Raymond J. Dezzani

Putting the "landscape" in landscape genetics.

Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term ‘landscape genetics’, provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.

Landscape genetics of high mountain frog metapopulations

Explaining functional connectivity among occupied habitats is crucial for understanding metapopulation dynamics and species ecology. Landscape genetics has primarily focused on elucidating how ecological features between observations influence gene flow. Functional connectivity, however, may be the result of both these between‐site (landscape resistance) landscape characteristics and at‐site (patch quality) landscape processes that can be captured using network based models. We test hypotheses of functional connectivity that include both between‐site and at‐site landscape processes in metapopulations of Columbia spotted frogs (Rana luteiventris) by employing a novel justification of gravity models for landscape genetics (eight microsatellite loci, 37 sites, n = 441). Primarily used in transportation and economic geography, gravity models are a unique approach as flow (e.g. gene flow) is explained as a function of three basic components: distance between sites, production/attraction (e.g. at‐site landscape process) and resistance (e.g. between‐site landscape process). The study system contains a network of nutrient poor high mountain lakes where we hypothesized a short growing season and complex topography between sites limit R. luteiventris gene flow. In addition, we hypothesized production of offspring is limited by breeding site characteristics such as the introduction of predatory fish and inherent site productivity. We found that R. luteiventris connectivity was negatively correlated with distance between sites, presence of predatory fish (at‐site) and topographic complexity (between‐site). Conversely, site productivity (as measured by heat load index, at‐site) and growing season (as measured by frost‐free period between‐sites) were positively correlated with gene flow. The negative effect of predation and positive effect of site productivity, in concert with bottleneck tests, support the presence of source–sink dynamics. In conclusion, gravity models provide a powerful new modelling approach for examining a wide range of both basic and applied questions in landscape genetics.

Terrain complexity and reduction of topographic data

Abstract. Digital terrain data are useful for a variety of applications in mapping and spatial analysis. Most available terrain data are organized in a raster format, among them being the most extensively-used Digital Elevation Models (DEM) of the U.S. Geological Survey. A common problem with DEM for spatial analysis at the landscape scale is that the raster encoding of topography is subject to data redundancy and, as such, data volumes may become prohibitively large. To improve efficiency in both data storage and information processing, the redundancy of the terrain data must be minimized by eliminating unnecessary elements. To what extent a set of terrain data can be reduced for improving storage and processing efficiency depends on the complexity of the terrain. In general, data elements for simpler, smoother surfaces can be substantially reduced without losing critical topographic information. For complex terrains, more data elements should be retained if the topography is to be adequately represented. In this paper, we present a measure of terrain complexity based on the behavior of selected data elements in representing the characteristics of a surface. The index of terrain complexity is derived from an estimated parameter which denotes the relationship between terrain representation (percentage surface representation) and relative data volume (percentage DEM elements). The index can be used to assess the required volume of topographic data and determine the appropriate level of data reduction. Two quadrangles of distinct topographic characteristics were examined to illustrate the efficacy of the developed methodology.

Geographies of U.S. Police Militarization and the Role of the 1033 Program

Concerns about police militarization have become an important public policy issue since the aggressive police response to the 2014 protests in Ferguson, Missouri, where police officers used military-style equipment to confront protestors. This event was a stark visual reminder that many U.S. police departments have used federal programs to acquire surplus military equipment, including weapons, armored vehicles, and body armor. We explore the geographies and histories of one the most important programs, called 1033, which supplies police with military equipment under the rationale of prosecuting the War on Drugs. We show that the legal blurring of the police and the military has been ongoing for decades at the national scale but this has resulted in an uneven landscape of police militarization at the county scale. We also investigate one of the most common global arguments for why police become militarized, which is the presence of Special Weapons and Tactics-style paramilitary teams, finding little support for that claim. More geographic inquiry is needed to understand the trajectories, causes, and consequences of police militarization.

