Linking Habitat Heterogeneity to Genetic Partitioning in the Rocky Subtidal Using Black Surfperch (Embiotica Jacksoni)

Habitat composition and complexity can play an important role in structuring populations of marine organisms. However, the interactions between the physical and biological landscape and their combined effect on marine population dynamics are not well understood. In this study, I explored the role of habitat complexity (three-dimensional habitat structure) on habitat composition (abundance and distribution of habitat types) and their combined role in structuring genetic variation in populations of the black surfperch Embiotoca jacksoni, within Monterey Bay, California. Black surfperch have no pelagic larval stage, limited adult dispersal, and associate strongly with benthic habitat making them an excellent model system for this study. Structural complexity of subtidal habitat was calculated using digital elevation models of the sea floor. Habitat composition was estimated from photoquadrats of the subtidal benthos and collections of benthic algal samples, which were sampled for the surfperch’s major prey sources in order to calculate prey biomass and distribution. Surfperch were collected for tissue samples and their stomach contents were analyzed for prey categorization (species and size distribution). We used 10 microsatellite markers to generate allele frequencies. GIS and spatial statistics were used to visualize and analyze the relationship between subtidal landscape variables and genetic diversity in black surfperch populations. This approach can provide rigorous quantitative estimates on the relationship between subtidal landscape complexity and genetic diversity in nearshore marine organisms

A Remote Sensing Analysis Of The Effects Of Watershed Disturbance And Riparian Integrity On Stream Fish Communities In The Red River Of The North Basin

The relationships between fish species guilds, riparian cover, and vegetation disturbances in the surrounding landscape were examined across the 11 western tributaries of the Red River of the North. Archival stream sampling data, collected from 1993-2011 by North Dakota state agencies, were analyzed relative to temporally-appropriate land-cover predictors generated from National Land Cover Database and National Agricultural Imagery Program products. The 0-30 m riparian cover width was the most influential landscape predictor influencing fish structure. The 0-30 m riparian cover displayed interactive effects with 30-50 m riparian cover width and watershed land-cover disturbance. These riparian scales were identified by a PCA of intact riparian area, determined from digitized 1m remotely sensed images. Tolerant and omnivorous species guilds had higher percent compositions where riparian cover in the 0-30 m scale was degraded. Conversely, insectivorous and benthic insectivorous species guilds had higher percent compositions where the 0-30 m riparian cover was more intact. Although suspended sediment loading resulting from riparian disturbance is suspected as a potential mechanism for the riparian effect, the limits of the 0-30 m riparian scale are recognized. The 0-30 m riparian scale is presently a proxy variable, as the results identify a structural relationship with the landscape and assumes mechanisms. The investigation of riparian scaling also has implications for the incorporation of riparian effects into fisheries landscape analysis. Relationships between fish communities and riparian integrity or riparian composition have been reported at a variety of arbitrarily selected scales. To test the effects of generalizing riparian scale, a 0-50 m riparian scale was used rather than the 0-30 m scale determined to be the most important. The more general scale displayed slightly different relationships than were shown to exist. Caution should therefore be exercised if arbitrarily selecting riparian scale widths for fisheries landscape analysis

Monitoring and modeling urban sprinkling: a new perspective of land take

According to the studies done until now on the recent urban transformation dynamics, namely urban sprinkling, this thesis aims to investigate the phenomenon from different points of view to bring out its unsustainable character. The urban dispersion phenomena, specific characteristic of low-density territories, will be examined through the sprinkling index by including new components in addition to the traditional settlement system components. It allows to evaluate the shape of the anthropic settlements and the distance between them which often results in fragmentation of the urban settlements which in turn generate landscape fragmentation. Nowadays, both in the proximity of large cities and in more external areas such as rural areas, there are often evidences of strong fragmentation of the anthropic settlements in which, even if the amount of occupied surface (land take) may not seem worrying, its configuration determines a general decrease in ecological connectivity, landscape quality and general degradation of soil functions. The general hypothesis is that fragmentation (of urban, landscape and habitat) can become an indicator of land take. In fact, it is not enough to consider only the loss of natural or agricultural areas, but also the distribution of buildings in the landscape matrix, i.e., its spatial component. An emblematic case is that of Basilicata region whose dynamics of transformation from the 50s to the present day will be investigated in this thesis. According to the latest report of the Italian Institute for Environmental Protection and Research (ISPRA 2020), the Basilicata region has only 3.15% of land consumption compared to the entire regional surface. This indicator is in contrast with the shape of the anthropic settlements which results fragmented and dispersed. It is essential that the effects of fragmentation as well as ecosystem disaggregation take on a "measurable" character, joining the list of indicators of urban and territorial quality such as land take and land consumption that European Union addresses to national communities currently consider essential and decisive to highlighting the efficiency/inefficiency of environmental and landscape management. It is crucial to understand and investigate what have been and will be in the future the most influential drivers on these dynamics that contribute intrinsically to land consumption and to define the addresses or the thresholds to contain this pulverized and disordered dissemination of anthropic settlements

