Términos relacionados

197 resultados encontrados para: TEMA: Modelos matemáticos
  • «
  • 1 de 20
  • »
- Artículo con arbitraje
Numerical and experimental assessment of the reflection coefficient of rubble mound breakwaters with an S shaped profile
Del Valle Morales, Jair ; Mendoza Baldwin, Edgar (coaut.) ; Alcérreca Huerta, Juan Carlos (coaut.) ; Silva Casarin, Rodolfo (coaut.) ;
Contenido en: Tecnología y Ciencias del Agua Vol. 10, no. 2 (April 2019), p. 128-152 ISSN: 0187-8336
Resumen en: Español | Inglés |
Resumen en español

La reflexión debida a obras de abrigo es un fenómeno importante de cuantificar dado que, si no se controla, puede provocar problemas a la navegación, a la operación portuaria y, en casos extremos, a la estabilidad de la misma estructura. Dada la forma en que interactúan los taludes de los rompeolas y el oleaje, el coeficiente de reflexión es prácticamente único para cada tipología y forma de dique, por lo que su cálculo depende, además de las condiciones de clima marítimo, de las propiedades geométricas de la estructura, así como de su porosidad. En este trabajo se presenta una evaluación del coeficiente de reflexión para diques rompeolas de piezas sueltas con perfil en forma de “S” a partir de resultados obtenidos con el modelo OpenFOAM. Dicha información se comparó contra datos de laboratorio publicados en otras fuentes y contra algunas formulaciones disponibles en la literatura. Los resultados que ofrece el modelo numérico son muy parecidos a los reportados de manera experimental y, el modelo para el cálculo del coeficiente de reflexión obtenido mostró poca dispersión y buena precisión, en comparación con las formulaciones previas, por lo que puede emplearse en los rompeolas originales del estudio y también en otro tipo de estructuras.

Resumen en inglés

Quantification of the wave reflection from breakwaters is important due to the problems to navigation, port operation and structure stability it may induce. Given the complex interaction between waves and rough slopes, the reflection coefficient is properly unique for each dike type and shape, thus, its estimation depends not only on the wave climate conditions but on the geometry of the structure and its porosity. In this paper a numerical estimation of the reflection coefficient from “S” shaped breakwaters is given from the hydrodynamic results obtained via OpenFoam numerical tool. This information was compared to that published elsewhere and against previous available formulations. The numerical results map well within the ranges experimentally reported, while the best fit model proposed herein showed low dispersion and good precision against the previous formulations. This means that the simple model developed can be used to estimate the reflection coefficient from the originally studied structures and other several kind of structures as well.

*Solicítelo con su bibliotecario/a
Optimal allocation of public parking spots in a smart city: problem characterisation and first algorithms
Arellano Verdejo, Javier ; Alonso Pecina, Federico (coaut.) ; Alba, Enrique (coaut.) ; Guzmán Arenas, Adolfo (coaut.) ;
Contenido en: Journal of Experimental and Theoretical Artificial Intelligence Vol. 31, no 4 (July 2019), p. 575-597 ISSN: 1362-3079
Nota: Solicítelo con su bibliotecario/a
Resumen en: Inglés |
Resumen en inglés

Having a mechanism to mathematically model the problem of the optimal allocation of parking spots within cities could bring great benefits to society. According to the International Parking Institute, about 38% of the cars circulating throughout a city are looking for available parking spots, leading to increased pollution and subsequent health problems, as well as economic losses due to wasted man-hours. In the work presented here, a new mathematical model describing the problem of the optimal allocation of parking spots is proposed, along with an evolutionary algorithm to demonstrate how this model can be used in practice. A simulated annealing algorithm was implemented to test the effectiveness of this approach. The proposed strategy will allow users to find parking more quickly and easily, as well as lead to new services for the hot-topic of smart mobility. For the definition of the problem, a real map of the city of Malaga, Spain, was used along with Sumo software to carry out the simulations. The results clearly demonstrated that the proposed mechanism is capable of minimising the global cost of parking, implying a direct benefit for users.

Habitat suitability and distribution models: with applications in R / Antoine Guisan, Wilfried Thuiller, Niklaus E. Zimmermann ; with contributions from Valeria di Cola, Damien Georges, Achilleas Psomas
Guisan, Antoine ; Thuiller, Wilfried (coaut.) (1975-) ; Zimmermann, Niklaus E. (coaut.) ;
Cambridge, United Kingdom : Cambridge University Press , 2017
Clasificación: 333.954 / G8
Bibliotecas: Chetumal
SIBE Chetumal
ECO030008671 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.


Foreword page
Authors’ Contributions
1 General Content of the Book
1.1 What Is This Book About?
1.2 How Is the Book Structured?
1.3 Why Write a Textbook with R Examples?
1.4 What Is This Book Not About?
1.5 Why Was This Book Needed?
1.6 Who Is This Book For?
1.7 Where Can I Find Supporting Material?
1.8 What Are Readers Assumed to Know Already?
1.9 How Does This Book Difer From Previous Ones?
1.10 What Terminology Is Used in This Book?
Part I Overview, Principles, Theory, and Assumptions Behind Habitat Suitability Modeling
2 Overview of the Habitat Suitability Modeling Procedure
2.1 The Diferent Methodological Steps of Habitat Suitability Modeling
2.2 The Initial Conceptual Step
3 What Drives Species Distributions?
3.1 The Overall Context: Dispersal, Habitat, and Biotic Filtering
3.2 Speciation, Dispersal, Species Pools, and Neutral Theory
3.3 The Abiotic Environment: Habitats and Fundamental Niches
3.4 The Biotic Environment: Species Interactions, Community Assembly, and Realized Niches
3.5 Further Discussion of the Realized Environmental Niche and Other Related Niche Concepts
4 From Niche to Distribution: Basic Modeling Principles and Applications
4.1 From Geographical Distribution to Niche Quantiication
4.2 From the Quantiied Niche to Spatial Predictions
4.3 From Individual Species Predictions to Communities
4.4 Main Fields of Application
5 Assumptions Behind Habitat Suitability Models
5.1 Theoretical Assumptions
5.2 Methodological Assumptions
Part II Data Acquisition, Sampling Design, and Spatial Scales
6 Environmental Predictors: Issues of Processing and Selection
6.1 Existing Environmental Databases
6.2 Performing Simple GIS Analyses in R
6.3 RS- Based Predictors
6.4 Properties and Selection of Variables
7 Species Data: Issues of Acquisition and Design
7.1 Existing Data and Databases

