Monitoring Ecosystems: Interdisciplinary Approaches For Evaluating Ecoregional Initiatives

Monitoring Ecosystems brings together leading scientists and researchers to offer a ground-breaking synthesis of lessons learned about ecological monitoring in major ecoregional initiatives around the United States. Contributors present insights and experiences gained from their work in designing, developing, and implementing comprehensive ecosystem monitoring programs in the Pacific Northwest, the lower Colorado River Basin, and the Florida Everglades. The book: outlines the conceptual and scientific underpinnings for regional-scale ecosystem monitoring examines the role and importance of data management, modeling, and integrative analyses considers techniques for and experience with monitoring habitats, populations, and communities Chapters by the editors synthesize and expand on points made throughout the volume and present recommendations for establishing frameworks for monitoring across scales, from local to international. Monitoring Ecosystems presents a critical examination of the lessons learned from direct experience along with generalized conclusions that can be applied to monitoring programs in the United States and around the world. It is a vital contribution to science-based monitoring efforts that will allow those responsible for developing and implementing ecoregional initiatives to make use of knowledge gained in previous efforts.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Towards simulation and emulation of large-scale computer networks

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today's networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. ^ First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. ^ Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. ^ Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation.^

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.

Application of excitation emission matrix fluorescence monitoring in the assessment of spatial and seasonal drivers of dissolved organic matter composition: Sources and physical disturbance controls

The environmental dynamics of dissolved organic matter (DOM) were characterized for a shallow, subtropical, seagrass-dominated estuarine bay, namely Florida Bay, USA. Large spatial and seasonal variations in DOM quantity and quality were assessed using dissolved organic C (DOC) measurements and spectrophotometric properties including excitation emission matrix (EEM) fluorescence with parallel factor analysis (PARAFAC). Surface water samples were collected monthly for 2 years across the bay. DOM characteristics were statistically different across the bay, and the bay was spatially characterized into four basins based on chemical characteristics of DOM as determined by EEM-PARAFAC. Differences between zones were explained based on hydrology, geomorphology, and primary productivity of the local seagrass community. In addition, potential disturbance effects from a very active hurricane season were identified. Although the overall seasonal patterns of DOM variations were not significantly affected on a bay-wide scale by this disturbance, enhanced freshwater delivery and associated P and DOM inputs (both quantity and quality) were suggested as potential drivers for the appearance of algal blooms in high impact areas. The application of EEM-PARAFAC proved to be ideally suited for studies requiring high sample throughput methods to assess spatial and temporal ecological drivers and to determine disturbance-induced impacts in aquatic ecosystems.

Norm-based comparison of SF -12 physical and mental health scale scores for a sample of employees of Broward County

In the new health paradigm, the connotation of health has extended beyond the measures of morbidity and mortality to include wellness and quality of life. Comprehensive assessments of health go beyond traditional biological indicators to include measures of physical and mental health status, social role-functioning, and general health perceptions. To meet these challenges, tools for assessment and outcome evaluation are being designed to collect information about functioning and well-being from the individual's point of view.^ The purpose of this study was to profile the physical and mental health status of a sample of county government employees against U.S. population norms. A second purpose of the study was to determine if significant relationships existed between respondent characteristics and personal health practices, lifestyle and other health how the tools and methods used in this investigation can be used to guide program development and facilitate monitoring of health promotion initiatives.^ The SF-12 Health Survey (Ware, Kosinski, & Keller, 1995), a validated measure of health status, was administered to a convenience sample of 450 employees attending one of nine health fairs at an urban worksite. The instrument has been utilized nationally which enabled a comparative analysis of findings of this study with national results.^ Results from this study demonstrated that several respondent characteristics and personal health practices were associated with a greater percentage of physical and/or mental scale scores that were significantly "worse" or significantly "better" than the general population. Respondent characteristics that were significantly related to the SF-12 physical and/or mental health scale scores were gender, age, education, ethnicity, and income status. Personal health practices that were significantly related to SF-12 physical and/or mental scale scores were frequency of vigorous exercise, presence of chronic illness, being at one's prescribed height and weight, eating breakfast, smoking and drinking status. This study provides an illustration of the methods used to analyze and interpret SF-12 Health Survey data, using norm-based interpretation guidelines which are useful for purposes of program development and collecting information on health at the community level. ^

Monitoring of Tree Island Condition in the Southern Everglades: Annual Report 2011

