Environmental Science & Technology

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Advanced imaging and data mining technologies for medical and food safety applications
(2009) Jiang, Lu; Tao, Yang; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
As one of the most fast-developing research areas, biological imaging and image analysis receive more and more attentions, and have been already widely applied in many scientific fields including medical diagnosis and food safety inspection. To further investigate such a very interesting area, this research is mainly focused on advanced imaging and pattern recognition technologies in both medical and food safety applications, which include 1) noise reduction of ultra-low-dose multi-slice helical CT imaging for early lung cancer screening, and 2) automated discrimination between walnut shell and meat under hyperspectral florescence imaging. In the medical imaging and diagnosis area, because X-ray computed tomography (CT) has been applied to screen large populations for early lung cancer detection during the last decade, more and more attentions have been paid to studying low-dose, even ultra-low-dose X-ray CTs. However, reducing CT radiation exposure inevitably increases the noise level in the sinogram, thereby degrading the quality of reconstructed CT images. Thus, how to reduce the noise levels in the low-dose CT images becomes a meaningful topic. In this research, a nonparametric smoothing method with block based thin plate smoothing splines and the roughness penalty was introduced to restore the ultra-low-dose helical CT raw data, which was acquired under 120 kVp / 10 mAs protocol. The objective thorax image quality evaluation was first conducted to assess the image quality and noise level of proposed method. A web-based subjective evaluation system was also built for the total of 23 radiologists to compare proposed approach with traditional sinogram restoration method. Both objective and subjective evaluation studies showed the effectiveness of proposed thin-plate based nonparametric regression method in sinogram restoration of multi-slice helical ultra-low-dose CT. In food quality inspection area, automated discrimination between walnut shell and meat has become an imperative task in the walnut postharvest processing industry in the U.S. This research developed two hyperspectral fluorescence imaging based approaches, which were capable of differentiating walnut small shell fragments from meat. Firstly, a principal component analysis (PCA) and Gaussian mixture model (PCA-GMM)-based Bayesian classification method was introduced. PCA was used to extract features, and then the optimal number of components in PCA was selected by a cross-validation technique. The PCA-GMM-based Bayesian classifier was further applied to differentiate the walnut shell and meat according to the class-conditional probability and the prior estimated by the Gaussian mixture model. The experimental results showed the effectiveness of this PCA-GMM approach, and an overall 98.2% recognition rate was achieved. Secondly, Gaussian-kernel based Support Vector Machine (SVM) was presented for the walnut shell and meat discrimination in the hyperspectral florescence imagery. SVM was applied to seek an optimal low to high dimensional mapping such that the nonlinear separable input data in the original input data space became separable on the mapped high dimensional space, and hence fulfilled the classification between walnut shell and meat. An overall recognition rate of 98.7% was achieved by this method. Although the hyperspectral fluorescence imaging is capable of differentiating between walnut shell and meat, one persistent problem is how to deal with huge amount of data acquired by the hyperspectral imaging system, and hence improve the efficiency of application system. To solve this problem, an Independent Component Analysis with k-Nearest Neighbor Classifier (ICA-kNN) approach was presented in this research to reduce the data redundancy while not sacrifice the classification performance too much. An overall 90.6% detection rate was achieved given 10 optimal wavelengths, which constituted only 13% of the total acquired hyperspectral image data. In order to further evaluate the proposed method, the classification results of the ICA-kNN approach were also compared to the kNN classifier method alone. The experimental results showed that the ICA-kNN method with fewer wavelengths had the same performance as the kNN classifier alone using information from all 79 wavelengths. This demonstrated the effectiveness of the proposed ICA-kNN method for the hyperspectral band selection in the walnut shell and meat classification.