7.2 Spatial Autocorrelation and Pseudo- Replicates
7.3 Sample Size, Prevalence, and Sample Accuracy
7.4 Sampling Design and Data Collection
7.5 Presence– Absence vs. Presence- Only Data
8 Ecological Scales: Issues of Resolution and Extent
8.1 Issues of Resolution
8.2 Issues of Extent
Part III Modeling Approaches and Model Calibration
9 Envelopes and Distance- Based Approaches
9.1 Concepts
9.2 Envelope Approaches
9.3 Distance- Based Methods
10 Regression- Based Approaches
10.1 Concepts
10.2 Generalized Linear Models
10.3 Generalized Additive Models
10.4 Multivariate Adaptive Regression Splines
11 Classiication Approaches and Machine- Learning Systems
11.1 Concepts
11.2 Recursive Partitioning
11.3 Linear Discriminant Analysis and Extensions
11.4 Artiicial Neural Networks
12 Boosting and Bagging Approaches
12.1 Concepts
12.2 Random Forests
12.3 Boosted Regression Trees
13 Maximum Entropy
13.1 Concepts
13.2 Maxent in R
14 Ensemble Modeling and Model Averaging
Part IV Evaluating Models: Errors and Uncertainty
15 Measuring Model Accuracy: Which Metrics to Use?
15.1 Comparing Predicted Probabilities of Presence to Presence– Absence Observations
15.2 Comparing Probabilistic Predictions to Presence- Only Observations
16 Assessing Model Performance: Which Data to Use?
16.1 Assessment of Model Fit Using Resubstitution and Randomization
16.2 Internal Evaluation by Resampling
16.3 External Evaluation (Fully Independent Data)
Part V Predictions in Space and Time
17 Projecting Models in Space and Time
17.1 Additional Considerations and Assumptions When Projecting Models: Analog Environment, Niche Completeness, and Niche Stability
17.2 Projecting Models in Space
17.3 Projecting Models in Time
17.4 Ensemble Projections
Part VI Data and Tools Used in this Book, with Developed Case Studies

18 Datasets and Tools Used for the Examples in this Book
19 The Biomod2 Modeling Package Examples
19.1 Example 1: Habitat Suitability Modeling of Protea laurifolia in South Africa
19.2 Example 2: Creating Diversity Maps for the Laurus Species
Part VII Conclusions and Future Perspectives
20 Conclusions and Future Perspectives in Habitat Suitability Modeling
20.1 Further Progress in HSMs through Metagenomics and Remote Sensing
20.2 Point- Process Models for Presence- Only Data
20.3 Hierarchical Bayesian Approaches to Integrate Models at Diferent Scales
20.4 Ensemble of Small Models for Rarer Species
20.5 Improving the Modeling Techniques to Fit Simple and Ensemble HSMs
20.6 Multi- Species Modeling and Joint- Species Distribution Modeling
20.7 Use of Artiicial Data
Glossary and Deinitions of Terms and Concepts
Methods, Approaches, Models, Techniques, Algorithms
ENM, SDM, HSM, etc.: Diferent Names and Acronyms for the Same Models!
Environment, Habitat, Niche, Niche-Biotope Duality, and Distribution
Technical Acronyms for the Most Commonly Used Modeling Techniques
Color plates can be found between pages 238 and 239

Algorithms for data science / Brian Steele, John Chandler, Swarna Reddy
Disponible en línea: Algorithms for data science.
Steele, Brian ; Chandler, John (coaut.) ; Reddy, Swarna (coaut.) ;
New York, New York, United States : Springer Science+Business Media , 2016
Clasificación: 518.1 / S8
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010019237 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.

This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.


1 Introduction
1.1 What Is Data Science?
1.2 Diabetes in America
1.3 Authors of the Federalist Papers
1.4 Forecasting NASDAQ Stock Prices
1.5 Remarks
1.6 The Book
1.7 Algorithms
1.8 Python
1.9 R
1.10 Terminology and Notation
1.10.1 Matrices and Vectors
1.11 Book Website
Part I Data Reduction
2 Data Mapping and Data Dictionaries
2.1 Data Reduction
2.2 Political Contributions
2.3 Dictionaries
2.4 Tutorial: Big Contributors
2.5 Data Reduction
2.5.1 Notation and Terminology
2.5.2 The Political Contributions Example
2.5.3 Mappings
2.6 Tutorial: Election Cycle Contributions
2.7 Similarity Measures
2.7.1 Computation
2.8 Tutorial: Computing Similarity
2.9 Concluding Remarks About Dictionaries
2.10 Exercises
2.10.1 Conceptual
2.10.2 Computational
3 Scalable Algorithms and Associative Statistics
3.1 Introduction
3.2 Example: Obesity in the United States
3.3 Associative Statistics
3.4 Univariate Observations
3.4.1 Histograms
3.4.2 Histogram Construction
3.5 Functions
3.6 Tutorial: Histogram Construction
3.6.1 Synopsis
3.7 Multivariate Data
3.7.1 Notation and Terminology
3.7.2 Estimators
3.7.3 The Augmented Moment Matrix
3.7.4 Synopsis
3.8 Tutorial: Computing the Correlation Matrix
3.8.1 Conclusion
3.9 Introduction to Linear Regression
3.9.1 The Linear Regression Model
3.9.2 The Estimator of β
3.9.3 Accuracy Assessment
3.9.4 Computing R²adjusted
3.10 Tutorial: Computing β
3.10.1 Conclusion
3.11 Exercises
3.11.1 Conceptual
3.11.2 Computational
4 Hadoop and MapReduce
4.1 Introduction
4.2 The Hadoop Ecosystem
4.2.1 The Hadoop Distributed File System
4.2.2 MapReduce
4.2.3 Mapping
4.2.4 Reduction
4.3 Developing a Hadoop Application
4.4 Medicare Payments
4.5 The Command Line Environment
4.6 Tutorial: Programming a MapReduce Algorithm
4.6.1 The Mapper
4.6.2 The Reducer
4.6.3 Synopsis

