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Biodiversity has faced a steep decline during the Anthropocene and as human influence on ecosystems through the direct impacts of development and indirect impact of climate change continue to increase the threat to global biodiversity is higher than ever. Coastal marine ecosystems are particularly susceptible to human activities due to their proximity to human settlements and ease of access. In order to protect coastal oceans, it is important to accurately catalog the biodiversity of these recreationally and commercially valuable ecosystems, but marine systems differ in the mechanisms that drive population structure and incipient speciation common to terrestrial systems. Large population size, a lack of physical barriers, and high dispersal potential make it a challenge to identify reproductively isolated populations in marine environments. Molecular techniques have provided solutions to some of these challenges as they are capable of determining where populations fall on the continuum between panmixia and speciation and allow investigators to identify previously undescribed biodiversity (interspecific and intraspecific genetic variation) in marine ecosystems. In this dissertation I use genome-wide sequencing techniques to search for patterns of cryptic speciation and explore the population dynamics of fishes from the family Clinidae in two dynamic coastal ecosystems, Southern Africa and Western North America.

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

The world of Research Methods is always changing and becoming ever more complex. Now completely up to date with the latest innovations, this book engages with recent controversies to give you the best start with your research. In each chapter you will find: · Key concept boxes to help you stay on track and focus on what’s most important · Real life examples which make the theory easier to understand · Exercises to check you’ve understood the chapter · Questions to help you develop your critical thinking. Also available online are: · Multiple choice questions to test your understanding · Datasets to allow you to practice your skills · A flashcard glossary to help with revision. Offering a complete package to anyone taking a research methods course as part of their degree.

The world of Research Methods is always changing and becoming ever more complex. Now completely up to date with the latest innovations, this book engages with recent controversies to give you the best start with your research. In each chapter you will find: · Key concept boxes to help you stay on track and focus on what’s most important · Real life examples which make the theory easier to understand · Exercises to check you’ve understood the chapter · Questions to help you develop your critical thinking. Also available online are: · Multiple choice questions to test your understanding · Datasets to allow you to practice your skills · A flashcard glossary to help with revision. Offering a complete package to anyone taking a research methods course as part of their degree.

Interactions between spatial and temporal variability of rainfall and catchment characteristics strongly influence hydrological response. In urban areas, where runoff generation is fast due to high imperviousness degree, it is especially relevant to capture the high spatiotemporal rainfall variability. Significant progress has been made in the development of spatially distributed rainfall measurements and of distributed hydrological models, to represent the variability of catchment's characteristics. Interactions between rainfall and basin scales on hydrological response sensitivity, however, needs deeper investigation. A previous study investigated the hydrological response in the small urbanized catchment of Cranbrook (8 km 2 , London, UK) and proposed three dimensionless “scale factors” to identify if the available rainfall resolution is sufficient to properly predict hydrological response. We aim to verify the applicability of these scale factors to larger scales, with a distinct physiographic setting, in Little Sugar Creek (111 km 2 , Charlotte, USA), to identify the required rainfall resolution and to predict model performance. Twenty-eight events were selected from a weather radar data set from the National Weather Radar Network, with a resolution of 1 km 2 and 15 min. Rainfall data were aggregated to coarser resolutions and used as input for a distributed hydrological model. Results show that scale factors and associated thresholds are generally applicable for characterization of urban flood response to rainfall across spatiotemporal scales. Additionally, application of scale factors in observation-based analysis supports identification of event characteristics that are poorly captured and critical improvements that need to be made before the model can benefit from high-resolution rainfall., Water Resources

Interactions between spatial and temporal variability of rainfall and catchment characteristics strongly influence hydrological response. In urban areas, where runoff generation is fast due to high imperviousness degree, it is especially relevant to capture the high spatiotemporal rainfall variability. Significant progress has been made in the development of spatially distributed rainfall measurements and of distributed hydrological models, to represent the variability of catchment's characteristics. Interactions between rainfall and basin scales on hydrological response sensitivity, however, needs deeper investigation. A previous study investigated the hydrological response in the small urbanized catchment of Cranbrook (8 km 2 , London, UK) and proposed three dimensionless “scale factors” to identify if the available rainfall resolution is sufficient to properly predict hydrological response. We aim to verify the applicability of these scale factors to larger scales, with a distinct physiographic setting, in Little Sugar Creek (111 km 2 , Charlotte, USA), to identify the required rainfall resolution and to predict model performance. Twenty-eight events were selected from a weather radar data set from the National Weather Radar Network, with a resolution of 1 km 2 and 15 min. Rainfall data were aggregated to coarser resolutions and used as input for a distributed hydrological model. Results show that scale factors and associated thresholds are generally applicable for characterization of urban flood response to rainfall across spatiotemporal scales. Additionally, application of scale factors in observation-based analysis supports identification of event characteristics that are poorly captured and critical improvements that need to be made before the model can benefit from high-resolution rainfall., Water Resources

Amphiphilic block copolymers can assemble into a variety of structures on the nanoscale in selective solvent. The micelle blending protocol offers a simple unique route to reproducibly produce polymer nanostructures. Here we expand this blending protocol to a range of polymer micelle systems and self-assembly routes. We found by exploring a range of variables that the systems must be able to reach global equilibrium at some point for the blending protocol to be successful. Our results demonstrate the kinetics requirements, specifically core block glass transition temperature, Tg, and length of the block limiting the exchange rates, for the blending protocol which can then be applied to a wide range of polymer systems to access this simple protocol for polymer self-assembly.

