on a quarterly (February, May, August, November) basis in 1987-1988 and then semi-annually (January and July) through 1996. Since 1996, invertebrate communities have been sampled intermittenly, with the last collection occurring in June 2000. Sampling was conducted at 18 fixed sites, with six sites each in mud (northern and mid-lake), sand (western), and peat (southern) habitat zones. Three pseudo-replicate samples were collected at each of the 18 sites during every sampling event, yielding 18 samples per habitat zone and a total of 54 samples. Samples were collected with a petite ponar dredge.

Stressed Lakes for 2015. This service is for the Open Data Download application for the Southwest Florida Water Management District.

Restaurants in Lake Park, Florida. name, maps, price Range, types, city, continent, Country, Website, email, administrative división, address, telephone

This is a georeferenced raster image of a printed paper map of the Florida Lake, Alberta region (Sheet No. 083O02), published in 1957. It is the first edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1957 and the information on the map is current as of 1952. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry.

Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay had not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay.

TheFlorida Department of Revenue’s Property Tax Oversight(PTO) program collects parcel level Geographic Information System (GIS) data files every April from all of Florida’s 67 county property appraisers’ offices. This GIS data was exported from these file submissions between March 15 to April 1, 2023. The GIS parcel polygon features have been joined with thereal property roll (Name – Address – Legal, or NAL)file. No line work was adjusted between county boundaries.The polygon data set represents the information property appraisers gathered from the legal description on deeds, lot layout of recorded plats, declaration of condominium documents, recorded and unrecorded surveys.Individual parcel data is updated continually by each county property appraiser as needed. The GIS linework and related attributions for the statewide parcel map are updated annually by the Department every August. The dataset extends countywide and is attribute by Federal Information Processing Standards (FIPS) code.DOR reference with FIPS county codes and attribution definitions - https://fgio.maps.arcgis.com/home/item.html?id=ff7b985e139c4c7ba844500053e8e185If you discover the inadvertent release of a confidential record exempt from disclosure pursuant to Chapter 119, Florida Statutes, public records laws, immediately notify the Department of Revenue at 850-717-6570 and your local Florida Property Appraisers’ Office.Please contact the county property appraiser with any parcel specific questions: Florida Property Appraisers’ Offices:Alachua County Property Appraiser – https://www.acpafl.org/Baker County Property Appraiser – https://www.bakerpa.com/Bay County Property Appraiser – https://baypa.net/Bradford County Property Appraiser – https://www.bradfordappraiser.com/Brevard County Property Appraiser – https://www.bcpao.us/Broward County Property Appraiser – https://bcpa.net/Calhoun County Property Appraiser – https://calhounpa.net/Charlotte County Property Appraiser – https://www.ccappraiser.com/Citrus County Property Appraiser – https://www.citruspa.org/Clay County Property Appraiser – https://ccpao.com/Collier County Property Appraiser – https://www.collierappraiser.com/Columbia County Property Appraiser – https://columbia.floridapa.com/DeSoto County Property Appraiser – https://www.desotopa.com/Dixie County Property Appraiser – https://www.qpublic.net/fl/dixie/Duval County Property Appraiser – https://www.coj.net/departments/property-appraiser.aspxEscambia County Property Appraiser – https://www.escpa.org/Flagler County Property Appraiser – https://flaglerpa.com/Franklin County Property Appraiser – https://franklincountypa.net/Gadsden County Property Appraiser – https://gadsdenpa.com/Gilchrist County Property Appraiser – https://www.qpublic.net/fl/gilchrist/Glades County Property Appraiser – https://qpublic.net/fl/glades/Gulf County Property Appraiser – https://gulfpa.com/Hamilton County Property Appraiser – https://hamiltonpa.com/Hardee County Property Appraiser – https://hardeepa.com/Hendry County Property Appraiser – https://hendryprop.com/Hernando County Property Appraiser – https://www.hernandopa-fl.us/PAWEBSITE/Default.aspxHighlands County Property Appraiser – https://www.hcpao.org/Hillsborough County Property Appraiser – https://www.hcpafl.org/Holmes County Property Appraiser – https://www.qpublic.net/fl/holmes/Indian River County Property Appraiser – https://www.ircpa.org/Jackson County Property Appraiser – https://www.qpublic.net/fl/jackson/Jefferson County Property Appraiser – https://jeffersonpa.net/Lafayette County Property Appraiser – https://www.lafayettepa.com/Lake County Property Appraiser – https://www.lakecopropappr.com/Lee County Property Appraiser – https://www.leepa.org/Leon County Property Appraiser – https://www.leonpa.gov/Levy County Property Appraiser – https://www.qpublic.net/fl/levy/Liberty County Property Appraiser – https://libertypa.org/Madison County Property Appraiser – https://madisonpa.com/Manatee County Property Appraiser – https://www.manateepao.gov/Marion County Property Appraiser – https://www.pa.marion.fl.us/Martin County Property Appraiser – https://www.pa.martin.fl.us/Miami-Dade County Property Appraiser – https://www.miamidade.gov/pa/Monroe County Property Appraiser – https://mcpafl.org/Nassau County Property Appraiser – https://www.nassauflpa.com/Okaloosa County Property Appraiser – https://okaloosapa.com/Okeechobee County Property Appraiser – https://www.okeechobeepa.com/Orange County Property Appraiser – https://ocpaweb.ocpafl.org/Osceola County Property Appraiser – https://www.property-appraiser.org/Palm Beach County Property Appraiser – https://www.pbcgov.org/papa/index.htmPasco County Property Appraiser – https://pascopa.com/Pinellas County Property Appraiser – https://www.pcpao.org/Polk County Property Appraiser – https://www.polkpa.org/Putnam County Property Appraiser – https://pa.putnam-fl.com/Santa Rosa County Property Appraiser – https://srcpa.gov/Sarasota County Property Appraiser – https://www.sc-pa.com/Seminole County Property Appraiser – https://www.scpafl.org/St. Johns County Property Appraiser – https://www.sjcpa.gov/St. Lucie County Property Appraiser – https://www.paslc.gov/Sumter County Property Appraiser – https://www.sumterpa.com/Suwannee County Property Appraiser – https://suwannee.floridapa.com/Taylor County Property Appraiser – https://qpublic.net/fl/taylor/Union County Property Appraiser – https://union.floridapa.com/Volusia County Property Appraiser – https://vcpa.vcgov.org/Wakulla County Property Appraiser – https://mywakullapa.com/Walton County Property Appraiser – https://waltonpa.com/Washington County Property Appraiser – https://www.qpublic.net/fl/washington/Florida Department of Revenue Property Tax Oversight https://floridarevenue.com/property/Pages/Home.aspx

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.