4.7 Tutorial: Using Amazon Web Services
4.7.1 Closing Remarks
4.8 Exercises
4.8.1 Conceptual
4.8.2 Computational
Part II Extracting Information from Data
5 Data Visualization
5.1 Introduction
5.2 Principles of Data Visualization
5.3 Making Good Choices
5.3.1 Univariate Data
5.3.2 Bivariate and Multivariate Data
5.4 Harnessing the Machine
5.4.1 Building Fig. 5.2
5.4.2 Building Fig. 5.3
5.4.3 Building Fig. 5.4
5.4.4 Building Fig. 5.5
5.4.5 Building Fig. 5.8
5.4.6 Building Fig. 5.10
5.4.7 Building Fig. 5.11
5.5 Exercises
6 Linear Regression Methods
6.1 Introduction
6.2 The Linear Regression Model
6.2.1 Example: Depression, Fatalism, and Simplicity
6.2.2 Least Squares
6.2.3 Confidence Intervals
6.2.4 Distributional Conditions
6.2.5 Hypothesis Testing
6.2.6 Cautionary Remarks
6.3 Introduction to R
6.4 Tutorial: R
6.4.1 Remark
6.5 Tutorial: Large Data Sets and R
6.6 Factors
6.6.1 Interaction
6.6.2 The Extra Sums-of-Squares F-test
6.7 Tutorial: Bike Share
6.7.1 An Incongruous Result
6.8 Analysis of Residuals
6.8.1 Linearity
6.8.2 Example: The Bike Share Problem
6.8.3 Independence
6.9 Tutorial: Residual Analysis
6.9.1 Final Remarks
6.10 Exercises
6.10.1 Conceptual
6.10.2 Computational
7 Healthcare Analytics
7.1 Introduction
7.2 The Behavioral Risk Factor Surveillance System
7.2.1 Estimation of Prevalence
7.2.2 Estimation of Incidence
7.3 Tutorial: Diabetes Prevalence and Incidence
7.4 Predicting At-Risk Individuals
7.4.1 Sensitivity and Specificity
7.5 Tutorial: Identifying At-Risk Individuals
7.6 Unusual Demographic Attribute Vectors
7.7 Tutorial: Building Neighborhood Sets
7.7.1 Synopsis
7.8 Exercises
7.8.1 Conceptual
7.8.2 Computational
8 Cluster Analysis
8.1 Introduction
8.2 Hierarchical Agglomerative Clustering
8.3 Comparison of States
8.4 Tutorial: Hierarchical Clustering of States

8.4.1 Synopsis
8.5 The k-Means Algorithm
8.6 Tutorial: The k-Means Algorithm
8.6.1 Synopsis
8.7 Exercises
8.7.1 Conceptual
8.7.2 Computational
Part III Predictive Analytics
9 k-Nearest Neighbor Prediction Functions
9.1 Introduction
9.1.1 The Prediction Task
9.2 Notation and Terminology
9.3 Distance Metrics
9.4 The k-Nearest Neighbor Prediction Function
9.5 Exponentially Weighted k-Nearest Neighbors
9.6 Tutorial: Digit Recognition
9.6.1 Remarks
9.7 Accuracy Assessment
9.7.1 Confusion Matrices
9.8 k-Nearest Neighbor Regression
9.9 Forecasting the S&P 500
9.10 Tutorial: Forecasting by Pattern Recognition
9.10.1 Remark
9.11 Cross-Validation
9.12 Exercises
9.12.1 Conceptual
9.12.2 Computational
10 The Multinomial Naïve Bayes Prediction Function
10.1 Introduction
10.2 The Federalist Papers
10.3 The Multinomial Naïve Bayes Prediction Function
10.3.1 Posterior Probabilities
10.4 Tutorial: Reducing the Federalist Papers
10.4.1 Summary
10.5 Tutorial: Predicting Authorship of the Disputed Federalist Papers
10.5.1 Remark
10.6 Tutorial: Customer Segmentation
10.6.1 Additive Smoothing
10.6.2 The Data
10.6.3 Remarks
10.7 Exercises
10.7.1 Conceptual
10.7.2 Computational
11 Forecasting
11.1 Introduction
11.2 Tutorial: Working with Time
11.3 Analytical Methods
11.3.1 Notation
11.3.2 Estimation of the Mean and Variance
11.3.3 Exponential Forecasting
11.3.4 Autocorrelation
11.4 Tutorial: Computing ρτ
11.4.1 Remarks
11.5 Drift and Forecasting
11.6 Holt-Winters Exponential Forecasting
11.6.1 Forecasting Error
11.7 Tutorial: Holt-Winters Forecasting
11.8 Regression-Based Forecasting of Stock Prices
11.9 Tutorial: Regression-Based Forecasting
11.9.1 Remarks
11.10 Time-Varying Regression Estimators
11.11 Tutorial: Time-Varying Regression Estimators
11.11.1 Remarks
11.12 Exercises
11.12.1 Conceptual

11.12.2 Computational
12 Real-time Analytics
12.1 Introduction
12.2 Forecasting with a NASDAQ Quotation Stream
12.2.1 Forecasting Algorithms
12.3 Tutorial: Forecasting the Apple Inc. Stream
12.3.1 Remarks
12.4 The Twitter Streaming API
12.5 Tutorial: Tapping the Twitter Stream
12.5.1 Remarks
12.6 Sentiment Analysis
12.7 Tutorial: Sentiment Analysis of Hashtag Groups
12.8 Exercises
A Solutions to Exercises
B Accessing the Twitter API

Nonlinear physics of ecosystems / Ehud Meron
Disponible en línea: Nonlinear physics of ecosystems.
Meron, Ehud ;
Boca Raton, FL : CRC Press :: Taylor & Francis Group , c2015
Clasificación: 574.524 / M4
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010018505 (Disponible)
Disponibles para prestamo: 1
Resumen en: Inglés |
Resumen en inglés

Nonlinear Physics of Ecosystems introduces the concepts and tools of pattern formation theory and demonstrates their utility in ecological research using problems from spatial ecology. Written in language understandable to both physicists and ecologists in most parts, the book reveals the mechanisms of pattern formation and pattern dynamics. It also explores the implications of these mechanisms in important ecological problems. The first part of the book gives an overview of pattern formation and spatial ecology, showing how these disparate research fields are strongly related to one another. The next part presents an advanced account of pattern formation theory. The final part describes applications of pattern formation theory to ecological problems, including self-organized vegetation patchiness, desertification, and biodiversity in changing environments. Focusing on the emerging interface between spatial ecology and pattern formation, this book shows how pattern formation methods address a variety of ecological problems using water-limited ecosystems as a case study. Readers with basic knowledge of linear algebra and ordinary differential equations will develop a general understanding of pattern formation theory while more advanced readers who are familiar with partial differential equations will appreciate the descriptions of analytical tools used to study pattern formation and dynamics.

