Using Accelerometer Data to Remotely Assess Predation Activity of Arctic Wolves

Arctic wolves (Canis lupus arctos) play an important role in ecosystems located in the far northern regions of the world; however, unlike the gray wolves in Yellowstone National Park, little information is available about High Arctic wolves and their impacts on prey populations. This research uses data received from two GPS radio-collared Arctic wolves located in the Fosheim Peninsula on Ellesmere Island. Each radio-collar was programmed to record a position every 30-60 minutes, as well as the wolfs activity movement (forwards - backwards and left - right), which was generated by an accelerometer housed within the radio-collar. This research project focused on using location clusters and their associated activity data to remotely identify the locations and the frequency of wolf predation events. The activity data can be used to identify potential kill sites because it takes both time and energy for the Arctic wolves to take down and consume their prey, thus clusters of locations with high levels of activity are generated at these places. Over fifty of the cluster sites were visited and assessed for remains of a kill, such as bone remnants, teeth, or hair. A key objective of this study was to identify predictors and develop a statistical model that distinguishes kill sites from non-kill sites, including rendezvous sites, which I also analyzed. I used AIC model selection methods to compare different multinomial logistic regression models that measured the probability a cluster included a kill, a rendezvous, or neither as a function of several variables, including the sum of activity, total timespan of the cluster, average activity, and the initial slope in activity within the first few hours of each cluster, which is the rate at which activity decreased following the establishment of the cluster. The most predictive variable was number of points; other useful predictors included the average distance between each point and the cluster centroid, and the average value in sideways and rotary acceleration (Activity Y) across the cluster lifespan. These three variables comprise the best-fit multinomial model to distinguish kill and rendezvous clusters, as supported by the AIC results. When excluding the rendezvous clusters, the best-fit multinomial model included the three variables (number of points, average distance, and average in Activity Y) in addition to the slope in activity within the first two hours since cluster formation. Use of accelerometer data and multinomial logistic regression models may help differentiate clusters and enable scientists and wildlife managers to remotely monitor the predatory impact of Arctic wolves

Invited Paper: Ingredients of a High-Quality Information Systems Program in a Changing IS Landscape

This paper describes James Madison University’s undergraduate major in Computer Information Systems as an example of a high- quality Information Systems (IS) program and discusses our planned evolution in the context of the rapid changes of technological, business, and social factors. We have determined what we consider to be five essential ingredients of what makes JMU’s program a high-quality IS major. These are: (1) building an integrated, rigorous curriculum with a strong technical foundation; (2) developing a vibrant community of faculty, students, alumni, employers, and community service organizations; (3) respecting and supporting pedagogical scholarship; (4) committing to continuous improvement and assessment; and (5) accreditation. We believe these ingredients will continue to be highly relevant as the IS discipline moves forward, but also that curriculum content will need to adjust to meet changing demand. We discuss the increasing relevance of topics such as analytics, security, and the cloud to the IS curriculum and their implications for pedagogy, accreditation, and scholarship. We hope that sharing JMU’s experience, insights, and future directions will be useful to JISE’s readership

The Complexity of Engineering Education in a Mission Driven PBL University

In this extended abstract, we will argue that education in a mission driven university holds particularities that call for the transformation of not only the research base of education but also the way in which education is conceptualised and practiced. Following a PBL approach, we put the students in the centre of educational activity and propose a conceptual framework for engineering education in a mission driven PBL university.<br/

Ndoto Au Ota: Jifundishe\u27s Strategic Planning Process: A Case Study of an Appreciative Approach to the Strategic Planning Process for a Small NGO

In January 2017, Jifundishe, a small, local nonprofit organization based in Ngongongare, Tanzania undertook its first strategic planning process. I was asked to design and lead this initiative for the organization, finding a way to introduce tools and resources so that they, or a similar organization, could replicate the process in the future. To better reflect the values, culture, and constraints of Jifundishe, the methodology selected to drive the process was Appreciative Inquiry (AI). AI is a change model focused on strengths, and an appreciative strategic plan looks to take the best of what you already have and work to develop that further. This Independent Practitioner Inquiry Capstone (IPIC) seeks to identify how AI was used in the strategic planning process, examining what tools and approaches were chosen and how they were implemented. And then to present how those who participated in the process experienced it. Through a series of portraits, it became evident that using appreciative inquiry as an approach to the strategic planning process allows for the participants to participate in an inclusive, positive way and to dream big. This study highlights the importance of using a tailored approach to strategic planning grounded in AI methodology