Factors influencing the participation in environmental stewardship programs: a case study of the agricultural and forestry sectors in Louisiana

Considerable research has been conducted to evaluate the adoption of agricultural best management practices and their overall impact on improving environmental quality. However, limited studies have been conducted to evaluate the behavioral factors that influence the adoption of these practices in the context of educational programs The goal of this study is to determine the factors that influence farmer conservation behavior that might lead to an increased probability of improving agriculture and forestry watersheds. A conceptual model was developed to identify the: 1) identify landowner participation in watershed conservation projects, and 2) determine the factors influencing agriculture and forestry landowners to participate in watershed conservation projects. The model includes four major sets of explanatory variables including: 1) social-psychological, 2) farm structural, 3) ecological, and 4) institutional. This study indicates that both farmers and loggers that are younger, more educated and of Caucasian ethnicity tend to participate in environmental stewardship programs which lead to the implementation of conservation practices. Farmers with strong local organization relationships have a greater tendency to participate in environmental stewardship programs which leads to the adoption of conservation practices. Agricultural producers with higher income resulting from farming, higher total acres, and farms legal structure indicated as incorporated tended to participate in environmental stewardship programs. Loggers that produced larger loads per week, which is an indicator of size, tended to participate in environmental stewardship programs. The study also found that agricultural producers who spend more time in a job off-farm and have a family owned operation have a lower tendency to participate in environmental stewardship programs. The study indicates that farmers that have modified their operation due to the Clean Water Act as well as awareness of efforts to control non-point source pollution through the Clean Water Act have a lower tendency to participate in environmental stewardship programs, thus viewed upon as institutional barriers. Also found was loggers with negative relationships toward regulatory agencies and lending institutions have a lower tendency to participate in environmental stewardship programs. Farmers have mixed attitudes toward government involvement in agriculture. These conclusions are supported by earlier studies

Measuring sovereign contagion in Europe

This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use oftwo econometric approaches based on quantile regressions (standard quantile regression and Bayesianquantile regression with heteroskedasticity) we find that the propagation of shocks in euro\u2019s bond yieldspreads shows almost no presence of shift-contagion in the sample periods considered (2003\u20132006,Nov. 2008\u2013Nov. 2011, Dec. 2011\u2013Apr. 2013). Shock transmission is no different on days with big spreadchanges and small changes. This is the case even though a significant number of the countries in our sample have been extremely affected by their sovereign debt and fiscal situations. The risk spillover amongthese countries is not affected by the size or sign of the shock, implying that so far contagion has remainedsubdued. However, the US crisis does generate a change in the intensity of the propagation of shocks inthe eurozone between the 2003\u20132006 pre-crisis period and the Nov. 2008\u2013Nov. 2011 post-Lehman one,but the coefficients actually go down, not up! All the increases in correlation we have witnessed overthe last years come from larger shocks and the heteroskedasticity in the data, not from similar shockspropagated with higher intensity across Europe. These surprising, but robust, results emerge becausethis is the first paper, to our knowledge, in which a Bayesian quantile regression approach allowing forheteroskedasticity is used to measure contagion. This methodology is particularly well-suited to dealwith nonlinear and unstable transmission mechanisms especially when asymmetric responses to signand size are suspected

Simulation of Wind Dispersal of Tree Seeds, Tree Colonization, and Growth of Bottomland Hardwood Reforestation Sites of the Mississippi Alluvial Valley

