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Bioassays with Arthropods

Handbook / Manual

By: J Robertson and HK Preisler

224 pages, no illustrations

Lewis Publishers

Hardback | Jun 2007 | Edition: 2 | #152883 | ISBN-13: 9780849323317
Availability: Usually dispatched within 6 days Details
NHBS Price: £63.99 $84/€73 approx

About this book

Expanded second edition that explains the statistical bases and analyses quantal-response bioassays in succinct terms.

When the first edition of this book was published in 1992, senior entomologists and students alike breathed a sigh of relief. Suddenly, we had a reference written in tractable language and relatively free of Greek symbols, or mathematical coding that sends many students of biological sciences into immediate mental block. In addition, now we had sensible, down to earth interpretations of POLO inputs and outputs. Even better for the non-mathematical or statistically inclined, their questions concerning how many doses, how many replicates, or how many insects to use in the design of their bioassay experiments, were answered in comprehensible language. !I very much look forward to the second edition. -- Susan P Worner, Bio-Protection and Ecology Division, Lincoln University, NEW ZEALAND "This small volume is a bible for those who design and/or conduct and interpret bioassays. !There are statistical formulae and excellent discussions of Probit, Logit, and goodness-of-fit tests. This is a must for anyone doing bioassays or for interpreting bioassay data." -- Florence V. Dunkel, Montana State University "There are not too many people who can write such a book and do justice to the statistics like these authors can." -- Thomas A. Miller, University of California, Riverside "! an excellent reference and guide for use in designing, conducting, and analyzing a wide variety of bioassays. ! Additions to the second edition include chapters on natural variation, quarantine statistics, microbial insecticide testing, and pesticide resistance. Many topics and discussions also have been expanded throughout the second edition. Another important and noteworthy revision is that much of the text is geared towards POLOPLUS a , which is an analytical software package developed and marketed by the co-authors. Other proprietary software described in the book includes PoloMixa and PoloEncorea . ! In conclusion, Bioassays with Arthropods is an excellent desktop reference and guide for use in designing, conducting, and analyzing a wide variety of bioassays that investigate a wide variety of chemistries and simulated environmental treatments. The book has utility for everyone from the beginning graduate student to the seasoned professional researcher. Additionally, this reviewer further recommends the book as a very suitable companion book for courses specifically dealing with arthropod toxicology and pest management science." --M. E. Scharf, Entomology & Nematology Dept., University of Florida, Gainesville, in Florida Entomologist 91(3), Sept. 2008 "! this book has an expanded scope. ! this one adds a sense of humor. The text does a good job of introducing the basic concepts of toxicity testing." -- Glenn Suter in Integrated Environmental 4(2) 2008 "! not only tremendously informative, but also a pleasure to read. ! the reader is provided not only with the correct design but also statistical equations with clear explanations of their meaning. The authors also provide guidance on the use of LeOra Software, including PoloPlus, PoloMix, PoloDose, and PoloEncore, for straightforward, user-friendly statistical analysis. It is this step-by-step process throughout the book that is so helpful in tying all of these complex topics together into a story that is fun to read. ! a wonderful reference text for both beginning and experienced researchers." --Denny Bruck, Horticultural Crops Research Laboratory, USDA-DARS, Corvallis, Oregon, USA, in Journal of Economic Entomology, February 2009


INTRODUCTIONQUANTAL RESPONSE BIOASSAYSTypes of Quantal Response BioassaysExperimental Design of BioassaysComputer ProgramsReferencesBINARY RESPONSE WITH ONE EXPLANATORY VARIABLETerminology and General Statistical ModelStatistical MethodsReferencesBINARY QUANTAL RESPONSE: DATA ANALYSESPoloPlusSASR and S-PlusReferencesBINARY QUANTAL RESPONSE: DOSE NUMBER, DOSE SELECTION AND SAMPLE SIZEPoloDoseBasic Binary BioassaysSpecialized Binary BioassaysPractical ConsiderationsReality Checklist for BioassaysReferencesNATURAL VARIATIONDefinitionStatistical Boundaries of Natural VariationLevels of VariationReferencesQUARANTINE STATISTICSTolerance Distributions in Quarantine SecurityEcological Approaches to Quarantine Security: RiskConclusionsReferencesSTATISTICAL ANALYSES OF DATA FROM BIOASSAYS WITH MICROBIAL INSECTICIDESBiological Units and StandardsRevised Definition of Relative PotencyEffects of Natural Variation on Product QualityConclusionsReferencesPESTICIDE RESISTANCEResistance DefinedNatural Variation Versus ResistanceUses of Bioassays to Separate Populations and StrainsStatistical Models of Modes of ResistanceHost-Insect Interaction and the Expression of ResistanceReferencesMIXTURESIndependent, Uncorrelated Joint Action of Pesticide MixturesSimilar (Additive) Joint ActionOther Theoretical Hypotheses of Joint Action of PesticidesSynergistsConclusionsReferencesTIME AS A VARIABLEPurposes of Studies Involving TimeSampling DesignsAnalysis of Independent Time-Mortality DataAnalysis of Serial Time-Mortality DataConclusionsReferencesBINARY QUANTAL RESPONSE WITH MULTIPLE EXPLANATORY VARIABLESEarly Examples and Inefficient AlternativesGeneral Statistical ModelTypes of Variables in Multiple Regression ModelsComputer ProgramsMultiple Probit Analysis: Example from PoloEncoreMultiple Logit Analysis: Sample of Analysis with GLIMConclusionsReferencesMULTIPLE EXPLANATORY VARIABLES: BODY WEIGHTEffects of Erroneous Assumptions About Body WeightTesting the Hypothesis of Proportional ResponseWhen Body Weight is a Significant Independent VariableStandardized Bioassay Techniques Involving WeightConclusionsReferencesPOLYTOMOUS (MULTINOMIAL) QUANTAL RESPONSEExampleMultinomial Logit ModelConclusionsReferencesIMPROVING PREDICTION BASED ON DOSE-RESPONSE BIOASSAYSReferences

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LeOra Software, Petaluma, California, USA LeOra Software, El Cerrito, California, USA USDA Forest Service, Albany, California, USA University of Iowa, Iowa City, USA

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