Thesis (Ph.D.)--Drexel University, 2008. / Includes abstract and vita. Includes bibliographical references (leaves 138-147).

Thesis (Ph. D.)--Rutgers University, 2010. / "Graduate Program in Environmental Science." Includes bibliographical references (p. 104-113).

<p>Previous research in the US Virgin Islands (USVI) has demonstrated that land-based sources of pollution associated with watershed development and climate change are local and global factors causing coral reef degradation. A good indicator that can be used to assess stress on these environments is the water quality. Conventional assessment methods based on <i>in situ</i> measurements are timely and costly. Satellite remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic nature of water quality parameters by applying bio-optical models. Chlorophyll-a, suspended sediments (TSM), and colored-dissolved organic matter are color-producing agents (CPAs) that define the water quality and can be measured remotely. However, the interference of multiple optically active constituents that characterize the water column as well as reflectance from the bottom poses a challenge in shallow coastal environments in USVI. In this study, field and laboratory based data were collected from sites on St. Thomas and St. John to characterize the CPAs and bottom reflectance of substrates. Results indicate that the optical properties of these waters are a function of multiple CPAs with chlorophyll-a values ranging from 0.10 to 2.35 ?g/L and TSM values from 8.97 to 15.7 mg/L. These data were combined with <i>in situ</i> hyperspectral radiometric and Landsat OLI satellite data to develop a regionally tiered model that can predict CPA concentrations using traditional band ratio and multivariate approaches. Band ratio models for the hyperspectral dataset (R² = 0.35; RMSE = 0.10 ?g/L) and Landsat OLI dataset (R² = 0.35; RMSE = 0.12 ?g/L) indicated promising accuracy. However, a stronger model was developed using a multivariate, partial least squares regression to identify wavelengths that are more sensitive to chlorophyll-a (R² = 0.62, RMSE = 0.08 ?g/L) and TSM (R² = 0.55). This approach takes advantage of the full spectrum of hyperspectral data, thus providing a more robust predictive model. Models developed in this study will significantly improve near-real time and long-term water quality monitoring in USVI and will provide insight to factors contributing to coral reef decline.

<p> What was the last interaction you had with a bird? Was it a cordial conversation with a parrot or indirectly, as while devouring deviled eggs? The colorful ways in which avian and human lives are connected are as nuanced as they are pervasive. Perhaps this is unsurprising, given that globally, birds are held in captivity by the billions. Despite the massive scale at which our lives intersect, we often fail to recognize the psychological aspects of bird confinement. This project dives below the surface to examine the largely unconscious forces that underlie bird captivity by exploring psychosocial dynamics between poultry, parrots, and people. Employing a heuristic methodology, emergent themes are woven into a 30-minute film, <i>A Bird Tail</i> to develop <i> conscientiza&ccedil;&atilde;o</i>, the cultivation of a critical awareness of how captivity shapes avian-human relationships, the psyches of individual humans and birds, and ultimately our collective, trans-species cultures. Told from the perspective of an avian alchemist, the film explicitly navigates across species lines through imagery and voice by providing a <i>bird&rsquo;s eye view</i> of numerous challenges faced by captive-held birds, including death, disease, and trauma. A central purpose of this exploration is to bring these subsurface currents to light so that we as humans can begin to dissolve those psychological constructs and projections that prevent authentic cross-species connection and cause such profound harm. </p><p>

