Ecological Modelling and Ecophysics (Second Edition) : Agricultural and Environmental Applications / Hugo Fort.

Format:

Book

Author/Creator:

Fort, Hugo, author.

Series:

IOP Ebooks Series

Language:

English

Subjects (All):

Ecology--Simulation methods.

Ecology.

Physical Description:

1 online resource (385 pages)

Edition:

Second edition.

Place of Publication:

Bristol, England : IOP Publishing, [2024]

Summary:

This book focuses on use-inspired basic science by connecting theoretical methods and mathematical developments in ecology with practical real-world problems, either in production or conservation.

Contents:

Outline placeholder

References

Acknowledgements

Author biography

Hugo Fort

Chapter Introduction

0.1 The goal of ecology: understanding the distribution and abundance of organisms from their interactions

0.2 Mathematical models

0.2.1 What is modeling?

0.2.2 Why mathematical modelling?

0.2.3 What kind of mathematical modelling?

0.2.4 Principles and some rules of mathematical modeling

0.3 Community and population ecology modeling

0.3.1 Parallelism with physics and the debate of the 'biology-as-physics approach'

0.3.2 Trade-offs and modeling strategies

Chapter From growth equations for a single species to Lotka-Volterra equations for two interacting species

1.1 Summary

1.2 From the Malthus to the logistic equation of growth for a single species

1.2.1 Exponential growth

1.2.2 Resource limitation, density-dependent per-capita growth rate and logistic growth

1.3 General models for single species populations and analysis of local equilibrium stability

1.3.1 General model and Taylor expansion

1.3.2 Algebraic and geometric analysis of local equilibrium stability

1.4 The Lotka-Volterra predator-prey equations

1.4.1 A general dynamical system for predator-prey

1.4.2 A first model for predator-prey: the original Lotka-Volterra predator-prey model

1.4.3 Realistic predator-prey models: logistic growth of prey and Holling predator functional responses

1.5 The Lotka-Volterra competition equations for a pair of species

1.5.1 A descriptive or phenomenological model

1.5.2 Stable equilibrium: competitive exclusion or species coexistence?

1.5.3 Transforming the competition model into a mechanistic model

1.6 The Lotka-Volterra equations for two mutualist species.

1.7 Worked example: Niche overlap and traits of nectar-producing plant species and nectar searching animal species

1.7.1 Mutualistic interaction requires matching of morphological traits of plants and pollinators

1.7.2 Niche overlap

1.7.3 The bi-partite network representation

1.7.4 Dataset1010Courtesy of Sebastien Ibanez.

1.7.5 Connecting niche position with traits through resource overlapping

1.8 Exercises

Exercise 1.1

Exercise 1.2

Exercise 1.3 (from Hale and McCarthy 2005)

Exercise 1.4

Exercise 1.5

Exercise 1.6

Exercise 1.7

Exercise 1.8

Exercise 1.9

Exercise 1.10

Exercise 1.11

Exercise 1.12

Exercise 1.13

Exercise 1.14

Exercise 1.15

Chapter Extensive livestock farming: a quantitative management model in terms of a predator-prey dynamical system

A1.1 Background information: the growing demand for quantitative livestock models

A1.2 A predator-prey model for grassland livestock or PPGL

A1.2.1 What is our goal?

A1.2.2 What do we know? and what do we assume?: identifying measurable relevant variables for grass and animals

A1.2.3 How? Adapting a predator-prey model

A1.2.4 What will our model predict?

A1.3 Model validation

A1.3.1 Are predictions valid?

A1.3.2 Sensitivity analysis

A1.3.3 Verdict: model validated

A1.4 Uses of PPGL by farmers: estimating gross margins in different productive scenarios

A1.5 How can we improve our model?

MATLAB codes

Main code: LVPPGL_Ap1%

Function 'Digest'

Chapter Lotka-Volterra models for multispecies communities and their usefulness as quantitative predicting tools

2.1 Summary

2.2 Many interacting species: the Lotka-Volterra generalized linear model

2.3 The Lotka-Volterra linear model for single trophic communities

2.3.1 Purely competitive communities.

2.3.2 Single trophic communities with interspecific interactions of different signs

2.3.3 Obtaining the parameters of the linear Lotka-Volterra generalized model from monoculture and biculture experiments

2.4 Food webs and trophic chains

2.5 Quantifying the accuracy of the linear model for predicting species yields in single trophic communities11This section is based on Fort (2018a).

