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• 21. [Article] Essays on the Recreational Value of Avian Biodiversity

  This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity ...

  Citation × Citation Citation Abstract Copy Title: Essays on the Recreational Value of Avian Biodiversity Author: Kolstoe, Sonja Year: 2016 This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity to bird watchers. Panel data (i.e. longitudinal data) are highly desirable for preference estimation. Fortuitously, the diaries of birding excursions by eBird members provide a rich source of spatial data on trips taken, over time by the same individuals, to a variety of birding destinations. Origin and destination data can be combined with exogenous species prevalence information. These combined data sources permit estimation of utility-theoretic choice models that allow derivation of the marginal utilities of avian biodiversity measures as well as the marginal utility of net income (i.e. consumption of other goods and services). Ratios of these marginal utilities yield marginal willingness to pay (MWTP) estimates for numbers of bird species (or numbers of species of different types, in richer specifications). MWTP for levels of other attributes of birding destinations are also derived (e.g. ecosystem type, management regime, seasonal variations, a time trend).\\ The chapters are organized as follows: Chapter 2 is a stand-alone paper that demonstrates the feasibility of a travel-cost based random utility model with the eBird data. This chapter focuses on measuring the total number of bird species at each birding hotspot in Washington and Oregon states. This chapter does not differentiate among types of birders beyond using their recent birding activities in an analysis of habit formation or variety-seeking behavior. For this model, beyond past behavior, a representative consumer is postulated. Chapter 3 starts from the basic specifications identified in Chapter 2 and explores heterogeneous preferences among consumers as well as their preferences for species richness and for different categories of birds. This chapter explores whether different types of birds are relatively more attractive to different types of birders (for example, by gender or by age or by neighborhood characteristics and educational attainment). Chapter 4 is an extension of the work in Chapter 3 to explore how changing site attributes in the face of climate change effects birder welfare. This dissertation includes previously unpublished co-authored material. Title: Essays on the Recreational Value of Avian Biodiversity Author: Kolstoe, Sonja Year: 2016 This dissertation uses a convenience sample of members of eBird, a large citizen science project maintained by the Cornell University's Laboratory of Ornithology, to explore the value of avian biodiversity to bird watchers. Panel data (i.e. longitudinal data) are highly desirable for preference estimation. Fortuitously, the diaries of birding excursions by eBird members provide a rich source of spatial data on trips taken, over time by the same individuals, to a variety of birding destinations. Origin and destination data can be combined with exogenous species prevalence information. These combined data sources permit estimation of utility-theoretic choice models that allow derivation of the marginal utilities of avian biodiversity measures as well as the marginal utility of net income (i.e. consumption of other goods and services). Ratios of these marginal utilities yield marginal willingness to pay (MWTP) estimates for numbers of bird species (or numbers of species of different types, in richer specifications). MWTP for levels of other attributes of birding destinations are also derived (e.g. ecosystem type, management regime, seasonal variations, a time trend).\\ The chapters are organized as follows: Chapter 2 is a stand-alone paper that demonstrates the feasibility of a travel-cost based random utility model with the eBird data. This chapter focuses on measuring the total number of bird species at each birding hotspot in Washington and Oregon states. This chapter does not differentiate among types of birders beyond using their recent birding activities in an analysis of habit formation or variety-seeking behavior. For this model, beyond past behavior, a representative consumer is postulated. Chapter 3 starts from the basic specifications identified in Chapter 2 and explores heterogeneous preferences among consumers as well as their preferences for species richness and for different categories of birds. This chapter explores whether different types of birds are relatively more attractive to different types of birders (for example, by gender or by age or by neighborhood characteristics and educational attainment). Chapter 4 is an extension of the work in Chapter 3 to explore how changing site attributes in the face of climate change effects birder welfare. This dissertation includes previously unpublished co-authored material. Close

• 22. [Article] Robust Large Margin Approaches for Machine Learning in Adversarial Settings

  Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform ...

  Citation × Citation Citation Abstract Copy Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Close

• 23. [Article] What Does Accuracy Get You? Empathic Accuracy During a Negotiation.

  Accurately guessing the thoughts and feelings of another person, known as empathic accuracy, is thought to be a useful skill across many domains. However, the evidence supporting the value of empathic ...

