Dynamics of ecosystem metabolism and flocculent detritus transport in estuarine Taylor River

Estuaries and estuarine wetlands are ecologically and societally important systems, exhibiting high rates of primary production that fuel offshore secondary production. Hydrological processes play a central role in shaping estuarine ecosystem structure and function by controlling nutrient loading and the relative contributions of marine and terrestrial influences on the estuary. The Comprehensive Everglades Restoration Plan includes plans to restore freshwater delivery to Taylor Slough, a shallow drainage basin in the southern Everglades, ultimately resulting in increased freshwater flow to the downstream Taylor River estuary. The existing seasonal and inter-annual variability of water flow and source in Taylor River affords the opportunity to investigate relationships between ecosystem function and hydrologic forcing. Estimates of aquatic ecosystem metabolism, derived from free-water, diel changes in dissolved oxygen, were combined with assessments of wetland flocculent detritus quality and transport within the context of seasonal changes in Everglades hydrology. Variation in ecosystem gross primary production and respiration were linked to seasonal changes in estuarine water quality using multiple autoregression models. Furthermore, Taylor River was observed to be net heterotrophic, indicating that an allochthonous source of carbon maintained ecosystem respiration in excess of autochthonous primary production. Wetland-derived detritus appears to be an important vector of energy and nutrients across the Everglades landscape; and in Taylor River, is seasonally flushed into ponded segments of the river where it is then respired. Lastly, seasonal water delivery appears to govern feedbacks regulating water column phosphorus availability in the Taylor River estuary.

Importance of storm events in controlling ecosystem structure and function in a Florida gulf coast estuary

From 8/95 to 2/01, we investigated the ecological effects of intra- and inter-annual variability in freshwater flow through Taylor Creek in southeastern Everglades National Park. Continuous monitoring and intensive sampling studies overlapped with an array of pulsed weather events that impacted physical, chemical, and biological attributes of this region. We quantified the effects of three events representing a range of characteristics (duration, amount of precipitation, storm intensity, wind direction) on the hydraulic connectivity, nutrient and sediment dynamics, and vegetation structure of the SE Everglades estuarine ecotone. These events included a strong winter storm in November 1996, Tropical Storm Harvey in September 1999, and Hurricane Irene in October 1999. Continuous hydrologic and daily water sample data were used to examine the effects of these events on the physical forcing and quality of water in Taylor Creek. A high resolution, flow-through sampling and mapping approach was used to characterize water quality in the adjacent bay. To understand the effects of these events on vegetation communities, we measured mangrove litter production and estimated seagrass cover in the bay at monthly intervals. We also quantified sediment deposition associated with Hurricane Irene's flood surge along the Buttonwood Ridge. These three events resulted in dramatic changes in surface water movement and chemistry in Taylor Creek and adjacent regions of Florida Bay as well as increased mangrove litterfall and flood surge scouring of seagrass beds. Up to 5 cm of bay-derived mud was deposited along the ridge adjacent to the creek in this single pulsed event. These short-term events can account for a substantial proportion of the annual flux of freshwater and materials between the mangrove zone and Florida Bay. Our findings shed light on the capacity of these storm events, especially when in succession, to have far reaching and long lasting effects on coastal ecosystems such as the estuarine ecotone of the SE Everglades.

Ecosystem Structure and Function Still Altered Two Decades After Short-Term Fertilization of a Seagrass Meadow

An oligotrophic phosphorus (P) limited seagrass ecosystem in Florida Bay was experimentally fertilized in a unique way. Perches were installed to encourage seabirds to roost and deliver an external source of nutrients via defecation. Two treatments were examined: (1) a chronic 23-year fertilization and (2) an earlier 28-month fertilization that was discontinued when the chronic treatment was initiated. Because of the low mobility of P in carbonate sediments, we hypothesized long-term changes to ecosystem structure and function in both treatments. Structural changes in the chronic treatment included a shift in the dominant seagrass species from Thalassia testudinum to Halodule wrightii, large increases in epiphytic biomass and sediment chlorophyll-a, and a decline in species richness. Functional changes included increased benthic metabolism and quantum efficiency. Initial changes in the 28-month fertilization were similar, but after 23 years of nutrient depuration T. testudinum has reestablished itself as the dominant species. However, P remains elevated in the sediment and H. wrightii has maintained a presence. Functionally the discontinued treatment remains altered. Biomass exceeds that in the chronic treatment and indices of productivity, elevated relative to control, are not different from the chronic fertilization. Cessation of nutrient loading has resulted in a superficial return to the pre-disturbance character of the community, but due to the nature of P cycles functional changes persist.

Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Diatom-based Models for Inferring Hydrology and Periphyton Abundance in a Subtropical Karstic Wetland: Implications for Ecosystem-Scale Bioassessment

We developed diatom-based prediction models of hydrology and periphyton abundance to inform assessment tools for a hydrologically managed wetland. Because hydrology is an important driver of ecosystem change, hydrologic alterations by restoration efforts could modify biological responses, such as periphyton characteristics. In karstic wetlands, diatoms are particularly important components of mat-forming calcareous periphyton assemblages that both respond and contribute to the structural organization and function of the periphyton matrix. We examined the distribution of diatoms across the Florida Everglades landscape and found hydroperiod and periphyton biovolume were strongly correlated with assemblage composition. We present species optima and tolerances for hydroperiod and periphyton biovolume, for use in interpreting the directionality of change in these important variables. Predictions of these variables were mapped to visualize landscape-scale spatial patterns in a dominant driver of change in this ecosystem (hydroperiod) and an ecosystem-level response metric of hydrologic change (periphyton biovolume). Specific diatom assemblages inhabiting periphyton mats of differing abundance can be used to infer past conditions and inform management decisions based on how assemblages are changing. This study captures diatom responses to wide gradients of hydrology and periphyton characteristics to inform ecosystem-scale bioassessment efforts in a large wetland.

Factors influencing movements and foraging ecology of American alligators (Alligator mississippiensis) in a dynamic subtropical coastal ecosystem

Top predators can have large effects on community and population dynamics but we still know relatively little about their roles in ecosystems and which biotic and abiotic factors potentially affect their behavioral patterns. Understanding the roles played by top predators is a pressing issue because many top predator populations around the world are declining rapidly yet we do not fully understand what the consequences of their potential extirpation could be for ecosystem structure and function. In addition, individual behavioral specialization is commonplace across many taxa, but studies of its prevalence, causes, and consequences in top predator populations are lacking. In this dissertation I investigated the movement, feeding patterns, and drivers and implications of individual specialization in an American alligator (Alligator mississippiensis ) population inhabiting a dynamic subtropical estuary. I found that alligator movement and feeding behaviors in this population were largely regulated by a combination of biotic and abiotic factors that varied seasonally. I also found that the population consisted of individuals that displayed an extremely wide range of movement and feeding behaviors, indicating that individual specialization is potentially an important determinant of the varied roles of alligators in ecosystems. Ultimately, I found that assuming top predator populations consist of individuals that all behave in similar ways in terms of their feeding, movements, and potential roles in ecosystems is likely incorrect. As climate change and ecosystem restoration and conservation activities continue to affect top predator populations worldwide, individuals will likely respond in different and possibly unexpected ways.

Factors Influencing Movements and Foraging Ecology of American Alligators (Alligator mississippiensis) in a Dynamic Subtropical Coastal Ecosystem

