ALBERT

Note: Erscheinungsjahr in Vorlageform:2009

Note: Erscheinungsjahr in Vorlageform:2009

Note: Contents List of contributors Introduction Part I Geophysical methods 1 Electrical methods / C. Kneisel and C. Hauck 1.1 Introduction 1.2 Measurement principles 1.3 Data acquisition 1.4 Data processing 1.5 Periglacial applications and particularities 1.6 Conclusions 1.7 Checklist References 2 Electromagnetic methods / A. Hardt and C. Hauck 2.1 Introduction 2.2 Background 2.3 Periglacial applications and particularities 2.4 Conclusions 2.5 Checklist References 3 Refraction seismics / L. Schrott and T. Hoffmann 3.1 Introduction 3.2 Measurement principles 3.3 Limitations of seismic refraction based on measurement principles 3.4 Data acquisition 3.5 Data processing 3.6 Periglacial applications and particularities 3.7 Checklist References 4 Ground-penetrating radar / I. Berthling and K. Melvold 4.1 Introduction 4.2 Measurement principles 4.3 Data acqusition 4.4 Data processing 4.5 Periglacial applications and particularities 4.6 Recommendations References Part Il Case studies 5 Typology of vertical electrical soundings for permafrost/ground ice investigation in the forefields of small alpine glaciers / R. Delaloye and C. Lambiel 5.1 Introduction 5.2 Method 5.3 Typology 5.4 Conclusions References 6 ERT imaging for frozen ground detection / M. Ishikawa 6.1 Introduction 6.2 Data acquisition and quality control 6.3 Case studies 6.4 Summary References 7 Electrical resistivity values of frozen soil from YES and TEM field observations and laboratory experiments / K. Harada 7.1 Introduction 7.2 Methods 7.3 Results 7.4 Summary References 8 Results of geophysical surveys on Kasprowy Wierch, the Tatra Mountains, Poland / W. Dobinski, B. Zogala, K. Wzietek and L. Litwin 8.1 Introduction 8.2 Field site 8.3 Methods 8.4 Measurements 8.5 Analysis and interpretation of the measurements 8.6 Conclusions References 9 Reassessment of DC resistivity in rock glaciers by comparing with P-wave velocity: a case study in the Swiss Alps / A. Ikeda 9.1 Introduction 9.2 Methods 9.3 Field sites with borehole information 9.4 Results 9.5 Discussion 9.6 Conclusions References 10 Quantifying the ice content in low-altitude scree slopes using geophysical methods / C. Hauck and C. Kneisel 10.1 Introduction 10.2 Methods 10.3 Field sites 10.4 Results 10.5 Discussion and conclusions References 11 The use of GPR in determining talus thickness and talus structure / O. Sass 11.1 Introduction 11.2 Study sites and data acquisition 11.3 Results 11.4 Conclusions References 12 GPR soundings of rock glaciers on Svalbard / I. Berthling, B. Etzelmüller, H. Farbrot, K. Isaksen, M. Wåle and R. Ødegård 12.1 Introduction 12.2 Methods 12.3 Results and interpretation 12.4 Discussion References 13 Arctic glaciers and ground-penetrating radar. Case study: Stagnation Glacier, Bylot Island, Canada / T. Irvine-Fynn and B. Moorman 13.1 Introduction 13.2 Field site 13.3 Field methods 13.4 Processing methods 13.5 Results 13.6 Discussion 13.7 Conclusions References 14 Mapping of subglacial topography using GPR for determining subglacial hydraulic conditions / K. Melvold and T. V. Schuler 14.1 Introduction 14.2 Field site 14.3 Methodology 14.4 Results 14.5 Discussion 14.6 Conclusions References 15 Snow measurements using GPR: example from Amundsenisen, Svalbard / K. Melvold 15.1 Introduction 15.2 GPR and GPS equipment and measurements 15:3 Data processing 15.4 Results and discussion 15.5 Conclusions References 16 Mapping frazil ice conditions in rivers using ground penetrating radar / I. Berthling, H. Benjaminsen and A. Kvambekk 16J Introduction 16.2 Setting and field procedures 16.3 Results 16.4 Discussion 16.5 Conclusions References Contents Appendix Tables of geophysical parameters for periglacial environments Index