Handbook of spatial point-pattern analysis in ecology / Thorsen Wiegand, Kirk A. Moloney
Wiegand, Thorsen ; Moloney, Kirk Adams (coaut.) (1952-) ;
Boca Raton, FL : Chapman and Hall/CRC , 2014
Clasificación: 577.015195 / W5
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010017632 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

Although numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Ideal for empirical ecologists who want to avoid advanced theoretical literature, the book covers statistical techniques for analyzing and interpreting the information contained in ecological patterns. It presents methods used to extract information hidden in spatial point-pattern data that may point to the underlying processes. The authors focus on point processes and null models that have proven their immediate utility for broad ecological applications, such as cluster processes. Along with the techniques, the handbook provides a comprehensive selection of real-world examples. Most of the examples are analyzed using Programita, a continuously updated software package based on the authors’ many years of teaching and collaborative research in ecological point-pattern analysis. Programita is tailored to meet the needs of real-world applications in ecology. The software and a manual are available online.


1. Application of Spatial Statistics in Ecology
1.1 Analysis of Spatial Patterns
2. Fundamentals of Point-Pattern Analysis
2.1 Fundamental Steps of Point-Pattern Analyses
2.2 Data Types
2.3 Summary Statistics
2.4 Null Models and Point-Process Models
2.5 Methods to Compare Data and Point-Process Models
2.6 Dealing with Heterogeneous Patterns
3. Estimators and Toolbox
3.1 Estimators of Summary Statistics
3.2 Replicate Patterns
3.3 Superposition of Point Processes
3.4 Toolbox
4. Examples
4.1 Analysis of Univariate Patterns
4.2 Analysis of Bivariate Patterns
4.3 Analysis of Multivariate Patterns
4.4 Analysis of Qualitatively Marked Patterns
4.5 Analysis of Quantitatively Marked Patterns
4.6 Analysis of Objects with Finite Size
5. A Course Outline Based on the Book
5.1 Introduction
5.2 Analysis of Univariate Patterns
5.3 Analysis of Bivariate Patterns
5.4 Analysis of Qualitatively Marked Patterns
5.5 Analysis of Quantitatively Marked Patterns
5.6 Analysis of Objects of Finite Size and Real Shape
Frequently Used Symbols

Plants and microclimate: a quantitative approach to environmental plant physiology / Hamlyn G. Jones
Jones, Hamlyn G. ;
Cambridge : Cambridge University Press , 2014
Clasificación: 581.5 / J6/2014
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010015982 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

This rigorous yet accessible text introduces the key physical and biochemical processes involved in plant interactions with the aerial environment. It is designed to make the more numerical aspects of the subject accessible to plant and environmental science students, and will also provide a valuable reference source to practitioners and researchers in the field. The third edition of this widely recognised text has been completely revised and updated to take account of key developments in the field. Approximately half of the references are new to this edition and relevant online resources are also incorporated for the first time. The recent proliferation of molecular and genetic research on plants is related to whole plant responses, showing how these new approaches can advance our understanding of the biophysical interactions between plants and the atmosphere. Remote sensing technologies and their applications in the study of plant function are also covered in greater detail. • Ideal for students with a limited background in mathematics, making numerical aspects of plant physiology accessible to a wider audience. • Contains questions covering the main topics, along with fully worked answers, allowing students to check their understanding of the key concepts. • Extensive glossary of symbols and abbreviations aids understanding of complex ideas and equations.


Main abbreviations and acronyms
Measurement of soil or plant water status
Hydraulic flow
Long-distance transport in the phloem
Sample problems
1 A quantitative approach to plant–environment interactions
1.1 Modelling
1.2 Use of experiments
2 Radiation
2.1 Introduction
2.2 Radiation laws
2.3 Radiation measurement
2.4 Radiation in natural environments
2.5 Radiation in plant communities
2.6 Radiation distribution within plant canopies
2.7 Canopy reflectance and remote sensing
2.8 Direct and subcanopy methods for determining canopy structure
2.9 Concluding comments
2.10 Sample problems
3 Heat, mass and momentum transfer
3.1 Measures of concentration
3.2 Molecular transport processes
3.3 Convective and turbulent transfer
3.4 Transfer processes within and above plant canopies
3.5 Sample problems
4 Plant water relations
4.1 Physical and chemical properties of water
4.2 Cell water relations
5 Energy balance and evaporation
5.1 Energy balance
5.2 Evaporation
5.3 Measurement of evaporation rates
5.4 Evaporation from plant communities
5.5 Dew
5.6 Sample problems
6 Stomata
6.1 Distribution of stomata
6.2 Stomatal mechanics and mechanisms
6.3 Methods of study
6.4 Stomatal response to environment
6.5 Stomatal resistance in relation to other resistances
6.6 Stomatal function and the control loops
6.7 Sample problems
7 Photosynthesis and respiration
7.1 Photosynthesis
7.2 Respiration
7.3 Measurement and analysis of carbon dioxide exchange
7.4 Photosynthetic models
7.5 Chlorophyll fluorescence
7.6 Control of photosynthesis and photosynthetic ‘limitations’
7.7 Carbon isotope discrimination
7.8 Response to environment
7.9 Photosynthetic efficiency and Productivity
7.10 Evolutionary and ecological aspects
7.11 Sample problems
8 Light and plant development
8.1 Introduction

