Browsing by Author "Kevin Gross, Committee Member"

Now showing 1 - 18 of 18

A Bioenergetics Assessment of Temperature and Food Consumption Effects on Growth of Reservoir Striped Bass.
(2006-08-07) Davias, Lori Anna; Kevin Gross, Committee Member; Jeffrey A. Buckel, Committee Member; James A. Rice, Committee Chair
I examined the influence of temperature and food availability on growth of striped bass Morone saxatilis in seven North Carolina reservoirs. Reduced condition and summer mortality events of stocked striped bass in some southern reservoirs have been attributed to the habitat 'squeeze' of high summer temperatures and low dissolved oxygen (DO). However, in a recent study of Lakes Badin and Norman, NC, Thompson et al. (2005) intensively studied striped bass thermal selection, diet, mortality, and energetics, and concluded that food consumption was more important than temperature in explaining the large differences in growth between the systems. Following on this study, I analyzed striped bass growth, diet, predator and prey energy densities, and temperature experience in each of an additional five reservoirs in 2003-2005. Striped bass growth and thermal experience varied widely across all seven reservoirs. Fish growth to age-7 varied from less than 2kg to nearly 6 kg, and fish spent between 0 and 87 days in very warm (≥27°C) water during summer stratification. Striped bass from lakes that experienced the most severe summer temperature also exhibited relatively fast growth. Simulations of a Wisconsin bioenergetics model parameterized for striped bass estimated that striped bass annual consumption varied between 3,144g for age-2 fish in Lake Gaston and 24,616g for age-5 fish in Jordan Lake. I also used the bioenergetics model to test for the relative effects of water temperature and food consumption on growth by conducting a series of "habitat exchange simulations." In this approach I simulated how much the growth of a particular size fish in one reservoir might change if it had experienced the temperature or food consumption of a similarsized fish in another reservoir. The difference in growth predicted when exchanging consumption was greater than that resulting from exchanging temperature for every pair of lakes compared except for those including Lake Rhodhiss. This lake had an extended period over the summer where striped bass could remain in preferred 20°C habitat, and simulations where these temperatures were combined with high food consumption resulted in the greatest annual growth for striped bass. However, my results stress that regardless of temperature, low food consumption will only yield moderate to slow growth of striped bass. On the other hand, striped bass subjected to very warm temperatures can maintain good growth but only if adequate forage exists. Knowledge of availability and abundance of clupeid prey, in addition to information on thermal conditions, is thus especially important for management of striped bass in southern reservoirs.
Catch Curve and Capture Recapture Models: A Bayesian Combined Approach
(2009-03-19) Griffith, Emily Hohmeister; Dennis Boos, Committee Member; Kenneth H. Pollock, Committee Chair; Sujit K. Ghosh, Committee Co-Chair; Kevin Gross, Committee Member
When studying animal populations, one demographic parameter of interest is the annual rate of survival. Methods for estimating survival rates of animal populations fall into two general categories: methods based on marked or non-marked animals. Catch curve analysis falls into the latter category of non-marked animal methods, and is based on strong assumptions about population dynamics. Capture-recapture methods, on the other hand, use marked animals and require assumptions about homogeneous individual capture and survival probabilities. We focus specifically on Chapman and RobsonÃ¢â‚¬â„¢s catch curve analysis, the Cormack-Jolly-Seber (CJS) open population model, and Udevtiz and BallacheyÃ¢â‚¬â„¢s augmentation of catch curve data with ages-at-death data, which are a random sample from the natural deaths that occur in a population between two time periods. In Chapter 1, we develop the Bayesian approach to catch curve analysis, beginning with the simple situation of a single catch curve. After extending our method to multiple years, we relax the model assumptions to include random effects for survival across years. The proposed model is validated using predictive distributions and compared with the traditional methods. We conclude that many benefits can be obtained from the Bayesian approach to the analysis of a single or multiple year catch curve. In Chapter 2, we augment catch curve data with capture-recapture data in a hierarchical Bayesian framework. We estimate the fidelity rate and the population growth rate. We illustrate these models with a data set and simulation study. In Chapter 3, we develop a Bayesian method for analyzing catch curve and ages-at-death data together, based on the likelihoods developed in Udevitz and Ballachey. We utilize the Bayesian framework and relax both the assumption of a stable age-distribution and that of a known population growth rate.
Developments and Applications of a Closed Capture-Recapture Robust Design Model to Avian Point Count Data.
