Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

/pdf/a-tutorial-on-support-vector-regression-10mfvogjkf.pdf

A tutorial on support vector regression

The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information...

https://faculty.washington.edu/skalski/classes/QERM597/papers_xtra/Burnham%20and%20Anderson.pdf

Multimodel Inference Understanding AIC and BIC in Model Selection

The richness of optical and electronic properties of graphene attracts enormous interest. Graphene has high mobility and optical transparency, in addition to flexibility, robustness and environmental stability. So far, the main focus has been on fundamental physics and electronic devices. However, we believe its true potential lies in photonics and optoelectronics, where the combination of its unique optical and electronic properties can be fully exploited, even in the absence of a bandgap, and the linear dispersion of the Dirac electrons enables ultrawideband tunability. The rise of graphene in photonics and optoelectronics is shown by several recent results, ranging from solar cells and light-emitting devices to touch screens, photodetectors and ultrafast lasers. Here we review the state-of-the-art in this emerging field.

/pdf/graphene-photonics-and-optoelectronics-3ddg6ukzcu.pdf

Graphene photonics and optoelectronics

https://www.wsl.ch/staff/niklaus.zimmermann/papers/ecomod135_147.pdf

Predictive habitat distribution models in ecology

An Introduction to the Bootstrap.

1. Introductory concepts 2. Assumptions and modelling philosophy 3. Statistical theory 4. Line transects 5. Point transects 6. Related methods 7. Study design and field methods 8. Illustrative examples

Introduction to Distance Sampling: Estimating Abundance of Biological Populations

Summary
1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.

2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.

3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.

4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance.

5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.

6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.

7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.

/pdf/distance-software-design-and-analysis-of-distance-sampling-5edm5dww9q.pdf

Distance software: design and analysis of distance sampling surveys for estimating population size.

Distance Sampling-Estimating Abundance of Biological Populations.

1 Introductory concepts.- 1.1 Introduction.- 1.2 Range of applications.- 1.3 Types of data.- 1.4 Known constants and parameters.- 1.5 Assumptions.- 1.6 Fundamental concept.- 1.7 Detection.- 1.8 History of methods.- 1.9 Program DISTANCE.- 2 Assumptions and modelling philosophy.- 2.1 Assumptions.- 2.2 Fundamental models.- 2.3 Philosophy and strategy.- 2.4 Robust models.- 2.5 Some analysis guidelines.- 3 Statistical theory.- 3.1 General formula.- 3.2 Hazard-rate modelling of the detection process.- 3.3 The key function formulation for distance data.- 3.4 Maximum likelihood methods.- 3.5 Choice of model.- 3.6 Estimation for clustered populations.- 3.7 Density, variance and interval estimation.- 3.8 Stratification and covariates.- 4 Line transects.- 4.1 Introduction.- 4.2 Example data.- 4.3 Truncation.- 4.4 Estimating the variance in sample size.- 4.5 Analysis of grouped or ungrouped data.- 4.6 Model selection.- 4.7 Estimation of density and measures of precision.- 4.8 Estimation when the objects are in clusters.- 4.9 Assumptions.- 4.10 Summary.- 5 Point transects.- 5.1 Introduction.- 5.2 Example data.- 5.3 Truncation.- 5.4 Estimating the variance in sample size.- 5.5 Analysis of grouped or ungrouped data.- 5.6 Model selection.- 5.7 Estimation of density and measures of precision.- 5.8 Estimation when the objects are in clusters.- 5.9 Assumptions.- 5.10 Summary.- 6 Extensions and related work.- 6.1 Introduction.- 6.2 Other models.- 6.3 Modelling variation in encounter rate and cluster size.- 6.4 Estimation of the probability of detection on the line or point.- 6.5 On the concept of detection search effort.- 6.6 Fixed versus random sample size.- 6.7 Efficient simulation of distance data.- 6.8 Thoughts about a full likelihood approach.- 6.9 Distance sampling in three dimensions.- 6.10 Cue counting.- 6.11 Trapping webs.- 6.12 Migration counts.- 6.13 Point-to-object and nearest neighbour methods.- 7 Study design and field methods.- 7.1 Introduction.- 7.2 Survey design.- 7.3 Searching behaviour.- 7.4 Measurements.- 7.5 Training observers.- 7.6 Field methods for mobile objects.- 7.7 Field methods when detection on the centerline is not certain.- 7.8 Field comparisons between line transects, point transects and mapping censuses.- 7.9 Summary.- 8 Illustrative examples.- 8.1 Introduction.- 8.2 Lake Huron brick data.- 8.3 Wooden stake data.- 8.4 Studies of nest density.- 8.5 Fin whale abundance in the North Atlantic.- 8.6 Use of tuna vessel observer data to assess trends in abundance of dolphins.- 8.7 House wren densities in South Platte River bottomland.- 8.8 Songbird surveys in Arapaho National Wildlife Refuge.- 8.9 Assessing the effects of habitat on density.- Appendix A List of common and scientific names cited.- Appendix B Notation and abbreviations, and their definitions.

Stephen T. Buckland

Papers

An Introduction to the Bootstrap.

Introduction to Distance Sampling: Estimating Abundance of Biological Populations

Distance software: design and analysis of distance sampling surveys for estimating population size.

Distance Sampling-Estimating Abundance of Biological Populations.

Distance Sampling: Estimating Abundance of Biological Populations