Advancing Ecosystem Based Fisheries Management: Biological Reference Points for Nutritional Status of Striped Bass (Morone saxatilis)
(2014) Haus, William; Harrell, Reginal M; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Nutritional condition is a valuable metric in ecosystem-based fisheries management. However, the need for lethal sampling for the most accurate indicators ethically and logistically limits sample sizes. Percent moisture has been recommended for management of striped bass Morone saxatilis and a management threshold has been suggested. Past researchers have used bioelectrical impedance analysis (BIA) to non-lethally estimate percent dry weight, the inverse of percent moisture. We sought to develop species-specific BIA models for striped bass in a controlled, laboratory setting and later validate those models with independent, field-collected data. BIA models were developed for five size classes and sampled across three temperatures. Results in the lab suggest BIA is an accurate and robust method for estimating percent dry weight in striped bass. However, when implemented in field surveys results are less conclusive. Possible differences between wild and hatchery-reared striped bass that effect BIA need further exploration. Additionally, the effects of salinity and stress response on BIA warrant further work.
Alternative Simulation of Soil Phosphorus for Agricultural Land Uses in the Chesapeake Bay Watershed Model
(2014) Mulkey, Alisha Spears; Coale, Frank J; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Current restoration efforts for the Chesapeake Bay watershed mandate reducing nutrient and sediment loads to receiving waters. The Chesapeake Bay Watershed Model (WSM) estimates loading; however, some WSM routines have not been updated to reflect recent research. This study's objective was to improve the simulation of soil phosphorus dynamics by using an independent modeling tool (APLE) as an alternative to the current WSM approach. Identical assumptions of acreage, soil properties, nutrient management, and transport factors from the WSM were used as inputs to APLE. Outcomes represent revised estimates of phosphorus edge-of-field losses and estimates of change in soil labile phosphorus concentration. The modification resulted in a greater mean phosphorus loss estimate compared to the WSM, and a relationship between nutrient application, tillage, and soil phosphorus concentrations. Outcomes support APLE as an appropriate alternative model for simulating soil phosphorus dynamics, and for informing mitigation of soil phosphorus losses through best management practices.
Anthropogenic disturbance alters plant and microbial communities in tidal freshwater wetlands in the Chesapeake Bay, USA
(2019) Gonzalez Mateu, Martina; Yarwood, Stephanie A; Baldwin, Andrew H; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Tidal freshwater wetlands are often found near urban centers, and as a result of human development they are subject to multiple environmental stressors. Increases in nutrient runoff, sedimentation, and hydrologic alterations have had significant impacts on these systems and on the ecosystem services they provide. One of the consequences of these stressors is the expansion of invasive species that can affect native biodiversity and the many biogeochemical processes that are key to wetland ecosystem function. This research looked at how human activities affect microbial communities in tidal freshwater wetlands, and explored various aspects of an invasive plant’s ecology in the Chesapeake Bay. In our first study, we found that microbial community composition differed along a rural to urban gradient and identified microbial taxa that were indicators of either habitat. Rural sites tended to have more methanogens and these were also indicators in these system, whereas in urban systems nitrifying bacteria were the main indicator taxa. This study suggested that urban wetlands have different microbial communities and likely different functions than those in rural areas, particularly concerning nitrogen and contaminant removal. Our second study looked at management of an invasive lineage of Phragmites australis which is commonly found in wetlands impacted by nitrogen enrichment. We evaluated the effects of different C:N ratios on the competitive ability of this lineage and a native North American lineage. Even though carbon addition did not improve the native’s competitive ability, we identified facilitative interactions when both lineages were growing together. This suggests that native and invasive Phragmites might coexist if there are no additional disturbances to the system. Our last study focused on plant-fungal interactions, and found that both Phragmites lineages benefitted from inoculation with fungal endophytes under salt stress. These results suggest that studies of plant-fungal interactions can yield insights into mechanisms of invasion, and could be further investigated in native wetland plants susceptible to increased salt stress following sea-level rise. Our results provide insights into plant and microbial ecology in the Chesapeake Bay’s tidal freshwater wetlands, and improve our understanding of the invasion process and management strategies of Phragmites australis.
