Poverty and Place in the Context of the American South

In the United States, poverty has been historically higher and disproportionately concentrated in the American South. Despite this fact, much of the conventional poverty literature in the United States has focused on urban poverty in cities, particularly in the Northeast and Midwest. Relatively less American poverty research has focused on the enduring economic distress in the South, which Wimberley (2008:899) calls “a neglected regional crisis of historic and contemporary urgency.” Accordingly, this dissertation contributes to the inequality literature by focusing much needed attention on poverty in the South.

Each empirical chapter focuses on a different aspect of poverty in the South. Chapter 2 examines why poverty is higher in the South relative to the Non-South. Chapter 3 focuses on poverty predictors within the South and whether there are differences in the sub-regions of the Deep South and Peripheral South. These two chapters compare the roles of family demography, economic structure, racial/ethnic composition and heterogeneity, and power resources in shaping poverty. Chapter 4 examines whether poverty in the South has been shaped by historical racial regimes.

The Luxembourg Income Study (LIS) United States datasets (2000, 2004, 2007, 2010, and 2013) (derived from the U.S. Census Current Population Survey (CPS) Annual Social and Economic Supplement) provide all the individual-level data for this study. The LIS sample of 745,135 individuals is nested in rich economic, political, and racial state-level data compiled from multiple sources (e.g. U.S. Census Bureau, U.S. Department of Agriculture, University of Kentucky Center for Poverty Research, etc.). Analyses involve a combination of techniques including linear probability regression models to predict poverty and binary decomposition of poverty differences.

Chapter 2 results suggest that power resources, followed by economic structure, are most important in explaining the higher poverty in the South. This underscores the salience of political and economic contexts in shaping poverty across place. Chapter 3 results indicate that individual-level economic factors are the largest predictors of poverty within the South, and even more so in the Deep South. Moreover, divergent results between the South, Deep South, and Peripheral South illustrate how the impact of poverty predictors can vary in different contexts. Chapter 4 results show significant bivariate associations between historical race regimes and poverty among Southern states, although regression models fail to yield significant effects. Conversely, historical race regimes do have a small, but significant effect in explaining the Black-White poverty gap. Results also suggest that employment and education are key to understanding poverty among Blacks and the Black-White poverty gap. Collectively, these chapters underscore why place is so important for understanding poverty and inequality. They also illustrate the salience of micro and macro characteristics of place for helping create, maintain, and reproduce systems of inequality across place.

Latent Space and Social Psychological Models of Diffusion

The problem of social diffusion has animated sociological thinking on topics ranging from the spread of an idea, an innovation or a disease, to the foundations of collective behavior and political polarization. While network diffusion has been a productive metaphor, the reality of diffusion processes is often muddier. Ideas and innovations diffuse differently from diseases, but, with a few exceptions, the diffusion of ideas and innovations has been modeled under the same assumptions as the diffusion of disease. In this dissertation, I develop two new diffusion models for "socially meaningful" contagions that address two of the most significant problems with current diffusion models: (1) that contagions can only spread along observed ties, and (2) that contagions do not change as they spread between people. I augment insights from these statistical and simulation models with an analysis of an empirical case of diffusion - the use of enterprise collaboration software in a large technology company. I focus the empirical study on when people abandon innovations, a crucial, and understudied aspect of the diffusion of innovations. Using timestamped posts, I analyze when people abandon software to a high degree of detail.

To address the first problem, I suggest a latent space diffusion model. Rather than treating ties as stable conduits for information, the latent space diffusion model treats ties as random draws from an underlying social space, and simulates diffusion over the social space. Theoretically, the social space model integrates both actor ties and attributes simultaneously in a single social plane, while incorporating schemas into diffusion processes gives an explicit form to the reciprocal influences that cognition and social environment have on each other. Practically, the latent space diffusion model produces statistically consistent diffusion estimates where using the network alone does not, and the diffusion with schemas model shows that introducing some cognitive processing into diffusion processes changes the rate and ultimate distribution of the spreading information. To address the second problem, I suggest a diffusion model with schemas. Rather than treating information as though it is spread without changes, the schema diffusion model allows people to modify information they receive to fit an underlying mental model of the information before they pass the information to others. Combining the latent space models with a schema notion for actors improves our models for social diffusion both theoretically and practically.

The empirical case study focuses on how the changing value of an innovation, introduced by the innovations' network externalities, influences when people abandon the innovation. In it, I find that people are least likely to abandon an innovation when other people in their neighborhood currently use the software as well. The effect is particularly pronounced for supervisors' current use and number of supervisory team members who currently use the software. This case study not only points to an important process in the diffusion of innovation, but also suggests a new approach -- computerized collaboration systems -- to collecting and analyzing data on organizational processes.

Data to Decision in a Dynamic Ocean: Robust Species Distribution Models and Spatial Decision Frameworks

Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.

For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.

Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.

Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.

In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.

For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.

Genetic evaluation of a proposed introduction: the case of the greater prairie chicken and the extinct heath hen.

Population introduction is an important tool for ecosystem restoration. However, before introductions should be conducted, it is important to evaluate the genetic, phenotypic and ecological suitability of possible replacement populations. Careful genetic analysis is particularly important if it is suspected that the extirpated population was unique or genetically divergent. On the island of Martha's Vineyard, Massachusetts, the introduction of greater prairie chickens (Tympanuchus cupido pinnatus) to replace the extinct heath hen (T. cupido cupido) is being considered as part of an ecosystem restoration project. Martha's Vineyard was home to the last remaining heath hen population until its extinction in 1932. We conducted this study to aid in determining the suitability of greater prairie chickens as a possible replacement for the heath hen. We examined mitochondrial control region sequences from extant populations of all prairie grouse species (Tympanuchus) and from museum skin heath hen specimens. Our data suggest that the Martha's Vineyard heath hen population represents a divergent mitochondrial lineage. This result is attributable either to a long period of geographical isolation from other prairie grouse populations or to a population bottleneck resulting from human disturbance. The mtDNA diagnosability of the heath hen contrasts with the network of mtDNA haplotypes of other prairie grouse (T. cupido attwateri, T. pallidicinctus and T. phasianellus), which do not form distinguishable mtDNA groupings. Our findings suggest that the Martha's Vineyard heath hen was more genetically isolated than are current populations of prairie grouse and place the emphasis for future research on examining prairie grouse adaptations to different habitat types to assess ecological exchangeability between heath hens and greater prairie chickens.