8.2 Detection of the signal
8.3 Phytochrome control of development
8.4 Physiological responses
8.5 The role of plant growth regulators
8.6 Sample problem
9 Temperature
9.1 Physical basis of the control of tissue temperature
9.2 Physiological effects of temperature
9.3 Effects of temperature on plant development
9.4 Temperature extremes
9.5 Comments on some ecological aspects of temperature adaptation
9.6 Sample problems
10 Drought and other abiotic stresses
10.1 Plant water deficits and physiological processes
10.2 Drought tolerance
10.3 Further analysis of water use efficiency
10.4 Irrigation and irrigation scheduling
10.5 Other abiotic stresses
11 Other environmental factors: wind, altitude, climate change and atmospheric pollutants
11.1 Wind
11.2 Altitude
11.3 Climate change and the ‘greenhouse effect’
11.4 Atmospheric pollutants
12 Physiology and crop yield improvement
12.1 Variety improvement
12.2 Modelling and determination of crop ideotype
12.3 Examples of applications
Appendix 1 Units and conversion factors
Appendix 2 Mutual diffusion coefficients for binary mixtures containing air or water at. 20°C. Appendix 3 Some temperature-dependent properties of air and water
Appendix 4 Temperature dependence of air humidity and associated quantities
Appendix 5 Thermal properties and densities of various materials and tissues at. 20°C. Appendix 6 Physical constants and other quantities
Appendix 7 Solar geometry and radiation approximations
Appendix 8 Measurement of leaf boundary layer conductance
Appendix 9 Derivation of Equation (9.9)
Appendix 10 Answers to sample problems

Spatial capture-recapture / J. Andrew Royle, Richard B. Chandler, Rahel Sollmann and Beth Gardner
Royle, J. Andrew ; Chandler, Richard B. (coaut.) ; Sollmann, Rahel (coaut.) ; Gardner, Beth (coaut.) ;
Amsterdam, The Netherlands : Elsevier Academic Press , 2014
Clasificación: 591.566 / S7
Bibliotecas: Chetumal , San Cristóbal
SIBE Chetumal
ECO030008406 (Disponible)
Disponibles para prestamo: 1
SIBE San Cristóbal
ECO010017628 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package. Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic Every methodological element has a detailed worked example with a code template, allowing you to learn by example Includes an R package that contains all computer code and data sets on companion website.


Part I Background and Concepts
Chapter 1 Introduction
Chapter 2 Statistical Models and SCR
Chapter 3 GLMs and Bayesian Analysis
Chapter 4 Closed Population Models
Part II Basic SCR Models
Chapter 5. Fully Spatial Capture-Recapture Models
Chapter 6 Likelihood Analysis of Spatial Capture-Recapture Models
Chapter 7 Modeling Variation In Encounter Probability
Chapter 8 Model Selection and Assessment
Chapter 9 Alternative Observation Models
10 Sampling Design
Part III Advanced SCR Models
Chapter 11 Modeling Spatial Variation in Density
Chapter 12 Modeling Landscape Connectivity
Chapter 13 Integrating Resource Selection with Spatial Capture-Recapture Models
Chapter 14 Stratified Populations: Multi-session and Multi-site Data
Chapter 15 Models for Search-Encounter Data
Chapter 16 Open Population Models
Part IV Super-Advanced SCR Models
Chapter 17 Developing Markov Chain Monte Carlo Samplers
18. Unmarked Populations
Chapter 19 Spatial Mark-Resight Models for partially identifiable populations
Chapter 20 2012: A Spatial Capture-Recapture Odyssey
Part V Appendix
Appendix I – Useful Softwares and R Packages

Data analysis in vegetation ecology / Otto Wildi
Wildi, Otto ;
Chichester, West Sussex, UK : John Wiley & Sons Inc , 2013
Clasificación: 581.70285 / W5
Bibliotecas: Chetumal , San Cristóbal
SIBE Chetumal
ECO030008144 (Disponible)
Disponibles para prestamo: 1
SIBE San Cristóbal
ECO010017659 (Prestado)
Disponibles para prestamo: 0
Índice | Resumen en: Inglés |
Resumen en inglés

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author’s extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the freely available statistical package R. The addition of R in this new edition has allowed coverage of additional methods for classification and ordination, and also logistic regression, GLMs, GAMs, regression trees as well as multinomial regression to simulate vegetation types. A package of statistical functions, specifically written for the book, covers topics not found elsewhere, such as analysis and plot routines for handling synoptic tables. All data sets presented in the book are now also part of the R package ‘dave’, which is freely available online at the R Archive webpage. The book and data analysis tools combined provide a complete and comprehensive guide to carrying out data analysis students, researchers and practitioners in vegetation science and plant ecology. A completely revised and updated edition of this popular introduction to data analysis in vegetation ecology • Now includes practical examples using the freely available statistical package ‘R’ • Written by a world renowned expert in the field. • Complex concepts and operations are explained using clear illustrations and case studies relating to real world phenomena. • Highlights both the potential and limitations of the methods used, and the final interpretations. • Gives suggestions on the use of the most widely used statistical software in vegetation ecology and how to start analysing data.


Preface to the second edition
Preface to the first edition
List of figures
List of tables
About the companion website
1 Introduction
2 Patterns in vegetation ecology
2.1 Pattern recognition
2.2 Interpretation of patterns
2.3 Sampling for pattern recognition
2.3.1 Getting a sample
2.3.2 Organizing the data
2.4 Pattern recognition in R
3 Transformation
3.1 Data types
3.2 Scalar transformation and the species enigma
3.3 Vector transformation
3.4 Example: Transformation of plant cover data
4 Multivariate comparison
4.1 Resemblance in multivariate space
4.2 Geometric approach
4.3 Contingency measures
4.4 Product moments
4.5 The resemblance matrix
4.6 Assessing the quality of classifications
5 Classification
5.1 Group structures
5.2 Linkage clustering
5.3 Average linkage clustering
5.4 Minimum-variance clustering
5.5 Forming groups
5.6 Silhouette plot and fuzzy representation
6 Ordination
6.1 Why ordination?
6.2 Principal component analysis
6.3 Principal coordinates analysis
6.4 Correspondence analysis
6.5 Heuristic ordination
6.5.1 The horseshoe or arch effect
6.5.2 Flexible shortest path adjustment
6.5.3 Nonmetric multidimensional scaling
6.5.4 Detrended correspondence analysis
6.6 How to interpret ordinations
6.7 Ranking by orthogonal components
6.7.1 RANK method
6.7.2 A sampling design based on RANK (example)
7 Ecological patterns
7.1 Pattern and ecological response
7.2 Evaluating groups
7.2.1 Variance testing
7.2.2 Variance ranking
7.2.3 Ranking by indicator values
7.2.4 Contingency tables
7.3 Correlating spaces
7.3.1 The Mantel test
7.3.2 Correlograms
7.3.3 More trends: ‘Schlaenggli’ data revisited
7.4 Multivariate linear models
7.4.1 Constrained ordination
7.4.2 Nonparametric multiple analysis of variance
7.5 Synoptic vegetation tables
7.5.1 The aim of ordering tables