(2009-08-10) Stanislav, Stephen Joseph Jr; Jason Osborne, Committee Member; Kevin Gross, Committee Member; Kimberly Weems, Committee Member; Kenneth Pollock, Committee Chair
Here we review various methods of estimating detection probabilities for avian point counts; distance sampling, multiple observer methods, and recently proposed time-of-detection methods. We provide a general model of detection where the total probability of detection is made up of the probability of a bird singing (availability) and the probability of detecting a bird, conditional on its having sung. This approach is shown to be a special case of Pollock's robust capture-recapture design where the probability that a bird does not sing is equivalent to the probability that an animal is a temporary emigrant. We show that the time-of-detection method provides an estimate combining both probabilities and by combining the time-of-detection method with a multiple observer method it is possible to estimate the two components of the detection process separately. These results are presented in Chapter 1. Chapter 2 presents the detailed model evaluation with model extensions and simulation studies. We report on the combined multiple-observer and time-of-detection method for estimation of the components of aural detection probabilities and population size through simulation. We focus on the dependent multiple-observer versus independent multiple-observer aspect of our combined method and evaluate which is the more effective in practice. We also evaluate the combined multiple-observer and time-of-detection method where the model assumptions may be violated. Finally, Chapter 3 presents the development of several modeling approaches allowing for competing detection cues in estimation of population size and components of the competing cues, aural and visual, and then study these models through simulation. We also investigate advantages and disadvantages of the competing cue modeling versus the more conventional pooled cue modeling with evaluation through simulation. After the detailed explanations of our research methods and our simulated and real data results, we focus on the implications and importance of our work to field ornithologists designing point count studies and suggest possibilities for future research.
The Effects of Habitat Fragmentation and Connectivity on Plant Disease
(2009-12-22) Johnson, Brenda Lynn; Nick M. Haddad, Committee Chair; Kevin Gross, Committee Member; Charles Mitchell, Committee Member
Within a large-scale habitat corridor experiment, I performed both experimental and observational studies to determine the effects of habitat fragmentation, habitat edge, and patch connectivity on the movement and incidence of fungal plant diseases. Increased spread of infectious disease is often cited as a potential negative effect of habitat corridors, and increases in the amount of habitat edge that are inevitable byproducts of corridor creation could also impact the incidence and development of plant disease. However, the impacts of corridors and habitat edges on plant disease dynamics remain empirically untested. Using sweet corn and southern corn leaf blight as a model plant-pathogen system, I experimentally tested the impacts of connectivity and habitat fragmentation on pathogen movement and disease development. I found that corridors do not facilitate the movement of wind dispersed plant pathogens, that connectivity of patches does not enhance levels of foliar fungal plant disease, and that edge effects are the key drivers of plant disease dynamics. Over time, less edgy patches had higher proportions of diseased plants, and distance of host plants to habitat edges was the greatest determinant of disease development. To test the effects of habitat connectivity and edge on the incidence of naturally occurring plant disease, I surveyed foliar lesions on three native Lespedeza species. I found that connectivity of habitat patches did not affect levels of disease and that incidence of wind dispersed foliar fungal diseases was significantly higher close to habitat edges, further demonstrating that edge effects play an important role in plant disease dynamics. I also found that density of host plants was significantly higher farther from habitat edges, contradicting previous studies that relate higher host densities to increased disease load. Environmental variables also showed strong edge effects, with significantly higher temperatures and light intensities at the interior of habitat patches, providing possible mechanisms for these disease patterns. Results from both studies show that worries over the potential harmful effects of connectivity on disease dynamics are misplaced, and that, in a conservation context, diseases can be better managed by mitigating edge effects.
