Beyond Precipitation: Reassessing Drought Severity Using Multiple Variables

Univariate assessments of drought such as the Standardized Precipitation Index (SPI) may be insufficient for detecting all types and severities of drought. Bivariate assessments of drought, such as combining SPI and the Standardized Soil Moisture Index (SSI) to create the Multivariate Standardized Drought Index, predict drought onset and longevity better than SSI and SPI compared to SSI alone. While drought risk is normally evaluated with precipitation alone, we investigate drought risk with precipitation and temperature combined. Using Weibull’s method and statistical copulas, we compare univariate and bivariate return periods in Northern Georgia and Central Iowa. Results show that using only a single variable to define drought gives the possibility of overestimating or underestimating drought risk. As shown in this study, using precipitation data joined with temperature data provides a return period that is more meaningful and more accurately describes drought conditions in an area. Methods to account for multiple variables are particularly important given the uncertain impacts of climate change; in which small changes in precipitation extremes may be exacerbated by large changes in temperature extremes. Understanding the interaction between precipitation and temperature will allow decision makers to plan ahead and act accordingly during times of drought

Texas after City of Dallas v. Stewart: Police Nuisances Or City Police Power Abated

The current economic recession has been the single worst in the United States since the Great Depression. In 2010, 46.2 million people were living at or below the poverty level. As a result of the nation\u27s economic downturn and unemployment rates, there has been an onslaught of municipal decay and abandoned buildings. Nonseasonal vacant properties increased from 7 million in the year 2000 to 10 million in the year 2010. Texas, in particular, experienced a 41% to 50% increase in the number of vacant properties in its cities. Public nuisances such as deteriorating buildings, pest infestations, and overgrown vegetation commonly result from these neglected properties. Their presence has a negative effect on the quality of life of people living in communities nationwide. In the wake of the recession more than ever, Texas has been faced with the need to abate public nuisances in an effort to keep its communities safe and to rehabilitate its cities

Preference and Motivation Testing

Since the early 1970s, scientists have used preference tests (tests that require animals to choose between two or more different options or environments) as a means of answering questions about animal welfare. Preference tests have been used to establish animals\u27 preferences for common housing options such as ambient temperature, illumination and preferred types of bedding and flooring; to improve the effectiveness of devices such as loading ramps and nest boxes; and to clarify how strongly animals avoid various aspects of confinement and methods of restraint. To use preference research to answer questions about animal welfare, three issues need to be addressed. First, we must ensure that experiments do adequately reflect the animals\u27 preferences. The preferences of an animal are likely to vary with the animal\u27s age and experience, the time of day, environmental conditions, and the animal\u27s on-going behaviour; therefore, preference experiments must be comprehensive enough to identify the relevant sources of variation. Experiments must also avoid confounding preference with familiarity, and avoid spurious results arising from the use of particular testing procedures and response measures. Second, to draw inferences about animal welfare from preference research requires that we establish how strongly an animal prefers a chosen option, avoids an unpref erred one, or is motivated to perform a certain behaviour (nest-building, exploration) that is prevented in some environments. Various methods to assess preference and motivation strength have been proposed. Third, the environments preferred by an animal will often, but not always, promote its welfare in the sense of health and psychological well-being. However, preferences may not correspond to welfare if the choices fall outside the animals\u27 sensory, cognitive and affective capacities, or if animals are required to choose between short- and long-term benefits. Future priorities for preference testing include more emphasis on identifying the factors underlying animals\u27 preferences, greater integration of preference research with other indicators of animal well-being, more reliance on the natural history of the species as a source of hypotheses about environmental preferences, and greater use of preference research in the design of animal environments

Cryptic protein interactions regulate DNA replication initiation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147744/1/mmi14142_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147744/2/mmi14142-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147744/3/mmi14142.pd

Climate Change and Winter Road Maintenance: Planning for Change in the City of Prince George, British Columbia