7.5.2 Steps involved in sorting tables
7.5.3 Example: ordering Ellenberg’s data
8 Static predictive modelling
8.1 Predictive or explanatory?
8.2 Evaluating environmental predictors
8.3 Generalized linear models
8.4 Generalized additive models
8.5 Classification and regression trees
8.6 Building scenarios
8.7 Modelling vegetation types
8.8 Expected wetland vegetation (example)
9 Vegetation change in time
9.1 Coping with time
9.2 Temporal autocorrelation
9.3 Rate of change and trend
9.4 Markov models
9.5 Space-for-time substitution
9.5.1 Principle and method
9.5.2 The Swiss National Park succession (example)
9.6 Dynamics in pollen diagrams (example)
10 Dynamic modelling
10.1 Simulating time processes
10.2 Simulating space processes
10.3 Processes in the Swiss National Park
10.3.1 The temporal model
10.3.2 The spatial model
11 Large data sets: wetland patterns
11.1 Large data sets differ
11.2 Phytosociology revisited
11.3 Suppressing outliers
11.4 Replacing species with new attributes
11.5 Large synoptic tables?
12 Swiss forests: a case study
12.1 Aim of the study
12.2 Structure of the data set
12.3 Selected questions
12.3.1 Is the similarity pattern discrete or continuous?
12.3.2 Is there a scale effect from plot size?
12.3.3 Does the vegetation pattern reflect environmental conditions?
12.3.4 Is tree species distribution man-made?
12.3.5 Is the tree species pattern expected to change?
12.4 Conclusions
Appendix A Functions in package dave
Appendix B Data sets used

Food webs and biodiversity: foundations, models, data / Axel G. Rossberg
Rossberg, Axel G. (1969-) ;
Chichester, West Sussex, UK : Wiley Blackwell , 2013
Clasificación: 577.16 / R6
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010015852 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

Food webs have now been addressed in empirical and theoretical research for more than 50 years. Yet, even elementary foundational issues are still hotly debated. One difficulty is that a multitude of processes need to be taken into account to understand the patterns found empirically in the structure of food webs and communities. Food Webs and Biodiversity develops a fresh, comprehensive perspective on food webs. Mechanistic explanations for several known macroecological patterns are derived from a few fundamental concepts, which are quantitatively linked to field-observables. An argument is developed that food webs will often be the key to understanding patterns of biodiversity at community level. Key Features: Predicts generic characteristics of ecological communities in invasion-extirpation equilibrium. Generalizes the theory of competition to food webs with arbitrary topologies. Presents a new, testable quantitative theory for the mechanisms determining species richness in food webs, and other new results. Written by an internationally respected expert in the field. With global warming and other pressures on ecosystems rising, understanding and protecting biodiversity is a cause of international concern. This highly topical book will be of interest to a wide ranging audience, including not only graduate students and practitioners in community and conservation ecology but also the complex-systems research community as well as mathematicians and physicists interested in the theory of networks.


List of Symbols
Part I Preliminaries
1 Introduction
2 Models and Theories
2.1 The usefulness of models
2.2 What models should model
2.3 The possibility of ecological theory
2.4 Theory-driven ecological research
3 Some Basic Concepts
3.1 Basic concepts of food-web studies
3.2 Physical quantities and dimensions
Part II Elements of Food-Web Models
4 Energy and Biomass Budgets
4.1 Currencies of accounting
4.2 Rates and efficiencies
4.3 Energy budgets in food webs
5 Allometric Scaling Relationships Between Body Size and Physiological Rates
5.1 Scales and scaling
5.2 Allometric scaling
6 Population Dynamics
6.1 Basic considerations
6.1.1 Exponential population growth
6.1.2 Five complications
6.1.3 Environmental variability
6.2 Structured populations and density-dependence
6.2.1 The dilemma between species and stages
6.2.2 Explicitly stage-structured population dynamics
6.2.3 Communities of structured populations
6.3 The Quasi-Neutral Approximation
6.3.1 The emergence of food webs
6.3.2 Rana catesbeiana and its resources
6.3.3 Numerical test of the approximation
6.4 Reproductive value
6.4.1 The concept of reproductive value
6.4.2 The role of reproductive value in the QNA
6.4.3 Body mass as a proxy for reproductive value
7 From Trophic Interactions to Trophic Link Strengths
7.1 Functional and numerical responses
7.2 Three models for functional responses
7.2.1 Linear response
7.2.2 Type II response
7.2.3 Type II response with prey switching
7.2.4 Strengths and weaknesses of these models
7.3 Food webs as networks of trophic link strengths
7.3.1 The ontology of trophic link strengths
7.3.2 Variability of trophic link strengths
8 Tropic Niche Space and Trophic Traits
8.1 Topology and dimensionality of trophic niche space
8.1.1 Formal setting
8.1.2 Definition of trophic niche-space dimensionality