An Empirical Assessment of the Role of Driver Motivation and Emotion State, and Driving Conditions in Perceived Safety Margins
(2009-06-29) Zhang, Yu; Kevin Gross, Committee Member; Denis Cormier, Committee Member; David Kaber, Committee Chair
Models of motivation in driver behavior have been developed to predict driver performance under different conditions. Among existing models, NÃƒÂ¤ÃƒÂ¤tÃƒÂ¤nen and SummalaÃ¢â‚¬â„¢s multi-dimensional threshold model (1976) was selected as basis for the current study. NÃƒÂ¤ÃƒÂ¤tÃƒÂ¤nen and Summala proposed that driver behavior is modified not only according the degree of difficulty of traffic situations, but also based on driver risk tolerance in specific tasks. They also observed that risk-taking is based on driver motives and emotional states. According to this model, various factors may influence driver behavior. For instance, traffic patterns (other driver behavior) and driver motivation to comply with social norms, extreme emotions triggered by special circumstances (emergencies), and long-term emotional tendencies. The latter factor has been assessed using Driver Stress Inventories (Matthews, Desmond, Joyner, Carcary, & Gilliland, 1996). NÃƒÂ¤ÃƒÂ¤tÃƒÂ¤nen and SummalaÃ¢â‚¬â„¢s model also indentified measures of change in driver risk-taking decisions, or safety margins, as being predictors of driver performance. The objectives of this study were to: 1) provide empirical evidence of the influence of motivation and emotional factors, as identified by NÃƒÂ¤ÃƒÂ¤tÃƒÂ¤nen and Summala, in driver risk-taking behavior; and 2) identify any additional variables that might mediate the effects of motivational factors on behavior, including roadway environment complexity. The study examined the following specific factors: 1) traffic patterns, including traffic jam, school zone, normal traffic flow and speeding conditions to assess the influence of social norms on driver behavior; 2) driver payment systems, including time-based and performance-based compensation to assess the influence of extreme emotions on performance; and 3) environment complexity, including rural and city conditions to assess the influence of changes in task difficulty on behavior. Response measures included safety margins and speed measures. Safety margin measures consisted of: 1) spatial variables, including headway distance (HW) and lateral distance (DH); and 2) time variables, including time headway (THW), time to collision (TTC) and time to line crossing (TTLC). Speed measures consisted of average speed, maximum speed and the percentage of time spent speeding. Ten participants drove a virtual car in a high-fidelity simulator and performed daily driving tasks (e.g., lane maintenance, lead-car following, passing, negotiating intersections, etc.). A split-plot experiment design was used with the whole-plot (trial) factors including environment complexity and the payment system. Traffic pattern was manipulated as the split-plot factor with each of the four patterns occurring during a single segment in each trial. Participants complete eight test trials, including two replications all combinations of complexity and payment system. Participants were also required to finish a DSI prior to simulated-driving tasks. The experiment results revealed the effect of the motivational factor/payment system. More risky driving behavior was associated with the performance-based payment system compared to the time-based system. The influence of environment complexity was also observed. Smaller safety margins appeared in the rural environment as compared to city. The effects of traffic pattern were significant across all response measures except TTLC: traffic jams led to minimum safety margins; speeding segments produced the highest driving speeds and largest safety margins; school zones were associated with conservative behavior, including lower speeds and larger safety margins. The traffic pattern also interacted with the roadway complexity condition in terms of THW. Drivers were influenced more by the behaviors of other drivers in the city versus rural setting. Correlation analyses showed significant linear associations between long-term emotional tendencies and safety margin and speed measures. In summary, the current research contributed to the further development of motivational models of driving behavior by providing reliable evidence of factors that are significantly influential in perceived safety margins and performance. The study also identified additional (lateral vs. longitudinal) measures for sensitively specifying safety margins. Future research should investigate a broader range of emotional factors that may be related with safety margins. The role of long-term emotional tendencies in driver performance should also be studied under a broader range of conditions, including roadway hazard exposure. In addition to this, future work might examine more diverse driving populations (beyond college students) to obtain a more comprehensive understanding of motivational/emotional factors in driving performance.
Exploring the Inverse Problem with Infectious Disease Models.
(2010-07-01) Capaldi, Alexander; Alun Lloyd, Committee Chair; Kevin Gross, Committee Member; James Selgrade, Committee Member; Hien Tran, Committee Member
Habitat Ecology of the Carolina Madtom, Noturus furiosus, an Imperiled Endemic Stream Fish
(2008-12-09) Midway, Stephen Russell; D. Derek Aday, Committee Co-Chair; Thomas J. Kwak, Committee Co-Chair; Nick M. Haddad, Committee Member; Kevin Gross, Committee Member