ANTHROPOGENIC INFLUENCES ON BOTTOM-UP AND TOP-DOWN REGULATION OF ANIMAL DISTRIBUTIONS, POPULATIONS, AND BEHAVIORS IN URBAN ENVIRONMENTS
(2024) Herrera, Daniel Joseph; Gallo, Travis; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Animal populations are simultaneously governed by both bottom-up (e.g., habitat availability) and top-down (e.g., predation) regulation. While ecologists historically sought to differentiate the roles of bottom-up and top-down regulation on ecosystems, the two are not so easily defined in urban ecosystems due to the immense influence humans have on ecological processes in cities. In Chapter One, I present this argument from a philosophical perspective and comment on how this philosophy has shaped my worldview. In Chapter Two, I examine the legacy of historical park planning on urban bird assemblages using archived municipal maps and historical bird data. My analysis found a positive correlation between percent park area and both species richness and functional richness of birds. Additionally, I found the effect size of park area was larger than the effect of certain life history traits thought to facilitate urban exploitation. These results indicate that landscape features and life history traits are equally responsible for the success of synurbic species. Chapter Three explores the effect of urbanization on animal behavior by analyzing anti-predator behavior of white-tailed deer (Odocoileus virginianus) in relation to ambient light, noise, and human activity. Despite negligible predation risk in my study area, deer expressed higher vigilance behavior in dark and noisy conditions, and increased their foraging group size during noisy conditions. These results suggest that anti-predator behaviors are a response to the perception of predation risk rather than a response to the actual presence of predators. Although predation of deer is rare in urban ecosystems, predation of smaller wildlife species by mesopredators, such as non-native domestic cats (Felis catus) is common. Chapter Four examines the potential for predation and zoonotic disease transmission between cats and eight native mammals by estimating the spatial and temporal overlap between species. I found that cat distribution was largely driven by anthropogenic features, whereas native wildlife was generally deterred by anthropogenic features and instead occupied forested areas. I also found that cats, as a species, were active on the landscape during the full 24-hour cycle. As a result, while spatial overlap between cats and wildlife varied across the study area, temporal overlap was possible anywhere cats and wildlife co-occurred. Chapter Five expands on Chapter Four and investigates predation directly by using observations of cats carrying prey documented by motion-activated cameras. I found that predation by cats was higher in areas where supplemental cat food was prevalent, but declined near forested areas. Additionally, my results indicate that cats within 250 meters of a forest edge predominantly preyed on native wildlife, whereas cats generally preyed on non-native rats (Rattus norvegicus) when greater than 250 meters from a forest edge. Each chapter provides applied recommendations to the management and conservation of urban wildlife, but together, my work demonstrates the entanglement of bottom-up, top-down, and anthropogenic forces in urban ecosystems. In light of these findings, I advocate for a more nuanced understanding of ecosystem regulation through a socio-ecological lens.
Application and evaluation of a subaqueous soil-landscape conceptual model in the West River subestuary, Maryland
(Wiley, 2022-10-18) Wessel, Barret M.; Rabenhorst, Martin C.; Needelman, Brian A.
A soil-landscape conceptual model developed in the Rhode River subestuary of Maryland was applied to create a soil survey for the adjacent West River subestuary. The survey for the West River subestuary was completed before samples were collected there to evaluate the soil-landscape conceptual model used to generate the soil survey. The West River subestuary was then sampled along transects that crossed soil map units to compare observed soil taxa with predicted soil taxa. Observed transect samples were classified and scored based on their similarity to predicted taxa in soil map units. These data were resampled via a bootstrapping method to determine if the predictions of the West River subestuary soil survey were significantly different from random predictions. Significant information was provided by the survey, and therefore by the soil-landscape conceptual model used to generate it.