8.2 Examples and ecological interpretations
8.2.1 A minimal example
8.2.2 Is the definition of dimensionality reasonable?
8.2.3 Dependencies between vulnerability and foraging traits of a species
8.2.4 The range of phenotypes considered affects niche-space dimensionality
8.3 Determination of trophic niche-space dimensionality
8.3.1 Typical empirical data
8.3.2 Direct estimation of dimensionality
8.3.3 Iterative estimation of dimensionality
8.4 Identification of trophic traits
8.4.1 Formal setting
8.4.2 Dimensional reduction
8.5 The geometry of trophic niche space
8.5.1 Abstract trophic traits
8.5.2 Indeterminacy in abstract trophic traits
8.5.3 The D-dimensional niche space as a pseudo-Euclidean space
8.5.4 Linear transformations of abstract trophic traits
8.5.5 Non-linear transformations of abstract trophic traits
8.5.6 Standardization and interpretation of abstract trophic traits
8.5.7 A hypothesis and a convention
8.5.8 Getting oriented in trophic niche space
8.6 Conclusions
9 Community Turnover and Evolution
9.1 The spatial scale of interest
9.2 How communities evolve
9.3 The mutation-for-dispersion trick
9.4 Mutation-for-dispersion in a neutral food-web model
10 The Population-Dynamical Matching Model
Part III Mechanisms and Processes
11 Basic Characterizations of Link-Strength Distributions
11.1 Modelling the distribution of logarithmic link strengths
11.1.1 General normally distributed trophic traits
11.1.2 Isotropically distributed trophic traits
11.2 High-dimensional trophic niche spaces
11.2.1 Understanding link stengths in high-dimensional trophic niche spaces
11.2.2 Log-normal probability distributions
11.2.3 The limit of log-normally distributed trophic link strength
11.2.4 Correlations between trophic link strengths
11.2.5 The distribution of the strengths of observable links
11.2.6 The probability of observing links (connectance)

11.2.7 Estimation of link-strength spread and Pareto exponent
11.2.8 Empirical examples
12 Diet Partitioning
12.1 The diet partitioning function
12.1.1 Relation to the probability distribution of diet proportions
12.1.2 Another probabilistic interpretation of the DPF
12.1.3 The normalization property of the DPF
12.1.4 Empirical determination of the DPF
12.2 Modelling the DPF
12.2.1 Formal setting
12.2.2 Diet ratios
12.2.3 The DPF for high-dimensional trophic niche spaces
12.2.4 Gini-Simpson dietary diversity
12.2.5 Dependence of the DPF on niche-space dimensionality
12.3 Comparison with data
12.4 Conclusions
13 Multivariate Link-Strength Distributions and Phylogenetic Patterns
13.1 Modelling phylogenetic structure in trophic traits
13.1.1 Phylogenetic correlations among logarithmic link strengths
13.1.2 Phylogenetic correlations among link strengths
13.1.3 Phylogenetic patterns in binary food webs
13.2 The matching model
13.2.1 A simple model for phylogenetic structure in food webs
13.2.2 Definition of the matching model
13.2.3 Sampling steady-state matching model food webs
13.2.4 Alternatives to the matching model
13.3 Characteristics of phylogenetically structured food webs
13.3.1 Graphical representation of food-web topologies
13.3.2 Standard parameter values
13.3.3 Intervality
13.3.4 Intervality and trophic niche-space dimensionality
13.3.5 Degree distributions
13.3.6 Other phylogenetic patterns
13.3.7 Is phylogeny just a nuisance?
14 A Framework Theory for Community Assembly
14.1 Ecological communities as dynamical systems
14.2 Existence, positivity, stability, and permanence
14.3 Generic bifurcations in community dynamics and their ecological phenomenology
14.3.1 General concepts
14.3.2 Saddle-node bifurcations
14.3.3 Hopf bifurcations
14.3.4 Transcritical bifurcations
14.3.5 Bifurcations of complicated attractors

14.4 Comparison with observations
14.4.1 Extirpations and invasions proceed slowly
14.4.2 The logistic equation works quite well
14.4.3 IUCN Red-List criteria highlight specific extinction scenarios
14.4.4 Conclusion
14.5 Invasion fitness and harvesting resistance
14.5.1 Invasion fitness
14.5.2 Harvesting resistance: definition
14.5.3 Harvesting resistance: interpretation
14.5.4 Harvesting resistance: computation
14.5.5 Interpretation of h → 0
14.6 Community assembly and stochastic species packing
14.6.1 Community saturation and species packing
14.6.2 Invasion probability
14.6.3 The steady-state distribution of harvesting resistance
14.6.4 The scenario of stochastic species packing
14.6.5 A numerical example
14.6.6 Biodiversity and ecosystem functioning
15 Competition in Food Webs
15.1 Basic concepts
15.1.1 Modes of competition
15.1.2 Interactions in communities
15.2 Competition in two-level food webs
15.2.1 The Lotka-Volterra two-level food-web model
15.2.2 Computation of the equilibrium point
15.2.3 Direct competition among producers
15.2.4 Resource-mediated competition in two-level food webs
15.2.5 Consumer-mediated competition in two-level food webs
15.3 Competition in arbitrary food webs
15.3.1 The general Lotka-Volterra food-web model
15.3.2 The competition matrix for general food webs
15.3.3 The L-R-P formalism
15.3.4 Ecological interpretations of the matrices L, R, and P
15.3.5 Formal computation of the equilibrium point
15.3.6 Consumer-mediated competition in general food webs
15.3.7 Consumer-mediated competitive exclusion
15.3.8 Conclusions
16 Mean-Field Theory of Resource-Mediated Competition
16.1 Transition to scaled variables
16.1.1 The competitive overlap matrix
16.1.2 Free abundances
16.2 The extended mean-field theory of competitive exclusion
16.2.1 Assumptions
16.2.2 Separation of means and residuals