The Carolina madtom Noturus furiosus is an imperiled stream catfish (Ictaluridae) endemic to the Tar and Neuse river basins in North Carolina. The species is listed as State Threatened, and whereas the Tar Basin population resembles its historical distribution, the Neuse Basin population has shown recent significant decline. Quantifying habitat use and availability is critical for effective management and subsequent survival of the species. This study combined field and laboratory research to investigate habitat use and suitability, as well as efficacy of an artificial cover unit. To assess habitat suitability, we investigated six reaches (three in each river basin) to (1) quantify Carolina madtom microhabitat use, availability, and suitability, (2) compare suitable microhabitat availability between the two basins, and (3) examine the effectiveness of an instream artificial cover unit. We also conducted laboratory experiments to examine madtomsâ€™ use of the same artificial cover unit relative to three natural cover types. Carolina madtom were located and their habitat use was quantified at four of six survey reaches; the species appeared to be absent at two reaches in the impacted Neuse Basin. Carolina madtom most frequently occupied shallow to moderate depths (0.5 m) of swift moving water over a sand substrate using cobble for cover. Univariate and principal components analyses both showed Carolina madtom use of instream habitat to be selective, or nonrandom. Interbasin comparisons suggested that most suitable microhabitats (particularly water depth and velocities) were more prevalent in the Neuse than in the Tar Basin, which is interesting considering that the Neuse population appears to be the more impacted of the two. Consequently, we suggest that other physical or biotic factors must be responsible for the decline in the Neuse Basin population. Our instream artificial cover units were occupied mainly by Carolina madtom (25% of the time), and rarely by a suite of other stream animals. Comparing areas with the artificial cover units (â€˜treated areasâ€™) to those without them (â€˜control areasâ€™), Carolina madtom abundance among all treated areas was statistically higher than the controls, demonstrating that madtoms will use suitable artificial cover when available. Microhabitat characteristics of occupied artificial cover units closely resembled those of natural microhabitat use. Results from the field component of the study provide habitat suitability criteria that can inform management and conservation of the Carolina madtom, and the artificial cover units present a cost-effective conservation and restoration option if increased management is deemed necessary. In the laboratory component of the study, Carolina madtom were placed in an experimental stream tank (44 cm x 88 cm in area and about 45 cm deep) and given 24 hours to make a selection among four cover options, three natural (one each of rock, leaf pack, and mussel shell) and the artificial cover unit. Among 30 experimental trials, Carolina madtom preferred the artificial cover unit, selecting it 63% of the time. Rock was selected 23% and leaf pack 13% of the time. Contrary to previous anecdotal observations, mussel shells were not selected during any trials. Results from the laboratory experiments, coupled with similar findings from instream work, indicate that artificial cover may be a viable option for species conservation and restoration. Given the State Threatened status and limited distribution, our results have implications for conservation and restoration of this native and endemic southeastern catfish. Successful management and conservation of declining Carolina madtom populations is dependent upon preserving Tar Basin habitat, identifying Neuse Basin impacts, and restoring Neuse Basin populations.
Hierarchical Bayesian application to instantaneous rates tag-return models
(2009-10-06) Krachey, Matthew James; Ken Pollock, Committee Chair; Kevin Gross, Committee Member; Sujit Ghosh, Committee Member; Joseph Hightower, Committee Co-Chair
Natural mortality has always been a challenging quantity to estimate in harvested populations. The most common approaches to estimation include a regression model based on life history parameters and more recently tag-return models. In recent years, Bayesian methods have been increasingly implemented in ecological models due to their ability to handle increased model complexity and auxiliary datasets. In this dissertation, I explore the implementation of Bayesian methods to analyze tag-return data focusing on natural mortality. Chapter 1 is focused on the addition of two components to the tag-return model framework: random effects and auxiliary data. Auxiliary information on the instantaneous rate of natural mortality is provided through Hoenig's equation relating lifespan to natural mortality, and also implemented through a hierarchical prior. A simulation study validates the performance of the model while an analysis of the classic Cayuga Lake trout dataset demonstrates its use. Chapter 2 adds a change-point allowing for the estimation of two levels of natural mortality and the timing of the discrete-time shift in mortality. Analysis is focused on a Chesapeake Bay striped bass tagging dataset of fish tagged at six years of age and older from 1991-2002. Results show the ability to account for shift in timing. Contrasting with Jiang et al.'s study on the same striped bass dataset, the timing of the change-point was different between the two studies, likely because the Jiang study assumed a fixed tag-reporting probability of 0.43 whereas estimates seem to indicate it may be closer to 0.3. Chapter 3 introduces a change-point allowing for a shift in the tag-reporting probability while assuming a constant natural mortality rate. High reward tags are included in a subset of the data time-series to improve estimation. A factorial simulation design was used to investigate the model performance with different reporting rate and high reward tag scenarios. In general, the model performed very well with little bias except in the case of no high-reward tags. The model performed surprisingly well in a six year study. The results suggest the importance of high reporting rates and/ or auxiliary data sources such as high reward tags.
Investigating Interactions between Largemouth and Spotted Bass, Lake Norman, North Carolina.
