The Influence of Aspen Chemistry and the Nutritional Context on Aspen Herbivory

Herbivory is one major force accelerating aspen decline in North America, but it is unclear why herbivores prefer certain aspen stands over others, or over other plant species in the understory. In this dissertation, I determined the influence of nutrients and plant secondary compounds (PSC), physiological state, chemical composition, and prior experience on aspen preference by sheep in controlled pen experiments. In addition, I explored the relationship between herbivory, regeneration, recruitment, and other landscape elements for specific aspen stands within Wolf Creek Ranch in northern Utah using biomass and chemical composition of the understory and chemical defenses of juvenile aspen trees (i.e., the foodscape). Aspen intake was enhanced when lamb diets contained a high crude protein to energy ratio or when the basal diet contained a low density of energy. Intake was depressed as concentrations of PG (phenolic glycosides) increased in aspen leaves or when lambs were fed a high energy to protein ratio. The effects of nutrients on aspen intake were greater when phenolic glycosides in aspen were present at low concentrations. However, when given a choice between aspen leaves of high or low PG content, lamb preference depended more on aspen nutrient and mineral availability, or on prior diet, than on defense chemistry. On the landscape, I found that stands at low elevations with low abundance of nutrients in the understory are more likely to experience less regeneration and recruitment than those growing within nutrient-rich sites. Aspen browsing was negatively correlated with PG content in aspen stands, and elk presence (measured via fecal pellets) was negatively correlated with abundance of understory protein. In conclusion, aspen herbivory appears to be controlled by the interplay between types and amounts of nutrients offered by the landscape and the chemical composition of aspen stands. A clear assessment of these variables on the landscape, i.e., the foodscape, will aid in the development of novel management strategies aimed at providing nutrients (e.g., through supplements, introduced forages) at strategic locations in order to reduce aspen herbivory within at-risk aspen stands

Are neighbourhood amenities associated with more walking and less driving? Yes, but predominantly for the wealthy

Cities are home to a vast array of amenities, from local barbers to science museums and shopping malls. But these are unequally distributed across urban space. Using Google Places data combined with trip-based mobility data for Bogotá, Colombia, we shed light on the impact of neighbourhood amenities on urban mobility patterns. By deriving a new accessibility metric that explicitly takes into account spatial range, we find that a higher density of local amenities is associated with a higher likelihood of walking as well as shorter bus and car trips. Digging deeper, we use an effect modification framework to show that this relationship varies by socioeconomic status. Our main focus is walking and driving, finding that amenities within about a 1-km radius from home are robustly associated with a higher propensity to walk and shorter driving time only for the wealthiest group. These results suggest that wealthier groups may weigh the proximity of local amenities more heavily into travel decisions, perhaps based on differentiated time-money trade-offs. As cities globally aim to boost public transport and green travel, these findings enable us to better understand how commercial structure shapes urban mobility in highly income-segregated settings

Unravelling the forces underlying urban industrial agglomeration

As early as the 1920's Marshall suggested that firms co-locate in cities to reduce the costs of moving goods, people, and ideas. These 'forces of agglomeration' have given rise, for example, to the high tech clusters of San Francisco and Boston, and the automobile cluster in Detroit. Yet, despite its importance for city planners and industrial policy-makers, until recently there has been little success in estimating the relative importance of each Marshallian channel to the location decisions of firms. Here we explore a burgeoning literature that aims to exploit the co-location patterns of industries in cities in order to disentangle the relationship between industry co-agglomeration and customer/supplier, labour and idea sharing. Building on previous approaches that focus on across- and between-industry estimates, we propose a network-based method to estimate the relative importance of each Marshallian channel at a meso scale. Specifically, we use a community detection technique to construct a hierarchical decomposition of the full set of industries into clusters based on co-agglomeration patterns, and show that these industry clusters exhibit distinct patterns in terms of their relative reliance on individual Marshallian channels

COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities' mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with more formalized and complex industrial structure experienced greater reductions in mobility. Within cities, wealthier residents are more likely to reduce mobility, and commuters are especially more likely to stay at home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities' employment characteristics and work-from-home capabilities are the primary determinants of mobility reduction. This finding underscores the need for mitigations aimed at lower income/informal workers, and sheds light on critical dependencies between socio-economic classes in Latin American cities

Rigidity Percolation in Disordered Fiber Systems: Theory and Applications

Nanocomposites, particularly carbon nanocomposites, find many applications spanning an impressive variety of industries on account of their impressive properties and versatility. However, the discrepancy between the performance of individual nanoparticles and that of nanocomposites suggests continued technological development and better theoretical understanding will provide much opportunity for further property enhancement. Study of computational renderings of disordered fiber systems has been successful in various nanocomposite modeling applications, particularly toward the characterization of electrical properties. Motivated by these successes, I develop an explanatory model for `mechanical' or `rheological percolation,' terms used by experimentalists to describe a nonlinear increase in elastic modulus/strength that occurs at particle inclusion volume fractions well above the electrical percolation threshold. Specifically, I formalize a hypothesis given by \\citet*{penu}, which states that these dramatic gains result from the formation of a `rigid CNT network.' Idealizing particle interactions as hinges, this amounts to the network property of \\emph{rigidity percolation}---the emergence of a giant component (within the inclusion contact network) that is not only connected, but furthermore the inherent contacts are patterned to constrain all internal degrees of freedom in the component. Rigidity percolation has been studied in various systems (particularly the characterization of glasses and proteins) but has never been applied to disordered systems of three-dimensional rod-like particles. With mathematically principled arguments from \\emph{rigidity matroid theory}, I develop a scalable algorithm (\\emph{Rigid Graph Compression}, or \\emph{RGC}), which can be used to detect rigidity percolation in such systems by iteratively compressing provably rigid subgraphs within the rod contact networks. Prior to approaching the 3D system, I confirm the usefulness of \\emph{RGC} by using it to accurately approximate the rigidity percolation threshold in disordered systems of 2D fibers---achieving $<1\\%$ error relative to a previous exact method. Then, I develop an implementation of \\emph{RGC} in three dimensions and determine an upper bound for the rigidity percolation threshold in disordered 3D fiber systems. More work is required to show that this approximation is sufficiently accurate---however, this work confirms that rigidity in the inclusion network is a viable explanation for the industrially useful mechanical percolation. Furthermore, I use \\emph{RGC} to quantitatively characterize the effects of interphase growth and spatial CNT clustering in a real polymer nanocomposite system of experimental interest.Doctor of Philosoph