Note: Contents Contributors 1 Introduction 1.1 Part 1: Applied statistical theory 1.2 Part 2: The case studies 1.3 Data, software and flowcharts 2 Data management and software 2.1 Introduction 2.2 Data management 2.3 Data preparation 2.4 Statistical software 3 Advice for teachers 3.1 Introduction 4 Exploration 4.1 The first steps 4.2 Outliers, transformations and standardisations 4.3 A final thought on data exploration 5 Linear regression 5.1 Bivariate linear regression 5.2 Multiple linear regression 5.3 Partial linear regression 6 Generalised linear modelling 6.1 Poisson regression 6.2 Logistic regression 7 Additive and generalised additive modelling 7.1 Introduction 7.2 The additive model 7.3 Example of an additive model 7.4 Estimate the smoother and amount of smoothing 7.5 Additive models with multiple explanatory variables 7.6 Choosing the amount of smoothing 7.7 Model selection and validation 7.8 Generalised additive modelling 7.9 Where to go from here 8 Introduction to mixed modelling 8.1 Introduction 8.2 The random intercept and slope model 8.3 Model selection and validation 8.4 A bit of theory 8.5 Another mixed modelling example 8.6 Additive mixed modelling 9 Univariate tree models 9.1 Introduction 9.2 Pruning the tree 9.3 Classification trees 9.4 A detailed example: Ditch data 10 Measures of association 10.1 Introduction 10.2 Association between sites: Q analysis 10.3 Association among species: R analysis 10.4 Q and R analysis: Concluding remarks 10.5 Hypothesis testing with measures of association 11 Ordination — First encounter 11.1 Bray-Curtis ordination 12 Principal component analysis and redundancy analysis 12.1 The underlying principle of PCA 12.2 PCA: Two easy explanations 12.3 PCA: Two technical explanations 12.4 Example of PCA 12.5 The biplot 12.6 General remarks 12.7 Chord and Hellinger transformations 12.8 Explanatory variables 12.9 Redundancy analysis 12.10 Partial RDA and variance partitioning 12.11 PCA regression to deal with collinearity 13 Correspondence analysis and canonical correspondence analysis 13.1 Gaussian regression and extensions 13.2 Three rationales for correspondence analysis 13.3 From RGR to CCA13.4 Understanding the CCA triplot 13.5 When to use PCA, CA, RDA or CCA 13.6 Problems with CA and CCA 14 Introduction to discriminant analysis 14.1 Introduction 14.2 Assumptions 14.3 Example 14.4 The mathematics 14.5 The numerical output for the sparrow data 15 Principal coordinate analysis and non-metric multidimensional scaling 15.1 Principal coordinate analysis 15.2 Non-metric multidimensional scaling 16 Time series analysis — Introduction 16.1 Using what we have already seen before 16.2 Auto-regressive integrated moving average models with exogenous variables 17 Common trends and sudden changes 17.1 Repeated LOESS smoothing 17.2 Identifying the seasonal component 17.3 Common trends: MAFA 17.4 Common trends: Dynamic factor analysis 17.5 Sudden changes: Chronological clustering 18 Analysis and modelling of lattice data 18.1 Lattice data 18.2 Numerical representation of the lattice structure 18.3 Spatial correlation 18.4 Modelling lattice data 18.5 More exotic models 18.6 Summary 19 Spatially continuous data analysis and modelling 19.1 Spatially continuous data 19.2 Geostatistical functions and assumptions 19.3 Exploratory variography analysis 19.4 Geostatistical modelling: Kriging 19.5 A full spatial analysis of the bird radar data 20 Univariate methods to analyse abundance of decapod larvae 20.1 Introduction 20.2 The data 20.3 Data exploration 20.4 Linear regression results 20.5 Additive modelling results 20.6 How many samples to take? 20.7 Discussion 21 Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal 21.1 Introduction 21.2 Data and materials 21.3 Data exploration 21.4 Classification trees 21.5 Generalised additive modelling 21.6 Generalised linear modelling 21.7 Discussion 22 Crop pollination by honeybees in Argentina using additive mixed