abstract: The presence of compounds such as pharmaceuticals and personal care products (PPCPs) in the environment is a cause for concern as they exhibit secondary effects on non-target organisms and are also indicative of incomplete removal by wastewater treatment plants (WWTPs) during water reclamation. Analytical methods and predictive models can help inform on the rates at which these contaminants enter the environment via biosolids use or wastewater effluent release to estimate the risk of adverse effects. The goals of this research project were to integrate the results obtained from the two different methods of risk assessment, (a) in silico modeling and (b) experimental analysis. Using a previously published empirical model, influent and effluent concentration ranges were predicted for 10 sterols and validated with peer-reviewed literature. The in silico risk assessment analysis performed for sterols and hormones in biosolids concluded that hormones possess high leaching potentials and that particularly 17-&#945;-ethinyl estradiol (EE2) can pose significant threat to fathead minnows (P. promelas) via leaching from terrestrial depositions of biosolids. Six mega-composite biosolids samples representative of 94 WWTPs were analyzed for a suite of 120 PPCPs using the extended U.S. EPA Method 1694 protocol. Results indicated the presence of 26 previously unmonitored PPCPs in the samples with estimated annual release rates of 5-15 tons yr-1 via land application of biosolids. A mesocosm sampling analysis that was included in the study concluded that four compounds amitriptyline, paroxetine, propranolol and sertraline warrant further monitoring due to their high release rates from land applied biosolids and their calculated extended half-lives in soils. There is a growing interest in the scientific community towards the development of new analytical protocols for analyzing solid matrices such as biosolids for the presence of PPCPs and other established and emerging contaminants of concern. The two studies presented here are timely and an important addition to the increasing base of scientific articles regarding environmental release of PPCPs and exposure risks associated with biosolids land application. This research study emphasizes the need for coupling experimental results with predictive analytical modeling output in order to more fully assess the risks posed by compounds detected in biosolids. / Dissertation/Thesis / M.S. Civil and Environmental Engineering 2012

<p> An observed 20-30% increase in forest net ecosystem exchange (<i> NEE</i>) on partly cloudy days is often attributed to there being more uniform canopy illumination by diffuse radiation when clouds are present. However, the sky on such days is typically populated by fair-weather cumulus clouds, bringing dynamically changing shadow-to-light conditions on the order of minutes to the forest, with radiation alternating from 1000 W m^-2 in the clear sky to less than 400 W m^-2 in under-cloud shadows. These dynamically changing conditions cannot be investigated by the conventional time-averaged eddy-covariance flux method, which requires nearly steady-state turbulent conditions over much longer 20-30-min periods in order for the fluxes to converge to stationary values. We examine the &ldquo;true&rdquo; dynamics of the whole-canopy response to the light change by using a practical ensemble-flux method applied to eddy-covariance flux measurements from two distinct forest ecosystems: Harvard Forest (HF, 42.53&deg;N, 72.17&deg;W), temperate mid-latitude forest near Petersham, Massachusetts, USA, and Tapaj&oacute;s National Forest (LBA, 2.86&deg;S, 54.96&deg;W), an Amazonian evergreen tropical forest near Santarem, Par&aacute;, Brazil. Using the rapid change in radiative flux that occurs during the transition from cloud-induced shadow to light as a reference starting point, we combine sets of conditional illumination-change shadow-to-light and light-to-shadow transition events characteristic of cumulus-cloud conditions and parametrize distributions of light and shadow durations and rates of light change of the radiative-flux time series for different cloud conditions reported by standard weather stations. We investigate the sensitivity of the dynamics of forest response to the illumination transitions initiated by these conditional events, and identify an unexpected transient <i> NEE</i> maxima when <i>NEE</i> increases above the clear-sky steady-state equilibrium values (<i>NEE</i>_eq) within the first 10 min of the light period after the shadow-to-light transition, that we hypothesize to be a physiological forest response to the abrupt light change due to presence of the intercellular CO₂ pool in the leaf tissues. Overall <i>NEE</i> builds up during the sunlit periods, but in shadow heat and water stresses are reduced, thus increasing the water use efficiency (<i>WUE</i>). To conduct this analysis, we obtain similarity criteria for realizations defined by conditional events to combine them into the ensembles. With 300 similar realizations grab-sampled at 1-s intervals, we can reliably estimate (&le; 5% standard error) dynamic ensemble fluxes resolved on a 5-s time scale. By the successful application of the first-order system of the delay differential equations with the exponential approach-to-equilibrium solutions, we are able to justify the utility of the &ldquo;Big-Leaf&rdquo;-model approach to describe whole-canopy fluxes and provide the dynamic parametrizations of the &ldquo;Big-Leaf&rdquo; Active Thermal Layer as well as of the Transient Internal Layer above both forests when the light switches on after the cloud pass. By combining results of sensitivity analysis with modelled solutions applied to the real day-long fluctuating-light time series, we show that the variable light during fair-weather clouds (Shadow period duration &lt; 100 s, Light period duration &ge; 300 s) is responsible for an increase in <i>NEE</i> above the <i>NEE</i>_eq of 15-25% for HF and 10-15% for LBA. This indicates that there is a fluctuating-light <i>NEE</i>-enhancement mechanism that can be considered to be a viable alternative to the existing hypothesis of a diffuse-radiation <i> NEE</i>-enhancement mechanism on partly-cloudy days. We show that on such days <i>Diffuse Fraction</i> can be linearly-related to <i> Cloudiness</i> estimated using the shadow-to-light change in a conditional-event radiative-flux ratio, connecting these two <i>NEE</i>-enhancement mechanisms. Combination of increased NEE with the relatively high plateau in <i>WUE</i> allows forests to operate efficiently in partly-cloudy conditions with maxima located in the <i>Cloudiness</i> range [0.1-0.3] and <i>Diffuse-Fraction</i> range [0.35-0.6], suggesting forest adaptations to the preferred lighting conditions and fair-weather cloudiness. </p><p>

