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UNIVERSITY OF AGRICULTURAL SCIENCES

Bioassays with arthropods / Jacqueline L. Robertson [et.al.].

By: Robertson, Jacqueline LContributor(s): Jones, Moneen M | Olguin, Efren | Alberts, BradMaterial type: TextTextPublisher: Boca Raton : CRC Press, Taylor & Francis Group, 2017Edition: 3rd ednDescription: xvii, 194 pages : illustrations ; 24 cmISBN: 9781482217087; 1482217082Subject(s): Pesticides -- Environmental aspects -- Measurement | Arthropoda -- Effect of pesticides on | Biological assay | Arthropoda -- Effect of pesticides on | Biological assay | Pesticides -- Environmental aspects -- MeasurementDDC classification: 595.2 LOC classification: QH545.P4 | R478 2017
Contents:
Introduction -- Quantal Response Bioassays -- Types of Quantal Response Bioassays -- Experimental Design of Bioassays -- Randomization -- Treatments -- Controls -- Replication -- Order of Treatments within a Replication -- Computer Programs -- References -- Binary Quantal Response with One Explanatory Variable -- Terminology and General Statistical Model -- Statistical Methods -- Probit or Logit Regression -- t Risk for Preferred Hosts and Heavy Infestations: Systems Approach -- Conclusions -- References -- Statistical Analyses of Data from Bioassays with Microbial Products -- Biological Units and Standards -- A Revised Definition of Relative Potency -- Effects of Natural Variation on Product Quality -- Bioassays for Nontarget Organisms or Host Animals -- Conclusions -- References -- Pesticide Resistance -- Resistance Defined -- Natural Variation versus Tolerance -- Use of Bioassays to Separate Populations and Strains -- Population Bioassays -- Response Ratios -- Use of a Discriminating Dose -- Statistical Models of Modes of Resistance Inheritance -- Standard Method of Analysis with Bioassay Data -- Degree of Dominance -- Hypothesis Testing -- Inferences Using the Standard Method -- Mode of Inheritance -- Types of Variation -- Both Mode of Inheritance and Binomial Distribution -- Other Causes for Bad Fit -- Examples -- Dose -- Response Bioassays -- Dose -- Mortality Lines -- Estimation of Overdispersion -- Mode of Inheritance of Cyhexatin Resistance -- Mode of Inheritance of Propargite Resistance -- Host -- Insect Interaction and the Expression of Resistance -- Insect Growth Regulators and Resistance -- Genetically Modified Crops -- References -- Mixtures -- Independent, Uncorrelated Joint Action of Pesticide Mixtures -- Statistical Model -- Test of Hypothesis of Independent Joint Action -- PoloMixture -- Program Input -- Running PoloMixture -- Program Output -- Similar (Additive) Joint Action -- Other Theoretical Hypotheses of Joint Action of Pesticides -- Synergists -- Conclusions -- References -- Time as a Variable -- Purposes of Studies Involving Time -- Sampling Designs -- Alternatives -- General Statistical Models -- Analysis of Independent Time -- Mortality Data -- Experimental Design -- Limitations and Constraints -- Analysis of Serial Time -- Mortality Data -- Experimental Design -- Statistical Methods -- Estimation -- Estimation of Response Probabilities -- Estimation of Lethal Doses over Time -- Conclusions -- References -- Binary Quantal Response with Multiple Explanatory Variables -- Early Examples and Inefficient Alternatives -- General Statistical Model -- Types of Variables in Multiple Regression Models -- Computer Programs -- Multiple Probit Analysis: Example from PoloMulti -- Statistical Model -- Hypotheses Tests -- Data Analysis with PoloMulti -- Multiple Logit Analysis of Dose -- Weight -- Temperature -- Photoperiod -- Response Data with R -- Statistical Model -- Hypothesis Tests -- Search for the "Best-Fitting" Dose -- Mortality Model -- Example: Acephate -- Significance of Average Body Weight -- Parallelism of the Logit Lines -- Model with the Best-Fitting Logit Line -- Conclusions -- References -- Multiple Explanatory Variables: Body Weight -- Effects of Erroneous Assumptions about Body Weight -- Testing the Hypothesis of Proportional Response -- When Body Weight Is a Significant Independent Variable -- Standardized Bioassay Techniques Involving Weight -- Conclusions -- References -- Polytomous (Multinomial) Quantal Response -- The Multinomial Logit Model -- Statistical Model -- Estimation of Parameters -- Estimation of Response Probabilities -- Data Analysis -- Conclusions -- References -- Improving Prediction Based on Dose-Response Bioassays -- Attempts to Improve Methods -- Exposure -- Scoring Process -- Significant Independent Variables -- Multiple Bioassays -- Optimal Time of Application -- Test Subjects -- Reasons for Failure -- References -- Population Toxicology.
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595.2 ROB (Browse shelf) Not for loan 144071

Includes bibliographical references.