(2009-06-14) Godbout, Jason; D. Derek Aday, Committee Co-Chair; Kevin Gross, Committee Member; James A. Rice, Committee Co-Chair
Spotted bass Micropterus punctulatus were recently introduced into Lake Norman, which already supported a healthy largemouth bass M. salmoides population. After only ten years, spotted bass now make up about half of the black bass fishery. Because the two species are ecologically similar, and numbers and biomass of largemouth bass have been declining, biologists were concerned that spotted bass were negatively affecting largemouth bass. Additionally, morphological observations suggested the two species were hybridizing. To better understand these issues, hybridization, diet overlap, and habitat use by black bass in Lake Norman were quantified. Genetic analyses confirmed largemouth and spotted bass were hybridizing. Genetic information on individuals was paired with morphological characteristics at juvenile (50 Ã¢â‚¬â€œ 100 mm total length, TL; n = 60) and adult (300 Ã¢â‚¬â€œ 500 mm TL; n = 78) life stages, and reliable patterns for field identification of spotted bass, largemouth bass, and hybrids were developed (78-88% correct). To understand potential competition between the taxa, juvenile (n = 132) and adult (n = 120) black bass were collected for diet and habitat comparisons. Diet information was collected from juveniles from 31 July Ã¢â‚¬â€œ 08 Aug 2007, 29 April 2008, and 10 June 2008 at different spatial scales. Zooplankton and benthic invertebrates were collected to estimate availability. Diet information was collected from adult fish from 29 April Ã¢â‚¬â€œ 01 May 2008 and from 12 May Ã¢â‚¬â€œ 16 May 2008. Habitat information on substrate and cover use was collected from locations of immobilized fish, and habitat availability of substrate and cover was estimated from 300-m transects (n = 12) throughout the reservoir. Proportion similarity index and MorisitaÃ¢â‚¬â„¢s index were used to quantify diet overlap by percent by number and percent by occurrence, and 10,000 bootstrap values were generated so that 95% confidence intervals could be estimated. Estimates of habitat selection were calculated with IvlevÃ¢â‚¬â„¢s index of electivity and the Strauss index of selectivity. Diet overlap values were high, and 95 % confidence intervals were typically in the upper half of the range of the indices. Based on prey availability samples, selection of invertebrate prey was largely opportunistic and similar between species. Habitat selection of both substrate and cover was similar with few exceptions. Because largemouth and spotted bass are hybridizing in Lake Norman, and overall, they show high overlap in use of prey items and habitat at both juvenile and adult life stages, they are likely competitors in Lake Norman. Our findings should guide future research and educate managers and anglers about the potential effects of introducing spotted bass or largemouth bass into lakes already containing a healthy black bass fishery.
Models of Virus-Immune Dynamics and Drug Resistant Virus Infections
(2006-09-26) Soberano, Lisa Albert; Kevin Gross, Committee Member; Charlie Smith, Committee Member; Alun Lloyd, Committee Chair
Models for the activity of a virus within a host describe the interaction between virus, the cells they infect and the attempts of the body's immune response to remove the infection. We aim to improve these models by incorporating more detailed, and more realistic, descriptions of the immune response. One particular application that is of interest involves the administration of drug treatment during acute infections, with the aim of understanding the dynamics of co-circulating wild-type and drug resistant viruses. Two models are presented that suggest improvements for the portrayal of the immune response. The first is based on existing basic infection models for acute viruses. The second is based upon recent experimental results show that a brief exposure to a pathogen can cause the CD8+ cytotoxic T lymphocytes (CTLs), of the immune system to undergo a programmed set of divisions, including sustained proliferation, giving rise to effector cells, which can kill infected cells, followed by the production and maintenance of memory cells. In the co-infection model of wild-type and drug resistant viruses, a target cell limited model is also used. Previous results suggested that the level of immune response maintained during therapy is the key to suppressing the peak load of resistant virus. Our results, however, suggest that suppression of a resistant strain is not solely dependent on the immune response, but also on the availability of target cells. A spatial virus-immune model is presented in an appendix, which allows multiple target regions within the host to be infected by one or more viruses. Another model is introduced which allows multiple viruses to infect the same target region, with immune specific responses to each virus.
Predator-prey Dynamics between Recently Established Stone Crabs (Menippe spp.) and Oyster Prey (Crassostrea virginica).
