Quantitative ecology and evolutionary biology: integrating Models with Data
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
[2016]
|
| Ausgabe: | First edition |
| Schriftenreihe: | Oxford series in ecology and evolution
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 285 Seiten Diagramme |
| ISBN: | 9780198714873 0198714874 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Quantitative Ecology
and Evolutionary Biology
Integrating Models with Data
OTSOOVASKAINEN
Department of Biosciences, University of Helsinki, Finland
HENRIKJOHANDEKNEGT
Department of Environmental Sciences, Wageningen University, The Netherlands
MARIADELMARDELGADO
Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Ovierfo University - Campus Mieres,
Mieres, Spain
OXFORD
UNIVERSITY PRESS
Contents
1 Approaches to ecological modelling I
1 1 Forward and inverse approaches 2
1 2 The interplay between models and data 3
1 3 The many choices with mathematical and statist ical models and methods 6
1 4 What a biologist should learn about modelling 8
2 Movement ecology 10
2 1 Why, where, when, and how do individual organisms move 10
211 Internal state: why to move 11
212 Motion capacity: how to move 12
213 Navigation capacity: when and where to move 13
214 Different types of movement 13
215 Approaches to movement research 14
216 Outline of this chapter 15
2 2 Movement models in homogeneous environments 17
221 The Lagrangian approach 18
222 Translating the Lagrangian model into an Eulerian model 20
223 Dispersal kernels 21
224 Adding directional persistence: correlated random walk models 23
225 Adding directional bias: home-range models 25
2 3 Movement models in heterogeneous environments 27
231 Random walk simulations in heterogeneous space 27
232 Diffusion models with continuous spatial variation in
movement parameters 29
233 Diffusion models with discrete spatial variation in
movement parameters 34
234 Using movement models to define and prcdict functional
connectivity 35
235 The influence of a movement corridor 40
2 4 Movements in a highly fragmented landscape 43
241 The case of a single habitat patch 44
242 The case of a patch network 47
x • Contents
2 5 Statistical approaches to analysing movement data 50
251 Exploratory data analysis of GPS data 51
252 Fitting a diffusion model to capture-mark-recapture data 60
2 6 Perspectives 63
261 Limitations and extensions of random walk and diffusion models 64
262 The many approaches of analysing mi ivement data 66
3 Population ecology 68
3 1 Scaling up from the individual level to population dynamics 68
311 Factors influencing population growth through birth and
death rates 69
312 How movements influence population dynamics 70
313 How population structure influences population dynamics 71
314 The outline of this chapter 72
3 2 Population models in homogeneous environments 73
321 Individual-based stochastic and spatial model 76
322 Simplifying the model: stochasticity without space 77
323 Simplifying the model further: without stochasticity and space 80
324 Another way of simplifying the model: space without stochasticity 81
3 3 Population models in heterogeneous environments 83
331 Environmental stochasticity 84
332 Spatial heterogeneity in continuous space: the plant
population model 86
333 Spatial heterogeneity in discrete space: the butterfly
metapopulation model 89
334 The Levins metapopulation model and its spatially realistic versions 93
3 4 The persistence of populations under habitat loss and fragmentation 97
341 Habitat loss and fragmentation in the plant population model 98
342 Habitat loss and fragmentation in the butterfly
metapopulation model 100
343 Habitat loss and fragmentation in the Levins
metapopulation model 100
3 5 Statistical approaches to analysing population ecological data 102
351 Time-series analyses of population abundance 102
352 Fitting Bayesian state-space models to time-series data 105
353 Species distribution models 112
354 Metapopulation models 115
3 6 Perspectives 117
361 The invisible choices made during a modelling process 118
362 Some key insights derived from population models 119
363 The many approaches to analysing population data 120
Contents • xi
4 Community ecology 122
4 1 Community assembly shaped by environmental filtering
and biotic interactions 122
411 Ecological interactions 124
412 Fundamental and realized niches and environmental filtering 125
413 Organizational frameworks for metacommunity ecology 126
414 The outline of this chapter 127
4 2 Community models in homogeneous environments 129
421 Competitive interactions 129
422 Resource-consumer interactions 135
423 Predator-prey interactions 138
4 3 Community models in heterogeneous environments 140
431 The case of two competing species 141
432 The case of many competing species 142
4 4 The response of communities to habitat loss and fragmentation 145
441 Endemics-area and species-area relationships generated by
the plant community model 145
4 5 Statistical approaches to analysing species communities 150
451 Time-series analyses of population size in species communities 150
452 Joint species distribution models 155
453 Ordination methods 161
454 Point-pattern analyses of distribution of individuals 162
4 6 Perspectives 164
461 Back to the metacommunity paradigms 164
462 Some insights derived from community models 165
463 The many approaches to modelling community data 167
5 Genetics and evolutionary ecology 168
5 1 Inheritance mechanisms and evolutionary processes 168
