Submitted by (ana.araujo@ufrpe.br) on 2016-08-01T19:09:05Z No. of bitstreams: 1 Leandro Ricardo Rodrigues de Lucena.pdf: 4745359 bytes, checksum: 74da71d24d73158d808a456fd5365dc9 (MD5) / Made available in DSpace on 2016-08-01T19:09:05Z (GMT). No. of bitstreams: 1 Leandro Ricardo Rodrigues de Lucena.pdf: 4745359 bytes, checksum: 74da71d24d73158d808a456fd5365dc9 (MD5) Previous issue date: 2015-08-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The phenomena studied in agricultural and environmental sciences are characterized by their natural evolution and are also affected by human activity. They are comprised of a large number of components with non linear interactions on some scales, which produce emergent properties on other scales. These are characteristics of complex systems and can be studied using various concepts developed over the last decades such as fractals, multifractals, self-organized criticality and entropy. In this paper we analyzed the temporal dynamics of climate variables rainfall, temperature and stream flow, and spatial distribution of vegetation fires represented by hot pixels detected by satellites. We used the method of lacunarity that serves to describe the distribution of gaps in a data set. Using lacunarity values for different time scales it was possible to describe the rainfall regime (fragmentation of rainy periods) and classify two precipitation patterns in the state of Sergipe, one formed by stations approaching the Sergipe coast, and other formed by stations that are located in semiarid region. The lacunarity analysis was successful for evaluation of impact of human activity on natural regime of river flow, in the case of rivers Atibaia and Jaguari, caused by the construction of Cantareira system reservoirs. After the construction of the reservoirs lacunarity values of stream flow decreased, indicating a reduction of heterogeneity of flow dynamics due to regulation of water flow. The results of lacunarity analysis of time series of temperature and precipitation in the state of Pernambuco showed that this method can be used to identify the geographic regions with characteristic temporal behavior of climatic variables. The lacunarity analysis for two-dimensional data was applied to spatial distribution of hot pixels detected in Amazonia during the period 2000-2013. In the years with high number of hot pixels the lacunarity values were lower, indicating a more homogeneous spatial distribution compared with periods with low number of hot pixels. The lacunarity method has also shown to be effective in identifying the dry and rainy seasons, by capturing the heterogeneity of spatial distribution of hot pixels. These results may be useful in planning the use of water resources, planning of agricultural activities, developing strategies to reduce the impact of extreme weather events (drought, flood) and natural disasters such as forest fires. / Os fenômenos estudados em ciências agrárias e ambientais além da própria evolução são fortemente influenciados pela atividade humana e caracterizam-se pelo grande número de componentes interagindo de forma não linear em uma escala e produzindo as propriedades emergentes em outras escalas. Com essas características os fenômenos agroambientais considerados sistemas complexos e estudados utilizando vários conceitos desenvolvidos durante as últimas décadas entre os quais: fractais, multifractais, criticalidade auto-organizada e entropias. Neste trabalho analisou-se a dinâmica temporal das variáveis climáticas: precipitação, temperatura e vazão do rio e a distribuição espacial de focos de queimadas. Foi utilizado o método de lacunaridade, que serve para avaliar a distribuição de lacunas em um conjunto de dados. Utilizando os valores de lacunaridade para diferentes escalas temporais foi possível descrever o regime da chuva (fragmentação dos períodos chuvosos) e classificar dois padrões de precipitação no estado de Sergipe, um padrão formado pelas estações que se aproximam da costa sergipana e outro das estações que se localizam nas regiões do semiárido e agreste. A análise de lacunaridade mostrou-se útil para avaliação do impacto da atividade humana no regime natural da vazão do rio, no caso dos rios Atibaia e Jaguari a construção dos reservatórios do sistema Cantareira. Depois da construção dos reservatórios os valores da lacunaridade diminuíram em relação ao período antes da construção dos reservatórios indicando a diminuição de heterogeneidade da dinâmica da vazão devido à regulação do fluxo hídrico. Os resultados da análise de lacunaridade de séries temporais de temperatura e de precipitação do estado de Pernambuco demostraram que este método pode ser usado para identificar as regiões geográficas com comportamento temporal característico das variáveis climáticas. A análise de lacunaridade para dados bidimensionais foi aplicada na distribuição espacial de focos de queimadas detectadas na Amazônia legal durante o período 2000-2013. Nos anos com maior número de queimadas os valores de lacunaridade indicaram uma distribuição espacial mais homogênea comparando com períodos com menor número das queimadas. O método de lacunaridade (avaliando a heterogeneidade da distribuição espacial de queimadas) também mostrou se eficaz em identificação das estações seca e chuvosa. Estes resultados podem ser úteis em planejamento do uso de recursos hídricos, em planejamento das atividades agrícolas, desenvolvimento de estratégias para diminuir as consequências de fenômenos climáticos extremos (seca, cheia) e desastres naturais como incêndios florestais.

