Integrated population biology and modeling, part B:
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier, North Holland
[2019]
|
| Schriftenreihe: | Handbook of statistics
volume 40 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 635 Seiten Ilustrationen, Diagramme 24 cm |
| ISBN: | 9780444641526 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Contributors Preface xiii xv Section VI Agent Based Models, Capture-Recapture Methods and Multi-Species Mutualism 1. An Agent-Based Model of the Spatial Distribution and Density of the SantaCruz Island Fox 3 Shelby M. Scott, Casey E. Middleton, and Erin N. Bodine 1 Introduction Incorporating CIS Data into an ABM 2.1 GIS Data Types 2.2 Projections and Coordinate Systems for GIS Data 3 Model Description 3.1 Overview 3.2 Design Concepts 3.3 Details 4 Results 4.1 Results of Model Analysis Without Golden Eagle Predation 4.2 Model Results with Golden Eagle Predation 5 Conclusions and Discussion Acknowledgments Appendix Supplemental Resources References 2 2. Capture-Recapture Methods and Models: Estimating Population Size 4 7 7 9 11 11 13 17 23 23 26 29 30 30 30 30 33 Ruth King and Rachel McCrea 1 2 Introduction Closed Population Capture-Recapture Studies 2.1 Simple Beginnings: Lincoln-Petersen Estimator 2.2 Multiple Capture Occasions 33 36 36 40 v
Contents 3 Heterogeneity 3.1 Unobserved Heterogeneity 3.2 Observed (Time-Invariant) Heterogeneity 3.3 Time-Varying Observed Individual Heterogeneity 4 Open Populations 5 Discussion References From Particular Models Toward a Generalization of the Concept 51 53 57 63 ^4 ^7 85 Paul Ceorgescu, Daniel Maxin, Laurentiu Sega, and Hong Zhang 1 2 Introduction Mutualism Within the Framework of a General Model 2.1 Mechanisms Behind Mutualisms 2.2 To Be or Not to Be Independent 2.3 The Generalization Paradigm 3 Modeling Considerations: From Specific Models to a More General Framework 3.1 The Lotka-Volterra Legacy 3.2 Saturating Functional Responses and More General Frameworks 3.3 An Unlikely Marriage: Two-Sex Reproduction in the Mutual istic Framework 3.4 Getting Away From the Logistic Growth: Allee Effects 4 Two-Dimensional Models: A Stability Analysis 4.1 Stability Results Using the LaSalle Invariance Principle 4.2 Stability Results Using the Dulac Criterion: Threshold-Like Parameters 4.3 Examples 5 n-Dimensional Models: Boundedness and Unboundedness 5.1 A Generic Framework 5.2 Boundedness vs Unboundedness: A Tale of n Parameters 5.3 Particular Growth Conditions: A Single Parameter to Rule Them All 5.4 Mutualism as Reduction of Mortality for the Benefiting Species 5.5 Mutualism as a Positive Contribution to the Fertility Rate of the Benefiting Species 5.6 Which Species Is Responsible for Unboundedness? 6 Final Considerations Acknowledgment References 86 87 87 88 89 92 92 94 96 98 99 101 104 110 115 116 118 119 121 123 125 127 -ļ 28 ļ շց
Contents víl Section VII Stochastic Complexity and Structural Dynamics 4. Stochastic Models for Structured Populations 133 Shripad Tuljapurkar and David Steinsaltz 1 2 3 Introduction Introduction: Population Dynamics in a Constant Environment Structured Populations in Stochastic Environments 3.1 The Environment 3.2 Population Dynamics 3.3 Limiting Distributions 3.4 A Key Limit: Stochastic Growth Rate 4 Stochastic Growth Rate: Small-Noise Approximation 5 Stochastic Growth Rate: Derivatives 5.1 Derivatives With Respect to Vital Rates 5.2 Derivatives With Respect to TransitionMatrix Elements 6 Discussion References 5. Studying Complexity and Risk Through Stochastic Population Dynamics: Persistence, Resonance, and Extinction in Ecosystems 133 135 137 137 139 144 145 149 151 152 153 153 154 157 Anuj Mubayi, Christopher Kribs, Viswanathan Arunachalam, and Carlos Castillo-Chavez 1 Introduction 1.1 Definitions 1.2 Examples of Basic Stochastic Processes 2 Persistence in Stochastic Models 2.1 Stochastic Logistic Growth Model for a Single Population Species 2.2 Quasi-Stationarity (QSD) of a Birth-Death Process 2.3 Stochastic Model for Interacting Population Species: Native and Invading Species 2.4 Stochastic Model for Heterogeneity in Population of a Single Type of Cells 3 Types of Stochasticity and Extinction in Stochastic Models 3.1 Time to Extinction of a Birth-Death Process 3.2 The Role of Stochasticity and Heterogeneity on Extinction Risk 4 Oscillations and Resonance in the Stochastic Models 4.1 Sustained Oscillations via Coherence Resonance 4.2 Sustained Oscillations via
