Valuation of Ecosystem Services for Environmental Decision Making in South Florida

The Greater Everglades system imparts vital ecosystem services (ES) to South Florida residents including high quality drinking water supplies and a habitat for threatened and endangered species. As a result of the altered Everglades system and regional dynamics, restoration may either improve the provision of these services or impose a tradeoff between enhanced environmental goods and services and competing societal demands. The current study aims at understanding public preferences for restoration and generating willingness to pay (WTP) values for restored ES through the implementation of a discrete choice experiment. A previous study (Milon et al., 1999) generated WTP values amongst Floridians of up to $3.42 -$4.07 billion for full restoration over a 10-year period. We have collected data from 2,905 respondents taken from two samples who participated in an online survey designed to elicit the WTP values for selected ecological and social attributes included in the earlier study (Milon et al. 1999). We estimate that the Florida general public is willing to pay up to $854.1- $954.1 million over 10 years to avoid restrictions on their water usage and up to $90.8- $183.7 million over 10 years to restore the hydrological flow within the Water Conservation Area.

Turning Passive Detection Systems into Field Experiments: An Application Using Wetland Fishes and Enclosures to Track Fine-Scale Movement and Habitat Choice

Understanding habitat selection and movement remains a key question in behavioral ecology. Yet, obtaining a sufficiently high spatiotemporal resolution of the movement paths of organisms remains a major challenge, despite recent technological advances. Observing fine-scale movement and habitat choice decisions in the field can prove to be difficult and expensive, particularly in expansive habitats such as wetlands. We describe the application of passive integrated transponder (PIT) systems to field enclosures for tracking detailed fish behaviors in an experimental setting. PIT systems have been applied to habitats with clear passageways, at fixed locations or in controlled laboratory and mesocosm settings, but their use in unconfined habitats and field-based experimental setups remains limited. In an Everglades enclosure, we continuously tracked the movement and habitat use of PIT-tagged centrarchids across three habitats of varying depth and complexity using multiple flatbed antennas for 14 days. Fish used all three habitats, with marked species-specific diel movement patterns across habitats, and short-lived movements that would be likely missed by other tracking techniques. Findings suggest that the application of PIT systems to field enclosures can be an insightful approach for gaining continuous, undisturbed and detailed movement data in unconfined habitats, and for experimentally manipulating both internal and external drivers of these behaviors.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^