APPLICATION OF RECLAIMED WASTEWATER FOR AGRICULTURAL IRRIGATION: DEVELOPING A DECISION SUPPORT TOOL USING SPATIAL MULTI-CRITERIA DECISION ANALYSIS
(2020) Paul, Manashi; Negahban-Azar, Masoud; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Intensified climate variability, depleting groundwater, and escalating water demand create severe stress on high-quality freshwater sources used for agricultural irrigation. These challenges necessitate the exploration of alternative water sources such as reclaimed water to reduce the pressure on freshwater sources. To do so, it is key to investigate the spatial pattern of areas that are more suitable for water reuse to determine the potential of reclaimed wastewater use for irrigation. This study provides a systematic decision-analysis framework for the decision-makers using an integrated process-based hydrologic model for sustainable agricultural water management. The outcomes of this study provide evidence of the feasibility of reclaimed wastewater use in the agricultural sector. The two objectives of this study were to: 1) identify the locations that are most suitable for the reclaimed wastewater use in agriculture (hotspots); and 2) develop the watershed-scale models to assess the agricultural water budget and crop production using different water conservation scenarios including reclaimed wastewater use. To achieve the first objective, a decision-making framework was developed by using the Geographic Information System and Multi-Criteria Decision Analysis (GIS-MCDA). This framework was then tested in the Southwest (California), and the Mid-Atlantic (Maryland) regions. Based on WWTPs’ proximity, sufficient water availability, and appropriate treatment process of the treated wastewater, the “Most Suitable” and “Moderately Suitable” agricultural areas were found to be approximately 145.5 km2, and 276 km2 for California and, 26.4 km2 and 798.8 km2 for Maryland, respectively. These results were then used to develop the hydrologic models to examine water conservation and water reuse scenarios under real-world conditions, using the Soil and Water Assessment Tool (SWAT). In California, the combination of auto irrigation (AI) and regulated deficit irrigation (RDI) resulted in higher WP for both almond and grape (> 0.50 kg/m3). Results also suggested that the wastewater reuse in almond and grape irrigation could reduce groundwater consumption more than 74% and 90% under RDI and AI scenarios, respectively. For Maryland, model simulations suggested that the green water productivity (only rainfall) can be improved up to 0.713 kg/m3 for corn and 0.37 kg/m3 for soybean under the reclaimed wastewater use scenario.
Assessing Crop Water Productivity under Different Irrigation Scenarios in the Mid–Atlantic Region
(MDPI, 2021-06-30) Paul, Manashi; Negahban-Azar, Masoud; Shirmohammadi, Adel
The continuous growth of irrigated agricultural has resulted in decline of groundwater levels in many regions of Maryland and the Mid–Atlantic. The main objective of this study was to use crop water productivity as an index to evaluate different irrigation strategies including rainfed, groundwater, and recycled water use. The Soil and Water Assessment Tool (SWAT) was used to simulate the watershed hydrology and crop yield. It was used to estimate corn and soybean water productivity using different irrigation sources, including treated wastewater from adjacent wastewater treatment plants (WWTPs). The SWAT model was able to estimate crop water productivity at both subbasin and hydrologic response unit (HRU) levels. Results suggest that using treated wastewater as supplemental irrigation can provide opportunities for improving water productivity and save fresh groundwater sources. The total water productivity (irrigation and rainfall) values for corn and soybean were found to be 0.617 kg/m3 and 0.173 kg/m3, respectively, while the water productivity values for rainfall plus treated wastewater use were found to be 0.713 kg/m3 and 0.37 kg/m3 for corn and soybean, respectively. The outcomes of this study provide information regarding enhancing water management in similar physiographic regions, especially in areas where crop productivity is low due to limited freshwater availability.
Assessing nest attentiveness of Common Terns via video cameras and temperature loggers
(Springer Nature, 2020-07-08) Sullivan, Jeffery D.; Marbán, Paul R.; Mullinax, Jennifer M.; Brinker, David F.; McGowan, Peter C.; Callahan, Carl R.; Prosser, Diann J.
While nest attentiveness plays a critical role in the reproductive success of avian species, nest attentiveness data with high temporal resolution is not available for many species. However, improvements in both video monitoring and temperature logging devices present an opportunity to increase our understanding of this aspect of avian behavior. To investigate nest attentiveness behaviors and evaluate these technologies, we monitored 13 nests across two Common Tern (Sterna hirundo) breeding colonies with a paired video camera - temperature logger approach, while monitoring 63 additional nests with temperature loggers alone. Observations occurred from May to August of 2017 on Poplar (Chesapeake Bay, Maryland, USA) and Skimmer Islands (Isle of Wight Bay, Maryland, USA). We examined data respective to four times of day: Morning (civil dawn‒11:59), Peak (12:00‒16:00), Cooling (16:01‒civil dusk), and Night (civil dusk‒civil dawn). While successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day, failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods (p < 0.05) with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl (Bubo virginianus). Incubation appeared to ameliorate ambient temperatures during Night, as nests were significantly warmer during Night when birds were on versus off the nest (p < 0.05). Meanwhile, off-bouts during the Peak period occurred during higher ambient temperatures, perhaps due to adults leaving the nest during the hottest periods to perform belly soaking. Unfortunately, temperature logger data alone had limited ability to predict nest attentiveness status during shorter bouts, with results highly dependent on time of day and bout duration. While our methods did not affect hatching success (p > 0.05), video-monitored nests did have significantly lower clutch sizes (p < 0.05). The paired use of iButtons and video cameras enabled a detailed description of the incubation behavior of COTE. However, while promising for future research, the logistical and potential biological complications involved in the use of these methods suggest that careful planning is needed before these devices are utilized to ensure data is collected in a safe and successful manner.