Stochastic Resonance 5 Computational Stochastic Approaches 5.1 Agent-Based Models and Parameter Estimation as Emergent Behavior 5.2 Algorithms for Simulating Stochastic Models 6 Conclusion References 157 160 162 165 166 167 169 171 175 175 176 180 180 182 185 185 188 190 190
Contents VIII 6. Analyzing Variety of Birth Intervals: A Stochastic Approach 195 Ram Chandra Yadava and Piyush Kant Rai 1 Introduction 1.1 Heterogeneity and Selection 1.2 Variety of Birth Intervals 2 Impact of Heterogeneity on Distributions of Duration Variables 2.1 Selection Bias in Postpartum Amenorrhea Period From Follow-Up Studies and Its Adjustment 2.2 Estimating Birth Interval Characteristic of Women 2.3 Impact of Heterogeneity on Time of First Conception 3 Sampling Frame as a Determinant of Duration Variable 3.1 Usual Closed Birth Interval vs Most Recent Closed Birth Interval 3.2 Most Recent Closed Birth Interval Assuming ЛҺ Order Births to Be Uniformly Distributed Over Time 3.3 Closed Birth Interval vs StraddlingBirth Interval 3.4 Open Birth Interval 3.5 Forward Birth Interval 3.6 Interior Birth Interval 4 Analyzing Consecutive Closed Birth Intervals: A Correlation Analysis 4.1 Consecutive Closed Birth Intervals 4.2 Application of the Analysis on NFHS-2 Data References 195 198 198 200 200 208 212 215 215 242 250 254 265 272 273 273 276 280 Section VIII GWAS, Species Divergence and Bayesian Item Response Theory 7. Detection of Quantitative Trait Loci From Genome-Wide Association Studies 287 David A. Spade 1 Introduction 2 SNP Data and Data Preparation 2.1 Description of the Data 2.2 Methods of Phasing 3 Working Example 4 Nontree-Based Methods of GWAS Analysis 4.1 Single-Marker Association 4.2 Haplotype Association Mapping 4.3 Regression-Based Methods of QTL Detection 5 Tree-Based Methods for Phased Data 5.1 Perfect Phylogeny Construction 5.2 QBIossoc 5.3 Models of
Allele Substitution 287 288 288 289 301 301 30շ 307 314 315 315 3ļ g 322
Contents ¡X 5.4 5.5 6 Likelihood Score Approach Bayesian SNP Detection Tree-Based GWAS Analysis FromUnphased Genetic Data 342 6.1 6.2 343 345 347 348 353 Local Semiperfect Phylogeny Construction Determination of Significance 7 Conclusion References Further Reading 8. 325 336 Bayesian Item Response Theory for Cancer Biomarker Discovery 355 Katabathula Ramachandra Murthy, Salendra Singh, David Tuck, and Vinay Varadan 1 2 3 Introduction Item Response Theory 356 358 2.1 2.2 2.3 358 359 362 368 369 369 369 370 372 374 Biomarker Information Function 3.1 3.2 3.3 3.4 3.5 3.6 4 5 6 9. Introduction Dichotomous Models Polytomous Models One Parameter Logistic Two Parameter Logistic Three Parameter Logistic Graded Response Model Partial Credit Model Rating Scale Model Bayesian Frame Work for IRT models 376 4.1 4.2 377 378 Markov Chain Monte Carlo Methods (MCMC) Model Selection Experimental Study 379 5.1 5.2 5.3 379 379 391 Model Selection Biomarker Selection Applications of IRT in CancerResearch Future Developments of IRT in Biological Modelling 397 7 Stan Codes for IRT Models Three-Parameter Logistic Graded Response 398 398 399 References 401 Effects of Phenotypic Plasticity and Unpredictability of Selection Environment on Niche Separation ana Species Divergence 405 Narayan Behera 1 2 Introduction Model 406 410 2.1 2.2 2.3 410 412 416 Life Cycle Model Specification Computersimulation
Contents x 3 Results 3.1 Haploid Population With Single Gaussian Food Supply 3.2 Haploid Population With Double Gaussian Food Supply 3.3 Diploid Population Having Additive Allelic Effect Gene Interaction and Double Food Supply 4 Discussion References Further Reading 410 418 421 423 425 429 431 Section IX Aging and Age-Structured Population Dynamics 10. Theory and Applications of Backward Probabilities and Prevalences in Cross-Longitudinal Surveys 435 Nicolas Brouard 1 Backward Probabilities Estimated From Chained Labor Force Surveys 1.1 Probability or Forward Probability 1.2 Backward Probability 1.3 Markov Chains and Strong Ergodicity 1.4 Weak Ergodicity 1.5 Backward Prevalence of a Specific Cohort 1.6 Forward Prevalence of a Specific Cohort 2 Backward Probabilities With Transient States and an Absorbing State 2.1 Chaining Forward for a Specific Cohort 2.2 Chaining Backward for a Specific Cohort 2.3 Some Estimations of Backward Prevalences 2.4 Limitations Concerning the Estimation of Forward and Backward Prevalences 2.5 Perspectives Concerning the Estimation of Backward Prevalences 3 Conclusion Acknowledgments References 11. Behavior of Stationary Population Identity in Two-Dimensions: Age and Proportion of Population Truncated in Follow-up 437 437 439 442 452 454 457 457 460 466 478 482 483 484 485 485 487 Arni S.R. Srinivasa Rao and James R. Carey 1 2 3 Introduction Structure of the Two-Dimensional Captive Cohort Truncation of the Follow-up Data of Captive Cohorts 487 489 491
