Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals

peer-reviewedSocial network analysis (SNA) is a technique to study behavioral dynamics within a social group. In SNA, it is an open question whether it is possible to characterize animal-level behaviors by using group-level information. Also, it was believed that the combined use of SNA would provide a more comprehensive understanding of social dynamics. In light of these two factors, here we explain an approach to evaluate animal importance to a group by considering the variability in group-level structural information, which is computed by joining the animal- and group-level SNA measures node centrality and network entropy, respectively. Moreover, two other metrics, animal social interaction range and nearest-neighbor frequency matrix, which represent a social affiliation of each animal within the group, are computed to help address the general challenges in graph-based SNA and, thereby, improve the precision of animal importance measures. Finally, we derive the joint distribution of animal importance of the group in detecting atypical social behaviors. The approach is tested using tracking data of dairy cows. The reliability of the derived animal importance was superior to the already existing animal importance measures. To illustrate the usability of the animal importance metric, a simulation study was conducted to identify sick and estrus animals in a group. The social affiliation of sick cows was less when compared to healthy cows. Also, their individual distributions of animal importance were shifted toward the left of the mean of the animal importance distributions of healthy cows. Consequently, the joint distribution of animal importance of the group exhibited a bimodal distribution with a left tailored shape. The behavior of cows in estrus was opposite to that of sick cows. Moreover, with the increasing number of sick and estrus cows in the group, respectively, the group entropy decreased with larger variance and slightly increased with less variance. Therefore, the entropy-based animal importance metric has superior performances when evaluating animal importance to the group compared to the existing metrics. It can be used for generating alerts for the early detection of atypical social behaviors associated with, for instance, animal health, veterinary, and welfare.Science Foundation Irelan

The interplay of the physical landscape and social dynamics in shaping movement of African savanna elephants (loxodonta africana)

Free ranging African savanna elephants (Loxodonta africana) are increasingly impacted by human-induced habitat loss and poaching for ivory. Because elephants live in tightly knit groups, this combination of threats not only reduces the size of their populations but also degrades their social interactions. Long-term relationships with socially competent individuals, such as experienced seniors, benefit the ability of other group members to access limiting resources and avoid danger. Understanding how anthropogenic pressure may affect persistence of elephant populations is important, because elephants are an economically important keystone species. This doctoral thesis characterizes how individual elephants influence the movement of their social partners, and how the social network properties of elephant groups related to information sharing may change when socially competent members are killed by poachers. To that end, two techniques commonly used to study movement of individuals in their habitat, and one used to study the consequences of repeated social interactions, are modified and extended to incorporate a number of the social processes typically found in groups of elephants. First, an established, choice-based statistical framework for movement analysis is modified and validated using synthetic and empirical data. It allows for simultaneous modeling of the effects of the habitat quality and social interactions on individual movement choices. Next, this new model is applied to a unique set of remotely sensed tracks from five male elephants navigating across the same habitat in southern Africa. A key result is that known dominance relationships observed at water points and other gathering places are determined to persist even when elephants are ranging more widely across the landscape. Lastly, an existing \u27social network and poaching\u27 simulation model is parameterized with data from wild elephants. It reveals debilitating effects of poaching on various network metrics thought to correlate with group communication efficiency. The modeling and simulation tools developed over the course of this doctoral research may be generalized to include the influence of \u27dynamic points\u27 other than social conspecifics, such as predators or poachers, on long-term movement patterns, and thus may provide a tool to both understand and mitigate human-wildlife conflict. In addition, they may aid hypothesis testing about disturbance of social dynamics in animal systems subject to exploitation by humans or lethal management

KC 1.1: Cultural Heritage and Climate Change: Exploring the Impacts and Issues

As noted at the 2017 ICOMOS Assembly in Delhi, cultural heritage is both under threat from climate change, and an asset in our attempts to adapt to and mitigate its impacts. The Paris Agreement emphasizes the need for urgency about climate change; cultural heritage can play a central role in this effort. For example, iconic sites at risk from storms, coastal erosion, wildfires or permafrost thaw can alert public to the very real impacts and costs of climate change. World Heritage Sites (WHS) around the world play a key role in alerting the public to the impacts of local climate change because they are highly visible, and are acknowledged as being important to national, regional and local heritage. As such, broad publicity about impacts and continuing losses such as the news coverage of the sea-level rise at Rapa Nui and Skara Brae and the degradation of the Cedars of Lebanon illustrate the value of both the iconic sites, their resources, and the wide media coverage they can project. Loss and damage due to climate change also includes the impacts on large landscapes and their associated communities. The loss of cultural heritage in these landscapes runs the gamut from intangible heritage such as folk tales, to immoveable cultural heritage, to the lifeways of cultures that have developed over centuries and millennia. Placing those impacts into a broader context is the role, and the goal, of the CCHWG Working Group. This session will address ongoing work by the Climate Change and Heritage Working Group (CCHWG) of ICOMOS that explores the nexus between climate change and heritage. Heritage interacts with climate change through a spectrum of impacts from the physical degradation of standing structures and site ecosystems, to the role that cultural heritage plays in the resilience of communities and their ontological security. Although the focus of the session will be on the impacts of climate change on rural landscapes, the discussion will cover the broad range of the work of the committee. Attached to this abstract is the full report of the Working Group, delivered to the UNESCO World Heritage Committee (WHC) on June 3, 2019 at the 43rd Meeting of the WHC in Baku, Azerbaijan. The audience will be asked to engage with the report to identify publications and case study examples that should be incorporated into the next steps of the work of the CCHWG

