Wild Turkey Resource Use on Food-subsidized Landscapes and the Relationship between Nesting Chronology and Gobbling Activity

Across the Southeast, heightened concern exists that wild turkey (Meleagris gallopavo) productivity and populations are declining, but the underlying reasons are largely unknown. Further concern stems from declining turkey harvest in several southeastern states. I answered questions germane to formulating turkey harvest regulations, specifically related to supplemental feeding and the correlation of gobbling timing with nest incubation and the timing of the hunting season. I examined turkey resource use in the Red Hills region of northern Florida and southern Georgia, where supplemental feeding for northern bobwhite (Colinus virginianus) is common. This supplements food availability and may alter resource use of both target and non-target species. A potential shift in individual behavior on non-target species may have negative consequences and warrants exploration to understand potential impacts on population dynamics of turkeys. Using hierarchical conditional logistic regression in a Bayesian framework, I evaluated turkey resource use at two spatial scales: landscape and within home range. Fields had the greatest probability of use at both scales. Drains also were important at the landscape scale but less important within home ranges. Areas near feed lines, drains, and roads, exhibited greater probabilities of use. Turkeys selected specifically for large drains. Responsible management decisions must balance the desires of stakeholders while being biologically sound for the target species. To gain an understanding of the relationship between nesting and gobbling activity I used linear mixed effects modeling to evaluate this relationship on 3 sites across Florida. A weak relationship existed between gobbling activity and the proportion of hens incubating nests. Additionally, I evaluated the correlation of the timing of Florida’s turkey hunting season with peaks of gobbling activity and proportion of hens incubating nests using incremental response modeling. Florida’s turkey hunting season may better correlate with the egg-laying stage if the hunting season was shifted one week later, especially for Tall Timbers and Dixie Plantation. Gobbling activity and incubation would be more closely correlated with the hunting season if the hunting season was shifted three weeks later. More regionally-based management zones would allow the hunting season to be timed more closely with turkey gobbling and nesting activity

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy - which is now possible with our approach

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz

Solar thermionic flight experiment study. volume iii- spacecraft design /phase ii/ final report

Solar thermionic flight experiment final report - spacecraft design phas

Evaluation of Reproductive Phenology and Ecology of Wild Turkey (Meleagris gallopavo) Across the Southeastern United States

The primary driver of population growth and sustainability of gallinaceous birds is annual recruitment. Habitat selection by wild turkeys (Meleagris gallopavo spp.) during reproductive activities could influence production at multiple temporal and spatial scales. Vegetation conditions at nest sites that could drive nest success have not been clearly identified, which suggests that other factors may drive reproductive success. Female wild turkeys maintain dominance hierarchies, which could influence how reproductively active females distribute themselves across the landscape during reproductive periods. Using high-frequency GPS data collected from reproductively active females, I analyzed nesting attempts for Eastern (n = 381), Gould’s (n = 17), and Rio Grande wild turkeys (n = 67) at 10 study sites during 2014 – 2017. I evaluated average daily distance traveled, size of utilization distributions, overlap of utilization distributions, and habitat selection during the pre-egg laying and egg-laying periods. I found that larger ranges during laying and less distance traveled daily during laying contributed to greater nest success. Overlap of 50% utilization distributions occurred in 59.6% of all nesting attempts (n = 465) and negatively impacted nest success for Eastern wild turkeys. These results suggest that movement behaviors and the spatial distribution of nesting females may be an additional component of wild turkey reproductive success. Identifying nest sites should govern all other components of habitat selection as female wild turkeys will be tied to these locations for the duration of the reproductive period. My objective was to evaluate vegetation conditions immediately before the selection of nest sites to determine if conditions at nests were different than those available. I evaluated vegetation conditions at nest sites and presumed travel paths used by 131 nesting female wild turkeys. I used 164 nesting attempts and measured vegetation at 37,976 locations along 492 movement paths. Average vegetation height at the nest site was met or exceeded at 61‒71% of random points, whereas visual obstruction was met or exceeded at 22-25%. These results indicate that vegetative conditions used by wild turkeys for nesting were not limited. This work illustrates that adequate nesting habitat may not be as limited across the landscape as previously thought, and that the process of nest site selection is time limited and likely occurs immediately prior to nest initiation

Monitoring Collective Intelligence in Lithuania’s Online Communities

This paper presents the findings of a systematic survey that evaluated the potential of online communities (or Civic Tech) in Lithuania to co-create collective intelligence. Traditional approaches to public engagement remain relevant, notwithstanding, our enquiry is more interested in the growing potential of digital-enabled citizens to increase efficient collective performance. Civic intelligence is a form of collective intelligence exercised by a group’s capacity to perceive societal problems and its ability to address them effectively. The subject of the research is “bottom up” digital-enabled online platforms initiated by Lithuanian public organizations, civic movements and/or business entities. This scientific project advances our understanding about the basic preconditions in online communities through which collective intelligence is being systematically co-created. By monitoring the performance of Civic Tech platforms, the scientific question was examined, what are the socio-technological conditions that led the communities to become more intelligent. The results of web-based monitoring were obtained by applying Collective intelligence Monitoring technique and Pearson correlation analysis. This provided information about the potential and limits of online communities, and what changes may be needed to overcome such limitations

Learning Education: An ‘Educational Big Data’ approach for monitoring, steering and assessment of the process of continuous improvement of education

Changing regulations, pedagogy and didactics worldwide, have ensured that the educational system has changed severely. But the entrance of Web 2.0 and other technologies had a significant impact on the way we educate and assess our education too. The Web 2.0 applications also increase the cooperation between stakeholders in education and has led to the phenomenon ‘Learning Education’. Learning Education is a term we use for the phenomenon where educational stakeholders (i.e. teachers, students, policy-makers, partners etc.) can learn from each other in order to ultimately improve education. The developments within the Interactive Internet (Web 2.0) enabled the development of innovative and sophisticated strategies for monitoring, steering and assessing the ‘learning of education’. These developments give teachers possibilities to enhance their education with digital applications, but also to monitor, steer and assess their own behavior. This process can be done with multiple sources, for example questionnaires, interviews, panel research, but also the more innovative sources like big social data and network interactions. In this article we use the term ‘educational big data’ for these sources and use it for monitoring, steering and assessing the developments within education, according to the Plan, Do, Check, Act principle (PDCA). We specifically analyze the Check-phase and describe it with the Learning Education Check Framework (LECF). We operationalize the LECF with a Learning Education Check System (LECS), which is capable to guide itself and change those directions as well in response to changing ways and trends in education and their practices. The system supports the data-driven decision making process within the learning education processes. So, in this article we work on the LECF and propose and describe a paper-based concept of the – by educational big data driven – LECS. Besides that, we show the possibilities, reliability and validity for measuring the ‘Educational Big Data’ within an educational setting