Tree islands, a prominent feature in both the marl prairie and ridge and slough landscapes of the Everglades, are sensitive to large-scale restoration actions associated with the Comprehensive Everglades Restoration Plan (CERP) authorized by the Water Resources Development Act (WRDA) 2000 to restore the south Florida ecosystem. More specifically, changes in hydrologic regimes at both local and landscape scales are likely to affect the internal water economy of islands, which in turn will influence plant community structure and function. To strengthen our ability to assess the “performance” of tree island ecosystems and predict how these hydrologic alterations would translate into ecosystem response, an improved understating of reference conditions of vegetation structure and function, and their responses to major stressors is important. In this regard, a study of vegetation structure and composition in relation to associated physical and biological processes was initiated in 2005 with initial funding from Everglades National Park and South Florida Water Management District (SFWMD). The study continued through 2011 with funding from US Army Corps of Engineers (USACOE) (Cooperative Agreement # W912HZ-09-2-0019 Modification No.: P00001).

Silicon photonic device for wavelength sensing and monitoring

Over the last decade advances and innovations from Silicon Photonics technology were observed in the telecommunications and computing industries. This technology which employs Silicon as an optical medium, relies on current CMOS micro-electronics fabrication processes to enable medium scale integration of many nano-photonic devices to produce photonic integrated circuitry. ^ However, other fields of research such as optical sensor processing can benefit from silicon photonics technology, specially in sensors where the physical measurement is wavelength encoded. ^ In this research work, we present a design and application of a thermally tuned silicon photonic device as an optical sensor interrogator. ^ The main device is a micro-ring resonator filter of 10 μm of diameter. A photonic design toolkit was developed based on open source software from the research community. With those tools it was possible to estimate the resonance and spectral characteristics of the filter. From the obtained design parameters, a 7.8 × 3.8 mm optical chip was fabricated using standard micro-photonics techniques. In order to tune a ring resonance, Nichrome micro-heaters were fabricated on top of the device. Some fabricated devices were systematically characterized and their tuning response were determined. From measurements, a ring resonator with a free-spectral-range of 18.4 nm and with a bandwidth of 0.14 nm was obtained. Using just 5 mA it was possible to tune the device resonance up to 3 nm. ^ In order to apply our device as a sensor interrogator in this research, a model of wavelength estimation using time interval between peaks measurement technique was developed and simulations were carried out to assess its performance. To test the technique, an experiment using a Fiber Bragg grating optical sensor was set, and estimations of the wavelength shift of this sensor due to axial strains yield an error within 22 pm compared to measurements from spectrum analyzer. ^ Results from this study implies that signals from FBG sensors can be processed with good accuracy using a micro-ring device with the advantage of ts compact size, scalability and versatility. Additionally, the system also has additional applications such as processing optical wavelength shifts from integrated photonic sensors and to be able to track resonances from laser sources.^

Silicon Photonic Device for Wavelength Sensing and Monitoring

Over the last decade advances and innovations from Silicon Photonics technology were observed in the telecommunications and computing industries. This technology which employs Silicon as an optical medium, relies on current CMOS micro-electronics fabrication processes to enable medium scale integration of many nano-photonic devices to produce photonic integrated circuitry. However, other fields of research such as optical sensor processing can benefit from silicon photonics technology, specially in sensors where the physical measurement is wavelength encoded. In this research work, we present a design and application of a thermally tuned silicon photonic device as an optical sensor interrogator. The main device is a micro-ring resonator filter of 10 $\mu m$ of diameter. A photonic design toolkit was developed based on open source software from the research community. With those tools it was possible to estimate the resonance and spectral characteristics of the filter. From the obtained design parameters, a 7.8 x 3.8 mm optical chip was fabricated using standard micro-photonics techniques. In order to tune a ring resonance, Nichrome micro-heaters were fabricated on top of the device. Some fabricated devices were systematically characterized and their tuning response were determined. From measurements, a ring resonator with a free-spectral-range of 18.4 nm and with a bandwidth of 0.14 nm was obtained. Using just 5 mA it was possible to tune the device resonance up to 3 nm. In order to apply our device as a sensor interrogator in this research, a model of wavelength estimation using time interval between peaks measurement technique was developed and simulations were carried out to assess its performance. To test the technique, an experiment using a Fiber Bragg grating optical sensor was set, and estimations of the wavelength shift of this sensor due to axial strains yield an error within 22 pm compared to measurements from spectrum analyzer. Results from this study implies that signals from FBG sensors can be processed with good accuracy using a micro-ring device with the advantage of ts compact size, scalability and versatility. Additionally, the system also has additional applications such as processing optical wavelength shifts from integrated photonic sensors and to be able to track resonances from laser sources.