Review: Perspective on high-performing dairy cows and herds

Milk and dairy products provide highly sustainable concentrations of essential amino acids and other required nutrients for humans; however, amount of milk currently produced per dairy cow globally is inadequate to meet future needs. Higher performing dairy cows and herds produce more milk with less environmental impact per kg than lower performing cows and herds. In 2018, 15.4% of the world\u27s dairy cows produced 45.4% of the world\u27s dairy cow milk, reflecting the global contribution of high-performing cows and herds. In high-performing herds, genomic evaluations are utilized for multiple trait selection, welfare is monitored by remote sensing, rations are formulated at micronutrient levels, health care is focused on prevention and reproduction is managed with precision. Higher performing herds require more inputs and generate more waste products per cow, thus innovations in environmental management on such farms are essential for lowering environmental impacts. Our focus is to provide perspectives on technologies and practices that contribute most to sustainable production of milk from high-performing dairy cows and herds

Forging forms of authority through the sociomateriality of food in partial organizations

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordThis study theorizes on the sociomateriality of food in authority-building processes of partial organizations by exploring Alternative Food Networks (AFNs). Through the construction of arenas for food provisioning, AFNs represent grassroots collectives that deliberately juxtapose their practices from mainstream forms of food provisioning. Based on a sequential mixed method analysis of 24 AFNs, where an inductive chronological analysis is followed by a Qualitative Comparative Analysis (QCA), we found that the entanglements between participants’ food provisioning practices and food itself shape how authority emerges in AFNs. Food generates biological, physiological and social struggles for AFN participants who, in turn, respond by embracing or avoiding them. As an outcome, most AFNs tend to bureaucratize over time according to four identified patterns while a few idiosyncratically build a more shared basis of authority. We conclude that the sociomateriality of food plays an important yet indirect role in understanding why and how food provisioning arenas re-organise and forge their forms of authority over time.European Commissio

Information network on Twitter regarding early warning of mount Semeru eruption

Background: Indonesia is a country that is highly susceptible to volcanic disasters. One potential measure the community can take is to utilize social media platforms to participate in disaster mitigation efforts. The hashtag #Semeru exemplifies the utilization of social media in disseminating information regarding volcanic disasters. It became a trending topic on Twitter regarding the information on the eruption of Mount Semeru at the end of 2021. Purpose: The primary objective of this research is to examine the operational mechanisms of the Mount Semeru eruption early warning system on Twitter. Furthermore, the objective is to determine the key actors responsible for disseminating early warning information on Twitter. Methods: This study employed the Social Network Analysis (SNA) method. Results: The findings show that the network distribution pattern of the Semeru eruption early warning system has a radial communication network pattern with indicators of low network density levels. The actors @fiersabesari, @bnonews, @asumsico, @disclose.tv, @jawafess, and @insiderpaper have a proximity centrality value of 0 due to their lack of acquaintance. On the other hand, two actors possess a closeness centrality value: @melodiysore with a value of 0.8 and @daryonoBMKG with a value of 0.2. This study highlighted that the actors involved in disaster management and mitigation had a level of popularity that ranked outside the top 10. Conclusions: The information network system for the early warning of the Mount Semeru eruption on Twitter forms a network distribution with a radial communication pattern that is concentrated at one point and acts as a key actor. Eight key actors play a role in disseminating early warning messages, specifically @fiersabesari, @daryonoBMKG, @bnonews, @asumsico, @disclose.tv, @theinsiderpaper, @melodiysore, and @jawafess (community). Implications: This study demonstrates the benefits of using Twitter as a timely indicator for disasters, notably the eruption of Mount Semeru. It can effectively engage the community and government in disseminating early-warning information about volcanic eruptions

Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking

Becoming an Ally: Multi-family Group Therapy Pilot with Low-income Families

The increasing demand for couple and family therapists (CFT) in integrated health care settings requires CFTs to learn to effectively serve low-income families. Resilience literature suggests that building families’ resilience and social support directly impacts a family’s chances for socioeconomic mobility. Multi-family group therapy (MFGT) offers an effective vehicle for increasing resilience and social support. This dissertation examines the link between family resilience and poverty and presents an ecological, solution-focused, family resilience lens applied through a pilot MFGT program, Bouncing Forward Family (BFF) Groups, for low-income families. This dissertation includes two publishable papers, and the first focuses on the BFF groups inclusive of key principles, their application and recommendations for maximizing the role of CFTs in their work with low-income families. This dissertation also tests the BFF program’s ability to benefit low-income families when in public housing assistance programs in San Bernardino, CA. In the second publishable paper, using a treatment-treatment as usual, within subjects design, we examined the benefits of using a pilot MFGT to help low-income families achieve socioeconomic goals. Results confirmed that families within the MFGT completed their socioeconomic goal significantly more than the treatment as usual group. These families also showed positive improvements in self-esteem and family cohesion. The results of this study are promising and suggest that the inclusion of MFGT may be an effective addition to comprehensive programs geared towards increasing families’ socioeconomic mobility. This study highlights the innovative benefits of Multi-family Group Therapy (MFGT) with low-income families

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development