Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 2: Dynamic and Advanced Models
Autor Marc Kéry, J. Andrew Royleen Limba Engleză Hardback – 8 oct 2020
- Makes ecological modeling accessible to people who are struggling to use complex or advanced modeling programs
- Synthesizes current ecological models and explains how they are inter-connected
- Contains numerous examples throughout the book, walking the reading through scenarios with both real and simulated data
- Provides an ideal resource for ecologists working in R software and in BUGS software for more flexible Bayesian analyses
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Specificații
ISBN-13: 9780128237687
ISBN-10: 0128237686
Pagini: 820
Dimensiuni: 191 x 235 x 46 mm
Greutate: 1.61 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128237686
Pagini: 820
Dimensiuni: 191 x 235 x 46 mm
Greutate: 1.61 kg
Editura: ELSEVIER SCIENCE
Public țintă
graduate students and professionals in ecology, biogeography, conservation biology, fisheries and wildlife managementCuprins
PART 1 MODELS FOR DYNAMIC SYSTEMS1. Relative Abundance Models for Population Dynamics2. Modeling Population Dynamics With Count Data3. Hierarchical Models of Survival4. Modeling Species Distribution and Range Dynamics, and Population Dynamics Using Dynamic Occupancy Models5. Modeling Metacommunity Dynamics Using Dynamic Community Models
PART 2 ADVANCED MODELS6. Multi-state Occupancy Models7. Modeling False Positives8. Modeling Interactions Among Species9. Spatial Models of Distribution and Abundance10. Integrated Models for Multiple Types of Data11. Spatially Explicit Distance Sampling Along Transects12. Conclusions
PART 2 ADVANCED MODELS6. Multi-state Occupancy Models7. Modeling False Positives8. Modeling Interactions Among Species9. Spatial Models of Distribution and Abundance10. Integrated Models for Multiple Types of Data11. Spatially Explicit Distance Sampling Along Transects12. Conclusions
Recenzii
"In terms of content, there is little more one could ask for. But even the detailed and careful analyses presented in each chapter cannot fully cover steps that would normally accompany statistical analyses: model diagnostics, quantification of uncertainty, particularly of model predictions, comparison with alternative model structures and so forth. The authors touch on these issues in the first volume, and are clearly aware of them, but it would be impossible and not really informative to add such common procedures to each and every single chapter. Overall this book is a must-have for any statistical ecologist who is working with data in the fields of conservation ecology or wildlife ecology, terrestrial or aquatic. It is not a book for casual reading, and experience with statistical analysis and R in particular are warranted." --Basic and Applied Ecology