<p> The effective impervious area (EIA) is the portion of the total impervious area (TIA) that is hydraulically connected to the storm drainage network; thus, it is responsible for a majority of the runoff and its adverse effects in watersheds. Empirical equations for determining EIA are frequently used since they are quick and easy to use, but their accuracy has been untested in the Southern California region. The goals of this study are to: (1) test the accuracy of empirical equations on watersheds within the Southern California region (2) develop an EIA equation for the region based on the EIA vs. TIA relationships (3) Calibrate a stormwater management model using the TIA and EIA values from the equations and see how they perform compared to the measured hydrograph. The results will help inform planners and engineers of the effectiveness of utilizing EIA empirical equations for Best Management Practices (BMP) structures and stormwater conveyance system sizing in the region. </p><p>

<p> Over the last several decades declines in pollinator populations, especially those of wild bees and other insects, have raised awareness of the economic impact pollination services have for crop production. This awareness and concern was heightened by an ongoing loss of millions of managed honey bee colonies since the early 1950s. Colonies are used predominantly for pollination services in fruit and vegetable crops. During 2007, an unusually large overwintering loss in colonies that was not characterized by the presence of dead bees was termed colony collapse disorder (CCD), a syndrome in which hives lacked sufficient worker caste bees to maintain the queen and brood. Potential factors hypothesized to be associated with CCD include parasite infestations (e.g., <i>Varroa </i> mite) and pathogen infections (<i>Nosema</i> spp. fungus and viruses), insecticide exposure (especially to the neonicotinoid class), and poor nutrition owing to a reduction in landscape areas containing high quality floral resources. Although no one stressor has been definitively associated with CCD, possible interactions among them have only recently been studied. Of particular interest are possible interactions of <i>Nosema </i> spp. with neonicotinoid insecticide exposure. The main objective of this dissertation was an examination of these potential interactions using a combination of literature analysis, empirical study of <i>Nosema</i> infection prevalence in adult bees, and simulation modeling of the combined effects of several stressors on worker population abundance. After the introduction, the dissertation is divided into four chapters addressing the following objectives: (1) Comparison of regulatory procedures for risk assessment of insecticides potentially impacting honey bees in the United States and in the European Union; (2) Analysis of published literature that document potential interactions between bee pathogens, parasites, and neonicotinoid insecticide residues; (3) Analysis of field-collected apiary bees for prevalence of <i>Nosema </i> spores in association with land uses and the presence of neonicotinoid residues; (4) Use of the honey bee colony model BEEHAVE to predict colony collapse in the presence of pathogens and insecticide-induced mortality. Results of the various analyses suggest a need for modifying risk assessment procedures to include the interaction of pesticide residues with parasite/pathogen stressors. </p><p>

