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Population Characteristics, Movement Dynamics, and Management of Nonnative Burbot in the Green River System, Wyoming
No full textSuppression programs targeting undesirable fish species are difficult and effort intensive, often requiring substantial effort by management agencies. Before suppression programs can be implemented, it is necessary to gain an in-depth understanding of the invader’s ecology to increase the effectiveness of removal efforts. This is especially true in the Green River system, Wyoming, where invasive Burbot have the potential to negatively influence native fishes and socially important fisheries. This thesis evaluated the efficacy of using angling data to monitor invasive Burbot Lota lota populations in the Green River basin and described the potential response of Burbot populations to various management scenarios. In addition, I evaluated the movement dynamics of Burbot in the Green River. My research suggests that angler-supplied Burbot are a cost-effective data source for monitoring Burbot populations. Results indicate that exploitation of mature Burbot must reach 33% to suppress lentic portions of the Green River Burbot population. Additionally, adfluvial Burbot in the upper Green River system seem to spawn near Fontenelle Reservoir. Focusing removal effort on this portion of the Green River could increase the efficiency of a suppression program.masters, M.S., Natural Resources -- University of Idaho - College of Graduate Studies, 2018-0
Nature Poems
No full textThis book was created by the second graders in Mrs.
McNally’s class at Lena Whitmore Elementary. Students
explored poetry elements, read a series of poems, and
learned about art elements. Students drew and
photographed their subject. Then students wrote their
poems about nature
REMOTE SENSING OF THE PACIFIC NORTHWEST BUNCHGRASS PRAIRIE FOR LANDSCAPE SCALE MONITORING AND MANAGEMENT
No full textTemperate grassland ecosystems are an at-risk biome type due to large amounts of conversion to other land use types as well as the mismanagement of livestock grazing. Livestock grazing is the largest land use type across the globe and can have both positive and negative feedbacks on ecosystem. Increasingly researchers and managers are wanting information regarding habitat heterogeneity due to its links to biodiversity. Monitoring meaningful grassland ecosystem indicators, as well as vegetation pattern at relevant management scales, and how they respond to management and environmental drivers has proven difficult with small plot scale research projects as well as in-field point based monitoring. Remote sensing technologies provide a different way to quantify grassland landscapes at various scales and can provide information on important and needed ecological patterns and management relevant grassland attributes such as biomass. In this dissertation I build upon the previous remote sensing science, by developing an algorithm that returns estimates of vegetation cover and biomass, driven by sensor and vegetation greenness using the Landsat surface reflectance products. These algorithms were significantly correlated to vegetation cover and biomass (R2 > 0.70) across varying phenological states enabling them to be used to monitor and analyze vegetation amounts across the grazing season for adaptive management. Next, I tested the ability of airborne lidar to provide estimates of grassland biomass at the landscape scale. In, finding that airborne lidar can in fact model grassland biomass (pseudo R2 =0.59; RMSD =139.4 g m-2) I then aggregate this biomass data to coarser cell sizes and compute geo-statistics on each of the resulting resolutions to determine the effect grazing has on vegetation heterogeneity at various spatial scales. Results showed that statistically different measures of heterogeneity were produced from the variogram models using the biomass estimates from the varying grid cell sizes. When relating the pasture level variogram statistics to stocking rate across the 23 pasture areas, we observed that the range statistic (a proxy for patch size) was only variogram metric sensitive to grazing and this was only significant across the 1m through 8m cell sizes tested. This research successfully quantifies spatial heterogeneity and finds that within the Zumwalt Prairie (a section of the Pacific Northwest Bunchgrass Prairie), grazing at low to moderate rates decreases spatial heterogeneity of vegetation amount. Lastly, I test the relationships between the Landsat-derived biomass algorithm tuned for the Pacific Northwest bunchgrass prairie with short term monitoring data on stocking rate and end of year utilization. In this study we find significant but weak to moderate correlations between the short-term monitoring indicators at both the pasture and plot scales and the biomass metrics including end of year residual biomass, and the relative difference in biomass between summer and fall. While the correlations were moderate to week, the ability to track pasture averages, and map across the study area biologically relevant thresholds of vegetation amount and change in vegetation between summer and fall provide a new way for land managers and rancher to monitor their management actions across the landscape. In this chapter I attempt to move beyond the statistics of modeling of vegetation amounts with remotely sensed data and demonstrated how remotely sensed vegetation data can directly inform adaptive management.doctoral, Ph.D., Natural Resources -- University of Idaho - College of Graduate Studies, 2018-1