16.2.3 Mean-field theory for the mean scaled abundance
16.2.4 Mean-field theory for the variance of scaled abundance
16.2.5 The coefficient of variation of scaled abundance
16.2.6 Related theories
17 Resource-Mediated Competition and Assembly
17.1 Preparation
17.1.1 Scaled vs. unscaled variables and parameters
17.1.2 Mean-field vs framework theory
17.2 Stochastic species packing under asymmetric competition
17.2.1 Species richness and distribution of invasion fitness (Part I)
17.2.2 Community response to invasion
17.2.3 Sensitivity of residents to invaders
17.2.4 Species richness and distribution of invasion fitness (Part II)
17.2.5 Random walks of abundances driven by invasions
17.2.6 Further discussion of the scenario
17.3 Stochastic species packing with competition symmetry
17.3.1 Community assembly with perfectly symmetric competition
17.3.2 Community assembly under nearly perfectly symmetric competition
17.3.3 Outline of mechanism limiting competition avoidance
17.3.4 The distribution of invasion fitness
17.3.5 Competition between residents and invaders
17.3.6 Balance of scaled biomass during assembly
17.3.7 Competition avoidance
17.3.8 Numerical test of the theory
18 Random-Matrix Competition Theory
18.1 Asymmetric competition
18.1.1 Girko’s Law
18.1.2 Application to competitive overlap matrices
18.1.3 Implications for sensitivity to invaders
18.1.4 Relation to mean-field theory
18.2 Stability vs feasibility limits to species richness
18.2.1 The result of May (1972)
18.2.2 Comparison of stability and feasibility criteria
18.3 Partially and fully symmetric competition
18.4 Sparse overlap matrices
18.4.1 Sparse competition
18.4.2 Eigenvalue distributions for sparse matrices
18.5 Resource overlap matrices
18.5.1 Diffuse resource competition
18.5.2 Sparse resource competition: the basic problem
18.5.3 The effect of trophic niche-space geometry

18.5.4 Competition among highly specialized consumers
18.5.5 Resource competition for varying ratios of producer to consumer richness
18.5.6 Competition for competing resources
18.6 Comparison with data
18.6.1 Gall-inducing insects on plants
18.6.2 Freshwater ecosystems
18.6.3 The North Sea
18.6.4 Conclusions
19 Species Richness, Size and Trophic Level
19.1 Predator-prey mass ratios
19.2 Modelling the joint distribution of size, trophic level, and species richness
19.2.1 Initial considerations
19.2.2 Model definition
19.2.3 Model simulation and comparison with data
20 Consumer-Mediated Competition and Assembly
20.1 A two-level food-web assembly model
20.2 Analytic characterization of the model steady state
20.2.1 Mechanism controlling producer richness
20.2.2 Other characteristics of the model steady state
20.3 Dependence of invader impacts on dietary diversity
20.3.1 Formal setting
20.3.2 Invadibility condition
20.3.3 Extirpation of resources during invasion
20.3.4 Extirpation of resources through consumer-mediated competition
20.3.5 Synthesis
20.4 Evolution of base attack rates
20.4.1 Motivation
20.4.2 Model definition
20.4.3 Numerical demonstration of attack rate evolution
20.4.4 Attack-rate evolution and prudent predation
21 Food Chains and Size Spectra
21.1 Concepts
21.1.1 Community size spectra
21.1.2 Species size spectra
21.2 Power-law food chains
21.2.1 Infinitely long power-law food chains
21.2.2 Top-down and bottom-up control
21.2.3 Power law-food chains of finite lengths and their stability to pulse perturbations
21.2.4 Food chains as approximations for size spectra
21.2.5 Adaptation of attack rates
21.3 Food chains with non-linear functional responses
21.3.1 Loss of stability with density-independent consumption
21.3.2 Linearization of a generalized food chain model
21.3.3 Linear responses to press perturbations

21.3.4 Linear stability to pulse perturbations
21.4 What are the mechanisms controlling the scaling laws?
21.4.1 Arguments for biological constraints on transfer efficiency
21.4.2 Arguments for stability constraints on transfer efficiency
21.4.3 Arguments for ecological constraints on biomass imbalance
21.4.4 Arguments for mechanical constraints on PPMR
21.4.5 Arguments for dynamical constraints on PPMR
21.4.6 Conclusions
21.5 Scavengers and detrivores
21.5.1 The general argument
21.5.2 The microbial loop and other detrital channels
22 Structure and Dynamics of PDMM Model Communities
22.1 PDMM model definition
22.1.1 Model states
22.1.2 Species sampling and community assembly
22.1.3 Population dynamics
22.2 PDMM simulations
22.2.1 Trophic niche space and phylogenetic correlations
22.2.2 Steady state and invasion fitness
22.2.3 Diet partitioning
22.2.4 Resource-mediated competition
22.2.5 Distribution of species over body sizes and trophic levels
22.2.6 The size spectrum and related distributions
22.3 The PDMM with evolving attack rates
22.3.1 Modelling and tracking evolving attack rates in the PDMM
22.3.2 Time series of species richness, aggressivity and dietary diversity
22.3.3 Mutual regulation of aggressivity and dietary diversity
22.4 Conclusions
Part IV Implications
23 Scientific Implications
23.1 Main mechanisms identified by the theory
23.1.1 Two trades – one currency
23.1.2 Resource-mediated competition
23.1.3 Randomness and structure in food webs
23.1.4 Consumer-mediated competition and attack-rate evolution
23.2 Testable assumptions and predictions
23.2.1 Link-strength distributions and trophic niche-space geometry
23.2.2 Diet-partitioning statistics and sampling curves
23.2.3 Prey switching
23.2.4 Adapted attack rates
23.2.5 Community assembly and turnover
23.2.6 Patterns in link-strength matrices
23.3 Some unsolved problems

23.3.1 Large plants
23.3.2 Interactions between modes of competition
23.3.3 Absolute species richness: the role of viruses
23.3.4 The role of prey switching for community structure
23.3.5 The role of phylogenetic correlations for community dynamics
23.3.6 Fundamental constraints determining size-spectrum slopes
23.3.7 Community assembly with non-trivial attractors
23.3.8 Solution of the Riccati Equation for resource competition
23.3.9 Eigenvalues of competition matrices
23.3.10 Geometry and topology of trophic niche space
23.4 The future of community ecology
24 Conservation Implications
24.1 Assessing biodiversity
24.1.1 Quantifying biodiversity
24.1.2 Biodiversity supporting biodiversity
24.1.3 Assessing community turnover
24.2 Modelling ecological communities
24.2.1 Unpredictability of long-term community responses
24.2.2 Short-term predictions of community responses
24.2.3 Coarse-grained and stochastic community models
24.3 Managing biodiversity
Appendix A
A.1 Mathematical concepts, formulae, and jargon
A.1.1 Sums
A.1.2 Complex numbers
A.1.3 Vectors and matrices
A.1.4 Sets and functions
A.1.5 Differential calculus
A.1.6 Integrals
A.1.7 Differential equations
A.1.8 Random variables and expectation values