<p> Soil studies have traditionally been limited to the upper 2-m, while deep regolith &ndash; semi-consolidated materials from soil to bedrock &ndash; remains relatively unexplored, leaving many questions about the weathering processes of the near earth surface. Regolith (soil plus weathered bedrock) was studied across an elevation transect (405 m to 2700 m) of the west slope of southern Sierra Nevada mountains as a bioclimosequence. </p><p> In chapter one, effects of subtle differences in lithology and temperature were evaluated to measure degree of pedogenesis in regolith at rain- and snow- dominated sites. Biotite content in fine sand fractions was positively correlated with regolith thickness at rain-dominated and snow-dominated sites. Mineral grain size was negatively correlated with regolith thickness at the rain-dominated site. Meanwhile, the degree of regolith transformation, evaluated based on clay and secondary Fe oxide concentrations, was related to annual heat energy load from 0.5 to 10-m hard bedrock, where the proxy of energy input was mediated by temperature with depth. </p><p> In chapter two, a significant reservoir of carbon (C) within weathered bedrock was discovered. Small measurable amounts of C detected in deep regolith summed to large pools at locations where the regolith was thick. Although temperature and precipitation not effected on C density in deep regolith, the influence of climate had an indirect effect on C stock because it governed weathered bedrock thickness. Weathered bedrock had been reported to store deep C by 9% to 30% of the total regolith C inventory due to thickness of 0.7 to 7.6 m. Furthermore, a hypothetical scenario of global C stocks in weathered bedrock of 4-m thickness is 201 Pg, which help to explain a &ldquo;hidden&rdquo; C sink in the global C budget. In the third chapter, physical, chemical and mineralogical properties were measured at three elevations to understand climatic controls on weathering in soil and weathered bedrock. Evidence of chemical weathering was most pronounced in soil at the mid-elevation, while evidence of physical weathering was greatest in weathered bedrock at the high elevation where deep water percolation was highest. The concentration of nutrients was lower in weathered bedrock than that of soil, but due its thickness weathered bedrock was reservoir of plant essential nutrients. Moreover, available water content was large when summed over weathered bedrock thickness.</p><p> New spatial trends in ecosystem services were revealed by expanding our concept of soil to include deep regolith. This work was first to document factors that influence spatial trends of regolith (soil to weathered bedrock) characteristics in the Sierra Nevada. Characterization of deep regolith under different weathering environments improves our understanding of regulating factors for ecosystems health. The differentiation between weathering trends of soil and weathered bedrock could be used in the future to predict conditions in weathered bedrock at large scales.</p><p>

<p> Empirical studies of graphs have contributed enormously to our understanding of complex systems, growing into a more scientific exploration of communities spanning the physical, biological, and social called network science. As the quantity and types of networks have grown so has their heterogeneity in quality and specificity resulting in a wealth of datasets that are not matched by existing theoretical methods. This is especially true in ecology where the majority of interactions are indirect and unobservable even in well-studied systems. As a result ecologists continue to grapple with three fundamental questions: Most basically, (i) `How do ecosystems function?' I answered this question by comparing networks to each other such that poorly-studied systems can be understood through their similarity to well-understood ones and theoretical models. To do this I created the alignment algorithm netcom which recasts ecosystem processes as statistical dynamics of diffusion kernels originating from a network's constituent nodes. Using netcom I constructed a supervised classifier which can distinguish processes in both synthetic and empirical network data. While this kind of inference works on currently available network data, I have shown how causality can serve as a more effective and unifying currency of ecological interaction. Measures of causality are even able to identify complex interactions across organizational scales of communities, answering the longstanding question (ii) `Can community structure causally determine dynamics of constituent species?' Moreover, causal inference can be readily combined with existing modeling frameworks to quantify dynamic interactions at the same scale as the underlying data. In this way we can answer the question (iii) `Which species in an ecosystem cause which other species?' These tools are part of a paradigm shift in ecology that offers the potential to make more reliable management decisions for dynamic ecosystems in real time using only observational data.</p><p>