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Quantitative ecology and evolutionary biology : integrating models with data / Otso Ovaskainen, Henrik Johan de Knegt, and Maria del Mar Delgado

Author
Ovaskainen, Otso
Published
Oxford : Oxford University Press, 2016.
Edition
First edition.
Physical Description
1 online resource
Additional Creators
Knegt, Henrik Johan de and Delgado, Maria del Mar, 1979-
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Contents
Machine generated contents note: 1.Approaches to ecological modelling -- 1.1.Forward and inverse approaches -- 1.2.The interplay between models and data -- 1.3.The many choices with mathematical and statistical models and methods -- 1.4.What a biologist should learn about modelling -- 2.Movement ecology -- 2.1.Why, where, when, and how do individual organisms move -- 2.1.1.Internal state: why to move -- 2.1.2.Motion capacity: how to move -- 2.1.3.Navigation capacity: when and where to move -- 2.1.4.Different types of movement -- 2.1.5.Approaches to movement research -- 2.1.6.Outline of this chapter -- 2.2.Movement models in homogeneous environments -- 2.2.1.The Lagrangian approach -- 2.2.2.Translating the Lagrangian model into an Eulerian model -- 2.2.3.Dispersal kernels -- 2.2.4.Adding directional persistence: correlated random walk models -- 2.2.5.Adding directional bias: home-range models -- 2.3.Movement models in heterogeneous environments -- 2.3.1.Random walk simulations in heterogeneous space -- 2.3.2.Diffusion models with continuous spatial variation in movement parameters -- 2.3.3.Diffusion models with discrete spatial variation in movement parameters -- 2.3.4.Using movement models to define and predict functional connectivity -- 2.3.5.The influence of a movement corridor -- 2.4.Movements in a highly fragmented landscape -- 2.4.1.The case of a single habitat patch -- 2.4.2.The case of a patch network -- 2.5.Statistical approaches to analysing movement data -- 2.5.1.Exploratory data analysis of GPS data -- 2.5.2.Fitting a diffusion model to capture-mark-recapture data -- 2.6.Perspectives -- 2.6.1.Limitations and extensions of random walk and diffusion models -- 2.6.2.The many approaches of analysing movement data -- 3.Population ecology -- 3.1.Scaling up from the individual level to population dynamics -- 3.1.1.Factors influencing population growth through birth and death rates -- 3.1.2.How movements influence population dynamics -- 3.1.3.How population structure influences population dynamics -- 3.1.4.The outline of this chapter -- 3.2.Population models in homogeneous environments -- 3.2.1.Individual-based stochastic and spatial model -- 3.2.2.Simplifying the model: stochasticity without space -- 3.2.3.Simplifying the model further: without stochasticity and space -- 3.2.4.Another way of simplifying the model: space without stochasticity -- 3.3.Population models in heterogeneous environments -- 3.3.1.Environmental stochasticity -- 3.3.2.Spatial heterogeneity in continuous space: the plant population model -- 3.3.3.Spatial heterogeneity in discrete space: the butterfly metapopulation model -- 3.3.4.The Levins metapopulation model and its spatially realistic versions -- 3.4.The persistence of populations under habitat loss and fragmentation -- 3.4.1.Habitat loss and fragmentation in the plant population model -- 3.4.2.Habitat loss and fragmentation in the butterfly metapopulation model -- 3.4.3.Habitat loss and fragmentation in the Levins metapopulation model -- 3.5.Statistical approaches to analysing population ecological data -- 3.5.1.Time-series analyses of population abundance -- 3.5.2.Fitting Bayesian state-space models to time-series data -- 3.5.3.Species distribution models -- 3.5.4.Metapopulation models -- 3.6.Perspectives -- 3.6.1.The invisible choices made during a modelling process -- 3.6.2.Some key insights derived from population models -- 3.6.3.The many approaches to analysing population data -- 4.Community ecology -- 4.1.Community assembly shaped by environmental filtering and biotic interactions -- 4.1.1.Ecological interactions -- 4.1.2.Fundamental and realized niches and environmental filtering -- 4.1.3.Organizational frameworks for metacommunity ecology -- 4.1.4.The outline of this chapter -- 4.2.Community