This book provides a practical introduction to analyzing ecological data using real data sets. It features 17 case studies covering topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader's own data analysis.

'Which test should I apply?' During the many years of working with ecologists, biologists and other environmental scientists, this is probably the question that the authors of this book hear the most often. The answer is always the same and along the lines of 'What are your underlying questions?', 'What do you want to show?'. The answers to these questions provide the starting point for a detailed discussion on the ecological background and purpose of the study. This then gives the basis for deciding on the most appropriate analytical approach. Therefore, a better start ing point for an ecologist is to avoid the phrase 'test' and think in terms of 'analy sis'. A test refers to something simple and unified that gives a clear answer in the form of a p-value: something rarely appropriate for ecological data. In practice, one has to apply a data exploration, check assumptions, validate the models, per haps apply a series of methods, and most importantly, interpret the results in terms of the underlying ecology and the ecological questions being investigated. Ecology is a quantitative science trying to answer difficult questions about the complex world we live in. Most ecologists are aware of these complexities, but few are fully equipped with the statistical sophistication and understanding to deal with them.

A comprehensive and practical guide to analysing ecological data based on courses given to researchers, environmental consultants and post graduate students. Provides comprehensive introductory chapters together with 17 detailed case study chapters written jointly with former course attendants. Each case study explores the statistical options most appropriate to the ecological questions being asked and will help the reader choose the best approach to analysing their own data. A non-mathematical, but modern approach (GLM, GAM, mixed models, tree models, neural networks) is used throughout the book, making it ideally suited to practicing ecologists and environmental scientists as well as professional statisticians. All data sets from the case studies are available for downloading from www.highstat.com Includes supplementary material: sn.pub/extras

Autorentext
Grad students, researchers

Klappentext

This book provides a practical introduction to analysing ecological data using real data sets collected as part of postgraduate ecological studies or research projects.

The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the first authors. The case studies include topics ranging from terrestrial ecology to marine biology. The case studies can be used as a template for your own data analysis; just try to find a case study that matches your own ecological questions and data structure, and use this as starting point for you own analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in Chapter 2.

Alain Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has contributed to a wide range of projects related to marine biology, oceanography, ecology, fisheries, etc. and has extensive experience teaching statistics to ecologists and environmental scientists in the form of academic and non-academic courses. He is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK.

Elena Ieno is senior marine biologist at Highland Statistics Ltd. In 2004 she left academia to work full time in statistical consultancy. She now teaches statistics to ecologists and has shown she can bridge the gap between the two disciplines and dispel the dread of statistics shown by most biologists. She is also involved in various international statistical consultancy projects, and is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK.

Graham M. Smith is a Senior Lecturer at Bath Spa University in the UK where he teaches statistics to biology undergraduates. He has a background in ecological consultancy, and continues to provide consultancy on the design and analysis of ecological monitoring programmes and the development of quantitative methods in Ecological Impact Assessment.

Inhalt
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.