2.5.1 Obtaining the theoretical yields: linear algebra solutions and simulations

2.5.2 Accuracy metrics to quantitatively evaluate the performance of the LLVGE

2.5.3 The linear Lotka-Volterra generalized equations can accurately predict species yields in many cases

2.5.4 Often a correction of measured parameters, within their experimental error bars, can greatly improve accuracy

2.6 Working with imperfect information

2.6.1 The 'Mean Field Matrix' (MFM) approximation for predicting global or aggregate quantities

2.6.2 The 'focal species' approximation for predicting the performance of a given species when our knowledge on the set of parameters is incomplete

2.7 Beyond equilibrium: testing the generalized linear model for predicting species trajectories

2.8 Conclusion

2.9 Exercises

Exercise 2.1

Exercise 2.2

Exercise 2.3 (from Goh 1977)

Exercise 2.4. A Lyapunov function for the Lotka-Volterra competition equations with symmetric interaction coefficients

Exercise 2.5. The reference point for the modified index of agreement d1

Exercise 2.6. Working with relative yields rather than yields

Exercise 2.7. Using the mean field approximation for predicting the RYT of a BIODEPTH 32 plant species experiment

Exercise 2.8. An example of application of the focal approximation

Chapter Predicting optimal mixtures of perennial crops by combining modelling and experiments

A2.1 Background information.

A2.2 Overview

A2.3 Experimental design and data

A2.4 Modelling

A2.4.1 Model equations

A2.4.2 Data curation

A2.4.3 Initial parameter estimation from experimental data

A2.4.4 Adjustment of the initial estimated parameters to meet stability conditions

A2.4.5 On the types of interspecific interactions

A2.5 Metrics for overyielding and equitability

A2.6 Model validation: theoretical versus experimental quantities

A2.6.1 Qualitative check: species ranking

A2.6.2 Quantitative check I: individual species yields

A2.6.3 Quantitative check II: overyielding, total biomasses and equitability

A2.6.4 Verdict: model validated

A2.7 Predictions: results from simulation of not sown treatments

A2.7.1 Similarities and differences between theoretical results for sown and not sown polycultures

A2.7.2 Using the model for predicting optimal mixtures

A2.8 Using the model attempting to elucidate the relationship between yield and diversity

A2.8.1 Positive correlation between productivity and species richness.

A2.8.2 No significant correlation between productivity and SE

A2.9 Possible extensions and some caveats

A2.10 Bottom line

MATLAB code

Chapter The maximum entropy method and the statistical mechanics of populations

3.1 Summary

3.2 Basics of statistical physics

3.2.1 The program of statistical physics

3.2.2 Boltzmann-Gibbs maximum entropy approach to statistical mechanics

3.3 MaxEnt in terms of Shannon's information theory as a general inference approach

3.3.1 Shannon's information entropy

3.3.2 MaxEnt as a method of making predictions from limited data by assuming maximal ignorance

3.3.3 Inference of model parameters from the statistical moments via MaxEnt

3.4 The statistical mechanics of populations

3.4.1 Rationale and first attempts.

3.4.2 Harte's MaxEnt theory of ecology (METE)33This subsection devoted to METE is based on chapter 7 of Harte's Maximum Entropy and Ecology (2011).

3.5 Neutral theories of ecology

3.6 Conclusion

3.7 Exercises

3.7.1 Exercise 3.1. An alternative way to obtain that the Lagrange multiplier of the Boltzmann distribution is λ1=1/kBT

3.7.2 Exercise 3.2

3.7.3 Exercise 3.3

3.7.4 Exercise 3.4

3.7.5 Exercise 3.5

3.7.6 Exercise 3.6. A toy community

3.7.7 Exercise 3.7

3.7.8 Exercise 3.8

3.7.9 Exercise 3.9

3.7.10 Exercise 3.10

3.7.11 Exercise 3.11

3.7.12 Exercise 3.12

Chapter Combining the generalized Lotka-Volterra model and MaxEnt method to predict changes of tree species composition in tropical forests

A3.1 Background information

A3.2 Overview

A3.3 Data for Barro Colorado Island (BCI) 50 ha tropical Forest Dynamics Plot

A3.3.1 Some facts about BCI

A3.3.2 Covariance matrices and species interactions

A3.4 Modeling

A3.4.1 Inference of the effective interaction matrix from the covariance matrix via MaxEnt

A3.4.2 Model equations

A3.5 Model validation using time series forecasting analysis

A3.5.1 Estimation of intrinsic growth rates and carrying capacities using a training set of data

A3.5.2 Generating predictions to be contrasted against a validation set of data

A3.5.3 Verdict: model validated

A3.6 Predictions

A3.7 Extensions, improvements and caveats

A3.8 Conclusion

Chapter Catastrophic shifts in ecology, early warnings and the phenomenology of phase transitions

4.1 Summary

4.2 Catastrophes

4.2.1 Catastrophic shifts and bifurcations

4.2.2 A simple population (mean field) model with a catastrophe

4.3 When does a catastrophic shift take place? Maxwell versus delay conventions.

4.4 Early warnings of catastrophic shifts22This section is mostly a summary of the material presented in chapter 9 of Gilmore (1981).

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9780750361590

075036159X

OCLC:

1432598454