Human cognition involves dynamic neural activities in distributed brain areas. For studying such neural mechanisms, magnetoencephalography (MEG) and electroencephalography (EEG) are two important techniques, as they non-invasively detect neural activities with a high temporal resolution. Recordings by MEG/EEG sensors can be approximated as a linear transformation of the neural activities in the brain space (i.e., the source space). However, we only have a limited number sensors compared with the many possible locations in the brain space; therefore it is challenging to estimate the source neural activities from the sensor recordings, in that we need to solve the underdetermined inverse problem of the linear transformation. Moreover, estimating source activities is typically an intermediate step, whereas the ultimate goal is to understand what information is coded and how information flows in the brain. This requires further statistical analysis of source activities. For example, to study what information is coded in different brain regions and temporal stages, we often regress neural activities on some external covariates; to study dynamic interactions between brain regions, we often quantify the statistical dependence among the activities in those regions through “connectivity” analysis. Traditionally, these analyses are done in two steps: Step 1, solve the linear problem under some regularization or prior assumptions, (e.g., each source location being independent); Step 2, do the regression or connectivity analysis. However, biases induced in the regularization in Step 1 can not be adapted in Step 2 and thus may yield inaccurate regression or connectivity results. To tackle this issue, we present novel one-step methods of regression or connectivity analysis in the source space, where we explicitly modeled the dependence of source activities on the external covariates (in the regression analysis) or the cross-region dependence (in the connectivity analysis), jointly with the source-to-sensor linear transformation. In simulations, we observed better performance by our models than by commonly used two-step approaches, when our model assumptions are reasonably satisfied. Besides the methodological contribution, we also applied our methods in a real MEG/EEG experiment, studying the spatio-temporal neural dynamics in the visual cortex. The human visual cortex is hypothesized to have a hierarchical organization, where low-level regions extract low-level features such as local edges, and high-level regions extract semantic features such as object categories. However, details about the spatio-temporal dynamics are less understood. Here, using both the two-step and our one-step regression models in the source space, we correlated neural responses to naturalistic scene images with the low-level and high-level features extracted from a well-trained convolutional neural network. Additionally, we also studied the interaction between regions along the hierarchy using the two-step and our one-step connectivity models. The results from the two-step and the one-step methods were generally consistent; however, the one-step methods demonstrated some intriguing advantages in the regression analysis, and slightly different patterns in the connectivity analysis. In the consistent results, we not only observed an early-to-late shift from low-level to high-level features, which support feedforward information flow along the hierarchy, but also some novel evidence indicating non-feedforward information flow (e.g., topdown feedback). These results can help us better understand the neural computation in the visual cortex. Finally, we compared the empirical sensitivity between MEG and EEG in this experiment, in detecting dependence between neural responses and visual features. Our results show that the less costly EEG was able to achieve comparable sensitivity with that in MEG when the number of observations was about twice of that in MEG. These results can help researchers empirically choose between MEG and EEG when planning their experiments with limited budgets.

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15-N hydrolysate from Ralstonia eutropha, we identified 15,297 non-redundant unlabeled peptides of which 10,839 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse gut microbiome.