Mathematical Modeling and Analysis of Gene Expression to Understand Phenotypic Heterogeneity and the Response of Methylobacterium extorquens to Formaldehyde Toxicity
No full textMethylobacterium extorquens is a facultative methylotrophic bacterium that lives on plant leaves. As part of the natural oxidation pathway of methanol secreted from the leaves, formaldehyde is generated. Experiments have shown there is phenotypic heterogeneity in tolerance to formaldehyde, and this heterogeneity varies continuously. Exposing M. extorquens to a high concentration (4 mM) of formaldehyde changed the distribution of tolerance to formaldehyde. In the second chapter of this dissertation, I introduced a mathematical model to investigate the processes involved in the change of the tolerance distribution. The model suggests there is an absolute threshold between survival and death in face of the stress from formaldehyde. In addition, I showed growth and death are not sufficient to explain the change of distribution of tolerance, and in fact, there is also a suggestion for phenotypic movements that permit the cells to change their phenotypic states. Moreover, the model showed that the phenotypic movements that occur depend upon the environmental conditions. In the third chapter, I investigated the genes involved in response to formaldehyde stress using RNA-seq analysis. In order to find specific mechanisms involved in formaldehyde-mediated translation inhibition, cultures of bacteria treated with either formaldehyde and kanamycin was investigated. To assess the role of the EfgA protein – which has a role in translation inhibition by formaldehyde – WT and ΔefgA strains were investigated in the mentioned treatments. I showed that a great portion of the response to formaldehyde is shared with the kanamycin response, and that having EfgA protein is crucial to the formaldehyde stress response. Analysis of functional gene groups showed that cytochromes, chaperones, DNA damage repair system, ABC transporters and flagellar proteins are among the highly affected genes in response to the formaldehyde. This analysis of RNA-seq data provides a set of candidate genes that potentially have role in the phenotypic heterogeneity in tolerance to formaldehyde.doctoral, Ph.D., Bioinformatics & Computational Biology -- University of Idaho - College of Graduate Studies, 2018-1
Use of Location-Sharing to Increase Situational Awareness and Improve Occupational Safety in Operational Forestry
No full textSituational awareness is imperative to maintaining safe workflow conditions on logging operations. Occupational injury and fatality risks are high for loggers, especially ground crew who work alongside highly mobile hazards like heavy machinery, or work in isolated conditions where injury response may be delayed. Situational awareness may be enhanced with location-sharing (LS) technology that allows users to send GNSS (Global Navigation Satellite System) coordinates to others, such as coworkers at a jobsite. To evaluate the potential success of LS to improve logging safety, we assessed a) device efficacy and accuracy through operational sampling and a controlled field experiment, and b) logger acceptance and adoption of LS technology through a survey of certified, Idaho loggers. First, using real-time, military-grade LS devices at three active logging operations, we were able to characterize rigging crew positions relative to three operational hazards. Ground crew spend approximately one third of the work day in potentially dangerous areas associated with machinery and equipment and about half of each day near snags. Simulated GNSS error associated with mature stands significantly impacted definitions of safe work distances, however, indicating a need for caution when using LS for proximity awareness, especially under forest canopy. Survey results indicate loggers perceive safety benefits to employing LS devices on logging operations, especially for injury response scenarios, such as alerting coworkers of an emergency and finding injured persons quickly. Loggers indicated intent to adopt safety practices involving location sharing, which is a strong indicator of action according to the Theory of Planned Behavior. Study results encourage further development of LS applications for logging safety but advise for recommendations outlining appropriate uses.masters, M.S., Natural Resources -- University of Idaho - College of Graduate Studies, 2018-0
Performance of Female Holstein Calves Grouped Post-Weaning According to their Individual Feed Intake