Assessing Soil Organic Carbon in Soils to Enhance and Track Future Carbon Stocks
(MDPI, 2020-08-05) Yang, Yun-Ya; Goldsmith, Avi; Herold, Ilana; Lecha, Sebastian; Toor, Gurpal S.
Soils represent the largest terrestrial sink of carbon (C) on Earth, yet the quantification of the amount of soil organic carbon (SOC) is challenging due to the spatial variability inherent in agricultural soils. Our objective was to use a grid sampling approach to assess the magnitude of SOC variability and determine the current SOC stocks in three typical agricultural fields in Maryland, United States. A selected area in each field (4000 m2) was divided into eight grids (20 m × 25 m) for soil sample collection at three fixed depth intervals (0–20 cm, 20–40 cm, and 40–60 cm). Soil pH in all fields was significantly (p < 0.05) greater in the surface soil layer (6.2–6.4) than lower soil layers (4.7–5.9). The mean SOC stocks in the surface layers (0–20 cm: 1.7–2.5 kg/m2) were 47% to 53% of the total SOC stocks at 0–60 cm depth, and were significantly greater than sub-surface layers (20–40 cm: 0.9–1.3 kg/m2; 40–60 cm: 0.8–0.9 kg/m2). Carbon to nitrogen (C/N) ratio and stable C isotopic composition (δ13C) were used to understand the characteristics of SOC in three fields. The C/N ratio was positively corelated (r > 0.96) with SOC stocks, which were lower in sub-surface than surface layers. Differences in C/N ratios and δ13C signatures were observed among the three fields. The calculated values of SOC stocks at 0–60 cm depth ranged from 37 to 47 Mg/ha and were not significantly different in three fields likely due to the similar parent material, soil types, climate, and a short history of changes in management practices. A small variability (~10% coefficient of variation) in SOC stocks across eight sampling grids in each field suggests that re-sampling these grids in the future can lead to accurately determining and tracking changes in SOC stocks.
Assessing the uncertainty of emergy analyses with Monte Carlo simulations
(2012) Hudson, Amy; Tilley, David R; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Crop production systems were used to show the presence and propagation of uncertainty in emergy analyses and the effect of source variance on the variance of the yield unit emergy value (UEV). Data on energy/masses and UEVs for each source and yield were collected from the emergy literature and considered as inputs for the Monte Carlo simulation. The inputs were assumed to follow normal, lognormal, or uniform probability distributions. Using these inputs and a tabular method, two models ran Monte Carlo simulations to generate yield UEVs. Supplemental excel files elucidate the Monte Carlo simulations' calculations. The nitrogen fertilizer UEV and net topsoil loss energy were the inputs with the largest impact on the variance of the yield's UEV. These two sources also make the largest emergy contributions to the yield and should be the focus of a manager intent on reducing total system uncertainty. The selection of a statistical distribution had an impact on the yield UEV and thus these analyses may need to remain system- or even source- specific.
Assessing Wetland Restoration on the Delmarva Peninsula using Vegetation Characteristics
(2015) McFarland, Eliza Katherine; Baldwin, Andrew H; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
With wetland restoration, post-restoration monitoring is essential for determining developmental trajectories, particularly when comparing to natural reference systems. As part of the Mid-Atlantic Conservation Effects Assessment Project, 15 depressional wetlands on the Delmarva Peninsula of Maryland and Delaware were surveyed for above-ground vegetation and seed bank community composition, annual biomass production, and vegetation carbon content (10 restorations from prior-converted cropland (aged 5-31 years), and 5 natural forested depressions). Within each wetland, hydrologic zones (emergent, transition, upland) were also denoted and sampled. Restored wetlands showed more seed bank community similarity to natural wetlands than above-ground vegetation communities. Restorations also produced more annual herbaceous biomass than natural systems, and lower annual leaf litter biomass. After this period of post-restoration development, restored wetlands do not perform vegetation-related functions identical to their natural counterparts; however, these restorations are performing important vegetation-based functions that require yet more time to truly develop.