(2010-08-13) Rindone, Richard; David Eggleston, Committee Chair; Kevin Gross, Committee Member; Geoffrey Bell, Committee Member
Probabilistic Allele Calling to Improve Population Size Estimates from Non-Invasive Genetic Mark-Recapture Analysis
(2009-08-10) Supple, Megan Ann; W. Owen McMillan, Committee Member; Kenneth H. Pollock, Committee Chair; Kevin Gross, Committee Member
Accurate estimates of population sizes are often necessary to help researchers better understand how wildlife populations are changing over time. Researchers often use traditional mark-recapture methods to estimate wildlife population sizes. A variety of models, with varying assumptions, are available to analyze traditional mark-recapture data. The utility of traditional mark-recapture methods is limited when sampling rare or elusive species. Capture probabilities may not be high enough due to the difficulties and cost of capturing the animals. In addition, physical capture can be stressful, even deadly, to the animals. The limitations of traditional mark-recapture methods can sometimes be addressed by utilizing non-invasive genetic mark-recapture methods. Using the non-invasive genetic method, individuals are not physically captured and tagged. Instead, non-invasive genetic samples, such as hair or scat, are collected and genotyped at multiple microsatellite markers. An individual's genotype serves as a DNA tag, uniquely identifying that individual. DNA is extracted from each sample and the extracted DNA is PCR amplified multiple times at several microsatellite loci. The results of each PCR amplification are visualized using capillary electrophoresis, resulting in an electropherogram. Alleles are called by interpreting the peak heights and/or peak areas on the electropherogram. While non-invasive genetic methods solve some of the problems of traditional mark-recapture, they also introduce some new problems. One major problem introduced by non-invasive genetic methods is the misidentification of individuals. The DNA from non-invasive samples is often low in quality and/or low in quantity, which increases the probability of genotyping errors. In addition, poor marker selection can result in individuals sharing a genotype. Traditional mark-recapture methods are not robust to violations of the assumption that individuals are correctly identified. Genotyping errors cause overestimation of population size; markers that lack the power to distinguish between individuals cause underestimation of population size. To achieve better population size estimates, I propose a new probabilistic allele calling method. In the traditional method, definitive allele calls are made independently for each PCR replicate of a sample. Then, the definitive allele calls are examined to determine the sample's genotype. The new method assigns probabilities to allele calls, rather than determining a definitive allele call. Probabilities are assigned to possible allele calls based on electropherogram peak heights. For cases of possible allelic drop out, a portion of the probability distribution for the PCR replicate is assigned to a heterozygous allele call with one undesignated allele. For each sample, the allele call probabilities at each locus, including allele calls with undesignated alleles, are averaged from the PCR replicates. Then, possible allele calls with undesignated alleles are assigned based on the allele frequencies in the averaged probabilities. The genotype with the highest probability is assigned as the sample's genotype. Using the probabilistic method, uncertainty remains in the allele calls until all the PCR replicates of a sample are examined. This allows more information from the electropherograms to be utilized when determining genotypes. To examine the proposed probabilistic allele calling method, I compared it to a traditional method by running computer simulations that examine a variety of scenarios. For each simulation scenario, a population was generated and sampled using non-invasive genetic mark-recapture methods. Each sample, which contained DNA of low quality and quantity, was genotyped at multiple microsatellite loci, with multiple PCR replicates for each locus. Genotypes were determined for samples using a traditional allele calling method and the new probabilistic allele calling method. The resulting genotypes were matched and the data was analyzed using four traditional closed mark-recapture models. The probabilistic method performed better than the traditional method in almost all cases. When more than two PCR replicates were examined, the estimates from the probabilistic method were less biased and more precise than estimates from the traditional method. Using the probabilistic method, good estimates can be achieved using fewer PCR replicates. This new method of analyzing non-invasive genetic mark-recapture data has the potential to allow wildlife population sizes to be accurately estimated using non-invasive methods in less time and at lower cost than current methods.
Properties of a Multilayer Coating for Applications in High Level Waste Packaging
(2005-04-27) Scheffing, Candice Camille; Jagannadham Kasichainula, Committee Member; Man-Sung Yim, Committee Co-Chair; Mohamed Bourham, Committee Co-Chair; Kevin Gross, Committee Member
