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E-raamat: Quantitative Ecology and Evolutionary Biology: Integrating models with data

4.33/5 (6 hinnangut Goodreads-ist)
(Professor, Metapopulation Research Center, Department of Biosciences, University of Helsinki), (Postdoctoral researcher, M), (Postdoctoral researcher, Resource Ecology Group, Department of Environmental Sciences, Wageningen University)
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Quantitative Ecology and Evolutionary Biology: Integrating models with dataSuurem pilt
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This novel, interdisciplinary text achieves an integration of empirical data and theory with the aid of mathematical models and statistical methods. The emphasis throughout is on spatial ecology and evolution, especially on the interplay between environmental heterogeneity and biological processes. The book provides a coherent theme by interlinking the modelling approaches used for different subfields of spatial ecology: movement ecology, population ecology, community ecology, and genetics and evolutionary ecology (each being represented by a separate chapter). Each chapter starts by describing the concept of each modelling approach in its biological context, goes on to present the relevant mathematical models and statistical methods, and ends with a discussion of the benefits and limitations of each approach. The concepts and techniques discussed throughout the book are illustrated throughout with the help of empirical examples.

This is an advanced text suitable for any biologist interested in the integration of empirical data and theory in spatial ecology/evolution through the use of quantitative/statistical methods and mathematical models. The book will also be of relevance and use as a textbook for graduate-level courses in spatial ecology, ecological modelling, theoretical ecology, and statistical ecology.

Arvustused

The book is a highly fascinating read for ecologists and evolutionary biologists interested in learning more about modelling approaches.It mightlikewise be interesting for mathematicians, statisticians and computer scientists interested in ecological and evolutionary questions.It is an advanced but essential text that surely stimulates further reading. * Yvonne Buchholz, Basic and Applied Ecology *