models in homogeneous environments -- 4.2.1.Competitive interactions -- 4.2.2.Resource-consumer interactions -- 4.2.3.Predator-prey interactions -- 4.3.Community models in heterogeneous environments -- 4.3.1.The case of two competing species -- 4.3.2.The case of many competing species -- 4.4.The response of communities to habitat loss and fragmentation -- 4.4.1.Endemics-area and species-area relationships generated by the plant community model -- 4.5.Statistical approaches to analysing species communities -- 4.5.1.Time-series analyses of population size in species communities -- 4.5.2.Joint species distribution models -- 4.5.3.Ordination methods -- 4.5.4.Point-pattern analyses of distribution of individuals -- 4.6.Perspectives -- 4.6.1.Back to the metacommunity paradigms -- 4.6.2.Some insights derived from community models -- 4.6.3.The many approaches to modelling community data -- 5.Genetics and evolutionary ecology -- 5.1.Inheritance mechanisms and evolutionary processes -- 5.1.1.Genetic building blocks and heritability -- 5.1.2.Selection, drift, mutation, and gene flow -- 5.1.3.Connections between ecological and evolutionary dynamics -- 5.1.4.The outline of this chapter -- 5.2.The evolution of quantitative traits under neutrality -- 5.2.1.An additive model for the map from genotype to phenotype -- 5.2.2.Coancestry and the additive genetic relationship matrix -- 5.2.3.Why related individuals resemble each other? -- 5.2.4.The animal model -- 5.2.5.Why related populations resemble each other? -- 5.3.The evolution of quantitative traits under selection -- 5.3.1.Evolution by drift, selection, mutation, recombination, and gene flow -- 5.3.2.Selection differential and the breeder's equation -- 5.3.3.Population divergence due to drift and selection -- 5.4.Evolutionary dynamics under habitat loss and fragmentation -- 5.4.1.Evolution of dispersal in the Hamilton-May model under adaptive dynamics -- 5.4.2.Evolution of dispersal in the plant population model under quantitative genetics -- 5.5.Statistical approaches to genetics and evolutionary ecology -- 5.5.1.Inferring population structure from neutral markers -- 5.5.2.Estimating additive genetic variance and heritability -- 5.5.3.Using association analysis to detect loci behind quantitative traits -- 5.5.4.Detecting loci under selection from genotypic data -- 5.5.5.Detecting traits under selection from genotypic and phenotypic data -- 5.6.Perspectives -- 5.6.1.Mathematical approaches to modelling genetics and evolution -- 5.6.2.Some insights derived from evolutionary models on dispersal evolution -- 5.6.3.The many uses of genetic data -- Appendix A Mathematical methods -- A.1.A very brief tutorial to linear algebra -- A.2.A very brief tutorial to calculus -- A.2.1.Derivatives, integrals, and convolutions -- A.2.2.Differential equations -- A.2.3.Systems of differential equations -- A.2.4.Partial differential equations -- A.2.5.Difference equations -- A.3.A very brief tutorial to random variables -- A.3.1.Discrete valued random variables -- A.3.2.Continuous valued random variables -- A.3.3.Joint distribution of two or more random variables -- A.3.4.Sums of random variables -- A.3.5.An application of random variables to quantitative genetics -- A.4.A very brief tutorial to stochastic processes -- A.4.1.Markov chains -- A.4.2.Markov processes -- Appendix B Statistical methods -- B.1.Generalized linear mixed models -- B.1.1.Linear models -- B.1.2.Link functions and error distributions -- B.1.3.Relaxing the assumption of independent residuals -- B.1.4.Random effects -- B.1.5.Multivariate models -- B.1.6.Hierarchical models -- B.2.Model fitting with Bayesian inference -- B.2.1.The concepts of likelihood, maximum likelihood, and parameter uncertainty -- B.2.2.Prior and posterior distributions, and the Bayes theorem -- B.2.3.Methods for sampling the posterior distribution.
Summary
This is an integration of empirical data and theory in quantitative ecology and evolution through the use of mathematical models and statistical methods.
Subject(s)
ISBN
9780191783210 (ebook)
Audience Notes
Specialized.
Note
This edition previously issued in print: 2016.
Bibliography Note
Includes bibliographical references and index.
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