The relationship of student and teacher variables on academic achievement in Florida schools

Since 1995, Florida has been one of the leading states in the country initiating a high-stakes school accountability system. Public schools in Florida receive letter grades based on their performance on the Florida Comprehensive Assessment Test (FCAT). These school grades have significant effects on schools' reputations and funding. Consequently, the plan has been criticized for grading all schools in the same manner, without taking into account such variables as student poverty and mobility rates which are beyond the control of the school. ^ The purpose of this study was to examine the relationship of student variables (poverty and mobility rates) and teacher variables (average years of teacher experience and attained degree level) on FCAT math and reading performance. This research utilized an education production function model to examine which set of inputs (student or teacher) has a stronger influence on student academic output as measured by the FCAT. ^ The data collected for this study was from over 1500 public elementary schools in Florida that listed all pertinent information for 2 school years (1998/1999 & 1999/2000) on the Florida Department of Education's website. ^ It was concluded that student poverty, teacher average years of experience and student mobility taken together, provide a strong predictive measure of FCAT reading and math performance. However, the set of student inputs was significantly stronger than the teacher inputs. High student poverty was highly correlated with low FCAT scores. Teacher experience and student mobility rates were not nearly as strongly related to FCAT scores as was student poverty. The results of this study provide evidence for educators and other school stakeholders of the relative degree to which student and teacher variables are related to student academic achievement. The underlying reasons for these relationships will require further examination in future studies. These results raise questions for Florida's school policymakers about the educational equity of the state's accountability system and its implementation. ^

Projected Reorganization of Florida Bay Seagrass Communities in Response to the Increased Freshwater Inflow of Everglades Restoration

Historic changes in water-use management in the Florida Everglades have caused the quantity of freshwater inflow to Florida Bay to decline by approximately 60% while altering its timing and spatial distribution. Two consequences have been (1) increased salinity throughout the bay, including occurrences of hypersalinity, coupled with a decrease in salinity variability, and (2) change in benthic habitat structure. Restoration goals have been proposed to return the salinity climates (salinity and its variability) of Florida Bay to more estuarine conditions through changes in upstream water management, thereby returning seagrass species cover to a more historic state. To assess the potential for meeting those goals, we used two modeling approaches and long-term monitoring data. First, we applied the hydrological mass balance model FATHOM to predict salinity climate changes in sub-basins throughout the bay in response to a broad range of freshwater inflow from the Everglades. Second, because seagrass species exhibit different sensitivities to salinity climates, we used the FATHOM-modeled salinity climates as input to a statistical discriminant function model that associates eight seagrass community types with water quality variables including salinity, salinity variability, total organic carbon, total phosphorus, nitrate, and ammonium, as well as sediment depth and light reaching the benthos. Salinity climates in the western sub-basins bordering the Gulf of Mexico were insensitive to even the largest (5-fold) modeled increases in freshwater inflow. However, the north, northeastern, and eastern sub-basins were highly sensitive to freshwater inflow and responded to comparatively small increases with decreased salinity and increased salinity variability. The discriminant function model predicted increased occurrences ofHalodule wrightii communities and decreased occurrences of Thalassia testudinum communities in response to the more estuarine salinity climates. The shift in community composition represents a return to the historically observed state and suggests that restoration goals for Florida Bay can be achieved through restoration of freshwater inflow from the Everglades.

Benthic Macrophyte Distribution and Abundance in Estuarine Mangrove Lakes and Estuaries: Relationships to Environmental Variables

Annual mean salinity, light availability, and sediment depth to bedrock structured the submerged aquatic vegetation (SAV) communities in subtropical mangrove-lined estuaries. Three distinct SAV communities (i.e., Chara group, Halodule group, and Low SAV coverage group) were identified along the Everglades–Florida Bay ecotone and related to water quality using a discriminant function model that predicted the type of plant community at a given site from salinity, light availability, and sediment depth to bedrock. Mean salinity alone was able to correctly classify 78% of the sites and reliably separated the Chara group from the Halodule group. The addition of light availability and sediment depth to bedrock increased model accuracy to 90% and further distinguished the Chara group from the Halodule group. Light availability was uniquely valuable in separating the Chara group from the Low SAV coverage group. Regression analyses identified significant relationships between phosphorus concentration, phytoplankton abundance, and light availability and suggest that a decline in water transparency, associated with increasing salinity, may have also contributed to the historical decline of Chara communities in the region. This investigation applies relationships between environmental variables and SAV distribution and provides a case study into the application of these general principals to ecosystem management.

Forecasting Responses of Seagrass Distributions to Changing Water Quality Using Monitoring Data

Extensive data sets on water quality and seagrass distributions in Florida Bay have been assembled under complementary, but independent, monitoring programs. This paper presents the landscape-scale results from these monitoring programs and outlines a method for exploring the relationships between two such data sets. Seagrass species occurrence and abundance data were used to define eight benthic habitat classes from 677 sampling locations in Florida Bay. Water quality data from 28 monitoring stations spread across the Bay were used to construct a discriminant function model that assigned a probability of a given benthic habitat class occurring for a given combination of water quality variables. Mean salinity, salinity variability, the amount of light reaching the benthos, sediment depth, and mean nutrient concentrations were important predictor variables in the discriminant function model. Using a cross-validated classification scheme, this discriminant function identified the most likely benthic habitat type as the actual habitat type in most cases. The model predicted that the distribution of benthic habitat types in Florida Bay would likely change if water quality and water delivery were changed by human engineering of freshwater discharge from the Everglades. Specifically, an increase in the seasonal delivery of freshwater to Florida Bay should cause an expansion of seagrass beds dominated by Ruppia maritima and Halodule wrightii at the expense of the Thalassia testudinum-dominated community that now occurs in northeast Florida Bay. These statistical techniques should prove useful for predicting landscape-scale changes in community composition in diverse systems where communities are in quasi-equilibrium with environmental drivers.