Assessment of nitrogen status and vegetation composition in tidal freshwater marshes using partial least squares regression models of hyperspectral canopy reflectance
(2006-04-28) Jenkins, Emily Poynter; Tilley, David R; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Hyperspectral canopy reflectance was used to predict sub-surface water nutrients, vegetation composition, and canopy nutrients, which could lead to more useful means for assessing the status of wetlands. Thirty field quadrats at two tidal freshwater marsh sites on the Nanticoke River (Maryland) were treated with five nitrogen levels. During the 2004-05 growing seasons, hyperspectral canopy reflectance was measured using a spectroradiometer with 1nm resolution across the visible and near - infrared spectrum (350-1075 nm), water samples were collected using lysimeters, species cover was quantified, and biomass was collected and analyzed for canopy nutrients. ANOVA was used to determine whether nitrogen affected reflectance, species composition, canopy N and P, and partial least squares regression was used to develop reflectance models predictive of these ecosystem properties. Results indicated that hyperspectral radiometry could be used as a remote sensing tool for quantifying sub-surface water nitrogen, vegetation composition, and canopy nutrients in tidal freshwater marshes.
Assessment of Petroleum-Based Plastic and Bioplastics Degradation Using Anaerobic Digestion
(MDPI, 2021-12-01) Nachod, Benjamin; Keller, Emily; Hassanein, Amro; Lansing, Stephanie
Bioplastics have emerged as a viable alternative to traditional petroleum-based plastic (PET). Three of the most common bioplastic polymers are polyhydroxybutyrate-valerate (PHBV), polylactide (PLA), and cellulose-based bioplastic (CBB). This study assessed biodegradation through anaerobic digestion (AD) of these three bioplastics and PET digested with food waste (FW) at mesophilic (35 °C) and thermophilic (55 °C) temperatures. The four plastic types were digested with FW in triplicate batch reactors. Additionally, two blank treatments (inoculum-only) and two PHBV treatments (with FW + inoculum and inoculum-only) were digested at 35 and 55 °C. The PHBV treatment without FW at 35 °C (PHBV-35) produced the most methane (CH4) normalized by the volatile solids (VS) of the bioplastics over the 104-day experimental period (271 mL CH4/g VS). Most bioplastics had more CH4 production than PET when normalized by digester volume or gram substrate added, with the PLA-FW-55 (5.80 m3 CH4/m3), PHBV-FW-55 (2.29 m3 CH4/m3), and PHBV-55 (4.05 m3 CH4/m3) having 848,275 and 561%, respectively, more CH4 production than the PET treatment. The scanning electron microscopy (SEM) showed full degradation of PHBV pellets after AD. The results show that when PHBV is used as bioplastic, it can be degraded with energy production through AD.
