Analyzing Ecological Data (2007)
Zuur, Ieno, Smith
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 of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics.
The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling 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. 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. Data from all case studies are available.
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Download data and R code
Most statistical analyses were carried out in the user-friendly software package Brodgar (it contains a GUI to some of the R functions, and this allows the user to apply the methods with a few mouse clicks). Below are the data sets and software code that can be used to carry out most of the analyses in R.
- Data and R code for Chapter 4.
- Data and R code for Chapter 5.
- Data and R code for Chapter 6.
- Data and R code for Chapter 7.
- Data and R code for Chapter 8.
- Data and R code for Chapter 9.
- Data and R code for Chapter 10.
- Chapter 11. There is no relevant R code in this chapter.
- Data and R code for Chapter 12.
- Data and R code for Chapter 13.
- Data and R code for Chapter 14.
- Chapter 15. (R code in prep).
- Data and R code for Chapter 16.
- Data and R code for Chapter 17.
- Data and R code for Chapter 18.
- Data and R code for Chapter 19.
- Data and R code for Chapter 20. Univariate methods to analyze abundance of decapod larvae. Pan M, Gallego A, Hay S, Ieno EN, Pierce GJ, Zuur AF and Smith GM.
- Data in an Excel file. Data and R code for Chapter 21. Analyzing presence and absence data for flatfish distribution in the Tagus estuary, Portugal. Cabral H, Zuur AF, Ieno EN and Smith GM.
- Data in an Excel file. Data and R code for Chapter 22. Crop pollination by honeybees in Argentina using additive mixed modeling. Basualdo, M., Ieno, E.N., Zuur, A.F. and Smith, G.M.
- Data and R code for Chapter 23. Investigating the effects of rice farming on aquatic birds with mixed modeling. Elphick CS, Zuur AF, Ieno EN, and Smith GM.
- Data and R code for Chapter 24. Classification trees and radar detection of birds for North Sea wind farms. Meesters HWG, Krijgsveld KL, Zuur AF, Ieno EN and Smith GM.
- Data and R code for Chapter 25. Fish stock identification through neural network analysis of parasite fauna. Campbell N, MacKenzie K, Zuur AF, Ieno EN and Smith GM.
- Chapter 26. Monitoring for change: Using generalized least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands.
- Sikkink, P.G., Zuur, A.F., Ieno, E.N. and Smith, G.M. R code in prep.
- Data and R code for Chapter 27. Univariate and multivariate analysis applied on a Dutch sandy beach community. Janssen, G.M., Mulder, S., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Chapter 28. Multivariate analyses of South-American zoobenthic species - spoilt for choice. Ieno E.N., Zuur A.F., Bastida R., Martin, J.P., Trassens M. and Smith G.M. R code in prep.
- Data and R code for Chapter 29. Principal component analysis applied to harbour porpoise fatty acid data. Jolliffe, I.T., Learmonth, J.A., Pierce, G.J., Santos, M.B., Trendafilov, N., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Data and R code for Chapter 30. Multivariate analyses of morphometric turtle data - size and shape. Claude, J., Jolliffe, I.T., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Data in Excel. Data and R code for Chapter 31. Redundancy analysis and additive modeling applied on savanna tree data. Lykke, A.M., Sambou, B., Mbow, C., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Data and R code for Chapter 32. Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico. Lira-Noriega, A., Laborde, J., Guevara, S., Sánchez-Ríos, G., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Data and R code for Chapter 33. Estimating common trends in Portuguese fisheries landings. Erzini, K., Zuur, A.F., Ieno, E.N., Pierce, G.J., Tuck, I. and Smith, G.M.
- Data for Chapter 34. Common trends in demersal communities on the Newfoundland-Labrador Shelf. Devine, J.A., Zuur, A.F., Ieno, E.N. and Smith, G.M. R code: See the R code for Chapter 33; it is identical.
- Data and R code for Chapter 35. Sea level change and salt marshes in the Wadden Sea: A time series analysis. Dijkema, K.S., Van Duin, W.E., Meesters, H.W.G., Zuur, A.F., Ieno. E.N. and Smith, G.M. Friesland data. Groningen Data.
- Data and R code for Chapter 36. Time series analysis of Hawaiian waterbirds. Reed, J.M., Elphick, C.S., Zuur, A.F., Ieno, E.N. and Smith, G.M.