  Citation × Citation Citation Abstract Copy Title: What Does Accuracy Get You? Empathic Accuracy During a Negotiation. Author: Howington, Devin Year: 2017 Accurately guessing the thoughts and feelings of another person, known as empathic accuracy, is thought to be a useful skill across many domains. However, the evidence supporting the value of empathic accuracy has been mixed, perhaps because the domains previously examined did not require accuracy for functionality. Negotiation may be one domain in which being accurate really matters for positive outcomes for the perceiver. Additionally, men and women may have different motivations to be accurate, including the presence of a competitive or cooperative situation. The primary research questions of this study are: 1) Is empathic accuracy related to outcomes in the negotiation?; 2) Is accuracy dependent on how the negotiation is framed?; and 3) Does how the negotiation is framed affect men’s and women’s empathic accuracy differently? Individual differences in personal power and Machiavellianism are also examined in relation to accuracy in this context. In this study, 336 participants interacted in same-sex dyads to negotiate over small items in either a competitive or cooperative context, resulting in a 2 (gender) x 2 (context) design. Accuracy was measured both as empathic accuracy for partner’s thoughts during the negotiation as well as accuracy for guessing their partner’s idiosyncratic item preferences (provided prior to the negotiation). Actor-Partner Interdependence models were used to estimate the contributions of each person in the dyad to the outcome of interest. Neither empathic accuracy nor accuracy for guessing item values led to any increases in personal gain in the negotiation. However, empathic accuracy was predictive of satisfaction with the outcome of the negotiation, such that more accurate actors and having more accurate partners both led to significantly increased satisfaction with the outcome. Contrary to the hypotheses, accuracy was not affected by gender or the framing of the negotiation or any interactions between the two variables. Individual differences in power and Machiavellianism did not lead to increases in perceiver empathic accuracy, but rather led to decreases in partner’s accuracy: actors that had partners who were high in power or high in Machiavellianism were less empathically accurate. The implications for negotiation research and future empathic accuracy research are discussed. Title: What Does Accuracy Get You? Empathic Accuracy During a Negotiation. Author: Howington, Devin Year: 2017 Accurately guessing the thoughts and feelings of another person, known as empathic accuracy, is thought to be a useful skill across many domains. However, the evidence supporting the value of empathic accuracy has been mixed, perhaps because the domains previously examined did not require accuracy for functionality. Negotiation may be one domain in which being accurate really matters for positive outcomes for the perceiver. Additionally, men and women may have different motivations to be accurate, including the presence of a competitive or cooperative situation. The primary research questions of this study are: 1) Is empathic accuracy related to outcomes in the negotiation?; 2) Is accuracy dependent on how the negotiation is framed?; and 3) Does how the negotiation is framed affect men’s and women’s empathic accuracy differently? Individual differences in personal power and Machiavellianism are also examined in relation to accuracy in this context. In this study, 336 participants interacted in same-sex dyads to negotiate over small items in either a competitive or cooperative context, resulting in a 2 (gender) x 2 (context) design. Accuracy was measured both as empathic accuracy for partner’s thoughts during the negotiation as well as accuracy for guessing their partner’s idiosyncratic item preferences (provided prior to the negotiation). Actor-Partner Interdependence models were used to estimate the contributions of each person in the dyad to the outcome of interest. Neither empathic accuracy nor accuracy for guessing item values led to any increases in personal gain in the negotiation. However, empathic accuracy was predictive of satisfaction with the outcome of the negotiation, such that more accurate actors and having more accurate partners both led to significantly increased satisfaction with the outcome. Contrary to the hypotheses, accuracy was not affected by gender or the framing of the negotiation or any interactions between the two variables. Individual differences in power and Machiavellianism did not lead to increases in perceiver empathic accuracy, but rather led to decreases in partner’s accuracy: actors that had partners who were high in power or high in Machiavellianism were less empathically accurate. The implications for negotiation research and future empathic accuracy research are discussed. Close

• 24. [Article] A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change

  Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from ...