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Encouraging greater implementation of conservation agriculture practices such as reduced tillage and cover crops may require better understanding of the effect of these practices on soil health. The overall objective of this study was to quantify soil health dynamics due to conservation agriculture practices and address methodological gaps in terms of measuring soil health parameters. We developed five sites across the state of Virginia; each site had replicated plots with combinations of reduced tillage versus disk tillage and wintertime cover crops versus no cover crops as experimental treatments. Soil and plant samples were collected 1-2 times per year for 3 years, and were analyzed for 30 soil health parameters. The parameters were first evaluated to determine if any consistently detected treatment differences. We then quantified the temporal dynamics of the eight most responsive soil health parameters, while considering influences of soil water content at time of tillage, cover crop biomass, and previous land management history. Of the analyzed parameters, only 2-4 mm aggregate stability and magnesium showed high responsiveness and consistency in identifying tillage and cover crop effects. None of the parameters detected treatment differences in all sites or at all times, yet samples collected after high biomass cover crops or after tillage in wet conditions tended to show significant treatment differences for multiple indicators. The previous history of management in each site may have affected trends in aggregate stability, but did not appear to influence other indicators. As soil aggregate stability was found to be the most important soil health parameter, our third study developed an improved method for measuring soil aggregate stability. This new method, Integrated Aggregate Stability (IAS), interprets aggregate stability using a laser diffraction machine. Overall, IAS showed higher correlation with the wet sieving method (R2 = 0.49 to 0.59) than widely used median aggregate size (d50) (R2 = 0.09 to 0.27). IAS can also quantify stability of macro- and micro-sized aggregates, which d50 cannot. When comparing between IAS and wet sieving, IAS requires considerably less time and sample amounts. Our fourth study focused on creating an inexpensive yet accurate tool for measuring soil respiration, as microbial assessments based on respiration rates have great potential for detecting rapid changes in soil health. Using an Arduino-based infrared gas analyzer (IRGA) sensor, we developed the Soil Microbial Activity Assessment Contraption (SMAAC) for less than $150. Our results show that SMAAC provided consistent readings with a commercial IRGA unit when tested using three different configurations. Altogether, the research presented in this dissertation identifies important soil health parameters and quantifies their temporal and between-site dynamics. Using this narrower set of indicators can help producers and practitioners save resources when conducting measurements to assess soil health effects of agricultural practices. Further, this work also provides improved measurement techniques for useful soil health parameters like aggregate stability and soil respiration. These findings and innovations should help to encourage greater adoption of agricultural management practices that build and preserve soil health. / Doctor of Philosophy / If we want to make sure that ample and safe food is available to future generations, then it is time that we produce food without damaging the soil. Many widely used soil management techniques like tillage and leaving the field bare can harm the soil and decrease productivity in the long run. One potential technique to produce food while protecting the soil and environment is conservation agriculture, which can include reduced tillage and cover cropping. Reduced tillage is a technique in which we grow food without majorly disturbing the soil, while cover crops are planted when cash crops are not in the field in order to improve or sustain the soil. Understanding the soil-related benefits of conservation agriculture practices is important to encourage farmers to adopt these practices. In this study we tested the effects on soils of reduced tillage and cover crop practices versus conventional tillage and bare soil practices, using five locations across Virginia. We also developed improved methods for measuring two informative soil parameters. We found that, when looking at all of our five sites, the stability of soil aggregates, the rate at which water enters soil, and the nutrients in surface soils were all affected by the type of management that the soils were subjected to. Reduced tillage increased stability of soil aggregates when compared with conventional till. This increased stability of aggregates indicators lower potential for surface water runoff, erosion, and flooding when we practice reduced tillage. Cover cropping also increased stability of soil aggregates, especially when the cover crops attained substantial above-ground mass. Soil nutrients (which are essential for plants to grow) were also overall higher in the surface soil layers under no-till. Since the stability of soil aggregates was found to be an important benefit of CA practices, we also perceived a need for a better method for measuring stability of these aggregates. In response, we developed a new index called Integrated Aggregate Stability (IAS). IAS was found to give similar results as established methods, but the time required to get IAS result is about 10 minutes, whereas the time required for established methods like wet sieving is around 2 days. IAS measurements are therefore both accurate and quick to perform. We also focused on developing an inexpensive tool for measuring soil respiration. Soil respiration-based measurements help us to understand the activity of microbes in the soil. These microbes are very important for soils to function. Our tool, Soil Microbial Activity Assessment Contraption (SMAAC), was very consistent with a currently used tool and shows high potential for future use. Altogether, we found that no-tillage and cover cropping can increase stability of soil aggregates even within 1-3 years of starting those practices. No-till can also increase nutrient concentrations in the top soil layer. The tools and innovations developed in this study have the potential to increase the ability of farmers to assess soil health and also encourage greater adoption of conservation agriculture practices.