Contents Captive Population Age Structure, Truncation, and Partition Functions 4.1 Time Left for a Captive Cohort andRight Truncation 5 Discussion 6 Conclusions References Further Reading xi 4 12. Demographic Situation of Manipur, India 492 493 498 499 499 500 501 Moirangthem Hemanta Meitei 1 Outline 1.1 The Origin of Communities in Manipur 1.2 Manipur at Crossroad 2 Demographic Composition of Manipur 2.1 Demographic Indicators 2.2 Mortality Situation in Manipur 2.3 Fertility in Manipur 2.4 Migration 3 Age Composition 3.1 Future Trajectory of Population References Further Reading 501 507 508 513 524 524 531 536 539 541 546 547 Section X Collective Behaviors in Ecology 13. Deriving Mesoscopic Models of Collective Behavior for Finite Populations 551 Jitesh Jhawar, Richard C. Morris, and Vishwesha Cuttal Nomenclature 1 Introduction 2 Background 3 Mesoscopic Description of a Pairwise Binary-Choice Model 3.1 The Model 3.2 Constructing Mesoscopic SDEs 3.3 Characterizing Mesoscopic Dynamics 3.4 Results for the Pairwise Pairwise Interaction Model 4 Ternary Interaction Model for Binary Choice 4.1 Constructing a Mesoscopic SDE for the Ternary Interaction Model 4.2 Characterizing Mesoscopic Dynamics 4.3 Results for the Ternary Interaction Model 5 Discussion 5.1 Comparison of System-Size Expansion With Chemical Langevin Approach 552 552 553 557 557 559 566 569 570 571 574 574 577 577
Contents xj¡ 5.2 5.3 5.4 Multiplicative Noiseat MesoscopicScales Extensions and ConcludingRemarks Resources Acknowledgments Appendices Appendix A The ChemicalLangevinEquation Appendix В Pairwise Interaction Model in Two Spatial Dimensions B.1 B.2 van Kampen s System-Size Expansion of Transition Rates ChemicalLangevinApproach References 14. Collective Behavior andEcology 578 579 581 581 581 581 584 584 586 590 595 Glenn R. Flierl 1 2 3 Introduction Individual-Based Models 595 596 2.1 2.2 2.3 597 598 601 Collective Behavior (Using IBMs) 3.1 4 5 6 Dispersion/Diffusion Taxis Kinesis Schooling 601 601 Fokker-Planck Equation 602 4.1 4.2 603 606 Common Behavior Steady Solutions Collective Behavior (Using FP) Collective Behavior and Ecology 610 614 6.1 6.2 615 618 One Variable Two-Variables 7 Concluding Remarks Acknowledgments References Further Reading 625 626 626 626
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| spelling | Integrated population biology and modeling, part B edited by Arni S. R. Srinivasa Rao, C.R. Rao Amsterdam Elsevier, North Holland [2019] © 2019 xvii, 635 Seiten Ilustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 40 Populationsbiologie (DE-588)4046800-8 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Populationsbiologie (DE-588)4046800-8 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Rao, Arni S. R. Srinivasa (DE-588)1143256220 edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 40 (DE-604)BV000002510 40 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030804550&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Integrated population biology and modeling, part B |
| title_auth | Integrated population biology and modeling, part B |
| title_exact_search | Integrated population biology and modeling, part B |
| title_full | Integrated population biology and modeling, part B edited by Arni S. R. Srinivasa Rao, C.R. Rao |
| title_fullStr | Integrated population biology and modeling, part B edited by Arni S. R. Srinivasa Rao, C.R. Rao |
| title_full_unstemmed | Integrated population biology and modeling, part B edited by Arni S. R. Srinivasa Rao, C.R. Rao |
| title_short | Integrated population biology and modeling, part B |
| title_sort | integrated population biology and modeling part b |
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