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Globally, universities have heavily invested in makerspaces. This investment requires an understanding of how students use tools and how tools to aid in engineering education, as well as how the spaces can be improved. Network analysis of human systems can often yield valuable information about how the networks work and function. Applying network techniques to makerspaces can yield helpful information that is otherwise not visible. This thesis’s primary focus is the application of a variety of bio-inspired network techniques to improve the understanding of the makerspace. Several parallels can be drawn between makerspace networks and other mutualistic networks, such as plant-and-pollinator networks where the system’s success depends on the interaction between the two species. The ecological metrics would establish measurable values that the health and conditions of a network can be evaluated using. These three metrics are nestedness, modularity, and connectance, which can provide structural information about the network and act as diagnostics tools that can change depending on different system conditions. The makerspace at the universities went through several regulatory changes due to COVID-19, providing a unique opportunity to utilize the metrics to analyze the health of the space under higher regulatory restrictions and return to normal operations. The makerspace is converted into a bipartite network to allow for ecological analysis techniques where the spaces are modeled with students interacting with tools. Null models evaluate the significance of the nestedness and modularity results. Findings indicate that makerspaces tend to be structurally nested, but when compared to normal operating conditions, they can be seen to exhibit modularity during the higher restriction environment. The makerspace network and subsequent analysis provide insight into the use of ecological metrics in human systems and provide potential ideas for results to be used in various networks. The following network analysis also yields valuable information identifying essential hub tools and student interactions within the space, showcasing the capabilities the ecological study of human networks can have on human systems.
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A roadmap towards predicting species interaction networks (across space and time)
Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
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- Award ID(s):
- 2021909
- PAR ID:
- 10312503
- Date Published:
- Journal Name:
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Volume:
- 376
- Issue:
- 1837
- ISSN:
- 0962-8436
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rstb.2021.0063