  Citation × Citation Citation Abstract Copy Title: A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change Author: Praskievicz, Sarah Year: 2014 Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from climate change. Climate-driven hydrological changes are potentially significant for the fluvial geomorphology of river systems. In unchanging climatic and tectonic conditions, a river's morphology will develop in equilibrium with inputs of water and sediment, but climate change represents a potential forcing on these variables that may push the system into disequilibrium and cause significant changes in river morphology. Geomorphic factors, such as channel geometry, planform, and sediment transport, are major determinants of the value of river systems, including their suitability for threatened and endangered species and for human uses of water. This dissertation research uses a hierarchical modeling approach to investigate potential impacts of anthropogenic climate change on river morphology in the interior Pacific Northwest. The research will address the following theoretical and methodological objectives: 1) Develop downscaled climate change scenarios, based on regional climate-model output, including changes in daily minimum and maximum temperature and precipitation. 2) Estimate how climate change scenarios affect river discharge and suspended-sediment load, using a basin-scale hydrologic model. 3) Examine potential impacts of climate-driven hydrologic changes on stream power and shear stress, bedload sediment transport, and river morphology, including channel geometry and planform. The downscaling approach, based on empirically-estimated local topographic lapse rates, produces high-resolution climate grids with positive forecast skill. The hydrologic modeling results indicate that projected climate change in the study rivers will change the annual cycle of hydrology, with increased winter discharge, a decrease in the magnitude of the spring snowmelt peak, and decreased summer discharge. Geomorphic modeling results suggest that changes in reach-averaged bedload transport are highly sensitive to likely changes in the recurrence interval of the critical discharge needed to mobilize bed sediments. This dissertation research makes an original contribution to the climate-change impacts literature by linking Earth processes across a wide range of spatial scales to project changes in river systems that may be significant for management of these systems for societal and ecological benefits. This dissertation includes unpublished co-authored material. Title: A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change Author: Praskievicz, Sarah Year: 2014 Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from climate change. Climate-driven hydrological changes are potentially significant for the fluvial geomorphology of river systems. In unchanging climatic and tectonic conditions, a river's morphology will develop in equilibrium with inputs of water and sediment, but climate change represents a potential forcing on these variables that may push the system into disequilibrium and cause significant changes in river morphology. Geomorphic factors, such as channel geometry, planform, and sediment transport, are major determinants of the value of river systems, including their suitability for threatened and endangered species and for human uses of water. This dissertation research uses a hierarchical modeling approach to investigate potential impacts of anthropogenic climate change on river morphology in the interior Pacific Northwest. The research will address the following theoretical and methodological objectives: 1) Develop downscaled climate change scenarios, based on regional climate-model output, including changes in daily minimum and maximum temperature and precipitation. 2) Estimate how climate change scenarios affect river discharge and suspended-sediment load, using a basin-scale hydrologic model. 3) Examine potential impacts of climate-driven hydrologic changes on stream power and shear stress, bedload sediment transport, and river morphology, including channel geometry and planform. The downscaling approach, based on empirically-estimated local topographic lapse rates, produces high-resolution climate grids with positive forecast skill. The hydrologic modeling results indicate that projected climate change in the study rivers will change the annual cycle of hydrology, with increased winter discharge, a decrease in the magnitude of the spring snowmelt peak, and decreased summer discharge. Geomorphic modeling results suggest that changes in reach-averaged bedload transport are highly sensitive to likely changes in the recurrence interval of the critical discharge needed to mobilize bed sediments. This dissertation research makes an original contribution to the climate-change impacts literature by linking Earth processes across a wide range of spatial scales to project changes in river systems that may be significant for management of these systems for societal and ecological benefits. This dissertation includes unpublished co-authored material. Close

• 25. [Article] Measurement of Membrane Rigidity and Its Modulation by the Vesicle Trafficking Protein Sar1

  Sculpting membranes into dynamic, curved shapes is central to intracellular cargo trafficking and other cellular functions. However, generation of membrane curvature during trafficking involves lipids ...