Materials features that are being considered for the Yucca Mountain high level waste repository include corrosion, hydrogen, and radiation effects as well as structural strength. The current plan for protection of the environment from high level waste placed inside Yucca Mountain includes a defense-in-depth design with multiple engineering barriers. The outer engineered barrier is a large drip shield made of titanium grade-7. Titanium was chosen for its high corrosion resistance and structural strength. This titanium drip shield is an elaborate design that will be expensive and may be susceptible to hydrogen embrittlement or hydrogen-induced cracking. An alternative, multi-layer coating design is proposed that will provide corrosion resistance and act as a barrier to hydrogen diffusion. The coating proposed is composed of the hydrogen barrier titanium nitride (TiN), corrosion resistant zirconium oxide (ZrO₂), and wear resistant diamond-like carbide (DLC). TiN and ZrO₂ coatings were deposited on stainless steel substrates using magnetron sputtering and laser ablation. Analysis of the corrosion resistance of TiN and the multilayer coating, TiN + ZrO₂, has been performed at Lawrence Livermore National Laboratory (LLNL) using simulated waters, representative of the Yucca Mountain environment. The hydrogen barrier properties of TiN have also been analyzed using low temperature resistance measurements and secondary ion mass spectrometry (SIMS) analysis. Using cyclic polarization testing, TiN was found to be resistant to SCW and BSW, Yucca Mountain simulated waters, with a passive region of 760 ± 342 mV in SCW and 408 ± 67 mV in BSW. The added ZrO₂ layer increased the passive region to 822 ± 108 mV in SCW, and increased the passive region in BSW to 1002 ± 260 mV. The ZrO₂ did not significantly increase the passive region in SCW but dramatically increased the passive region in BSW, considered to be the worst-case scenario for Yucca Mountain. Further testing in SCW using multiple coatings of TiN increased the passive region to 1050 ¬± 31.1 mV. Long term corrosion tests were also performed on TiN coated 316L weight-loss samples. After exposure for 6 months in corrosion tanks, the maximum corrosion rate observed was 0.530 μm y⁻¹. This level was observed in the 90 °C aqueous SCW environment, and the coating had been completely stripped away while immersed in the tank. Hydrogen diffusion testing was done on TiN coated stainless steel samples by exposing coated and non-coated samples to hydrogen at an elevated temperature for 3 hours. SIMS analysis indicated that for the TiN-coated samples there was no increase in hydrogen for the exposed sample; rather the hydrogen content of the substrate was lower than the non-exposed sample. The non-coated samples tested in SIMS did not show a difference in hydrogen content between the hydrogen exposed and non-exposed samples. More tests need to be done to confirm claims that TiN is a good diffusion barrier to hydrogen. The multilayer TiN + ZrO₂ coating provides good resistance against corrosion as shown by the large cyclic polarization passive regions. It does not, however, provide comparable corrosion resistance to titanium, indicating it is not as protective as titanium. The hydrogen barrier property of TiN is an additional property that pure titanium does not have and may be useful for Yucca Mountain. Additional corrosion tests can be done on the ZrO₂ coatings to find a more accurate corrosion passive region for the various simulated Yucca Mountain water. The feasibility of this multi-layer coating will also be an important next step to determine in what ways the coating can be a better choice than titanium as an alternative to the drip shield design at Yucca Mountain. *This research was performed under appointment of the Office of Civilian Radioactive Waste Management Fellowship Program administered by Oak Ridge Institute for Science and Education under a contract between the U.S. Department of Energy and the Oak Ridge Associated Universities.
Reproductive Ecology and Habitat Use of the Robust Redhorse in the Pee Dee River, North Carolina and South Carolina.
(2010-08-19) Fisk, James; Thomas Kwak, Committee Chair; Ryan Heise, Committee Chair; Kevin Gross, Committee Member
A Serosurvey of Feral Pigs (Sus scrofa) in Eastern North Carolina.
(2010-10-12) Sandfoss, Mark; Christopher DePerno, Committee Chair; Kevin Gross, Committee Member; Richard Lancia, Committee Member; Suzanne Kennedy-Stoskopf, Committee Member
Spatial Modeling of Detection and Abundance from Count Surveys of Animal Populations
(2006-12-27) Webster, Raymond Anthony; Kenneth H. Pollock, Committee Chair; Sujit K. Ghosh, Committee Member; Cavell Brownie, Committee Member; Kevin Gross, Committee Member
When analyzing data from surveys of animal populations, it has been common in the past to ignore important factors such as variation in animal detection probabilities across space, and spatial dependence in animal density. We present a unified framework for modeling animal survey data collected at spatially replicated survey sites in the form of repeated counts, "removal" counts, or "capture" history counts, that simultaneously models spatial variation in density and variation in detection probabilities due to changes in covariates across the landscape. The models have a complex hierarchical structure that makes them suited to Bayesian analysis using Markov chain Monte Carlo (MCMC) algorithms. To ensure that these algorithms are computationally efficient, we use conditional autogressive (CAR) models for modeling spatial dependence. We apply our models to two examples of animal survey data. In the first, an intensive repeated count survey of juvenile Coho Salmon in McGarvey Creek, Northern California, we detected moderate spatial dependence in density, and models which account for spatial dependence produced more precise predictions at unsurveyed habitat units, and thus more precise estimates of total stream abundance, than models which assumed spatial independence. Through a small simulation study, we show that ignoring heterogeneity in detection probabilities can lead to significant underestimation of total abundance. However, inclusion of heterogeneity using a random effect in the detection component of the model can lead to problems in Bayesian MCMC modeling for typical survey designs, and for this reason we stress the importance of accounting for heterogeneity by incorporating covariates in modeling detection probability. In our second example, we consider a large survey of birds in the Great Smoky Mountains