Rainfall spatial and temporal variability are key points in the prediction of hydrological response. At the same time, catchment scale and characteristics also play important roles, especially in urban areas, where the high level of imperviousness combined with intense and localised rainfall causes fast responses (Ochoa-Rodriguez et al., 2015). New instruments such as weather radars have been developed in recent decades able to better capture the spatial and temporal variability of storm events. At the same time, large improvements have been made to create high-resolution hydrological models that are able to represent the catchment with a high level of detail. However, the interactions between rainfall and catchment variability and their effects on the hydrological response remains poorly understood. In this work, we aim to evaluate the critical space and time scales that characterize rainfall variability and catchment characteristics in relation to hydrological response in urban areas. Critical scales based on dimensionless parameters developed in a previous work (Cristiano et al, 2018) will be evaluated for two urban areas in different climatological regions, one in Europe and one in the US., Water Resources

Rainfall spatial and temporal variability are key points in the prediction of hydrological response. At the same time, catchment scale and characteristics also play important roles, especially in urban areas, where the high level of imperviousness combined with intense and localised rainfall causes fast responses (Ochoa-Rodriguez et al., 2015). New instruments such as weather radars have been developed in recent decades able to better capture the spatial and temporal variability of storm events. At the same time, large improvements have been made to create high-resolution hydrological models that are able to represent the catchment with a high level of detail. However, the interactions between rainfall and catchment variability and their effects on the hydrological response remains poorly understood. In this work, we aim to evaluate the critical space and time scales that characterize rainfall variability and catchment characteristics in relation to hydrological response in urban areas. Critical scales based on dimensionless parameters developed in a previous work (Cristiano et al, 2018) will be evaluated for two urban areas in different climatological regions, one in Europe and one in the US., Water Resources

'This engagingly written and nicely opinionated book is a blend of friendly introduction and concisely applicable detail. No-one can recall every statistical formula, but if they have this book they will know where to look' - Professor Jon May, University of Plymouth 'This is one of the best books I have come across for teaching introductory statistics. The illustrative examples are engaging and often humorous and the explanations of 'difficult' concepts are written in a wonderfully clear and intuitive way' - Nick Allum, University of Essex Selected as an Outstanding Academic Title by Choice Magazine, January 2010 First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the social and behavioural sciences and is perfect for the statistics student starting their journey. The rationale and procedure for analyzing data are presented through exciting examples with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics given its gentle beginning, yet progressive treatment of topics. In addition to descriptive statistics, graphs, t-tests, oneway ANOVAs, Chi-square, and simple linear regression, this Second Edition now includes some new, more advanced topic areas as well as a host of additional examples to help students confidently progress through their studies and apply the techniques in lab work, reports and research projects. Key features of this new edition: - the reoganization of the first three chapters giving more attention to univariate statistics and providing more examples to work through at this level - more advanced 'second step' content has been added on factorial ANOVA and multiple regression - the robust methods chapter from the first edition is now spread throughout the book, and is linked with common teaching practices. - many more examples have been added to enhance the book's practical potential. - a host of exerc

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

'This engagingly written and nicely opinionated book is a blend of friendly introduction and concisely applicable detail. No-one can recall every statistical formula, but if they have this book they will know where to look' - Professor Jon May, University of Plymouth 'This is one of the best books I have come across for teaching introductory statistics. The illustrative examples are engaging and often humorous and the explanations of 'difficult' concepts are written in a wonderfully clear and intuitive way' - Nick Allum, University of Essex Selected as an Outstanding Academic Title by Choice Magazine, January 2010 First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the social and behavioural sciences and is perfect for the statistics student starting their journey. The rationale and procedure for analyzing data are presented through exciting examples with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics given its gentle beginning, yet progressive treatment of topics. In addition to descriptive statistics, graphs, t-tests, oneway ANOVAs, Chi-square, and simple linear regression, this Second Edition now includes some new, more advanced topic areas as well as a host of additional examples to help students confidently progress through their studies and apply the techniques in lab work, reports and research projects. Key features of this new edition: - the reoganization of the first three chapters giving more attention to univariate statistics and providing more examples to work through at this level - more advanced 'second step' content has been added on factorial ANOVA and multiple regression - the robust methods chapter from the first edition is now spread throughout the book, and is linked with common teaching practices. - many more examples have been added to enhance the book's practical potential. - a host of exerc

'This engagingly written and nicely opinionated book is a blend of friendly introduction and concisely applicable detail. No-one can recall every statistical formula, but if they have this book they will know where to look' - Professor Jon May, University of Plymouth 'This is one of the best books I have come across for teaching introductory statistics. The illustrative examples are engaging and often humorous and the explanations of 'difficult' concepts are written in a wonderfully clear and intuitive way' - Nick Allum, University of Essex Selected as an Outstanding Academic Title by Choice Magazine, January 2010 First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the social and behavioural sciences and is perfect for the statistics student starting their journey. The rationale and procedure for analyzing data are presented through exciting examples with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics given its gentle beginning, yet progressive treatment of topics. In addition to descriptive statistics, graphs, t-tests, oneway ANOVAs, Chi-square, and simple linear regression, this Second Edition now includes some new, more advanced topic areas as well as a host of additional examples to help students confidently progress through their studies and apply the techniques in lab work, reports and research projects. Key features of this new edition: - the reoganization of the first three chapters giving more attention to univariate statistics and providing more examples to work through at this level - more advanced 'second step' content has been added on factorial ANOVA and multiple regression - the robust methods chapter from the first edition is now spread throughout the book, and is linked with common teaching practices. - many more examples have been added to enhance the book's practical potential. - a host of exerc

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.