Throughout Canada, significant resources are dedicated to winter road maintenance (WRM) activities. While changes in technology and materials are affecting WRM decisions, climate variability and change will also be of considerable importance in long-term decision making. This research explores how anticipated changes in winter weather may affect WRM activities in Prince George, British Columbia. The goal of this thesis is to contribute to our understanding of adaptation planning in the municipal transportation sector, and in particular to explore the ways in which empirical estimates of change may affect adaptation decisions. The link between weather and snow and ice control are analyzed using WRM data made available by the City of Prince George and meteorological observations from Environment Canada. The approach taken to document the association between winter weather and WRM expenditures is a winter severity index. Findings show that, notwithstanding changes in maintenance strategies, much of the historic variability in WRM can be attributed to weather. This winter severity index was applied to simulated climate data based on 65 global climate models from the Canadian Climate Change Scenarios Network. Based on the mid-range of the 65 projections, climate models indicate that the Prince George Region is expected to be 1.5°C to 2.4°C degrees warmer and have 3.7% to 10.6% more precipitation. The expected net effect for winter maintenance is reductions in expenditures by 15.3% to 22.7% by the 2050s. The empirical results of this thesis were presented to decision-makers in the City of Prince George using a semi-structured interview process to establish the extent to which site-specific climate change impact assessments could help to overcome the barrier of lack of local knowledge in climate change adaptation planning. Results indicate that the empirical analysis of projected changes in the demand for WRM activities led to the development of new knowledge; however, the degree to which this knowledge creates climate-change readiness remains unclear. Overall, the semi-structured interview process highlighted a number of barriers and enablers of adaptation planning action at the municipal level. Institutional inertial, path dependency, the role of governance structure, political timelines, resident influence, and uncertainty surrounding weather and climate information were all identified as being influential

Data-driven climate indices as a climate translation service

Weather and climate have a powerful influence on humans and society. The ways in which individuals, organizations, and communities are sensitive to weather and climate varies considerably due to social, economic, institutional, and technological factors (Kirchhoff et al. 2013). The complexity and variability across space and time of the human-environment interface motivates the demand for tools and techniques that are able to effectively translate climatic information into usable products and services for decision-making. Furthermore, notwithstanding the extensive availability of weather and climate information, its use in informing both weather risk-management decisions and climate-change adaptation initiatives remains limited. One factor in the underutilization of weather and climate information stems from the difficulty of translating weather and climate data into useable information for decision-makers (Rayner et al. 2005, Lemos 2008, Weaver et al. 2013, Fellman 2012, Kirchhoff et al. 2013, Soares & Dessai 2015). Organizations have been increasingly seeking tools that can inform decision-making for both short-term weather risk management and long-term climate change adaptation measures (WMO 2016). Regardless of the temporal scope of a decision, there is a need to identify and quantify the climatic sensitivity and associated risks and opportunities of climatic stimuli (Damm et al. 2019). The non-linearity of climate-society interactions combined with the highly context-dependent nature of societal sensitivities to climatic stimuli poses a number of practical challenges. This gap in research, and in practice, provides a novel research opportunity to investigate the prospect of developing techniques that can quantify weather sensitivity in a variety of applications. These context-specific and user-driven climatic information products and services are often referred to as climate translation products and services (Damm et al. 2019). A core impediment to the development of climate translation services is an incomplete understanding of how individuals, organizations, and sectors are sensitive to climatic stimuli. A number of methods has been used to define this sensitivity but to date and there has been a dominant focus on stated-preference methods to ascertain user needs and sectoral climatic sensitivities. Expert consultations, user interviews, and participant surveys have been used extensively to define context-specific weather and climate sensitivities. However, a growing literature explores the use of data-driven techniques to explore societal sensitivity to weather and climate. Focusing on the highly climate-sensitive transportation and tourism sectors, this dissertation proposes a conceptualization of climatic sensitivity that is premised on the need for multiple climatic thresholds. This dissertation proposes a framework for data-driven techniques that can be used to develop climatic indices based on the underlying relationships between weather and society and presents the first data-driven approach to define multiple climatic thresholds for the climate-society nexus in two climate-sensitive sectors. The overarching purpose of this dissertation is to further the development of climate services and increase the scholarly understanding of context-specific climatic thresholds that communicate a societal response and can be applied to weather forecasts and climate projections at different temporal scales. The first manuscript uses expert knowledge in combination with mathematical optimization to develop a data-driven winter severity index that works well in predicting winter maintenance activity across 20 road maintenance jurisdictions in Ontario. The second manuscript builds on the first paper through an extension to include climate change projections, and provides greater focus on role of co-production in climate services development. This second manuscript explores the frequency, and intensity of past and future winter weather as it relates to winter road maintenance of provincial highways in Ontario, Canada. The climate change analysis reveals that winter severity, as it relates to snow and ice control, is projected to decrease through to the end of the century. The third manuscript of this dissertation explores the feasibility of transferring the methods developed in the first two manuscripts to develop a data-driven tourism climate index for Ontario Provincial Parks. This third study advances our understanding of beach park-visitor’s climatic sensitivity and provides tourism planners, managers, and decision-makers with enhanced information to inform decision-making. The final manuscript of the dissertation examines the intra-annual effect of weather on tourism demand to three Caribbean destinations (Barbados, Antigua and Barbuda, and Saint Lucia) from Ontario, Canada. This study refines the Holiday Climate Index: Beach through optimization to develop two new indices which estimate the climatic pull-factor of the destination, and the climatic push-factor from the source market. Findings reveal that the data-driven indices have greater predictive accuracy than the extant climate indices for tourism. In conclusion, this dissertation demonstrates the feasibility of developing data-driven indices in the transportation and tourism sectors that can form the foundation of climate service translation tools