Tree community composition in bottomland hardwood reforestation sites is considerably less diverse than natural bottomland hardwood forests. This study sought to understand the mechanisms behind failure to develop diverse tree communities. First, I developed a mechanistic model of seed dispersal by wind in spatially variable environments. Second, I developed a spatially explicit simulation model of forest dynamics that includes this wind-dispersal model to investigate whether diversity is limited by dispersal or interactions among species and individuals. Finally, I performed model experiments to determine if manipulations of stand structure might help improve conditions for colonization of various species, thus enhancing diversity of reforestation sites. The wind dispersal model was unbiased and accurate for predicting seed dispersal patterns of four species of wind-dispersed trees, demonstrating the utility of my algorithm for making predictions of seed arrival in a forest simulation model. The forest simulation model accurately predicted basal area growth and general patterns of species relative abundance in natural and reforested bottomland hardwoods, and predicted that reforestation sites will probably never attain diversity levels of natural forests under the current management scenario. Development of diversity was hindered by competition from the species planted and limited dispersal from forests. Hence, the only reasonably successful option to enhance diversity is probably to establish sites with mixed-species plantings at the outset. However, if stands are thinned at relatively young ages (15 yr for acorn-established stands, 25 yr for seedling-established stands), before canopy closure from planted individuals results in mortality of colonizing individuals, diversity may be enhanced if adequate numbers of colonizers are able to disperse to the site. Further research is necessary on mechanistic dispersal by animals, transition rates from seeds to seedlings, and the factors that affect such transitions in order to more accurately predict forest community development

Model-based approaches to discovering diversity : new implementations, tests of adequacy and an empirical application to central American Diptera

Most of the earth’s biodiversity is unknown to science. With global climate change set to drastically alter its distribution, it is imperative to catalogue it and understand its function in order to preserve it and better understand how this change will impact humanity. Recent technological and statistical advances have in theory made possible increasingly rapid discovery and description of diversity. The statistical properties and performance of these new approaches are still poorly known, however, their integration with complementary methods from disparate disciplines has not been achieved. In this dissertation we present three chapters of original research that advance these areas of biodiversity science. The first introduces a new implementation of the GMYC, a statistical model used for species delimitation. This implementation fully accounts for uncertainty in model parameters, and we test its performance under various historical scenarios. We find that the model generally performs well, but that failing to account for uncertainty in nuisance parameters inflates confidence in species limits. The second introduces a method to examine the fit of empirical data to a multispecies coalescent model commonly used in phylogenetic inference. Systematic and phylogeographic studies are generating ever-larger datasets which often range up to the genome scale and often wish to use coalescent models to infer parameters such as phylogenies, divergence times and effective population sizes. Though the multispecies coalescent can infer these parameters, it is unclear the extent to which it is a good fit for these new empirical datasets. We employ our new approach to 25 published datasets and find that a majority of them show poor fit to the data and that for some of them, that poor fit affects inference. In the last chapter we integrate statistical approaches from both ecology and systematics to infer species limits, phylogeny, population genetic structure and ecological community structure in a study of a poorly known tropical alpine fly fauna. We find that we can effectively describe patterns of diversity in the absence of a low-level taxonomic framework, but that inference of the processes structuring that diversity remains difficult. We also find that some of our inferences of community structure are sensitive to uncertainty in species limits and phylogeny

Functional Organization of the Human Brain: How We See, Feel, and Decide.

The human brain is responsible for constructing how we perceive, think, and act in the world around us. The organization of these functions is intricately distributed throughout the brain. Here, I discuss how functional magnetic resonance imaging (fMRI) was employed to understand three broad questions: how do we see, feel, and decide? First, high-resolution fMRI was used to measure the polar angle representation of saccadic eye movements in the superior colliculus. We found that eye movements along the superior-inferior visual field are mapped across the medial-lateral anatomy of a subcortical midbrain structure, the superior colliculus (SC). This result is consistent with the topography in monkey SC. Second, we measured the empathic responses of the brain as people watched a hand get painfully stabbed with a needle. We found that if the hand was labeled as belonging to the same religion as the observer, the empathic neural response was heightened, creating a strong ingroup bias that could not be readily manipulated. Third, we measured brain activity in individuals as they made free decisions (i.e., choosing randomly which of two buttons to press) and found the activity within fronto-thalamic networks to be significantly decreased compared to being instructed (forced) to press a particular button. I also summarize findings from several other projects ranging from addiction therapies to decoding visual imagination to how corporations are represented as people. Together, these approaches illustrate how functional neuroimaging can be used to understand the organization of the human brain

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Modeling ecological disturbances in the Southeastern United States

