Bioassays with Arthropods

Jacqueline L. Robertson, Moneen Marie Jones, Efren Olguin, Brad Alberts

• Publisher: CRC Press
• Year: 2017
• eBook ISBN: 9781315356273
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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.

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• Replaces out-of-date GLIM examples with R program samples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

Building on the foundation set by the much-cited first two editions, the authors clearly delineate applications and ideas that are exceptionally challenging for those not already familiar with their use. They lead you through the methods with such ease and organization, that you suddenly find yourself readily able to apply concepts that you never thought you would understand.

To order the PoloSuite computer software described in Bioassays with Arthropods, Third Edition, use the order form found at www.leora-software.com or contact the LeOra Software Company at [email protected].

1Introduction

Mā´ven, mā´vin, n. [Yiddish, from late Hebrew meven]. An expert or connoisseur, often a self-proclaimed one.

Webster’s Unabridged Dictionary

A bioassay is any experiment in which a living organism is used as a test subject. When a stimulus is applied, the organism responds. A bioassay provides a means to quantify the response or responses. In a general sense, a pesticide is any natural or synthetic substance or organism that harms an undesirable organism—a pest. Pest has no scientific definition. Instead, this designation is strictly a product of human activities and needs and is totally anthropocentric. Most pests are members of the invertebrate Phylum Arthropoda; within this phylum, most arthropod pests are members of the classes Insecta or Arachnida. Some pests interfere with production of food, fuel, or fiber by humans.

Nuisance pests, with which many of us are familiar, include cockroaches, ants, and gnats. Others, such as wasps, yellowjackets, mosquitoes, and some species of spiders, can cause severe allergic reactions in humans and other mammals.1 The most subjectively defined group of pests includes those that are ugly or those that provoke a fear response. The category of insects is vague, but large quantities of pesticides are purchased each year2 to control urban pests that breach our territory boundaries.

A single pest is never the problem. If it was, I would write about my research experiences with bedbugs, and how a nymph (I named him Henry) survived 6 weeks without food (i.e., a blood meal) in a Ziploc baggie after he hitchhiked on the bottom of my shoe to my home, or the best way to reduce swelling and itching from said bedbug bite. Instead, we study how a pest population (an interbreeding group of individuals of the same species3) is responsible for damage, disease transmission, or annoyance. Pesticide bioassays are experiments done with a pesticide to estimate the probability that a pest population will respond in the desired manner (e.g., die, become sterile, or, at the very least, suffer horribly) and so be made innocuous.

Principles of valid bioassay apply not just to pests but to beneficial organisms as well. The same methods used to evaluate effectiveness on target (pest) species also can be used to estimate safety to nontarget species, such as parasitoids or predators.

Beyond tests with arthropods, the statistical methods and principles described in this book are also applicable to any tests in which an agent (drug, pesticide, herbicide, radiation) or treatment (heat, cold) is tested on any living thing.

To illustrate some of the problems involved in a pesticide bioassay, consider a novice in the field, Dr. Paula Maven. She wants to test an insect growth regulator (IGR) as a possible chemical to reduce populations of some very large and dangerous Lepidoptera (Patronius giganticus Ubetterduck). The resulting new species, first discovered in Marin County, California, was given the common name Sicilian Godfather for its love of all Italian vegetables. After its release into the ecosystem, the new species spread through California’s Central Valley and swiftly chewed its way into the southern United States. Clearly, the Godfather must be controlled. At stake is an important part of the national economy (e.g., salsa and pizza) that depends on tomato, bell pepper, olive, onion, and garlic production. Her assignment is to establish an integrated pest management program for tomatoes.

The crown jewel of the University of Schaeferville is its International Research Center, a renowned institution that recruits graduate students for their skills in competitive insect pinning, quick taxonomy, and precision sweep netting.

The purpose of Dr. Maven’s first experiment is quite deceptively simple. She wants to find a dose of the IGR that will control the population or at least kill about 90% of the larvae from hell. The first problem is practical: How is she to keep the ginormous larvae alive in the laboratory long enough to find her answer? After four hours of collecting infested vegetable stems that fill eight large trash bags, she has a grand total of 55 mature caterpillars, and a nice sunburn. The stems are placed into screened containers, where Dr. Maven discovers that the caterpillars eat tomato plants and each other. That leaves 35 test subjects, each of which must be kept in a separate 25-cm-diameter Petri dish. Dr. Maven begins the experiment by diluting the IGR to the concentration that the manufacturer recommends for mosquito control (i.e., at least 90% mortality of the larvae).

A second major problem arises: Should the IGR be applied directly to the caterpillar, to the tomato leaves, or to the filter paper lining the Petri dish? Or, should she wait until the larvae mature to adults, have them mate, and test the IGR on eggs? Dr. Maven chooses direct application to the larvae. After all, the IGR contacts mosquitoes and all other targets listed on the label. If it is effective by contacting the other pest species, she reasons that it should be effective on the Godfather in the same way.

Next, how many caterpillars should she test? Dr. Maven decides to use 25 of the 35 survivors (the other 10 will be used to start a laboratory colony, just in case she decides to test eggs at a later date). Now, how should the chemical be applied? She chooses microapplication (a process by which a small, measured drop of the pesticide is put on each insect’s body) because published literature suggests that many other researchers use that method. Should the IGR be applied in proportion to the weight of each larva? Certainly, that is how physicians prescribe medication, so it seems reasonable to apply the chemical to each caterpillar in the same way.

With only 25 caterpillars, Dr. Maven next wonders how many dilutions to make from the stock solution. Five points are necessary to estimate a good regression curve, so five would be the minimum doses needed. Given her intention to construct a dose–respons...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Author
• Chapter 1 Introduction
• Chapter 2 Quantal Response Bioassays
• Chapter 3 Binary Quantal Response with One Explanatory Variable
• Chapter 4 Binary Quantal Response: Data Analyses
• Chapter 5 Binary Quantal Response: Dose Number, Dose Selection, and Sample Size
• Chapter 6 Natural Variation in Response
• Chapter 7 Invasive Species Statistics
• Chapter 8 Statistical Analyses of Data from Bioassays with Microbial Products
• Chapter 9 Pesticide Resistance
• Chapter 10 Mixtures
• Chapter 11 Time as a Variable
• Chapter 12 Binary Quantal Response with Multiple Explanatory Variables
• Chapter 13 Multiple Explanatory Variables: Body Weight
• Chapter 14 Polytomous (Multinomial) Quantal Response
• Chapter 15 Improving Prediction Based on Dose–Response Bioassays
• Chapter 16 Population Toxicology
• Index