Buch, Englisch, 672 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1042 g
Buch, Englisch, 672 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1042 g
Reihe: Statistics for Biology and Health
ISBN: 978-1-4419-2357-8
Verlag: Springer
This book provides a practical introduction to analyzing ecological data using real data sets collected as part of postgraduate ecological studies or research projects. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the authors. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader’s own data analysis. The case studies also show how to deal with certain levels of difficulty during the key process of selecting the correct or appropriate statistical technique. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften Meeres- und Süßwasserökologie
- Naturwissenschaften Biowissenschaften Biowissenschaften Ökologie
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Naturwissenschaften Biowissenschaften Biowissenschaften Naturschutzbiologie, Biodiversität
- Geowissenschaften Umweltwissenschaften Umweltwissenschaften
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Naturwissenschaften Biowissenschaften Biowissenschaften Terrestrische Ökologie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
Weitere Infos & Material
Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- to mixed modelling.- Univariate tree models.- Measures of association.- Ordination — First encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis — Introduction.- Common trends and sudden changes.- Analysis and modelling of lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal.- Crop pollination by honeybees in Argentina using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species — spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analyses of morphometric turtle data — size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisherieslandings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: A time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.