Denitrification and inference of nitrogen sources in the karstic Floridan Aquifer

Aquifer denitrification is among the most poorly constrained fluxes in global and regional nitrogen budgets. The few direct measurements of denitrification in groundwaters provide limited information about its spatial and temporal variability, particularly at the scale of whole aquifers. Uncertainty in estimates of denitrification may also lead to underestimates of its effect on isotopic signatures of inorganic N, and thereby confound the inference of N source from these data. In this study, our objectives are to quantify the magnitude and variability of denitrification in the Upper Floridan Aquifer (UFA) and evaluate its effect on N isotopic signatures at the regional scale. Using dual noble gas tracers (Ne, Ar) to generate physical predictions of N2 gas concentrations for 112 observations from 61 UFA springs, we show that excess (i.e. denitrification-derived) N2 is highly variable in space and inversely correlated with dissolved oxygen (O2). Negative relationships between O2 and δ15N NO3 across a larger dataset of 113 springs, well-constrained isotopic fractionation coefficients, and strong 15N:18O covariation further support inferences of denitrification in this uniquely organic-matter-poor system. Despite relatively low average rates, denitrification accounted for 32 % of estimated aquifer N inputs across all sampled UFA springs. Back-calculations of source δ15N NO3 based on denitrification progression suggest that isotopically-enriched nitrate (NO3-) in many springs of the UFA reflects groundwater denitrification rather than urban- or animal-derived inputs. © Author(s) 2012.

The Effects of Geography, Environment and Phylogeny on Community Assembly and Gene Flow Dynamics

It is increasingly evident that evolutionary processes play a role in how ecological communities are assembled. However the extend to which evolution influences how plants respond to spatial and environmental gradients and interact with each other is less clear. In this dissertation I leverage evolutionary tools and thinking to understand how space and environment affect community composition and patterns of gene flow in a unique system of Atlantic rainforest and restinga (sandy coastal plains) habitats in Southeastern Brazil.

In chapter one I investigate how space and environment affect the population genetic structure and gene flow of Aechmea nudicaulis, a bromeliad species that co-occurs in forest and restinga habitats. I genotyped seven microsatellite loci and sequenced one chloroplast DNA region for individuals collected in 7 pairs of forest / restinga sites. Bayesian genetic clustering analyses show that populations of A. nudicaulis are geographically structured in northern and southern populations, a pattern consistent with broader scale phylogeographic dynamics of the Atlantic rainforest. On the other hand, explicit migration models based on the coalescent estimate that inter-habitat gene flow is less common than gene flow between populations in the same habitat type, despite their geographic discontinuity. I conclude that there is evidence for repeated colonization of the restingas from forest populations even though the steep environmental gradient between habitats is a stronger barrier to gene flow than geographic distance.

In chapter two I use data on 2800 individual plants finely mapped in a restinga plot and on first-year survival of 500 seedlings to understand the roles of phylogeny, functional traits and abiotic conditions in the spatial structuring of that community. I demonstrate that phylogeny is a poor predictor of functional traits in and that convergence in these traits is pervasive. In general, the community is not phylogenetically structured, with at best 14% of the plots deviating significantly from the null model. The functional traits SLA, leaf dry matter content (LDMC), and maximum height also showed no clear pattern of spatial structuring. On the other hand, leaf area is strongly overdispersed across all spatial scales. Although leaf area overdispersion would be generally taken as evidence of competition, I argue that interpretation is probably misleading. Finally, I show that seedling survival is dramatically increased when they grow shaded by an adult individual, suggesting that seedlings are being facilitated. Phylogenetic distance to their adult neighbor has no influence on rates of survival though. Taken together, these results indicate that phylogeny has very limited influence on the fine scale assembly of restinga communities.

Macrosystems ecology: Understanding ecological patterns and processes at continental scales

Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisciplines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents. © The Ecological Society of America.

Event memory: A theory of memory for laboratory, autobiographical, and fictional events.

An event memory is a mental construction of a scene recalled as a single occurrence. It therefore requires the hippocampus and ventral visual stream needed for all scene construction. The construction need not come with a sense of reliving or be made by a participant in the event, and it can be a summary of occurrences from more than one encoding. The mental construction, or physical rendering, of any scene must be done from a specific location and time; this introduces a "self" located in space and time, which is a necessary, but need not be a sufficient, condition for a sense of reliving. We base our theory on scene construction rather than reliving because this allows the integration of many literatures and because there is more accumulated knowledge about scene construction's phenomenology, behavior, and neural basis. Event memory differs from episodic memory in that it does not conflate the independent dimensions of whether or not a memory is relived, is about the self, is recalled voluntarily, or is based on a single encoding with whether it is recalled as a single occurrence of a scene. Thus, we argue that event memory provides a clearer contrast to semantic memory, which also can be about the self, be recalled voluntarily, and be from a unique encoding; allows for a more comprehensive dimensional account of the structure of explicit memory; and better accounts for laboratory and real-world behavioral and neural results, including those from neuropsychology and neuroimaging, than does episodic memory.