AUGMENTING SEQUENCING TECHNOLOGY FOR BETTER INFERENCE IN SOIL MICROBIOME ANALYSIS
(2023) Epp Schmidt, Dietrich; Yarwood, Stephanie A; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The advent of DNA sequencing revolutionized the field of microbiome research. Many organisms, by virtue of their codependence and/or growth rate, are either impossible or extremely challenging to get into pure culture. Sequencing allows important taxonomic and phylogenetic information to be obtained independent of culturing. Development of the sequencing technology itself has allowed for high throughput workflow that has allowed low cost and extensive sampling of microbiomes across environments. The co-development of reference datasets for taxonomy and functional assignments, along with open-source bioinformatics pipelines has further empowered scientists to explore microbiomes in many environments. However, there are limitations to sequence data that have constrained the ecological inferences in microbiome research. One such limitation, the compositional nature of sequence data, has impeded our ability to make accurate inferences about the environmental drivers of taxon abundance and covariance across conditions. In this dissertation I explore the use of quantitative PCR in combination with sequencing techniques to generate “Quantitative Sequencing” data (QSeq) that mitigates the limitations of compositionality on inferences relating to taxon abundance and covariance across environmental gradients. In chapter 1, I reviewed key characteristics of the soil environment and sequencing as a mechanism for sampling. In chapter 2, I leveraged modeling, synthesis, and literature review methods to establish the questions and data characteristics that demand QSeq methodology. I show that even small amounts of variation in total abundance make determining the effects of environment (biotic and abiotic factors) on any given taxon unreliable without QSeq. In Chapter 3, I extend the logic of quantitative sequencing to improve metagenome prediction from PICRUSt2. Using data synthesis methods, accounting for 16S gene abundance consistently improved the accuracy of predicted functional genes. This was confirmed by high correlations between predicted and measured gene abundance (QPCR). There was however a large variation in prediction accuracy, likely due in part to database biases and in part to decoupling of bacterial function from taxonomy. In Chapter 4, I applied QSeq in the context of an experimental, long-term farming system that has large gradients in total abundance with depth, and I used QSeq to identify taxa that changed in abundance due to different farming system management and soil depth. Finally in Chapter 5, I used QSeq to identify putative N-fixing taxa that responded to glyphosate in four experimental farming systems. I show that the abundance of these taxa were decoupled from other effects of glyphosate on N-fixation in soybean across farming systems.
Auxin regulates adventitious root formation in tomato cuttings
(Springer Nature, 2019-10-21) Guan, Ling; Tayengwa, Reuben; Cheng, Zongming (Max); Peer, Wendy Ann; Murphy, Angus S.; Zhao, Mizhen
Adventitious root (AR) formation is a critical developmental process in cutting propagation for the horticultural industry. While auxin has been shown to regulate this process, the exact mechanism and details preceding AR formation remain unclear. Even though AR and lateral root (LR) formation share common developmental processes, there are exist some differences that need to be closely examined at the cytological level. Tomato stem cuttings, which readily form adventitious roots, represent the perfect system to study the influence of auxin on AR formation and to compare AR and LR organogenesis.
Back to Earth: Molecular Approaches to Microbial Ecology Must Consider Soil Morphology and Physicochemical Properties
(2015) Dlott, Glade; Yarwood, Stephanie A; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This project studied the influence of different long-term agricultural management regimes on soil microbial communities, and compared survival strategies of individual prokaryotic OTUs in diverse soils subjected to long-term incubation. Together these would show whether alterations to microbial communities affect rates of soil carbon cycling. Agricultural soils were sampled at arbitrary depths above and below the plow layer, and relative abundances of microbes were measured using high-throughput sequencing. `Activity' (rRNA:rDNA) ratios were calculated for individual OTUs identified by high-throughput sequencing of tropical rainforest and temperate cornfield soils after incubation for one year with differing water and carbon availabilities. It was found that depth controls microbial communities to a greater degree than agricultural management, and that the characterization of microbial trophic strategies might be complicated by the often-ignored DNA preservation potential of soil. The study highlights the need for holistic approaches to testing hypotheses in modern microbial ecology.
BALD EAGLE (HALIAEETUS LEUCOCEPHALUS) POPULATION PRODUCTIVITY AND DENSITY DEPENDENT EFFECTS IN MICHGAN, 1961-2010
(2013) Simon, Kendall Lyn; Bowerman, William W; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The bald eagle (Haliaeetus leucocephalus) population in Michigan has undergone a significant recovery following the ban of the pesticide dichlorodiphenyltrichloroethane (DDT), and its subsequent derivatives, mainly dichlorodiphenyl-dichloroethylene (p,p'-DDE). This recovery however, has not been uniform throughout the state. Michigan is a heterogeneous habitat, causing the best-fit, experienced breeding pairs to settle in high quality breeding areas first. This high quality habitat mainly occurs in the inland regions of Michigan. These areas experienced the greatest productivity until the 1990's, quickly recovering from the detrimental effects of DDT. Great Lakes breeding areas, particularly Lake Michigan and Lake Huron, are now more productive than inland breeding areas. These Great Lakes breeding pairs however, are the least efficient breeders with greater amounts of changeover between nesting pairs within one breeding area in comparison to inland pairs. A constant turnover of breeding pairs may overshadow any underlying effects causing decreased reproductive fitness in Great Lakes adults.