Using Spatiotemporal Methods to Fill Gaps In Energy Usage Interval Data

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best

Exploration of Stream Habitat Spatial Modeling; Using Geographically Weighted Regression, Ordinary Least Squares Regression, and Natural Neighbor Interpolation to Model Depth, Flow, and Benthic Substrate in Streams

Assessment and modeling of stream habitat are integral to understanding streams and the biota within them. In the past several decades, assessment sophistication of ecologic systems increased due to analysis power afforded by gains in computing capability. Spatial data analysis methodology grew alongside computing power and incorporated spatial qualities of ecological data, thereby providing new insights. New methods like geographically weighted regression (GWR) and more established methods like interpolation are now being used in ecological studies to guide assessments and management decisions. However, their accuracy and utility for analysis of stream habitat data have not been fully explored. To clarify their impacts on stream habitat data, the five chapters of this dissertation examined spatial qualities (e.g. heterogeneity, scale, sample pattern) and the use of interpolation and GWR on depth, flow velocity, and benthic substrate.;Benthic substrate, depth, and flow velocity data were collected from four streams between July 2005 and August 2010. Data were collected from Aarons Creek, Monongalia County, WV, Elk River, Kanawha County, WV, Little Wapiti and Grayling creeks in Gallatin County, MT. Using GIS, the datasets were mapped, modeled, and analyzed between fall 2009 and summer 2011.;Results from our studies demonstrated GWR outperformed non-spatial ordinary least squares regression (OLS) when modeling benthic substrate. Our study showed stream data collected at a single scale may be used to generate meaningful results at scales other than that at which it was collected. This finding is important for stream habitat studies where data are often collected at varying spatial scales. As spatial heterogeneity of benthic substrate increased, accuracy levels of models decreased showing heterogeneity must be quantified in analysis of stream habitat variables. Large (\u3e20m width) and small (\u3c10m width) wadeable streams may be analyzed using the same type of spatial analysis though substrate deposition pattern may vary in different size streams. Benthic substrate depositional pattern was most effectively captured by non-random point selection which created more accurate maps than grid and random point sample methods.;Combined results demonstrated the need to address spatial qualities of stream habitat data in analysis, assessment, and how spatial attributes may guide data collection. Further, failure to quantify spatial attributes in stream habitat data can cause erroneous results and thus minimize effectiveness for useful ecologic conclusions and management decisions

Autocorrelação espacial dos índices ndvi e gvi derivados de imagens landsat/tm para cultura da soja no oeste paranaense e ano agrícola de 2004/2005

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.Este trabalho apresenta um estudo de estatística espacial de áreas baseado no NDVI (índice de vegetação por diferença normalizada) e no GVI (índice de vegetação verde) da cultura da soja, obtidos de imagens de sensoriamento remoto da região oeste do Paraná. As imagens foram coletadas pelo sensor TM (Thematic Mapper) do satélite Landsat-5, durante a safra de 2004/2005. Os dados foram agrupados em cinco classes de igual amplitude, o que permitiu criar um mapa da evolução temporal da cultura da soja. Foi utilizado o índice I de Moran para estudar a autocorrelação espacial em um nível global e o índice LISA (Local Indicators of Spatial Association) para estudar a autocorrelação espacial em um nível local. Por meio destes índices, foi possível conhecer o perfil da cultura de soja nos municípios da região oeste do Paraná, permitindo identificar épocas diferentes do plantio desta cultura e subsidiar os membros da cadeia produtiva da soja que utilizam dados de produtividade em seus planejamentos.525537Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Biking equity : the unresolved puzzle piece in San Francisco’s biking renaissance