modelling 22.1 Introduction 22.2 Experimental setup 22.3 Abstracting the information 22.4 First steps of the analyses: Data exploration 22.5 Additive mixed modelling 22.6 Discussion and conclusions 23 Investigating the effects of rice farming on aquatic birds with mixed modelling 23.1 Introduction 23.2 The data 23.3 Getting familiar with the data: Exploration 23.4 Building a mixed model 23.5 The optimal model in terms of random components 23.6 Validating the optimal linear mixed model 23.7 More numerical output for the optimal model 23.8 Discussion 24 Classification trees and radar detection of birds for North Sea wind farms 24.1 Introduction 24.2 From radars to data 24.3 Classification trees 24.4 A tree for the birds 24.5 A tree for birds, clutter and more clutter 24.6 Discussion and conclusions 25 Fish stock identification through neural network analysis of parasite fauna 25.1 Introduction 25.2 Horse mackerel in the northeast Atlantic 25.3 Neural networks 25.4 Collection of data 25.5 Data exploration 25.6 Neural network results 25.7 Discussion 26 Monitoring for change: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands 26.1 Introduction 26.2 The data 26.3 Data exploration 26.4 Linear regression results 26.5 Generalised least squares results 26.6 Multivariate analysis results 26.7 Discussion 27 Univariate and multivariate analysis applied on a Dutch sandy beach community 27.1 Introduction 27.2 The variables 27.3 Analysing the data using univariate methods 27.4 Analysing the data using multivariate methods 27.5 Discussion and conclusions 28 Multivariate analyses of South-American zoobenthic species — spoilt for choice 28.1 Introduction and the underlying questions 28.2 Study site and sample collection 28.3 Data exploration 28.4 The Mantel test approach 28.5 The transformation plus RDA approach 28.6 Discussion and conclusions 29 Principal component analysis applied to harbour porpoise fatty acid data 29.1 Introduction 29.2 The data 29.3 Principal component analysis 29.4 Data exploration 29.5 Principal component analysis results 29.6 Simpler alternatives to PCA 29.7 Discussion 30 Multivariate analyses of morphometric turtle data — size and shape 30.1 Introduction 30.2 The turtle data 30.3 Data exploration 30.4 Overview of classic approaches related to PCA 30.5 Applying PCA to the original turtle data 30.6 Classic morphometric data analysis approaches 30.7 A geometric morphometric approach 31 Redundancy analysis and additive modelling applied on savanna tree data 31.1 Introduction 31.2 Study area 31.3 Methods 31.4 Results 31.5 Discussion 32 Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico 32.1 Introduction 32.2 The study area 32.3 The data 32.4 Data exploration 32.5 Canonical correspondence analysis results 32.6 African star grass 32.7 Discussion and conclusion 33 Estimating common trends in Portuguese fisheries landings 33.1 Introduction 33.2 The time series data 33.3 MAFA and DFA 33.4 MAFA results 33.5 DFA results 33.6 Discussion 34 Common trends in demersal communities on the Newfoundland-Labrador Shelf 34.1 Introduction 34.2 Data 34.3 Time series analysis 34.4 Discussion 35 Sea level change and salt marshes in the Wadden Sea: A time series analysis 35.1 Interaction between hydrodynamical and biological factors 35.2 The data 35.3 Data exploration 35.4 Additive mixed modelling 35.5 Additive mixed modelling results 35.6 Discussion 36 Time series analysis of Hawaiian waterbirds 36.1 Introduction 36.2 Endangered Hawaiian waterbirds 36.3 Data exploration 36.4 Three ways to estimate trends 36.5 Additive mixed modelling 36.6 Sudden breakpoints 36.7 Discussion 37 Spatial modelling of forest community features in the Volzhsko-Kamsky reserve 37.1 Introduction 37.2 Study area 37.3 Data exploration 37.4 Models of boreality without spatial auto-correlation 37.5 Models of boreality with spatial auto-correlation 37.6 Conclusion References Index