No full textOut of 755 calves, 480 calves (60±3 d old) from the high and the low quartile level of feed intake in the last three weeks in hutches, were chosen for the treatments (4-repetitions; 6-treatments). The treatments were 20-control (CTRL), 20-high-high (HH), 20-low-low (LL), 10-low-10-high (HL), 15-high-5-low (HHL), and 5-high-15-low (HLL). Calves were fed 95% grain and 5% alfalfa hay (TMR) and recorded 2-days a week for 4-weeks. Pen was the experimental unit. Data were analyzed using a mixed-effects model. Average DMI after grouping was greatest in HH (2.24 kg/d) and HHL (2.15 kg/d) followed by HL (2.07 kg/d), Control (2.06 kg/d), LLH (1.92 kg/d) and LL (1.77 kg/d). Similarly, ADG was greatest in HH (690 g/d) and HHL (674 g/d) than in HL (585 g/d), LLH (571 g/d), Control (545 g/d), and LL (526 g/d). Grouping calves according to pre-weaning intake improves feed efficiency (FE) and feed management. Low eater calves improved the overall visits to the feed bunk when grouped with high eater calves.masters, M.S., Animal and Veterinary Science -- University of Idaho - College of Graduate Studies, 2018-0
Low Complexity Algorithms for Automatic Modulation Classification Based on Machine Learning
No full textIn this thesis, we discuss two different approaches to modulation classifiers: we first propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good trade-off between classification accuracy and complexity relative to the Maximum Likelihood (ML) classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbor (GP-KNN) classifiers, the linear support vector machine classifier (LSVM) and the fold-based Kolmogorov-Smirnov (FB-KS) algorithm. In the second part of thesis, we propose a distribution-based modulation classifier using neural networks. We show that our proposed classifier outperform state-of-the-art classifiers, even when the pool of possible candidate modulations are unknown to the receiver.masters, M.S., Electrical and Computer Engineering -- University of Idaho - College of Graduate Studies, 2018-0
The Effects of Plyometric Training on Muscle Activation Characteristics in Post-Pubescent Adolescent Females
No full textThe purpose of this study was to (a) assess the symmetry of muscle activation onset, duration, and time to peak muscle activation during a jump-landing task in the left and right vastus lateralis, vastus medialis, biceps femoris, semitendinosus, and to (b) determine how a 6-week plyometric training intervention may impact the symmetry of muscle activation. Previous research has demonstrated that females are at an increased risk for anterior cruciate ligament (ACL) injury. Both modifiable and non-modifiable risk factors have been proposed, including that of neuromuscular control. Past research has demonstrated that coactivation of the quadriceps and hamstrings is suggested to be a favorable movement strategy, thereby, reducing the risk of ACL injury. Electromyography was used to record onset, duration, and time to peak muscle activation of the right and left limbs during a jump-landing task, prior to and following a 6-week plyometric training intervention. Repeated-measures ANOVAs were used to identify if significant differences in activation characteristics were present prior to and following training, as well as between right and left limbs. Results from the present study revealed increased quadriceps activity without equivalent increases in hamstring activity following plyometric training, thus, less favorable coactivation recruitment patterns. These findings prompt inquiry into the value and merit of prescribing plyometric training programs for post-pubescent adolescent females. Key words: ACL, neuromuscular control, coactivationdoctoral, Ph.D., Movement & Leisure Sciences -- University of Idaho - College of Graduate Studies, 2018-0
MODELING PLANT SPECIES DISTRIBUTIONS ACROSS IDAHO TO INFORM UNGULATE NUTRITION
No full textAbstract Mechanisms driving ungulate population declines are complex and poorly understood. Limitations in forage availability or quality may be contributing, but current habitat assessments lack fine-scale vegetation information needed to evaluate nutrition. To fill this gap, I developed predictive distribution models for ungulate forage species across Idaho using existing vegetation surveys, maps, and remotely-sensed data. Models predict plant species presence, and provide key insight to species-environment relationships that can aide habitat management strategies to improve nutritional quality. Additionally, I examined elk habitat selection on a summer range in north-central Idaho. Selection was influenced by the presence of herbaceous plant species and wildfire disturbance. Management strategies that re-open matured forest canopies that currently limit herbaceous understory vegetation will be useful for enhancing the nutritional quality of elk summer habitat. Considerations for non-native plant infestations in areas of highly recurrent and severe wildfires will also be important.masters, M.S., Natural Resources -- University of Idaho - College of Graduate Studies, 2018-0