For marine fishes, the early larval phase is considered a critical stage for survivorship and recruitment. The spatial and temporal dynamics of larval fish assemblages can influence their success and trophic structure of marine communities and entire ecosystems. This thesis will provide the first characterization of larval fish assemblage in the Strait of Georgia (SoG) in terms of diversity, abundance and composition, and their variability over multiple temporal scales, as well as the first quantification of variability in larval fish distribution in the SoG across multiple spatial scales. On the interdecadal scale, a significant decrease in larval abundance of several dominant fish taxa (Pacific hake, walleye Pollock, northern smoothtongue and rockfishes) contributed to a decline in total larval abundance and turnover in the composition structure between the early 1980s and the late 2000s. In contrast, both abundance and the relative composition of flatfishes and several demersal forage fish taxa increased during the same period. On interannual scales, abundance, diversity and community structure of the spring larval assemblages varied dramatically through 2007-2010, a period which alternated between strong La Niña and El Niño events. Higher overall larval concentrations were associated with warm conditions in the SoG in 2007 and 2010, while the lowest larval concentration was associated with cooler condition in 2009. Examination of associations between larval fish assemblages and environmental fluctuations suggests a potential influence of large-scale climate processes between the early 1980s and the late 2000s, but a primary association with local environmental factors on interannual scales. Spatial patterns in larval density of three dominant fish taxa (Pacific herring, Pacific hake and northern smoothtongue) were mostly structured on predefined broad (> 40km) and medium (20~40km) scales. Although their scale-dependent associations with environmental factors varied interannually, larval distributions in the central-southern SoG were generally associated with salinity, temperature and vertical stability of water column in the upper layer (0-50m). Our results emphasize the role of local estuarine circulation in structuring hierarchical spatial distributions of planktonic fish larvae in the SoG. These findings will provide considerable implications in fisheries resource management and conservation strategies. / Graduate / 0416 / 0329 / guanlu129@gmail.com

La diversité des très petits eucaryotes (<5 µm) a essentiellement été étudiée par des méthodes moléculaires dans les océans ou de grands lacs. La diversité dans les écosystèmes d'eau douce peu profonds reste très peu explorée, bien que ces systèmes soient très nombreux et écologiquement importants en régions tempérées. Dans ce travail, nous avons voulu explorer la diversité et certains aspects de l'écologie des micro-organismes eucaryotes dans ce type d'écosystèmes, à l'aide de méthodes moléculaires ciblant l'ADNr 18S de cellules planctoniques de surface, dans la fraction de taille théorique 0,2-5 µm. Nous nous sommes d'abord concentrés sur les haptophytes, un groupe important en milieu marin mais beaucoup moins bien connu en eaux douces. Nous avons exploré leur diversité à l'aide d'amorces spécifiques pour amplifier les gènes des ARNr 18S du groupe suivi du clonage / séquençage Sanger de ces marqueurs, dans 17 écosystèmes continentaux et 2 colonnes d'eau marines pour comparer la diversité dans différents milieux, ainsi qu'à l'aide du pyroséquençage de ce même marqueur dans 4 mares et 1 ru au cours d'un suivi mensuel sur 2 ans. La diversité des haptophytes était moindre en eau douce qu'en milieu marin, mais nous avons pu y détecter un nouveau groupe, divergeant au sein des Isochrysidales, présentant une saisonnalité marquée. Les phylotypes d'eau douce étaient majoritairement distincts de ceux détectés en milieu marin, et ont confirmé l'existence de plusieurs transitions marin/eau douce dans l'histoire des haptophytes. Dans un second temps, nous avons exploré par pyroséquençage 454 des ADNr 18S la diversité des micro-organismes eucaryotes dans 4 mares et 1 ru, échantillonnés au printemps, et différant par leur taille, leur forme et leur environnement proche. Nous avons détecté une grande diversité dans chaque système étudié, avec des séquences affiliées à tous les