National Park. We fit models to the three types of survey data, repeated counts, "removal" counts, and "capture" history counts. Our methods lead to maps of predicted relative density which are an improvement over those that would follow from ignoring spatial dependence. Modeling shows that variation in detection probability can also affect inference, particularly when a species is relatively difficult to detect. Our work also highlights the importance of good survey design for bird species modeling. We point out that these types of bird survey data, particularly removal and capture-recapture counts (which require individual birds to be identified), are prone to errors in bird identification. Although we obtain similar results for all three types of survey data, which implies that the effect of identification errors may be small, the consequences of such errors in the data requires further investigation. Finally, we present parametric models for combined distance and capture-recapture survey data from both line and point transect surveys that allow for two types of animal movement: permanent avoidance or attraction to a transect, or temporary displacement of animals in the vicinity of a transect. The models have a simple form, with parameters that quantify the impact transects and observers have on local density. We combine these density models with logistic-linear models for detection probability using the likelihood framework of Borchers et al. (1998) for combined distance and capture-recapture data. This allows us to separately estimate the parameters of both the density and detection components of the model, which is not possible using the standard methods of distance sampling. Through a simulation study, we show that, provided sufficient animals are detected, the model parameters have little bias, and lead to improved estimates of density over a simple uniform density model, particularly for line transect surveys. Model selection by AIC generally chooses the correct density model. We apply our models to the Great Smoky Mountains bird survey data, and find some evidence of observer effects on local bird density.
Stochastic Modeling of the Behavior of Dynein
(2005-04-11) Goedecke, David Michael; John F. Monahan, Committee Member; Kevin Gross, Committee Member; Carla Mattos, Committee Member; Sharon R. Lubkin, Committee Co-Chair; Timothy C. Elston, Committee Co-Chair
Molecular motors are proteins that convert stored energy into physical work inside cells, and thus are the engines that drive many cellular functions. An individual motor can be studied using a laser trap to measure its response to working against an external force. Axonemal dynein is the molecular motor responsible for the rhythmic beating of eukaryotic cilia and flagella. An individual axonemal dynein molecule is capable of both unidirectional, processive motion and bidirectional motion when placed under a load (Shingyoji et al., 1998). This capability may be an important underlying factor in the mechanism for flagellar and ciliary motion. A detailed stochastic model is proposed which links the physical motion of a two-headed dynein molecule to the biochemical steps of its ATP hydrolysis cycle. Forward motion is driven by ATP hydrolysis, while backward motion is due to a passive process of biased diffusion. The model exhibits both processive and bidirectional behaviors. A simplified model which can be more easily analyzed is derived, as is an alternate version which steps backward actively, rather than sliding passively. The simplified models are then used to predict motor characteristics such as the load-velocity profile, the stall force, and the effective diffusion coefficient, which can be determined experimentally and used to distinguish among competing mechanisms.
Towards a Movement Ecology: Modeling the Behavioral Response of Invasive Snails to Resources and Competition.
(2008-03-03) Snider, Sunny Brooke; Nick M. Haddad, Committee Member; James F. Gilliam, Committee Chair; Jay F. Levine, Committee Member; Kevin Gross, Committee Member
The movement of individuals is one of the fundamental components of contemporary ecological problems such as metapopulation theory, epidemic models, competitive coexistence, and invasion dynamics. Advection-diffusion models, sometimes with a reaction term, have been usefully applied to such problems. For this dissertation, I broadened this approach by seeking to understand the effects of certain biotic and abiotic factors on movement ecology, and asking how to incorporate flexible behavioral responses into classical advection-diffusion models. I asked how resources, competitive environment, and habitat structure, interacting with body size or not, affect the movement behaviors of two coexisting invasive snails (Melanoides tuberculata and Tarebia granifera), and whether including the behavioral response to these factors improves advection-diffusion models of movement. I also made natural history observations regarding the snail system to provide a biological context for my empirical work. To address these questions, I conducted replicated experiments and observational studies, extended advection-diffusion models, and arbitrated among candidate models using AIC (Akaike's Information Criterion) model selection. Specific studies included (1) behavioral response to phenotypic and resource heterogeneities, and their interaction, (2) behavioral response to intraspecific and interspecific competition, and (3) behavioral response to spatially uniform versus spatially heterogeneous environments. In summary, this dissertation provides insights into modeling movement behaviors, using two coexisting invasive snails as the model system. I advocate for a behaviorally informed modeling framework that integrates sentient responses of individuals in terms of movement, improving our ability to accurately model ecological processes that depend on movement ecology.