Society requires better insights into how disturbances will alter the global carbon cycle. Ecosystem models help us understand the carbon cycle and make predictions about how the terrestrial land sink will change under future climate regimes. Disturbances drive ecosystem cycling, but modeling disturbances has unique challenges, particularly in incorporating heterogeneity and parameter uncertainty. In this dissertation, I explore two questions. 1) How can we capture disturbance ecology in models?, which I explore in my first and second chapters, and 2) How can we use those models to make projections for the Southeastern US?, which I explore in my third and fourth chapters. Both my first and second chapters point to the practical trade-offs in model structure and realism. In my first chapter, I found that representing spatially implicit contagious disturbances in terms of shape and frequency accurately captured structural changes over time and separated the disturbance regimes of different regions. Representing spatially implicit disturbances in terms of shape and frequency sacrificed the specificity of a space-based approach but may be more computationally efficient. In my second chapter, I developed a framework for calibrating models based on an iterative cycle between uncertainty analysis and literature synthesis, targeted field campaigns, and statistical constraint. I found that targeted field work and statistical constraint reduced parameter uncertainty until structural uncertainty began to dominate. Models that capture disturbance dynamics can help us anticipate effects of global change factors like climate change and invasive species. In my third chapter, I found that elevated temperatures reduce cogongrass biomass, and that cogongrass facilitates pine dominance over oaks in a mixed pine-oak stand. This suggests that cogongrass mediates inter-species competition at an ecosystem scale. Prescribed burns are a management technique used to suppress cogongrass and has an add-on benefit of reducing tick populations. However, climate change may threaten how frequently prescribed fires can be safely deployed. In my fourth chapter, I found that tick populations are most sensitive to leaf litter and humidity, which allows for management strategies as an alternative to prescribed burns

Brain structure and function in Huntington's disease gene carriers far from predicted disease onset

Whilst there are currently no available disease modifying therapies for Huntington’s Disease (HD), recent progress in huntingtin-lowering strategies hold great promise. Initiating therapies early in the disease course will be important and a complete characterisation of the premanifest period will help inform when to initiate disease modifying therapies and the biomarkers that may be useful in such trials. Previous research has characterised the premanifest period up to approximately 15 years from predicted onset, but even at this early stage the disease process is already underway as evidenced by striatal and white matter atrophy, reductions in structural connectivity within brain networks, rising biofluid biomarkers of neuronal dysfunction, elevations in psychiatric symptoms and emerging subtle cognitive impairments. In order to understand how early neurodegeneration can be detected and which measures are most sensitive to the early disease processes, we need to look even earlier in the disease course. This thesis documents the recruitment and analysis of the HD Young Adult Study: a premanifest cohort further from predicted clinical onset than previously studied with an average of 24 years prior to predicted onset. Differences between gene carriers and controls were examined across a range of imaging, cognitive, neuropsychiatric and biofluid measures. The structural and functional brain connectivity in this cohort is then investigated in further detail. By providing a detailed characterisation of brain structure and function in the early premanifest period along with the most sensitive biomarkers at this stage, this work will inform future treatment strategies that may seek to delay the onset of functional impairments in HD

Synthesis of Aluminum-Titanium Carbide Nanocomposites by the Rotating Impeller Gas-Liquid In-situ Method

The next generation of aluminum alloys will have to operate at temperatures approaching 300°C. Traditional aluminum alloys cannot perform at these temperatures, but aluminum alloys reinforced with fine ceramic particles can. The objective of this research is to develop a process to synthesize Al-TiC composites by the Rotating Impeller Gas-Liquid In-situ method. This method relies on injecting methane into molten aluminum that has been pre-alloyed with titanium. The gas is introduced by means of a rotating impeller into the molten alloy, and under the correct conditions of temperature, gas flow, and rotation speed, it reacts preferentially with titanium to form titanium carbide particles. The design of the apparatus, the multi-physics phenomena underlying the mechanism responsible for particle formation and size control, and the operation window for the process are first elucidated. Then a parametric study that leads to the synthesis of aluminum reinforced with TiC microparticles and nanoparticles is described. Finally, potential technical obstacles that may stand in the way of commercializing the process are discussed and ways to overcome them are proposed