Top predators can have large effects on community and population dynamics but we still know relatively little about their roles in ecosystems and which biotic and abiotic factors potentially affect their behavioral patterns. Understanding the roles played by top predators is a pressing issue because many top predator populations around the world are declining rapidly yet we do not fully understand what the consequences of their potential extirpation could be for ecosystem structure and function. In addition, individual behavioral specialization is commonplace across many taxa, but studies of its prevalence, causes, and consequences in top predator populations are lacking. In this dissertation I investigated the movement, feeding patterns, and drivers and implications of individual specialization in an American alligator (Alligator mississippiensis) population inhabiting a dynamic subtropical estuary. I found that alligator movement and feeding behaviors in this population were largely regulated by a combination of biotic and abiotic factors that varied seasonally. I also found that the population consisted of individuals that displayed an extremely wide range of movement and feeding behaviors, indicating that individual specialization is potentially an important determinant of the varied roles of alligators in ecosystems. Ultimately, I found that assuming top predator populations consist of individuals that all behave in similar ways in terms of their feeding, movements, and potential roles in ecosystems is likely incorrect. As climate change and ecosystem restoration and conservation activities continue to affect top predator populations worldwide, individuals will likely respond in different and possibly unexpected ways.

Nonlinear dynamics and chaos simulation system analysis for improved design

Small errors proved catastrophic. Our purpose to remark that a very small cause which escapes our notice determined a considerable effect that we cannot fail to see, and then we say that the effect is due to chance. Small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter. When dealing with any kind of electrical device specification, it is important to note that there exists a pair of test conditions that define a test: the forcing function and the limit. Forcing functions define the external operating constraints placed upon the device tested. The actual test defines how well the device responds to these constraints. Forcing inputs to threshold for example, represents the most difficult testing because this put those inputs as close as possible to the actual switching critical points and guarantees that the device will meet the Input-Output specifications. ^ Prediction becomes impossible by classical analytical analysis bounded by Newton and Euclides. We have found that non linear dynamics characteristics is the natural state of being in all circuits and devices. Opportunities exist for effective error detection in a nonlinear dynamics and chaos environment. ^ Nowadays there are a set of linear limits established around every aspect of a digital or analog circuits out of which devices are consider bad after failing the test. Deterministic chaos circuit is a fact not a possibility as it has been revived by our Ph.D. research. In practice for linear standard informational methodologies, this chaotic data product is usually undesirable and we are educated to be interested in obtaining a more regular stream of output data. ^ This Ph.D. research explored the possibilities of taking the foundation of a very well known simulation and modeling methodology, introducing nonlinear dynamics and chaos precepts, to produce a new error detector instrument able to put together streams of data scattered in space and time. Therefore, mastering deterministic chaos and changing the bad reputation of chaotic data as a potential risk for practical system status determination. ^

Relative significance of pelagic, benthic and allochthonous organic matter to microbial carbon cycling in a phosphorus-depleted subtropical estuary (Florida Bay)

Heterotrophic bacteria are important decomposers and transformers of primary production and provide an important link between detritus and the aquatic food web. In seagrass ecosystems, much of seagrass primary production is unavailable through direct grazing and must undergo microbial reworking before seagrass production can enter the aquatic food web. The goal of my dissertation research is to understand better the role heterotrophic bacteria play in carbon cycling in seagrass estuaries. My dissertation research focuses on Florida Bay, a seagrass estuary that has experienced recent changes in carbon source availability, which may have altered ecosystem function. My dissertation research investigates the importance of seagrass, algal and/or cyanobacterial, and allochthonous-derived organic matter to heterotrophic bacteria in Florida Bay and helps establish the carbon base of the estuarine food web. ^ A three tiered approach to the study of heterotrophic bacterial carbon cycling and trophic influences in Florida Bay was used: (1) Spatiotemporal observations of environmental parameters (hydrology, nutrients, extracellular enzymes, and microbial abundance, biomass, and production); (2) Microbial grazing experiments under different levels of top-down and bottom-up influence; and (3) Bulk and compound-specific (bacteria-biomarker fatty acid analysis) stable carbon isotope analysis. ^ In Florida Bay, spatiotemporal patterns in microbial extracellular enzyme (also called ectoenzyme) activities indicate that microorganisms hydrolyzed selectively fractions of the estuarine organic matter pool. The microbial community hydrolyzed organic acids, peptides, and phosphate esters and did not use storage and structural carbohydrates. Organic matter use by heterotrophic bacterioplankton in Florida Bay was co-regulated by bottom-up (resource availability) and top-down (grazer mediated) processes. A bacterial carbon budget based on bacterial, epiphytic, and seagrass production indicates that heterotrophic bacterial carbon cycles are supported primarily through epiphytic production with mixing from seagrass production. Stable carbon isotope analysis of bacteria biomarkers and carbon sources in Florida Bay corroborate the results of the bacterial carbon budget. These results support previous studies of aquatic consumers in Florida Bay, indicating that epiphytic/benthic algal and/or cyanobacterial production with mixing from seagrass-derived organic matter is the carbon base of the seagrass estuarine food web. ^

