Bioassays with arthropods / Jacqueline L. Robertson [and three others].

Format:

Book

Author/Creator:

Robertson, Jacqueline L.

Language:

English

Subjects (All):

Pesticides--Environmental aspects--Measurement.

Pesticides.

Arthropoda--Effect of pesticides on.

Arthropoda.

Biological assay.

Physical Description:

xvii, 194 pages : illustrations ; 24 cm

regular print

Edition:

Third edition.

Place of Publication:

Boca Raton : CRC Press, Taylor & Francis Group, [2017]

Summary:

Imagine a statistics book for bioassays written by a statistician. Next, imagine a statistics book for bioassays written for a layman. Bioassays with Arthropods, Third Edition offers the best of both worlds by translating the terse, precise language of the statistician into language used by the laboratory scientist. The book explains the statistical basis and analysis for each kind of quantal response bioassay in just the right amount of detail. The first two editions were a great reference for designing, conducting, and interpreting bioassays: this completely revised and updated third edition will also train the laboratory scientist to be an expert in estimation of dose response curves. See What's New in the Third Edition: Introduces four new Windows and Apple-based computer programs (PoloJR, OptiDose, PoloMixture and PoloMulti) for the analyses of binary and multiple response analyses, respectively, Replaces out-of-date GLIM examples with R program samples, Includes a new chapter, Population Toxicology, and takes a systems approach to bioassays, Expands the coverage of invasive species and quarantine statistics Book jacket.

Contents:

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. Goodness of Fit

3.2.1.2. Lethal Dose Ratios

3.2.1.3. Comparison of Lethal Dose Ratios

3.3. Risk of Erroneous Conclusions

3.3.1. Interpreting the Results of Hypotheses Tests

3.3.1.1. Regression Lines

3.3.1.2. Point Estimates

3.3.1.3. Groups of Lines with Equal Response

3.4. Alternatives to Probit and Logit Analysis

ch. 4 Binary Quantal Response: Data Analyses

4.1. PoloJR

4.1.1. Data

4.1.2. Choice Screens and Program Options

4.1.3. Display Results

4.1.3.1. Parameters

4.1.3.2. Individual Regressions

4.1.3.3. Hypotheses Tests

4.1.3.4. LD Ratios

4.1.4. Plot

4.1.5. Conclusions

4.2. SAS

4.3. R and S-Plus

ch. 5 Binary Quantal Response: Dose Number, Dose Selection, and Sample Size

5.1. Experimental Design

5.1.1. Dose Selection and Sample Size

5.1.2. Number of Doses

5.2. OptiDose Statistics

5.3. Basic Binary Bioassays

5.4. Specialized Binary Bioassays

5.5. Practical Considerations

5.5.1. Basic Bioassays

5.5.2. Specialized Bioassays

5.6. Reality Checklist for Bioassays

5.7. Conclusions

ch. 6 Natural Variation in Response

6.1. Definition

6.2. Statistical Boundaries of Natural Variation

6.3. Levels of Variation

6.3.1. Sibling Groups

6.3.2. Cohorts within a Single Generation

6.3.3. Developmental Stage

6.4. Effects of Natural Variation on Product Quality

ch. 7 Invasive Species Statistics

7.1. Probit of 9

7.1.1. Laboratory Bioassays to Estimate Q9 in a Confirmatory Test

7.1.1.1. Differences in Estimates Depending on Tolerance Distribution

7.1.1.2. General Formula for Selection of Dose in a Confirmatory Test Based on Laboratory Bioassays

7.1.1.3. Dose Placement and Sample Size Requirements for Estimation of Q9 in Bioassays

7.1.1.4. Varietal Differences

7.1.2. Confirmatory Tests

7.2. Ecological Approaches to Invasive Species: Risk

7.2.1. The Alternative Efficacy Approach for Species on Poor Hosts

7.2.2. Risk for Lethal and Sublethal Effects on Beneficial Insects

7.2.3. Risk for Preferred Hosts and Heavy Infestations: Systems Approach

7.3. Conclusions

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

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

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

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

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. Example

11.5. Conclusions

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

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

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

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

ch. 16 Population Toxicology.

Notes:

Includes bibliographical references.

ISBN:

9781482217087

1482217082

OCLC:

951949898