511 Genetic building blocks and heritability 168
512 Selection, drift, mutation, and gene flow 170
513 Connections between ecological and evolutionary dynamics 172
514 The outline of this chapter 173
5 2 The evolution of quantitative traits under neutrality 175
521 An additive model for the map from genotype to phenotype 175
522 Coancestry and the additive genetic relationship matrix 177
523 Why related individuals resemble each other? 179
524 The animal model 180
525 Why related populations resemble each other? 181
5 3 The evolution of quantitative traits under selection 184
531 Evolution by drift, selection, mutation, recombination,
and gene flow 185
532 Selection differential and the breeder s equ ition 186
533 Population divergence due to drift and selection 190
xii • Contents
5 4 Evolutionary dynamics under habitat loss and fragmentation 194
541 Evolution of dispersal in the Hamilton-May model under
adaptive dynamics 194
542 Evolution of dispersal in the plant population model under
quantitative genetics 198
5 5 Statistical approaches to genetics and evolut ionary ecology 201
551 Inferring population structure from neutral markers 201
552 Estimating additive genetic variance and heritability 203
553 Using association analysis to detect loci behind quantitative traits 204
554 Detecting loci under selection from genotypic data 206
555 Detecting traits under selection from genotypic and
phenotypic data 207
5 6 Perspectives 209
561 Mathematical approaches to modelling genetics and evolution 209
562 Some insights derived from evolutionary models on
dispersal evolution 211
563 The many uses of genetic data 212
Appendix A: Mathematical methods 215
AlA very brief tutorial to linear algebra 215
A2A very brief tutorial to calculus 217
A21 Derivatives, integrals, and convolutions 217
A22 Differential equations 218
A23 Systems of differential equations 220
A24 Partial differential equations 221
A25 Difference equations 222
A3A very brief tutorial to random variables 222
A31 Discrete valued random variables 222
A32 Continuous valued random variables 223
A33 Joint distribution of two or more random variables 225
A34 Sums of random variables 226
A35 An application of random variables to quantitative genetics 228
A4A very brief tutorial to stochastic processes 229
A41 Markov chains 229
A42 Markov processes 231
Appendix B: Statistical methods 233
B l Generalized linear mixed models 233
Bll Linear models 233
Bl2 Link functions and error distributions 234
B13 Relaxing the assumption of independent residuals 236
B14 Random effects 238
Contents • xiii
B15 Multivariate models 241
Bl6 Hierarchical models 244
B 2 Model fitting with Bayesian inference 245
B21 The concepts of likelihood, maximum likelihood,
and parameter uncertainty 246
B22 Prior and posterior distributions, and the Bayes theorem 246
B23 Methods for sampling the posterior distribution 249
References _ 253
Index 277
|
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:23:15Z |
| institution | BVB |
| isbn | 9780198714873 0198714874 |
| language | English |
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| spelling | Ovaskainen, Otso aut Quantitative ecology and evolutionary biology integrating Models with Data Otso Ovaskainen, Henrik Johan de Knegt, Maria del Mar Delgado First edition Oxford Oxford University Press [2016] XIII, 285 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Oxford series in ecology and evolution Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Evolutionsbiologie (DE-588)4153283-1 gnd rswk-swf Ökologie (DE-588)4043207-5 gnd rswk-swf Ökologie (DE-588)4043207-5 s Evolutionsbiologie (DE-588)4153283-1 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Knegt, Henrik Johan de (DE-588)1101856955 aut Delgado, Maria del Mar 1979- (DE-588)1101856580 aut Erscheint auch als Druck-Ausgabe, hardcover 978-0-19-871486-6 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028750525&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ovaskainen, Otso Knegt, Henrik Johan de Delgado, Maria del Mar 1979- Quantitative ecology and evolutionary biology integrating Models with Data Mathematisches Modell (DE-588)4114528-8 gnd Evolutionsbiologie (DE-588)4153283-1 gnd Ökologie (DE-588)4043207-5 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4153283-1 (DE-588)4043207-5 |
| title | Quantitative ecology and evolutionary biology integrating Models with Data |
| title_auth | Quantitative ecology and evolutionary biology integrating Models with Data |
| title_exact_search | Quantitative ecology and evolutionary biology integrating Models with Data |
| title_full | Quantitative ecology and evolutionary biology integrating Models with Data Otso Ovaskainen, Henrik Johan de Knegt, Maria del Mar Delgado |
| title_fullStr | Quantitative ecology and evolutionary biology integrating Models with Data Otso Ovaskainen, Henrik Johan de Knegt, Maria del Mar Delgado |
| title_full_unstemmed | Quantitative ecology and evolutionary biology integrating Models with Data Otso Ovaskainen, Henrik Johan de Knegt, Maria del Mar Delgado |
| title_short | Quantitative ecology and evolutionary biology |
| title_sort | quantitative ecology and evolutionary biology integrating models with data |
| title_sub | integrating Models with Data |
| topic | Mathematisches Modell (DE-588)4114528-8 gnd Evolutionsbiologie (DE-588)4153283-1 gnd Ökologie (DE-588)4043207-5 gnd |
| topic_facet | Mathematisches Modell Evolutionsbiologie Ökologie |
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