Home | Archived October 29, 2018 | (i) |
Frank J. Mazzotti
This included: 1. Compile a list of studies and data sets relating to alligators in south Florida. 2. Determine the accessibility of data sets. Rank the data sets as to their importance and need for compilation (rankings will be made in cooperation with BRD modeling staff and crocodilian researchers and mangers). 3. Obtain and compile at least the 3 highest ranking data sets. 4. Develop a standardized format for collecting and managing data on alligators. 5. Develop a project plan for obtaining the remaining data sets and producing a digital library of historic reports. 6. Use the historical data assembled above to develop a method and to compare body condition among alligator populations in south Florida both spatially and temporally.
Evaluating long-term trends and developing population models require a large amount of data collected over a number of years and a number of locations. Information on alligator densities, nesting and growth have been collected in south Florida since the 1950s by rangers and researchers in Everglades National Park and Big Cypress National Preserve, Florida Fish and Wildlife Conservation Commission personnel, University researchers, and private consultants. Many of the most critical data sets (those having the largest amount of data or those from particular areas or years) are not accessible for use in evaluating restoration alternatives or developing models. The data are not available in a centralized, easily accessible, well documented database. Further, the size and scope of these data sets are not fully known. Certainly, thousands of individual records need to be evaluated, compiled, and entered into an appropriate database.
It is critical that these data sets are accessible to establish restoration targets for alligator populations, develop models, and design short and long-term monitoring tools for evaluating restoration success.
Brandt, Laura A.
Steven M. Davis and John C. Ogden, editors
Gutreuter, S. J.
The full article is available via journal subscription or single article purchase. The abstract may be viewed on the American Fisheries Society website
Kushlan, J. A.
The full article is available for single article purchase. The abstract may be viewed on the Wiley InterScience website
Jacobsen, T.
Brown, M. L.; Springer. T. A.
The full article is available via journal subscription or single article purchase. The abstract may be viewed on the American Fisheries Society website
Anderson, R. O.
Permits for alligator capture were obtained in 1999 from the following agencies: 1. Loxahatchee National Wildlife Refuge. 2. Florida Fish and Wildlife Conservation Commission. 3. Everglades National Park. 4. Big Cypress National Preserve.
We have conducted alligator capture and measurements for current alligator condition throughout the Everglades Ecosystem. Animals have been captured from Loxahatchee NWR, WCA 2A, WCA 2B, WCA 3A North, WCA 3A South, Everglades National Park (Shark Slough and estuarine areas), and Big Cypress National Preserve.
We have developed an initial condition model for comparison of current alligator condition to historical information in the collected data sets.
Historical Data Sets. -- A list of historic and current alligator projects and data sets will be compiled by sending a questionnaire to FFWCC, NPS, USFWS, University researchers, and private consultants who are currently or who have conducted research on alligators in south Florida. The questionnaire will ask for the project title, type of data, project dates, project PIs and current addresses, location and form of data (e.g. field notes, computer file etc.), and a list of reports in which the data are used. Each project will be evaluated as to the relevance of the data to restoration success criteria, modeling, and monitoring efforts, the amount of data, and the effort needed to get the data into a usable form. Based on the above information the projects will be ranked in order of importance.
The 3 highest ranking data sets will be obtained and compiled during the first year. This may involve physically retrieving field notes from locations outside of south Florida, entering data from field notes, reading data from old tapes, converting computerized data sets into a form usable by our chosen database housing software (ORACLE, ACCESS), and reviewing data sets with the PIs.
Guidelines for establishing standardized databases for the alligator research projects will be developed. This will include metadata requirements. These guidelines will be distributed to all researchers working on alligators in south Florida so that data currently being collected can be easily combined with historic data sets.
A project plan will be developed for obtaining and compiling the remaining data sets based on the successes and challenges experienced during the first year. In addition, a plan will be developed to collect and make available in digital form historic reports and papers on alligators in south Florida. The present proposal has been developed as a 1 year project. However, the project plan outlined above will design and discuss a more thorough and longer term project.
Condition. -- The definition of a reasonable "condition factor" is not trivial. This is true in part because our informal evaluations are often normative. For example, we proclaim that one animal is in good condition; another, we say, looks terrible. Even when applied to individuals within one population these terms are not objectively informative. In crocodilians we tend to believe that fat is good. Amongst crocodilians it is probably true that fatter females do produce larger clutches in a given year; however we have no strong evidence that their lifetime productivity is higher. Furthermore, even when our condition-assessments have been value-free, they have usually been qualitative rather than quantitative. So long as our definitions of condition remain unquantified, we shall confront serious difficulties when we attempt to compare across populations.
