The bioaccumulation of persistent contaminants by zebra mussels and their effects on state-endangered common terns

Common terns currently are listed as endangered or threatened in many states, including Illinois, Vermont, Pennsylvania, Ohio, Wisconsin, Michigan, and New York, and a species of special concern by the U.S. Fish and Wildlife Service (USFWS, 2002). The sole remaining nesting colony in Illinois is located at the Naval Station Great Lakes (NSGL) in Lake County where intensive management by the Illinois Department of Natural Resources has reduced nest predation and increased the number of eggs that hatch. However, the overall reproductive success (the number of young successfully reaching independence) has not improved. Observations of gross deformities in hatchlings (i.e. compromised feather development and cross-bill), lethargic behavior of young birds, and lesions, suggested the influence of environmental contaminants (Jablonski et al., 2005). I investigated if there were significant levels of environmental contaminants in eggs and nestlings of common terns. While there were minimal concentration of selenium, mercury, lead, and cadmium, there were large concentration of polychlorinated biphenyls (PCBs) in both the eggs and nestlings. The greater amounts of PCBs in older chicks than younger chicks suggest local contamination. In order to potentially manage the factors responsible for exposing the terns to PCBs I investigated the pathway by which PCBs were exposed to terns. The two most likely biological pathways as determined by research on Great Lake fishes were investigated. The first pathway is through atmospheric deposition of PCBs and resuspension of PCB-ladel sediment which are subsequently acquired by filter-feeding fish (e.g. alewives, Alosa pseudoharengus) and then pelagic fish (e.g. lake trout, Salvelinus namaychus) or in this case terns. The second pathway explored was via the biodeposits of zebra mussels which are consumed by round gobies (Neogobius melanostromus) and ultimately littoral fish (e.g. small-mouthed bass, Micropterus dolomieui) or terns. Because common terns breed in near-shore sites where concentrations of zebra mussels are found, as well as forage in more pelagic environments it is possible that either or both pathways may be contributing to their PCB exposure. Field experiments and stable isotope analyses demonstrated that the most likely pathway by which terns are exposed to PCBs is via alewives, similar to how apex predators such as lake trout acquire PCBs. Biodeposits from zebra mussels do not appear to be a significant factor in PCB accumulation in terns. The impact of PCB exposure on birds can vary widely, however in this situation we choise to investigate one specific behavior often affected by PCB exposure, parental attentiveness. PCBs are known to cause endocrine disruption which ultimately results in reduced brooding of young and incubation of eggs. I used temperature sensors to quantify nest temperatures and parental attentiveness during incubation. High concentrations of PCBs in our study population appear to be leading to poor parental attentiveness, and extended periods of absence during incubation and brooding, ultimately leading to poor reproductive success. Common terns are perilously close to being extirpated in Illinois and management of PCB exposure will be difficult. I propose that additional testing should be conducted to locate a site with less PCB contamination and then to move the tern colony to this location, possibly using social cues as has been done with other tern species in Illinois. PCBs are having a profound impact on common tern populations in Illinois and without moving the colony it is likely that the population will continue to decline.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.