- Data for Chapter 37. R code for Chapter 37. Spatial modeling of forest community features in the Volzhsko-Kamsky reserve. Rogova TV, Chizhikova NA, Lyubina OE, Saveliev AA, Mukharamova SS, Zuur AF, Ieno EN and Smith GM. The R code was kindly provided by Dr. Nelly Chizhikova.
Journal of the American Statistical Association (2008), 103, 1317-1317(1), by Loveday Conquest. "Overall, this book is worth the purchase price based on their case studies alone. No other book combines as many good ecological data sets with such thoughtfully written analyses. I give this book two enthusiastic thumbs up!"
Biometrics (2008). 64, 309–319, by Carl James Schwarz "I liked the compact style of the book and really enjoyed the case studies. The book would be a suitable companion to statistics courses for both ecologists and statisticians at the introductory graduate level….All in all, I enjoyed reading the book and marvel at the wide range of sophisticated statistical models used in modern ecology."
International Statistical Review (2007). 75, Issue 3, 426-427, by C.M. O'Brien. "Readership: Undergraduates, postgraduates, and scientists engaged in areas of the environmental sciences and ecological research. The material presented in this book has been developed and used by the authors in teaching statistics to its intended readership. The text is divided into two parts … . I have no doubt that for undergraduate students the main strength of the book will be the breadth of topics covered by the case studies – ranging from terrestrial ecology to marine biology.
Times Higher Education, May, 2008 by Michael Bonsall. "This is a practical way of analyzing ecological data in which methodological approaches are combined with real data sets with the advantages and disadvantages of each strategy discussed. Who is it for? Upper undergraduates, postgraduates and researchers in ecology. Presentation It links ecological data, data analysis and discussion of the approaches. Would you recommend it? If you want an edited volume on different methods of ecological data analysis, then this book is worth looking through."
I don’t normally write emails out of the blue, however, I wish to inform you that I recently purchased ‘Analyzing Ecological Data’. The book is brilliant, it is clear, concise and the examples are fantastic. It has instantly become my ‘go to’ book. Congratulations on producing such a fine piece of work. I will be preaching your virtues to all the unconverted. Cheers. Dr. Anthony Chariton. CSIRO, Australia.
I just finished reading your book "Analyzing Ecological Data". I liked it very much and learned a lot! Professor Antero Jarvinen, Director Kilpisjarvi Biological Station. University of Helsinki, Finland.
I love your recent book "Analyzing Ecological Data". I think it is an extraordinary platform for being taught in basic stats and analysis, as well as for reaching chaos due to the new techniques that one will want to test with his own data sets. I would appreciate too much the possibility of being provided with the R code. Thanks for you effort. Dr. Jaime Otero Villar. ECOBIOMAR Instituto de Investigaciones Marinas, CSIC, Spain.
I just purchased your book "Analyzing Ecological Data" a few days ago and from the first impression I have to say you did a fantastic job. Thanks a lot for this brilliant textbook, it will guide me through my future research. I noticed on your web page that you are thinking about publishing the R codes. As I am in the process of learning R, I would really appreciate this step. Many thanks again. Klaus Birkhofer, Department of Animal Ecology, Justus-Liebig-University, Giessen, Germany.
I just purchased your book "Analyzing Ecological Data" and I would first of all like to congratulate you on having produced such a well-written, well-structured and highly informative book. It will definitely be a good companion for the people in our lab when doing statistical analyses. I would also like to ask you if you could make more of the R code available online - that would be really more than helpful! With best wishes, Christoph Scherber. University of Goettingen, Germany.
Thank you for allowing me to have such a great time learning more about statistics. You folks have created one of the most readable texts I have ever seen. I very much appreciate the effort that has gone into introducing more advanced topics through those more familiar to the traditional biological practitioner. Jon Loehrke, Department of Fisheries Oceanography, School for Marine Science and Technology, University of Massachusetts, USA.
I want to thank you and your colleagues for your book “Analysing ecological data”. I’ve just begun to read it but I have the feeling that I’m entering in a new world! As an ecologist, I never thought that one day it will be a pleasure to read a statistics book! Everything becomes clearer and friendlier! So thank you for this great book and keep going! Séverine Drouhot, Université de Franche-Comté, France.