1 Approaches to ecological modelling 1(9)
1.1 Forward and inverse approaches
2(1)
1.2 The interplay between models and data
3(3)
1.3 The many choices with mathematical and statistical models and methods
6(2)
1.4 What a biologist should learn about modelling
8(2)
2 Movement ecology 10(58)
2.1 Why, where, when, and how do individual organisms move
10(7)
2.1.1 Internal state: why to move
11(1)
2.1.2 Motion capacity: how to move
12(1)
2.1.3 Navigation capacity: when and where to move
13(1)
2.1.4 Different types of movement
13(1)
2.1.5 Approaches to movement research
14(1)
2.1.6 Outline of this chapter
15(2)
2.2 Movement models in homogeneous environments
17(10)
2.2.1 The Lagrangian approach
18(2)
2.2.2 Translating the Lagrangian model into an Eulerian model
20(1)
2.2.3 Dispersal kernels
21(2)
2.2.4 Adding directional persistence: correlated random walk models
23(2)
2.2.5 Adding directional bias: home-range models
25(2)
2.3 Movement models in heterogeneous environments
27(16)
2.3.1 Random walk simulations in heterogeneous space
27(2)
2.3.2 Diffusion models with continuous spatial variation in movement parameters
29(5)
2.3.3 Diffusion models with discrete spatial variation in movement parameters
34(1)
2.3.4 Using movement models to define and predict functional connectivity
35(5)
2.3.5 The influence of a movement corridor
40(3)
2.4 Movements in a highly fragmented landscape
43(7)
2.4.1 The case of a single habitat patch
44(3)
2.4.2 The case of a patch network
47(3)
2.5 Statistical approaches to analysing movement data
50(13)
2.5.1 Exploratory data analysis of GPS data
51(9)
2.5.2 Fitting a diffusion model to capture-mark-recapture data
60(3)
2.6 Perspectives
63(5)
2.6.1 Limitations and extensions of random walk and diffusion models
64(2)
2.6.2 The many approaches of analysing movement data
66(2)
3 Population ecology 68(54)
3.1 Scaling up from the individual level to population dynamics
68(5)
3.1.1 Factors influencing population growth through birth and death rates
69(1)
3.1.2 How movements influence population dynamics
70(1)
3.1.3 How population structure influences population dynamics
71(1)
3.1.4 The outline of this chapter
72(1)
3.2 Population models in homogeneous environments
73(10)
3.2.1 Individual-based stochastic and spatial model
76(1)
3.2.2 Simplifying the model: stochasticity without space
77(3)
3.2.3 Simplifying the model further: without stochasticity and space
80(1)
3.2.4 Another way of simplifying the model: space without stochasticity
81(2)
3.3 Population models in heterogeneous environments
83(14)
3.3.1 Environmental stochasticity
84(2)
3.3.2 Spatial heterogeneity in continuous space: the plant population model
86(3)
3.3.3 Spatial heterogeneity in discrete space: the butterfly metapopulation model
89(4)
3.3.4 The Levins metapopulation model and its spatially realistic versions
93(4)
3.4 The persistence of populations under habitat loss and fragmentation
97(5)
3.4.1 Habitat loss and fragmentation in the plant population model
98(2)
3.4.2 Habitat loss and fragmentation in the butterfly metapopulation model
100(1)
3.4.3 Habitat loss and fragmentation in the Levins metapopulation model
100(2)
3.5 Statistical approaches to analysing population ecological data
102(15)
3.5.1 Time-series analyses of population abundance
102(3)
3.5.2 Fitting Bayesian state-space models to time-series data
105(7)
3.5.3 Species distribution models
112(3)
3.5.4 Metapopulation models
115(2)
3.6 Perspectives
117(5)
3.6.1 The invisible choices made during a modelling process
118(1)
3.6.2 Some key insights derived from population models
119(1)
3.6.3 The many approaches to analysing population data
120(2)
4 Community ecology 122(46)
4.1 Community assembly shaped by environmental filtering and biotic interactions
122(7)
4.1.1 Ecological interactions
124(1)
4.1.2 Fundamental and realized niches and environmental filtering
125(1)
4.1.3 Organizational frameworks for metacommunity ecology
126(1)
4.1.4 The outline of this chapter
127(2)
4.2 Community models in homogeneous environments
129(11)
4.2.1 Competitive interactions
129(6)
4.2.2 Resource-consumer interactions
135(3)
4.2.3 Predator-prey interactions
138(2)
4.3 Community models in heterogeneous environments
140(5)
4.3.1 The case of two competing species
141(1)
4.3.2 The case of many competing species
142(3)
4.4 The response of communities to habitat loss and fragmentation
145(5)
4.4.1 Endemics-area and species-area relationships generated by the plant community model
145(5)
4.5 Statistical approaches to analysing species communities
150(14)
4.5.1 Time-series analyses of population size in species communities
150(5)
4.5.2 Joint species distribution models
155(6)
4.5.3 Ordination methods
161(1)
4.5.4 Point-pattern analyses of distribution of individuals
162(2)
4.6 Perspectives
164(4)
4.6.1 Back to the metacommunity paradigms
164(1)
4.6.2 Some insights derived from community models
165(2)
4.6.3 The many approaches to modelling community data
167(1)
5 Genetics and evolutionary ecology 168(47)
5.1 Inheritance mechanisms and evolutionary processes
168(7)
5.1.1 Genetic building blocks and heritability
168(2)
5.1.2 Selection, drift, mutation, and gene flow
170(2)
5.1.3 Connections between ecological and evolutionary dynamics
172(1)
5.1.4 The outline of this chapter
173(2)
5.2 The evolution of quantitative traits under neutrality
175(9)
5.2.1 An additive model for the map from genotype to phenotype
175(2)
5.2.2 Coancestry and the additive genetic relationship matrix
177(2)
5.2.3 Why related individuals resemble each other?
179(1)
5.2.4 The animal model
180(1)
5.2.5 Why related populations resemble each other?
181(3)
5.3 The evolution of quantitative traits under selection
184(10)
5.3.1 Evolution by drift, selection, mutation, recombination, and gene flow
185(1)
5.3.2 Selection differential and the breeder's equation
186(4)
5.3.3 Population divergence due to drift and selection
190(4)
5.4 Evolutionary dynamics under habitat loss and fragmentation
194(7)
5.4.1 Evolution of dispersal in the Hamilton-May model under adaptive dynamics
194(4)
5.4.2 Evolution of dispersal in the plant population model under quantitative genetics
198(3)
5.5 Statistical approaches to genetics and evolutionary ecology
201(8)
5.5.1 Inferring population structure from neutral markers
201(2)
5.5.2 Estimating additive genetic variance and heritability
203(1)
5.5.3 Using association analysis to detect loci behind quantitative traits
204(2)
5.5.4 Detecting loci under selection from genotypic data
206(1)
5.5.5 Detecting traits under selection from genotypic and phenotypic data
207(2)
5.6 Perspectives
209(6)
5.6.1 Mathematical approaches to modelling genetics and evolution
209(2)
5.6.2 Some insights derived from evolutionary models on dispersal evolution
211(1)
5.6.3 The many uses of genetic data
212(3)
Appendix A: Mathematical methods 215(18)
A.1 A very brief tutorial to linear algebra
215(2)
A.2 A very brief tutorial to calculus
217(5)
A.2.1 Derivatives, integrals, and convolutions
217(1)
A.2.2 Differential equations
218(2)
A.2.3 Systems of differential equations
220(1)
A.2.4 Partial differential equations
221(1)
A.2.5 Difference equations
222(1)
A.3 A very brief tutorial to random variables
222(7)
A.3.1 Discrete valued random variables
222(1)
A.3.2 Continuous valued random variables
223(2)
A.3.3 Joint distribution of two or more random variables
225(1)
A.3.4 Sums of random variables
226(2)
A.3.5 An application of random variables to quantitative genetics
228(1)
A.4 A very brief tutorial to stochastic processes
229(4)
A.4.1 Markov chains
229(2)
A.4.2 Markov processes
231(2)
Appendix B: Statistical methods 233(20)
B.1 Generalized linear mixed models
233(12)
B.1.1 Linear models
233(1)
B.1.2 Link functions and error distributions
234(2)
B.1.3 Relaxing the assumption of independent residuals
236(2)
B.1.4 Random effects
238(3)
B.1.5 Multivariate models
241(3)
B.1.6 Hierarchical models
244(1)
B.2 Model fitting with Bayesian inference
245(8)
B.2.1 The concepts of likelihood, maximum likelihood, and parameter uncertainty
246(1)
B.2.2 Prior and posterior distributions, and the Bayes theorem
246(3)
B.2.3 Methods for sampling the posterior distribution
249(4)
References 253(24)
Index 277
Otso Ovaskainen obtained his PhD in mathematics in 1998 at the Helsinki University of Technology. To combine his mathematical training with his interest in ecology, he did his postdoctoral training with Professor Ilkka Hanski in Helsinki University and with Professor Bryan Grenfell in Cambridge University. He became a research fellow funded by the Academy of Finland in 2003, at which time he founded the Mathematical Biology Group, which is part of Ilkka Hanski's Metapopulation Research Centre. He became a professor in Helsinki University in 2009, and a visiting professor in Trondheim University in 2014. He is broadly interested in mathematical and statistical approaches in ecology and evolutionary biology.