supergroupes reconnus (Archaeplastida, Stramenopiles, Alveolata, Rhizaria, Excavata, Amoebozoa et Opisthokonta), ainsi qu'à des taxa de position phylogénétique mal résolue (i.e. Cryptophyta, Haptophyta, Centroheliozoa Katablepharida). Notamment, certaines OTU étaient affiliées au groupe MAST-3 (MArine STramenopiles) jusque-là considéré comme exclusivement marin. Les communautés de petits eucaryotes étaient différentes dans chacun des écosystèmes ; ces différences ne corrélaient pas avec les distances géographiques entre sites (test de Mantel), et des analyses multivariées n'ont pas mis en évidence de relation claire entre la distribution d'un groupe et un paramètre environnemental. Par la suite, nous avons suivi la diversité des eucaryotes microbiens sur 2 ans dans les mêmes 5 écosystèmes. Nous avons collecté des échantillons de plancton et mesuré différents paramètres physico-chimiques chaque mois, sauf pour 2 des écosystèmes lorsqu'ils étaient à sec. La diversité détectée sur 2 ans était bien plus grande que celle identifiée lors de l'étude ponctuelle. Cryptophytes, ciliés, chrysophytes et champignons stricto sensu étaient globalement les plus abondants. La composition et la structure des communautés différaient d'un écosystème à l'autre sur l'ensemble du suivi. Ces communautés étaient très dynamiques, et montraient une saisonnalité claire. La distribution spatio-temporelle des champignons sensu stricto était clairement corrélée aux hautes valeurs de conductivité. Enfin, nous avons décrit la dynamique des communautés de petits eucaryotes dans l'une des mares et le ru lors d'épisodes de sécheresse. Nous avons collecté du sédiment dans le lit asséché des écosystèmes lors des sécheresses, et du plancton le reste du temps. Les communautés du sédiment présentaient une signature différente des assemblages planctoniques. Ces derniers montraient une résilience élevée, et retrouvaient une signature planctonique moins d'un mois après que les écosystèmes soient de nouveau en eau. / The diversity of very small eukaryotes (<5 µm) has mainly been studied by molecular methods in marine systems or in large lakes. However, that of small shallow systems remains practically unexplored, despite the fact that these systems are extensive and ecologically important in temperate regions. We thus aimed at describing the diversity and community composition of small eukaryotes in shallow freshwater systems, using molecular methods targeting the 18S rRNA gene of planktonic cells in the 0.2-5 µm size range. We first focused on haptophytes, an important group in marine environments but much less known in freshwaters. We explored their diversity using newly designed specific primers to amplify haptophyte 18S rRNA genes, followed by their subsequent cloning and Sanger sequencing in seventeen continental ecosystems and in two marine water columns to allow comparisons between different environments, as well as using 454-pyrosequencing in 4 ponds and one brook during a 2-years monthly survey. Even if freshwater haptophytes were less diverse than marine lineages, we revealed the presence of a divergent lineage belonging to the Isochrysidales never recorded so far, which presented a marked seasonality. Freshwater phylotypes were usually distinct from their marine counterparts, and confirmed the occurrence of multiple marine–freshwater transitions in haptophyte evolution. In a second step, we explored the microbial eukaryote diversity in 5 distinct shallow ecosystems sampled at spring and that differ in size, shape and surrounding environment, by 454-pyrosequencing their 18S rDNA. Diversity was high in the studied systems, with sequences affiliated to the 7 recognized eukaryotic supergroups (Archaeplastida, Stramenopiles, Alveolata, Rhizaria, Excavata, Amoebozoa and Opisthokonta) as well as groups of unresolved phylogenetic position including, among others, Cryptophyta, Haptophyta, Centroheliozoa or Katablepharida. Especially, we detected OTUs affiliated to the previously thought exclusively marine lineage MAST-3 (MArine STramenopiles), and potentially to other MAST groups with no known representative from freshwaters. Small eukaryote community structures were different in the five ecosystems. Differences in community compositions did not correlate with geographical distances (Mantel test), and multivariate statistical analyses did not reveal clear