The surging bicycling rates in U.S. cities and the growing interest to improve avenues of active transportation substantiates the growing presence of the American biking renaissance. San Francisco’s sizeable share of bike-related improvements in the planning pipeline along with its third highest bike mode share amongst U.S. cities affirms that the city is in its most bike-conducive planning phase of history. As cities continue to invest their public dollars towards “Let’s Make Our Cities Bikeable” vision, growing number of planning studies are beginning to show that bike shares and biking infrastructure are inequitably distributed throughout the cities and in a manner that low-income households or communities of color do not use them as often or as comfortably (Smith, 2015) and San Francisco’s case is no different. With numerous Federal and State level grants being used to develop and expand the biking infrastructure in U.S. cities, communities are beginning to realize that biking can be a means to social justice. Additionally, for a high cost of living and housing price area like San Francisco, the low-income communities might benefit the most from the positive externalities accrued from improved access to the biking infrastructure. These benefits include but are not limited to - improved household transportation savings, lower fuel consumption, lowered health risks related to cardiovascular diseases and improved carbon footprint. The intent of this study is to inquire whether San Francisco’s existing biking infrastructure (including bike share programs) are absent or less accessible in communities of lower socioeconomic status. And if yes, how is this persisting inequity being influenced by the upcoming bikeway improvement projects and bike share programs. The study finds that bikers in San Francisco tend to be young, white, males with lower-to-middle income background. The study ran Geographically Weighted Regression (GWR) between perceived bike accessibility index and socioeconomic indicators to observe that the low-income neighborhoods in San Francisco have an inequitable access to biking infrastructure. Households in low-income neighborhoods of San Francisco with high dependency rates, low educational levels and with no access to health insurance show low bike accessibility. The study elicits that although the city’s long-term bike and ped planning projects are geared towards addressing this persisting inequity, a closer look at bikeway improvement projects implementation since 2012 hasn’t mended the equity gap. Improving access to safe and convenient biking infrastructure through physical planning and design is a traditional model of addressing inequitable distribution of civic amenities. The study gathers evidence from other U.S. cities in promoting equitable bike share lessons. It postulates that San Francisco with its bike sharing expansion stands at an opportune moment, where appropriate sequencing of bike infrastructure expansion, bike share station siting in low-income communities, active bike sharing advocacy, discounted membership for low-income households, improved bike share and transit integration, and predicted surge in ridership in the newly expanded residential neighborhoods might bridge the equity gap that traditional modes of bike infrastructure improvements have not been able to accomplishCommunity and Regional Plannin

Chronic kidney disease mortality in Costa Rica ; geographical distribution, spatial analysis and non-traditional risk factors

Central America in general and Costa Rica in particular have been facing with increasing number of Chronic Kidney Disease (CKD). Experts have recently (2013) recommended spatial analysis of the relevant data for better understanding of the situation. This study was performed to evaluate geographical distribution of CKD mortality in Costa Rica through spatial analysis of CKD mortality data. The study also looked at associations between CKD mortality and environmental factors. Moreover, this thesis evaluated physicians’ knowledge about non-traditional factors affecting CKD. CKD mortality data (1980 - 2012) were statistically and spatially analysed. Over the study period, CKD mortality showed an upward trend and geographically progressed to the neighbouring areas. Northern parts of the country were identified as the hot spot. Significant associations between CKD mortality and temperature, permanent crops and precipitation were observed (p< 0.05). There were inconsistencies in the effect of temperature and precipitation in different parts of the country. The study also showed an inadequate knowledge of physicians on the possible environmental risk factors for CKD. The findings of this study provided objective evidence on the progressive nature of the CKD problem in Costa Rica. This study also provided further evidence in support of the newly emerging non-traditional risk factors for CKD (agricultural occupation, heat stress etc.). Further investigations are recommended.The two kidneys are vital organs in the body with the main function of filtering out waste products from the blood stream. Chronic kidney disease (CKD) happens when the function of the kidney is not as before which means the kidney is damaged. CKD is now recognised as a global public health issue, but there are areas in the world in which CKD is a more prominent public health issue. One of those areas is Central America. So far, investigations have pointed out several factors as the possible underlying causes of the current CKD increase in Central America, including environmental and occupational factors. In order to better understand the current CKD increase in Central America, we looked at the publicly available data related to CKD deaths in Costa Rica and visualised the findings on Costa Rica’s map. The visualisation was carried out through a modern and sophisticated system called “GIS” or “Geographic Information System”. The maps identified northern part of the country as the hot spot which requires further attention by authorities when allocating resources for public health issues. The maps also showed more CKD related deaths in the geographic areas with more likelihood of exposure to heat and with more farming activities. These findings provide more evidence in support of the likely association between CKD and environmental and occupational factors. Further investigations are recommended

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment