» » Design and Analysis of Ecological Experiments

ePub Design and Analysis of Ecological Experiments download

by Samuel M. Scheiner,Jessica Gurevitch

ePub Design and Analysis of Ecological Experiments download
Samuel M. Scheiner,Jessica Gurevitch
Oxford University Press; 2 edition (April 26, 2001)
Science & Mathematics
ePub file:
1959 kb
Fb2 file:
1211 kb
Other formats:
lrf lit mobi lrf

Samuel M. Scheiner is at Arizona State University. Jessica Gurevitch is at SUNY at Stony Brook. Old shibboleths and new syntheses. Scheiner and J. Gurevitch, Chapman & Hall, 1993.

Samuel M. ISBN 0 412 03551 0. In recent years there has been an almost exponential growth in publications dealing with the design and analysis of experiments in ecology. Several factors have been responsible.

Автор: Scheiner Название: Design and Analysis of Ecological Experiments ISBN: 0195131878 ISBN-13 .

Each chapter presents a particular statistical technique or set of techniques in the context of resolving an ecological issue.

Start by marking Design and Analysis of Ecological Experiments as Want to Read . Meta-analysis: Combining the Results of Independent Experiments, Jessica Gurevitch and Larry V. HedgesReferences Index.

Start by marking Design and Analysis of Ecological Experiments as Want to Read: Want to Read savin. ant to Read. Read by Samuel M. Scheiner. Scheiner and Jessica Gurevitch. Samuel M. Each chapter presents a particular statistical technique or set of techniques in the context of resolving an ecological issue. Scheiner, Jessica Gurevitch.

Design and analysis of ecological experiments. SM Scheiner, J Gurevitch. The metaanalysis of response ratios in experimental ecology. LV Hedges, J Gurevitch, PS Curtis. Oxford University Press, 2001. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. L Rustad, J Campbell, G Marion, R Norby, M Mitchell, A Hartley,. Oecologia 126 (4), 543-562, 2001. Ecology 80 (4), 1150-1156, 1999. Are invasive species a major cause of extinctions? J Gurevitch, DK Padilla. Trends in ecology & evolution 19 (9), 470-474, 2004.

New Biological Books. Design and Analysis of Ecological Experiments. Allen Keast, "Design and Analysis of Ecological Experiments.

Ecological research and the way that ecologists use statistics continues to change rapidly. This second edition of the best-selling Design and Analysis of Ecological Experiments leads these trends with an update of this now-standard reference book, with a discussion of the latest developments in experimental ecology and statistical practice.The goal of this volume is to encourage the correct use of some of the more well known statistical techniques and to make some of the less well known but potentially very useful techniques available. Chapters from the first edition have been substantially revised and new chapters have been added. Readers are introduced to statistical techniques that may be unfamiliar to many ecologists, including power analysis, logistic regression, randomization tests and empirical Bayesian analysis. In addition, a strong foundation is laid in more established statistical techniques in ecology including exploratory data analysis, spatial statistics, path analysis and meta-analysis. Each technique is presented in the context of resolving an ecological issue. Anyone from graduate students to established research ecologists will find a great deal of new practical and useful information in this current edition.
  • the chapters can be really difficult to understand, I'm giving it 2 stars because 3 chapter are very informative and easy ti grasps.

  • This review is for the 1st edition and first appeared in Trends in Ecology and Evolution 9:495-496 (1994). Quick inspection of 2d edition shows that most of the problems identified here remain.


    Design and Analysis of Ecological Experiments, edited by S.M. Scheiner and J. Gurevitch, Chapman & Hall, 1993. $79.00 hbk (xiv + 445 pages). ISBN 0 412 03551 0

    In recent years there has been an almost exponential growth in publications dealing with the design and analysis of experiments in ecology. Several factors have been responsible. Various critiques have shown that erroneous statistical analyses are abundant in the ecological literature; this is equally true of other fields in the biological and social sciences, but ecologists like to think that they take a more rigorous approach to these matters. Editors and reviewers of ecological manuscripts frequently suggest incorrect statistical procedures to authors and at the same time let many errors in manuscripts go unquestioned. Many of the classical statistics textbooks and treatises omit advice on some topics important to ecologists (for example, repeated measures designs) and give poor advice on others (for example, the 'need' and procedures for fixing 'experiment-wise' error rates). Many sound designs developed by ecologists on commonsense grounds and for straightforward objectives have, in fact, rather complex structures and require correspondingly complex statistical analyses. And, finally, many field experimentalists have high hopes for the discovery of statistical techniques (magical potions?) that will increase the power of experiments that have low treatment replication and high within-treatment variances.
    The present book is a welcome synthesis of recent thinking, especially in connection with these last two factors. Its premise is that 'ecological experiments...present many statistical difficulties...Correct answers may require complex designs or elaborate and unusual techniques'. Its objective is to provide 'a toolbox containing the equipment needed to access advanced statistical techniques'.

    The book is a compendium of 17 review articles written by 22 authors. Collectively these reviews provide an excellent entry into the most recent statistical developments of interest to ecologists; all the authors are themselves ecologists. They present basic theory, analysis of assumptions, and worked ecological examples generally in clear prose. The technical level of many chapters is, however, quite advanced. Many ecologists, like this reviewer, will be unable to judge these critically without considerable additional reading and study. The book's greatest value may be as a text for graduate seminars In ecology and biostatistics. The instructors for such seminars will need to be highly competent statisticians, the students will need to have had at least a full year of coursework in statistics, including an Introduction to experimental design, and all will have to be on their toes, for reasons I discuss below.

    Among the most useful chapters are those focused on basic statistical matters. The chapters by Ellison (graphical data display), Potvin (growth chamber heterogeneity), and Philippi (multiple regression) I thought especially good. This in part reflects my lack of familiarity with many of the advanced methodologies presented in other chapters. It also reflects my belief that for most (>95%) experimentation in ecology, simple classical statistical methods are sufficient and that lack of understanding of basic principles and simple methods by practising ecologists is a serious problem, while under­use of advanced statistical methods is not.

    Such a belief finds strong support in this book. The editors and some of the other authors make or implicitly support numerous claims about basic statistical logic and procedure that are unjustified. Principal claims are:

    * that in testing situations it is necessary to specify a value for a, the probability of a type Ierror (pp. 10,128);
    * that a higher value of a may be selected if power is low because, for example, replication is low (pp. 8,135);
    * that when multiple tests are carried out, procedures should be used that fix the 'experiment-wise' type I error rate (pp. 85, 96,101,278,385);
    * that when there are multiple response variables, MANOVA should be used (pp. 90, 94, 371);
    * that when there is prediction of results, one-tailed tests should be used (pp. 89, 367, 390);
    * that experiments with repeated-measures designs should be analysed by repeated­measures ANOVA, and that separate date­by-date analyses are not appropriate (pp.90,257);
    * that experiments with a randomized block design should never be analysed as completely randomized designs lest there be 'erroneous results' (p. 59).

    These claims are among the commonest shibboleths of statistical criticism in contemporary ecology. None is original with these authors. All can be found elsewhere in the ecological and statistical literature, either as bare ex cathedra statements or justified by superficial and incomplete arguments. But none is logically defensible as a general prescription. The literature, often decades old, presenting contrary and more rational positions on these issues is, once again, ignored.

    The imposition of these shibboleths on authors by editors, reviewers, thesis advisors and other nouveaux statisticians is a serious problem. It is having a negative impact on the quality of the ecological literature. Those who go along with these shibboleths breeze through the review process - contributing additional negative models for future investigators. Those who refuse often have their manuscripts criticized anonymously and publication delayed, if not denied.

    It is true that an analysis of such basic issues is not the primary objective of this book. But even the casual repetition of these shibboleths in a work such as this one is bound to make matters worse. Moreover, one must ask what the uncritical acceptance by the editors and authors suggests about how carefully they may have evaluated the literature on the more advanced topics. Until experts in those areas can give us their evaluations, the book is better regarded as a good point of departure for debates than as a reference work.

    Another unfortunate conventional practice is embraced by the chapter on meta­analysis. When combining the results from different experimental studies for a meta­analysis, it is necessary that measurement of effect size be standardized. Given that there are two treatments with a common standard deviation, s, and respective means of m1 and m2 for a particular response variable, the authors follow older literature and recommend that the standardized effect size be measured as d = (m2 - m1)/s. For most ecological variables this is illogical, and meta-analyses carried out with the approach cannot yield meaningful results. For many variables, either m2/m1 or (m2 - m1)/m1 would serve very well as a standardized measure of effect size. But what grounds can there be for allowing the within-treatment standard deviation, that is, the variability among experimental units, to influence effect size? If the response variable shows an increase of 50% over its value in a control treatment, of what relevance is the heterogeneity of the experimental units? For a given percent change, why should effect size be defined to be larger for experiments that use indoor microcosms rather than outdoor microcosms, that use artificially composed field plots rather than natural come-as-they­are field plots, and that use genetically homogeneous experimental animals rather than genetically heterogeneous ones? It should not be so defined. Major problems clearly exist not only in the meta-analysis literature but also in that on power analysis, which has often used the same inappropriate measure of effect size.

    Though there are other points of statistics, logic and terminology in the book with which one might take issue, the preceding comments should sufficiently define the product. It is a big, juicy hamburger 'with everything' - including a lot of gristle and many bone fragments. It is nutritious but requires very careful chewing and a long digestion period.

    STUART H. HURLBERT, Dept of Biology, San Diego State University, San Diego, CA 92182, USA

  • This book indeed is useful for what it was written for. Easy enough language, yet detailed. I would like, however, that the dataset downloading website actually worked.