BALD EAGLES (HALIAEETUS LEUCOCEPHALUS) AS INDICATORS OF GREAT LAKES ECOSYSTEM HEALTH
(2016) Simon, Kendall Lyn; Bowerman, William W; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Environmental indicators have been proposed as a means to assess ecological integrity, monitoring both chemical and biological stressors. In this study, we used nestling bald eagles as indicators to quantify direct or indirect tertiary-level contaminant exposure. The spatial and temporal trends of polychlorinated biphenyl (PCB) congeners were evaluated in nestling plasma from 1999–2014. Two hexa-chlorinated congeners, PCB-138 and 153, were detected with the highest frequency and greatest concentrations throughout Michigan. Less-chlorinated congeners such as PCB-52 and 66 however, comprised a greater percentage of total PCB concentrations in nestlings proximate to urbanized areas, such as along the shorelines of Lake Erie. Toxic equivalents were greatest in the samples collected from nestlings located on Lake Erie, followed by the other Great Lakes spatial regions. Nestling plasma samples were also used to measure concentrations of the most heavily-used group of flame retardants, brominated diphenyl ethers (BDEs), and three groups of alternative flame retardants, non-BDE Brominated Flame Retardants (NBFRS), Dechloranes, and organophosphate esters (OPs). BDE-47, 99 and 100 contributed the greatest to total BDE concentrations. Concentrations of structurally similar NBFRs found in this study and recent atmospheric studies indicate that they are largely used as replacements to previously used BDE mixtures. A variety of Dechloranes, or derivatives of Mirex and Dechlorane Plus, were measured. Although, measured at lesser concentrations, environmental behavior of these compounds may be similar to mirex and warrant future research in aquatic species. Concentrations of OPs in nestling plasma were two to three orders of magnitude greater than all other groups of flame retardants. In addition to chemical indicators, bald eagles have also been proposed as indicators to identify ecological stressors using population measures that are tied to the fitness of individuals and populations. Using mortality as a population vitality rate, vehicle collisions were found to be the main source of mortality with a greater incidence for females during white-tailed deer (Odocoileus virginianus) hunting months and spring snow-melt. Lead poisoning was the second greatest source of mortality, with sources likely due to unretrieved hunter-killed, white-tailed deer carcasses, and possibly exacerbated by density-dependent effects due to the growing population in Michigan.
Bio-Electrochemical Enhancement of Hydrogen and Methane Production in a Combined Anaerobic Digester (AD) and Microbial Electrolysis Cell (MEC) from Dairy Manure
(MDPI, 2020-10-14) Hassanein, Amro; Witarsa, Freddy; Lansing, Stephanie; Qiu, Ling; Liang, Yong
Anaerobic digestion (AD) is a biological-based technology that generates methane-enriched biogas. A microbial electrolysis cell (MEC) uses electricity to initiate bacterial oxidization of organic matter to produce hydrogen. This study determined the effect of energy production and waste treatment when using dairy manure in a combined AD and MEC (AD-MEC) system compared to AD without MEC (AD-only). In the AD-MEC system, a single chamber MEC (150 mL) was placed inside a 10 L digester on day 20 of the digestion process and run for 272 h (11 days) to determine residual treatment and energy capacity with an MEC included. Cumulative H2 and CH4 production in the AD-MEC (2.43 L H2 and 23.6 L CH4) was higher than AD-only (0.00 L H2 and 10.9 L CH4). Hydrogen concentration during the first 24 h of MEC introduction constituted 20% of the produced biogas, after which time the H2 decreased as the CH4 concentration increased from 50% to 63%. The efficiency of electrical energy recovery (ηE) in the MEC was 73% (ηE min.) to 324% (ηE max.), with an average increase of 170% in total energy compared to AD-only. Chemical oxygen demand (COD) removal was higher in the AD-MEC (7.09 kJ/g COD removed) system compared to AD-only (6.19 kJ/g COD removed). This study showed that adding an MEC during the digestion process could increase overall energy production and organic removal from dairy manure.