Henrik de Knegt obtained his PhD in ecology in 2010 at Wageningen University (the Netherlands), after which he moved to Finland as a postdoctoral researcher at the University of Helsinki. He is broadly interested in spatial ecology, especially the small-scale mechanisms behind the movement and habitat selection of organisms. This is because the movement process couples organisms to their spatial-temporal environment, and is vital to coupling individual-level behaviour to population-level dynamics. He recently moved back to the Netherlands where his work at Wageningen University aims at contributing to the prevention of rhino poaching, through the development of a response system that highlights where and when poaching is taking place, based on anomalies in animal movement patterns that might signal poaching-induced disturbances.

Maria del Mar Delgado did her degree in Biology at the University of Seville (Spain), and then a PhD in ecology at Doñana Biological Station (CSIC, Spain). She then moved to Finland where she has spent her last seven years as a postdoctoral researcher. She is interested in a wide array of issues within behavioural and evolutionary ecology, biodiversity and conservation biology. The main goal of her scientific trajectory is to carry out multidisciplinary, synthetic ecological and evolutionary research with a strong collaborative basis. Ecology and evolution are essentially trans-disciplinary areas, and thus she has always been interested in combining experimental, theoretical, and observational approaches. The main focus of her research is on gaining an integrated understanding of the structure and dynamics of natural populations and communities by combining rigorous statistical analyses with long-term monitoring data.
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