Arctic ecosystem responses to changes in water availability and warming: Short and long-term responses

Arctic soils store close to 14% of the global soil carbon. Most of arctic carbon is stored below ground in the permafrost. With climate warming the decomposition of the soil carbon could represent a significant positive feedback to global greenhouse warming. Recent evidence has shown that the temperature of the Arctic is already increasing, and this change is associated mostly with anthropogenic activities. Warmer soils will contribute to permafrost degradation and accelerate organic matter decay and thus increase the flux of carbon dioxide and methane into the atmosphere. Temperature and water availability are also important drivers of ecosystem performance, but effects can be complex and in opposition. Temperature and moisture changes can affect ecosystem respiration (ER) and gross primary productivity (GPP) independently; an increase in the net ecosystem exchange can be a result of either a decrease in ER or an increase in GPP. Therefore, understanding the effects of changes in ecosystem water and temperature on the carbon flux components becomes key to predicting the responses of the Arctic to climate change. The overall goal of this work was to determine the response of arctic systems to simulated climate change scenarios with simultaneous changes in temperature and moisture. A temperature and hydrological manipulation in a naturally-drained lakebed was used to assess the short-term effect of changes in water and temperature on the carbon cycle. Also, as part of International Tundra Experiment Network (ITEX), I determined the long-term effect of warming on the carbon cycle in a natural hydrological gradient established in the mid 90's. I found that the carbon balance is highly sensitive to short-term changes in water table and warming. However, over longer time periods, hydrological and temperature changed soil biophysical properties, nutrient cycles, and other ecosystem structural and functional components that down regulated GPP and ER, especially in wet areas.

Developing performance measures of mangrove wetlands using simulation models of hydrology, nutrient biogeochemistry, and community dynamics

The goal of mangrove restoration projects should be to improve community structure and ecosystem function of degraded coastal landscapes. This requires the ability to forecast how mangrove structure and function will respond to prescribed changes in site conditions including hydrology, topography, and geophysical energies. There are global, regional, and local factors that can explain gradients of regulators (e.g., salinity, sulfides), resources (nutrients, light, water), and hydroperiod (frequency, duration of flooding) that collectively account for stressors that result in diverse patterns of mangrove properties across a variety of environmental settings. Simulation models of hydrology, nutrient biogeochemistry, and vegetation dynamics have been developed to forecast patterns in mangroves in the Florida Coastal Everglades. These models provide insight to mangrove response to specific restoration alternatives, testing causal mechanisms of system degradation. We propose that these models can also assist in selecting performance measures for monitoring programs that evaluate project effectiveness. This selection process in turn improves model development and calibration for forecasting mangrove response to restoration alternatives. Hydrologic performance measures include soil regulators, particularly soil salinity, surface topography of mangrove landscape, and hydroperiod, including both the frequency and duration of flooding. Estuarine performance measures should include salinity of the bay, tidal amplitude, and conditions of fresh water discharge (included in the salinity value). The most important performance measures from the mangrove biogeochemistry model should include soil resources (bulk density, total nitrogen, and phosphorus) and soil accretion. Mangrove ecology performance measures should include forest dimension analysis (transects and/or plots), sapling recruitment, leaf area index, and faunal relationships. Estuarine ecology performance measures should include the habitat function of mangroves, which can be evaluated with growth rate of key species, habitat suitability analysis, isotope abundance of indicator species, and bird census. The list of performance measures can be modified according to the model output that is used to define the scientific goals during the restoration planning process that reflect specific goals of the project.