The 10kTrees Website: A New Online Resource for Primate Phylogeny

The comparative method plays a central role in efforts to uncover the adaptive basis for primate behaviors, morphological traits, and cognitive abilities.[1-4] The comparative method has been used, for example, to infer that living in a larger group selects for a larger neocortex,[5][6] that primate territoriality favors a longer day range relative to home range size,[7] and that sperm competition can account for the evolution of primate testes size.[8][9] Comparison is fundamental for reconstructing behavioral traits in the fossil record, for example, in studies of locomotion and diet.[10-13] Recent advances in comparative methods require phylogenetic information,[2][14-16] but our knowledge of phylogenetic information is imperfect. In the face of uncertainty about evolutionary relationships, which phylogeny should one use? Here we provide a new resource for comparative studies of primates that enables users to run comparative analyses on multiple primate phylogenies Importantly, the 10,000 trees that we provide are not random, but instead use recent systematic methods to create a plausible set of topologies that reflect our certainty about some nodes on the tree and uncertainty about other nodes, given the dataset. The trees also reflect uncertainty about branch lengths.Human Evolutionary Biolog

Primate Extinction Risk and Historical Patterns of Speciation and Extinction in Relation to Body Mass

Body mass is thought to influence diversification rates, but previous studies have produced ambiguous results. We investigated patterns of diversification across 100 trees obtained from a new Bayesian inference of primate phylogeny that sampled trees in proportion to their posterior probabilities. First, we used simulations to assess the validity of previous studies that used linear models to investigate the links between IUCN Red List status and body mass. These analyses support the use of linear models for ordinal ranked data on threat status, and phylogenetic generalized linear models revealed a significant positive correlation between current extinction risk and body mass across our tree block. We then investigated historical patterns of speciation and extinction rates using a recently developed maximum-likelihood method. Specifically, we predicted that body mass correlates positively with extinction rate because larger bodied organisms reproduce more slowly, and body mass correlates negatively with speciation rate because smaller bodied organisms are better able to partition niche space. We failed to find evidence that extinction rates covary with body mass across primate phylogeny. Similarly, the speciation rate was generally unrelated to body mass, except in some tests that indicated an increase in the speciation rate with increasing body mass. Importantly, we discovered that our data violated a key assumption of sample randomness with respect to body mass. After correcting for this bias, we found no association between diversification rates and mass.Human Evolutionary Biolog

More Than Eggs – Relationship Between Productivity and Learning in Laying Hens

The intense selection of chickens for production traits, such as egg laying, is thought to cause undesirable side effects and changes in behavior. Trade-offs resulting from energy expenditure in productivity may influence other traits: in order to sustain energetic costs for high egg production, energy expenditure may be redirected away from specific behavioral traits. For example, such energetic trade-offs may change the hens’ cognitive abilities. Therefore, we hypothesized highly productive laying hens to show reduced learning performance in comparison to moderate productive lines. We examined the learning ability of four chicken lines that differed in laying performance (200 versus 300 eggs/year) and phylogenetic origin (brown/white layer; respectively, within performance). In total 61 hens were tested in semi-automated Skinner boxes in a three-phase learning paradigm (initial learning, reversal learning, extinction). To measure the hens’ learning performance within each phase, we compared the number of active decisions needed to fulfill a learning criteria (80% correct choices for learning, 70% no responses at extinction) using linear models. Differences between the proportions of hens per line that reached criterion on each phase of the learning tasks were analyzed by using a Kaplan–Meier (KM) survival analysis. A greater proportion of high productive hens achieved the learning criteria on each phase compared to less productive hens (Chi23 = 8.25, p = 0.041). Furthermore, high productive hens accomplished the learning criteria after fewer active decisions in the initial phase (p = 0.012) and in extinction (p = 0.004) compared to the less selected lines. Phylogenetic origin was associated with differences in learning in extinction. Our results contradict our hypothesis and indicate that the selection for productivity traits has led to changes in learning behavior and the high productive laying hens possessed a better learning strategy compared to moderate productive hens in a feeding-rewarding context. This better performance may be a response to constraints resulting from high selection as it may enable these hens to efficiently acquire additional energy resources. Underlying mechanisms for this may be directly related to differences in neuronal structure or indirectly to foraging strategies and changes in personality traits such as fearfulness and sociality