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Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. To facilitate the use of wildlife monitoring data for improved adaptive management, we developed a novel approach to define hierarchical tiers (multiple scales) of population structure.We defined population structure by combining graph theory with biological inference about dispersal capability (based on movement, gene flow, and habitat condition) and functional processes affecting movement (e.g. habitat selection across scales of landscape preferences). First, we developed least‐cost paths between high fidelity sites (habitat patches) using a cost surface, informed from functional processes of habitat characteristics to account for resistance of inter‐patch movements. Second, we combined the paths into a multi‐path graph construct. Third, we used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into hierarchical tiers of connected subpopulations, denoting the degree that dispersal affected population structure.As a case study, we applied our approach across the greater sage‐grouse (Centrocercus urophasianus) range, a species of conservation concern in western United States. We described the relative importance of local populations and where to potentially avoid landscape disturbances that may negatively affect population connectivity using centrality measures supported by graph theory, and we demonstrated close alignment of the resulting population structure with population densities.This method can be adapted for other species with site fidelity and used as a management tool to evaluate population trends and responses to landscape changes across different temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Objective: Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread., Methods: Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years., Results: Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model., Conclusions: Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States., Competing Interests: The authors declare that they have no competing interests., (© 2024 Ojwang’ et al.)

Traffic congestion negatively impacts our society. Most of the traditional transportation planning techniques - though effective - require rigorous amounts of data and analysis which consumes time and resources. This paper uses social network analysis (SNA) to analyze transportation networks, and consequently corroborate the effectiveness of SNA as a complementary tool for improved transportation planning. After creating the connection between the language and concepts of SNA and those of transportation systems - as well as developing a model that utilizes different SNA centrality measures within the transportation context - the authors utilize SNA to investigate traffic networks in three case studies in the state of Louisiana, analyze the results and draw conclusions. To this effect, with minimal cost and time, the model identifies the most critical intersections that should be further investigated using traditional techniques. These results are in agreement with the findings of Louisiana’s Department of Transportation and Development. [ABSTRACT FROM AUTHOR]

Given the global increase in public funding for nanotechnology research and development, it is even more important to support projects with promising return on investment. A main return is the benefit to other researchers and to the entire field through knowledge diffusion, invention, and innovation. The social network of researchers is one of the channels through which this happens. This study considers the scientific publication network in the field of nanotechnology, and evaluates how knowledge diffusion through coauthorship and citations is affected in large institutions by the location and connectivity of individual researchers in the network. The relative position and connectivity of a researcher is measured by various social network metrics, including degree centrality, Bonacich Power centrality, structural holes, and betweenness centrality. Leveraging the Cox regression model, we analyzed the temporal relationships between knowledge diffusion and social network measures of researchers in five leading universities in the United States using papers published from 2000 to 2010. The results showed that the most significant effects on knowledge diffusion in the field of nanotechnology were from the structural holes of the network and the degree centrality of individual researchers. The data suggest that a researcher has potential to perform better in knowledge creation and diffusion on boundary-spanning positions between different communities and when he or she has a high level of connectivity in the knowledge network. These observations may lead to improved strategies in planning, conducting, and evaluating multidisciplinary nanotechnology research. The paper also identifies the researchers who made most significant contributions to nanotechnology knowledge diffusion in the networks of five leading U.S. universities. [ABSTRACT FROM AUTHOR]

This paper examines the co-authorship network in the field of scientometrics using social network analysis techniques with the aim of developing an understanding of research collaboration in this scientific community. Using co-authorship data from 3125 articles published in the journal Scientometrics with a time span of more than three decades (1980-2012), we construct an evolving co-authorship network and calculate three centrality measures (closeness, betweenness, and degree) for 3024 authors, 1207 institutions, 68 countries and 22 academic fields in this network. This paper also discusses the usability of centrality measures in author ranking, and suggests that centrality measures can be useful indicators for impact analysis. Findings revealed that scientometrics was not dominated by a couple of key researchers as quite a significant number of popular researchers were identified. The United States occupies the topmost position in all measures except for degree centrality. The most active, central and collaborative academic discipline in scientometrics is Information & Library Science. [ABSTRACT FROM AUTHOR]