relationships between any group distribution and specific environmental parameters. Then, we conducted a 2-years survey of eukaryotic micro-organisms diversity in the same 5 small ecosystems. To do so, we collected plankton and measured several physical and chemical parameters on a monthly basis, except for two systems when they were totally dry. The total diversity encountered during the 24 months was much broader than that identified in the previous snapshot study. The most abundant detected groups were Cryptophytes, Ciliates, Chrysophytes and Fungi sensu stricto. Community structures and compositions were different in the five systems along the two years. In all systems, communities were highly dynamic, and revealed a marked seasonality, notably with summer and winter communities being always distinct. Multivariate statistical analyses were used to analyze simultaneously physico-chemical data and community compositions. The clearest correlation associated fungi distribution and high conductivity. Finally, we described the dynamics of small-eukaryote communities in a pond and a brook through drought events. We collected sediment on the system beds when they were dry, and plankton the rest of time. Communities in the sediment and in the water presented distinct signatures. Surface water communities presented (a high) resilience, and recovered a planktonic signature within a month after the systems were filled up again with water.

Invasive exotic species (IES) responses to silvicultural treatments eight years after timber harvesting were examined and compared to one-year post-harvest IES survey in University of Kentucky’s Robinson Forest. The temporal effects of harvesting were further compared between harvested and non-harvested watersheds. Analyses were performed to identify IES spatial distribution and determine the relationships between IES presence and disturbance effects, biological, and environmental characteristics. IES prevalence was higher in the harvested watersheds and was influenced by canopy cover, shrub cover and disturbance proximity. Ailanthus altissima and Microstegium vimineum presence in the study area has decreased over time. Comparing to the 1-yr post-harvest study which only identified direct harvesting effects (e.g. canopy cover and disturbance proximities) as significant predictors, the 8-yr post-harvest survey results suggest that while harvesting effects and disturbance proximity still play an important role, environmental characteristics have also taken precedence in predicting IES presence. Overall IES prevalence has decreased but invasive plant species richness has increased over time. Results indicate that IES eradication may not need to be conducted immediately after harvesting, and when needed, can primarily target IES hotspots where low canopy cover, proximity to disturbance, and southwest facing slopes convene on the landscape.

Agricultural intensification reliant on monocrops could change soil health in a way that does not support maximum crop productivity. Twenty-nine-year-old no-till field plots at the University of Kentucky Spindletop research farm showed a significant reduction in corn yields from continuous corn plots compared to those from plots in various types of rotation. The objective of this study was to determine what role soil microbes might play in yield reduction and how management and time effects microbial community structure. Samples were collected from the following treatments: continuous corn (CC), continuous soybean (SS), a 2-year corn/soybean rotation (CCSS), Corn in rotation with soybean with winter wheat cover (C/W/S), and sod controls (SOD). Soil health-related parameters were determined along with microbial community structure using phospholipid fatty acid analysis (PLFA). Results show that there is a strong seasonal dynamic in microbial communities with May, July and September showing the greatest differentiation between treatments. Nonparametric multidimensional analysis (NMDS) shows that microbial communities under SS, CC treatments were significantly different from the CS and CWS treatments across all four years of the study. My findings will prove useful for assessing the contribution of biological indicators to agroecosystem function and will aid in making recommendations of when and how to manage these parameters to improve soil health and maximize yield.