  Citation × Citation Citation Abstract Copy Title: Measurement of Membrane Rigidity and Its Modulation by the Vesicle Trafficking Protein Sar1 Author: Loftus, Andrew Year: 2014 Sculpting membranes into dynamic, curved shapes is central to intracellular cargo trafficking and other cellular functions. However, generation of membrane curvature during trafficking involves lipids and membrane-associated proteins; current mechanisms focus on creating rigid cages, curved scaffolds, or membrane surface area changes by proteins. This dissertation provides an alternative mechanistic example for the control of membrane deformations, involving modulation of membrane material properties. Sar1, a GTPase of the COPII family, regulates vesicle trafficking from the endoplasmic reticulum. We find that Sar1p lowers the rigidity of the lipid bilayer membrane to which it binds. We examine the behavior of Saccharomyces cerevisiae Sar1 (Sar1p) and Homo sapiens paralogs of Sar1 (Sar1A and Sar1B). Like Sar1p, human Sar1s lower membrane rigidity. Unlike Sar1p, the rigidity is not a monotonically decreasing function of concentration. At high concentrations, we find increased bending rigidity and decreased protein mobility. These features imply a model in which human Sar1 clustering governs membrane mechanical properties. Membrane rigidity measurements remain rare, however, and show a large variance, a situation that can be addressed by improving techniques and comparing disparate techniques applied to the same systems. I introduce applying selective plane illumination microscopy (SPIM) to image thermal fluctuations of giant vesicles. SPIM's optical sectioning enables high-speed fluorescence imaging of freely suspended vesicles and quantification of edge localization precision, yielding robust fluctuation spectra and rigidity estimates. For lipid-only membranes and membranes bound by the intracellular trafficking protein Sar1p, we show rigidity values from giant unilamellar vesicle fluctuations in close agreement with those derived from our independent assay based on membrane tether pulling. We also show that a model of homogeneous quasi-spherical vesicles poorly fits fluctuation spectra of vesicles bound by Sar1A at high concentrations, suggesting that SPIM-based analysis can offer insights into spatially inhomogeneous properties. I conclude by discussing our current work on amphipathic alpha helices, their ability to reduce membrane rigidity, and our hopes to create artificial helical structures capable of mimicking trafficking systems. Supplemental videos represent membrane disintegration with Sar1p and fluctuations of membrane only and Sar1p incubated vesicles. This dissertation contains previously published co-authored material. Title: Measurement of Membrane Rigidity and Its Modulation by the Vesicle Trafficking Protein Sar1 Author: Loftus, Andrew Year: 2014 Sculpting membranes into dynamic, curved shapes is central to intracellular cargo trafficking and other cellular functions. However, generation of membrane curvature during trafficking involves lipids and membrane-associated proteins; current mechanisms focus on creating rigid cages, curved scaffolds, or membrane surface area changes by proteins. This dissertation provides an alternative mechanistic example for the control of membrane deformations, involving modulation of membrane material properties. Sar1, a GTPase of the COPII family, regulates vesicle trafficking from the endoplasmic reticulum. We find that Sar1p lowers the rigidity of the lipid bilayer membrane to which it binds. We examine the behavior of Saccharomyces cerevisiae Sar1 (Sar1p) and Homo sapiens paralogs of Sar1 (Sar1A and Sar1B). Like Sar1p, human Sar1s lower membrane rigidity. Unlike Sar1p, the rigidity is not a monotonically decreasing function of concentration. At high concentrations, we find increased bending rigidity and decreased protein mobility. These features imply a model in which human Sar1 clustering governs membrane mechanical properties. Membrane rigidity measurements remain rare, however, and show a large variance, a situation that can be addressed by improving techniques and comparing disparate techniques applied to the same systems. I introduce applying selective plane illumination microscopy (SPIM) to image thermal fluctuations of giant vesicles. SPIM's optical sectioning enables high-speed fluorescence imaging of freely suspended vesicles and quantification of edge localization precision, yielding robust fluctuation spectra and rigidity estimates. For lipid-only membranes and membranes bound by the intracellular trafficking protein Sar1p, we show rigidity values from giant unilamellar vesicle fluctuations in close agreement with those derived from our independent assay based on membrane tether pulling. We also show that a model of homogeneous quasi-spherical vesicles poorly fits fluctuation spectra of vesicles bound by Sar1A at high concentrations, suggesting that SPIM-based analysis can offer insights into spatially inhomogeneous properties. I conclude by discussing our current work on amphipathic alpha helices, their ability to reduce membrane rigidity, and our hopes to create artificial helical structures capable of mimicking trafficking systems. Supplemental videos represent membrane disintegration with Sar1p and fluctuations of membrane only and Sar1p incubated vesicles. This dissertation contains previously published co-authored material. Close

• 26. [Article] Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene

  8 pages

  Citation × Citation Citation Abstract Copy Title: Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene Author: Houle, W. A., Brown, H. M., Griffith, O. H. Year: 2016, 1979 8 pages Title: Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene Author: Houle, W. A., Brown, H. M., Griffith, O. H. Year: 2016, 1979 8 pages Close

• 27. [Article] Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon

  144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree ...

  Citation × Citation Citation Abstract Copy Title: Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon Author: Greenblatt, Dana Rebecca Year: 2017, 2016 144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016 Title: Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon Author: Greenblatt, Dana Rebecca Year: 2017, 2016 144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016 Close

• 28. [Article] The impact hazard.

  48 pages

  Citation × Citation Citation Abstract Copy Title: The impact hazard. Author: Morrison, David, Chapman, Clark R., Slovic, Paul Year: 2017, 1994 48 pages Title: The impact hazard. Author: Morrison, David, Chapman, Clark R., Slovic, Paul Year: 2017, 1994 48 pages Close

• 29. [Article] Citizen Access to Complex Environmental Decision Making: A Case Study of the Proposed LNG Pipeline in Oregon

  144 pages. A thesis presented to the Department of Public Policy, Planning and Management, and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree ...