Note: Contents: Preface. - Acknowledgements. - List of Abbreviations. - 1 The evolution of knowledge about the Arctic and its climate. - 1.1 Historical exploration. - 1.2 The beginning of systematic observations. - 1.3 The modern era. - 2 Physical characteristics and basic climatic features. - 2.1 The Arctic ocean. - 2.2 The Arctic lands. - 2.3 Basic climatic elements. - 3 The basic atmospheric heat budget. - 3.1 The Arctic and the global heat budget. - 3.2 The basic Arctic heat budget. - 3.3 Further analysis of Fwall. - 4 The atmospheric circulation. - 4.1 Historical perspective. - 4.2 The stratospheric circulation. - 4.3 The Arctic tropopause. - 4.4 The mid-tropospheric circulation. - 4.5 Surface and near-surface circulation. - 4.6 Polar Lows. - 5 The surface energy budget. - 5.1 The energy balance equations. - 5.2 The downward solar radiation flux. - 5.3 Surface albedo. - 5.4 Longwave radiation fluxes. - 5.5 Distribution of net radiation. - 5.6 Cloud radiative forcing. - 5.7 Radiation fluxes from surface observations: examples from SHEBA. - 5.8 Partitioning of net radiation. - 5.9 Skin temperature, SAT and vertical structure. - 5.10 Radiation-climate feedbacks. - 6 Precipitation, net precipitation and river discharge. - 6.1 Precipitation. - 6.2 Evapo-transpiration and net precipitation. - 6.3 Mean annual cycles for the major terrestrial drainages. - 6.4 River discharge and runoff. - 7 Arctic ocean-sea ice-climate interactions. - 7.1 Sea ice formation, growth and melt. - 7.2 Mean circulation, ice zones and concentration. - 7.3 Sea ice motion. - 7.4 Examples of large-scale ocean-sea ice-climate interactions. - 7.5 The Fram Strait outflow and the thermohaline circulation. - 8 Climate regimes of the Arctic. - 8.1 The Greenland Ice Sheet. - 8.2 Polar desert. - 8.3 Maritime Arctic. - 8.4 Central Arctic Ocean. - 8.5 Mountains and uplands. - 8.6 Urban modifications of local climate. - 9 Modeling the Arctic climate system. - 9.1 General model types. - 9.2 Single-column models. - 9.3 Land surface models. - 9.4 Sea ice and ice-ocean models. - 9.5 Global climate models. - 9.6 Regional climate models. - 9.7 Numerical weather prediction models. - 9.8 Ecosystem models. - 9.9 Summary of model errors. - 10 Arctic paleoclimates. - 10.1 The distant past. - 10.2 Paleoclimate records for the Quaternary. - 10.3 Features of the Quaternary. - 10.4 Rapid climate shifts. - 10.5 Regional aspects of the LGM. - 10.6 Deglaciation. - 10.7 The Holocene. - 11 Recent climate variability, trends and the future. - 11.1 Setting the stage. - 11.2 Summary of observed variability and change. - 11.3 The NAO and AO. - 11.4 The NAO/AO framework: merits and shortcomings. - 11.5 Related multiyear climate variability. - 11.6 The future. - References. - List of selected websites. - Index.

Note: Contents: 1. Modeling party competition ; 2. How voters decide: the components of the unified theory of voting ; 3. Linking voter choice to party strategies: illustrating the role of non-policy factors ; 4. Factors influencing the link between party strategy and the variables ; 5. Policy competition under the unified theory: empirical applications to the 1988 French Presidential Election ; 6. Policy competition under the unified voting model: empirical applications to the 1989 Norwegian parliamentary election ; 7. The threat of abstention: candidate strategies and policy representation in US presidential elections ; 8. Candidate strategies with voter abstention in US presidential elections: 1980, 1984, 1988, 1996, and 2000 ; 9. Policy competition in Britain: the 1997 general election ; 10. The consequences of voter projection: assimilation and contrast effects ; 11. Policy-seeking motivations of parties in two-party elections: theory ; 12. Policy-seeking motivations of parties in two-party elections: empirical analysis ; 13. Concluding remarks.