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.

Education policy and national income distribution: New evidence from recent cross -country data

In the mid 19th century, Horace Mann insisted that a broad provision of public schooling should take precedence over the liberal education of an elite group. In that regard, his generation constructed a state sponsored common schooling enterprise to educate the masses. More than 100 years later, the institution of public schooling fails to maintain an image fully representative of the ideals of equity and inclusion. Critical theory in educational thought associates the dominant practice of functional schooling with maintenance of the status quo, an unequal distribution of financial, political, and social resources. This study examined the empirical basis for the association of public schooling with the status quo using the most recent and comparable cross-country income inequality data. Multiple regression analysis evaluated the possible relationship between national income inequality change over the period 1985-2005 and variables representative of national measures of education supply in the prior decade. The estimated model of income inequality development attempted to quantify the relationship between education supply factors and subsequent income inequality developments by controlling for economic, demographic, and exogenous factors. The sample included all nations with comparable income inequality data over the measurement period, N = 56. Does public school supply affect national income distribution? The estimated model suggested that an increase in the average years of schooling among the population age 15 years or older, measured over the period 1975-1985, provided a mechanism that resulted in a more equal distribution of income over the period 1985-2005 among low and lower-middle income nations. The model also suggested that income inequality increased less or decreased more in smaller economies and when the percentage of the population age < 15 years grew more slowly over the period 1985-2000. In contrast, this study identified no significant relationship between school supply changes measured over prior periods and income inequality development over the period 1985-2005 among upper-middle and high income nations.

Inferences about Parameters of Trivariate Normal Distribution with Missing Data

Multivariate normal distribution is commonly encountered in any field, a frequent issue is the missing values in practice. The purpose of this research was to estimate the parameters in three-dimensional covariance permutation-symmetric normal distribution with complete data and all possible patterns of incomplete data. In this study, MLE with missing data were derived, and the properties of the MLE as well as the sampling distributions were obtained. A Monte Carlo simulation study was used to evaluate the performance of the considered estimators for both cases when ρ was known and unknown. All results indicated that, compared to estimators in the case of omitting observations with missing data, the estimators derived in this article led to better performance. Furthermore, when ρ was unknown, using the estimate of ρ would lead to the same conclusion.