Measuring Transition and Mobility in the Hierarchical World-Economy

This paper provides a temporal stochastic framework that is used to analyze economic transitions of countries in the world-system. As such, it provides a contribution to a general quantitative rendering of the world-systems perspective. State space modeling using Markov chains provides a powerful stochastic instrument for global economic modeling when structure is known but relational uncertainty is present as well as for examining temporal change of geographic phenomena. Two phenomena are examined: (1) country mobility among regional classes within the world-economy; and (2) the stability of the rate of country-level transitions. Results suggest that although a moderate amount of movement has taken place in the period 1960-1990, the overall structure of the world-economy has not changed significantly. Thus, although the developmental hypothesis that countries are upwardly mobile has merit, empirical results suggest that very little impact is observed in the world-system because countries moving upward in the world-economy region sequence are nearly balanced by countries moving down the sequence. Copyright 2002 Blackwell Publishers Inc.

Development of a spatially explicit vulnerability-resilience model for community level hazard mitigation enhancement

Community vulnerability to coastal hazards can be difficult to analyze at a local level without proper modeling techniques. Societal assets and human populations are dispersed unequally across landscapes, causing vulnerability to vary from one community to another. A common method of quantifying vulnerability has developed in the form of vulnerability indexes, typically conducted at the county scale. These indexes attempt to measure community vulnerability by assessing exposure of traditional vulnerability indicators. Sensitivity and adaptive capacity analyses are excluded from these assessments, creating a less than holistic vulnerability analysis. Traditional vulnerability assessments also neglect the inclusion of place-specific differentially weighted indicators, and the effects of spatial autocorrelation. These limitations make indexes less effective for community level analysis. In response to these challenges, a resilience index that incorporates place, spatial, and scale specific indicators that are more appropriate for community level analysis was developed. The model developed in this research determines varying distributions of vulnerability across the study region using several socioeconomic, spatial and place specific indicators. Spatial statistics (such as spatial autocorrelation techniques) and multivariate techniques (such as factor analysis) were employed to determine the differential influence of each vulnerability and adaptive capacity indicator. The results of the model enable decision makers to target mitigation efforts toward place-specific, differentially weighted indicators that most impact vulnerability at the community level. The model also depicts that traditional vulnerability indicators are differentially impactful at varying spatial scales.

Spatial analysis in a Markov random field framework: The case of burning oil wells in Kuwait

Abstract. This paper discusses a modeling approach for spatial-temporal prediction of environmental phenomena using classified satellite images. This research was prompted by the analysis of change and landscape redistribution of petroleum residues formed from the residue of the burning oil wells in Kuwait (1991). These surface residues have been termed “tarcrete” (El-Baz et al. 1994). The tarcrete forms a thick layer over sand and desert pavement covering a significant portion of south-central Kuwait. The purpose of this study is to develop a method that utilizes satellite images from different time steps to examine the rate-of-change of the oil residue deposits and determine where redistribution is are likely to occur. This problem exhibits general characteristics of environmental diffusion and dispersion phenomena so a theoretical framework for a general solution is sought. The use of a lagged-clique, Markov random field framework and entropy measures is deduced to be an effective solution to satisfy the criteria of determination of time-rate-of-change of the surface deposits and to forecast likely locations of redistribution of dispersed, aggraded residues. The method minimally requires image classification, the determination of time stationarity of classes and the measurement of the level of organization of the state-space information derived from the images. Analysis occurs at levels of both the individual pixels and the system to determine specific states and suites of states in space and time. Convergence of the observed landscape disorder with respect to an analytical maximum provide information on the total dispersion of the residual system.

The Role of Foreign Direct Investment as a Structural Development Indicator of the Hierarchical World Economy

The world-system perspective has been little employed for the examination of foreign direct investment (FDI) distributions in the world economy. We approach FDI distributions as a function of the structural hierarchy of the world economy. The goal is to examine some fundamental relationships between the structure of the world economy and the flows and stocks of FDI. A state-space approach is used such that the world-economy classification serves as a dependent variable to be explained by FDI behavior. An FDI capital distribution model is developed to generate hypotheses for statistical validation. Results of the analyses suggest that, while the world-economy classification accounts for a significant proportion of variability, FDI is more complex than the core–periphery capital distribution model predicts. Differences in average profit rate, investment risk, and investment purpose all serve to adjust variation in the FDI distributions. Investment risk exhibits a stable relationship among world-economy classes over at least a twenty-year period and seems to be a primary driver of investment behavior. FDI may therefore be a statistically reasonable covariate with country-level position in the world economy as well as a predictor of development potential.