Fisheries biologists routinely face the task of evaluating various populations of a target species. Consequently they have been assiduous in their quest for appropriate measures of condition (Anderson and Gutreuter, 1983; Carlander, 1977; Gabelhouse, 1984; Wege and Anderson, 1978). Clearly this enterprise has two components. The preliminary problem is to define the condition of an individual animal. The more complex objective is to establish a protocol for comparison across populations.
Study areas include sampled sites from each major compartment in the Everglades: ARM Loxahatchee NWR, Water Conservation Area 2, Water Conservation Area 3, Everglades National Park, and Big Cypress National Preserve. Animals were captured by cable nooses from airboats at night. For each animal, we measured total length (TL), snout-vent length (SVL), tailgirth, chestgirth, neckgirth (all in cm), and mass (kg). We also determined the sex of each alligator, and we noted any deformities, particularly the loss of tail tips. Because condition among small alligators appears to be unstable over time, we included only animals with SVL > 50cm.
We must obtain suitable length-mass samples from a reasonable number of separate populations (perhaps at least 5-10; Murphy et al. [1990] examined 16 populations of largemouth bass and 114 populations for walleye). To be of value, these samples should probably measure at least 30-50 crocs (samples for Murphy et al. [1990] ranged from 12 to 5984) and must include animals from a broad spectrum of SVL’s. This same technique would be suitable for evaluating populations’ conditions across time rather than space.
(2) The most important datasets have been identified. Several have been acquired and assimilated into an ACCESS database. Other databases have been identified and are being acquired;
(3) We have evaluated alligator condition throughout the Everglades Ecosystem in response to hydrology. Animals have been captured from Loxahatchee NWR, WCA 2A, WCA 2B, WCA 3A North, WCA 3A South, Everglades National Park (Shark Slough and estuarine areas), and Big Cypress National Preserve.
Model Constraints
1. Spatial Constraints. - The spatial resolution for the model is 500 meters by 500 meters. All data (water depth, vegetation type, ground elevation, breeding indices) represent values for a 500x500 meter area.
2. Temporal Constraints. - The temporal resolution for the model is one day for all water data (height and depth) and is static for the vegetation habitat types. The model produces a single yearly value for each spatial cell that takes account of the daily water data affecting the nesting and offspring production during that year.
Model Components consist of:
1. Breeding. - Water levels encountered during the period ranging from May 16 of the current nesting year to April 15 of the previous year are used as an indicator of the probability of breeding occurrence in an area. The probability that nesting will occur correlates positively with the amount of time spent in flooded conditions during this period. This model component is defined to be the proportion of this period for which there was water depth greater than 0.5 feet.
2. Nest Construction. - The mean water depth during the peak of the mating season from April 16 through May 15 is used as an indicator of the probability that mating and nest construction will occur in a given area. Two linear functions are applied to indicate the value of this model component such that the highest probability of nest construction occurs at a mean level of 1.3 feet. Mean water depth values higher or lower than this reduce the probability of nest construction.
3. Nest Flooding. - The probability of a nest being flooding is calculated from a combination of the mean water level during nest construction and the maximum water level during egg incubation. Field observations indicate that the mean water level between June 15 and June 30 will determine the elevation at which a nest will be constructed. A linear function is applied to the difference between the maximum water level during the the egg incubation period (July 1 through September 1) and the mean water level during nest construction to give the probability of nest flooding.
4. Relative Habitat Quality - Available evidence suggests that the type of vegetative cover and elevation within an area greatly influence the probability of nesting. This model uses a static ranking of the dominant vegetation type within a 500 meter spatial cell as a measure of habitat quality.
5. Output - The overall API is calculated as a weighted product of the above described model components. This uses (1 - the probability of nest flooding) in the product and applies highest weight to the nest flooding component, a lower weight to the breeding and nesting components, and the lowest weight to habitat quality factor. All output is produced as maps in the standard ATLSS format, comparing one hydrologic scenario to another and displaying a map of the differences between the two scenarios.
This metadata record may have been copied from the SOFIA website and may not be the most recent version. Please check <https://sofia.usgs.gov/metadata> to be sure you have the most recent version.
U.S. Department of the Interior, U.S. Geological Survey
Comments and suggestions? Contact: Heather
Henkel - Webmaster
Generated by mp version 2.8.18 on Tue Nov 02 13:54:02 2010
Home | Archived October 29, 2018 |