Machine generated contents note: ch. 1 Introduction -- ch. 2 Quantal Response Bioassays -- 2.1. Types of Quantal Response Bioassays -- 2.2. Experimental Design of Bioassays -- 2.2.1. Randomization -- 2.2.2. Treatments -- 2.2.3. Controls -- 2.2.4. Replication -- 2.2.5. Order of Treatments within a Replication -- 2.3. Computer Programs -- References -- ch. 3 Binary Quantal Response with One Explanatory Variable -- 3.1. Terminology and General Statistical Model -- 3.2. Statistical Methods -- 3.2.1. Probit or Logit Regression -- 3.2.1.1. t g t g t g t g t g t g t g t g t t g t g t g t g t g t g t g t g t g t g t g t g t g t t g t g t g t g t g t g t g t g t g t g t g t g t t g t g t g t g t g t g t g t g t t g t g t g t g t g t g t g t g g t g t g t g t Risk for Preferred Hosts and Heavy Infestations: Systems Approach -- 7.3. Conclusions -- References -- ch. 8 Statistical Analyses of Data from Bioassays with Microbial Products -- 8.1. Biological Units and Standards -- 8.2. A Revised Definition of Relative Potency -- 8.3. Effects of Natural Variation on Product Quality -- 8.4. Bioassays for Nontarget Organisms or Host Animals -- 8.5. Conclusions -- References -- ch. 9 Pesticide Resistance -- 9.1. Resistance Defined -- 9.2. Natural Variation versus Tolerance -- 9.3. Use of Bioassays to Separate Populations and Strains -- 9.3.1. Population Bioassays -- 9.3.2. Response Ratios -- 9.3.3. Use of a Discriminating Dose -- 9.4. Statistical Models of Modes of Resistance Inheritance -- 9.4.1. Standard Method of Analysis with Bioassay Data -- 9.4.1.1. Degree of Dominance -- 9.4.1.2. Hypothesis Testing -- 9.4.2. Inferences Using the Standard Method -- 9.4.2.1. Mode of Inheritance -- 9.4.2.2. Types of Variation -- 9.4.2.3. Both Mode of Inheritance and Binomial Distribution -- 9.4.2.4. Other Causes for Bad Fit -- 9.4.3. Examples -- 9.4.3.1. Dose -- Response Bioassays -- 9.4.3.2. Dose -- Mortality Lines -- 9.4.3.3. Estimation of Overdispersion -- 9.4.3.4. Mode of Inheritance of Cyhexatin Resistance -- 9.4.3.5. Mode of Inheritance of Propargite Resistance -- 9.5. Host -- Insect Interaction and the Expression of Resistance -- 9.6. Insect Growth Regulators and Resistance -- 9.7. Genetically Modified Crops -- References -- ch. 10 Mixtures -- 10.1. Independent, Uncorrelated Joint Action of Pesticide Mixtures -- 10.1.1. Statistical Model -- 10.1.2. Test of Hypothesis of Independent Joint Action -- 10.1.3. PoloMixture -- 10.1.3.1. Program Input -- 10.1.3.2. Running PoloMixture -- 10.1.3.3. Program Output -- 10.2. Similar (Additive) Joint Action -- 10.3. Other Theoretical Hypotheses of Joint Action of Pesticides -- 10.4. Synergists -- 10.5. Conclusions -- References -- ch. 11 Time as a Variable -- 11.1. Purposes of Studies Involving Time -- 11.2. Sampling Designs -- 11.2.1. Alternatives -- 11.2.2. General Statistical Models -- 11.3. Analysis of Independent Time -- Mortality Data -- 11.3.1. Experimental Design -- 11.3.2. Limitations and Constraints -- 11.4. Analysis of Serial Time -- Mortality Data -- 11.4.1. Experimental Design -- 11.4.2. Statistical Methods -- 11.4.3. Estimation -- 11.4.3.1. Estimation of Response Probabilities -- 11.4.3.2. Estimation of Lethal Doses over Time -- 11.4.3.3. 11.5. Conclusions -- References -- ch. 12 Binary Quantal Response with Multiple Explanatory Variables -- 12.1. Early Examples and Inefficient Alternatives -- 12.2. General Statistical Model -- 12.3. Types of Variables in Multiple Regression Models -- 12.4. Computer Programs -- 12.5. Multiple Probit Analysis: Example from PoloMulti -- 12.5.1. Statistical Model -- 12.5.2. Hypotheses Tests -- 12.5.3. Data Analysis with PoloMulti -- 12.6. Multiple Logit Analysis of Dose -- Weight -- Temperature -- Photoperiod -- Response Data with R -- 12.6.1. Statistical Model -- 12.6.2. Hypothesis Tests -- 12.6.3. Search for the "Best-Fitting" Dose -- Mortality Model -- 12.6.4. Example: Acephate -- 12.6.4.1. Significance of Average Body Weight -- 12.6.4.2. Parallelism of the Logit Lines -- 12.6.4.3. Model with the Best-Fitting Logit Line -- 12.7. Conclusions -- References -- ch. 13 Multiple Explanatory Variables: Body Weight -- 13.1. Effects of Erroneous Assumptions about Body Weight -- 13.2. Testing the Hypothesis of Proportional Response -- 13.3. When Body Weight Is a Significant Independent Variable -- 13.4. Standardized Bioassay Techniques Involving Weight -- 13.5. Conclusions -- References -- ch. 14 Polytomous (Multinomial) Quantal Response -- 14.1. The Multinomial Logit Model -- 14.1.1. Statistical Model -- 14.1.2. Estimation of Parameters -- 14.1.3. Estimation of Response Probabilities -- 14.1.4. Data Analysis -- 14.2. Conclusions -- References -- ch. 15 Improving Prediction Based on Dose-Response Bioassays -- 15.1. Attempts to Improve Methods -- 15.1.1. Exposure -- 15.1.2. Scoring Process -- 15.1.3. Significant Independent Variables -- 15.1.4. Multiple Bioassays -- 15.1.5. Optimal Time of Application -- 15.1.6. Test Subjects -- 15.1.7. Reasons for Failure -- References -- ch. 16 Population Toxicology.

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