  Citation × Citation Citation Abstract Copy Title: Citizen Access to Complex Environmental Decision Making: A Case Study of the Proposed LNG Pipeline in Oregon Author: Greenblatt, Dana Year: 2017, 2016 144 pages. A thesis presented to the Department of Public Policy, Planning and Management, and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016. Title: Citizen Access to Complex Environmental Decision Making: A Case Study of the Proposed LNG Pipeline in Oregon Author: Greenblatt, Dana Year: 2017, 2016 144 pages. A thesis presented to the Department of Public Policy, Planning and Management, and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016. Close

• 30. [Article] Measuring Stress in Captive Bonobos: A Look to the Past and Future to Improve Methods

  Understanding stress in primates has wide ranging implications. It impacts how we understand human stress from an evolutionary perspective and how captive and laboratory primates are kept to best impact ...

  Citation × Citation Citation Abstract Copy Title: Measuring Stress in Captive Bonobos: A Look to the Past and Future to Improve Methods Author: Midttveit, Erica Year: 2016 Understanding stress in primates has wide ranging implications. It impacts how we understand human stress from an evolutionary perspective and how captive and laboratory primates are kept to best impact their health and well-being. Stress studies in non-human primates often focus on measuring cortisol. Cortisol can be measured in blood, urine, feces, saliva, or hair in primates. Quantification of cortisol is typically achieved by enzyme or radio immunoassay, high performance liquid chromatography, or mass spectroscopy. Once cortisol is quantified, it is traditionally related to stress in primates by determining associations to variables classically seen as potential stressors, such as dominance rank, aggression received, food availability, or moving facilities for captive primates. It is vitally important that researchers engaging in non-human primate cortisol research properly select the sample type and quantification method best suited to answer their particular research questions. It is also important that the quantification of cortisol and the subsequent reporting of methods and results obtained is done correctly and transparently so that other researchers are able to interpret and build upon previous results. In this dissertation, the past instances of non-human primate cortisol analyses are reviewed with a particular focus on urinary analyses. A critical view is taken of past methods and means of reporting results, and suggestions for better practices are made. Researchers should be reporting ranges of raw values measured for cortisol in order to help establish expected values in specific species, as well as explicit justifications for protocol modifications if any are made. A new method for assessment of urinary cortisol in bonobos (Pan paniscus) is validated and reported. A longitudinal study of captive bonobos at the Columbus Zoo and Aquarium contributed 154 urine samples for analyses over three field seasons (2012, 2013, and 2014). A commercially available cortisol EIA kit (Arbor Assays, Ann Arbor, MI) was determined to be appropriate for use in bonobos and subsequently used to test 154 urine samples. A diurnal cortisol rhythm was detected in bonobos for the first time. Individual differences were identified in AM and PM samples and will be the foundation for future behavioral association investigations. Title: Measuring Stress in Captive Bonobos: A Look to the Past and Future to Improve Methods Author: Midttveit, Erica Year: 2016 Understanding stress in primates has wide ranging implications. It impacts how we understand human stress from an evolutionary perspective and how captive and laboratory primates are kept to best impact their health and well-being. Stress studies in non-human primates often focus on measuring cortisol. Cortisol can be measured in blood, urine, feces, saliva, or hair in primates. Quantification of cortisol is typically achieved by enzyme or radio immunoassay, high performance liquid chromatography, or mass spectroscopy. Once cortisol is quantified, it is traditionally related to stress in primates by determining associations to variables classically seen as potential stressors, such as dominance rank, aggression received, food availability, or moving facilities for captive primates. It is vitally important that researchers engaging in non-human primate cortisol research properly select the sample type and quantification method best suited to answer their particular research questions. It is also important that the quantification of cortisol and the subsequent reporting of methods and results obtained is done correctly and transparently so that other researchers are able to interpret and build upon previous results. In this dissertation, the past instances of non-human primate cortisol analyses are reviewed with a particular focus on urinary analyses. A critical view is taken of past methods and means of reporting results, and suggestions for better practices are made. Researchers should be reporting ranges of raw values measured for cortisol in order to help establish expected values in specific species, as well as explicit justifications for protocol modifications if any are made. A new method for assessment of urinary cortisol in bonobos (Pan paniscus) is validated and reported. A longitudinal study of captive bonobos at the Columbus Zoo and Aquarium contributed 154 urine samples for analyses over three field seasons (2012, 2013, and 2014). A commercially available cortisol EIA kit (Arbor Assays, Ann Arbor, MI) was determined to be appropriate for use in bonobos and subsequently used to test 154 urine samples. A diurnal cortisol rhythm was detected in bonobos for the first time. Individual differences were identified in AM and PM samples and will be the foundation for future behavioral association investigations. Close