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Trapping of small organisms moving randomly: principles and applications to pest monitoring and management / James R. Miller, Christopher G. Adams, Paul A. Weston, Jeffrey H. Schenker
Miller, James R. ; Adams, Christopher G. (coaut.) ; Weston, Paul A. (coaut.) ; Schenker, Jeffrey H. (coaut.) ;
New York, New York, United States : Springer International Publishing , c2015
Clasificación: 632.9 / T7
Bibliotecas: Tapachula
SIBE Tapachula
ECO020013411 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

Most small animals such as insects follow simple behavioral rules when foraging, including moving randomly when receiving no cues from potential resources. Nevertheless, various insects, mites, nematodes, and some mollusks are sufficiently successful to become severe pests. Some transmit devastating diseases. An imperative of a civilized world is that these pests and disease vectors be accurately monitored so that pesticide applications or other control measures are made only when the benefits clearly outweigh the risks. The key to making efficient pest management decisions is knowledge of absolute pest density. Unfortunately, the available methods for measuring absolute pest density are prohibitively expensive because they require much labor. Traps baited with attractants such as sex pheromones play a critical role in efficiently revealing what pests are present and when they are active. However, progress has been slow in translating catch numbers into absolute pest density. This book aims to improve understanding of the science and mathematics of trapping so as to precipitate a break-through in pest management efficiency.


1 Why Care About Trapping Small Organisms Moving Randomly?
1.1 Most Animals Are Small and Forage Using Simple Behavioral Rules
1.2 The Most Serious Animal Pests Are Small
1.3 Responsible Pest Management Decisions Require Knowledge of Pest Numbers
1.4 Current Methods of Estimating Absolute Densities of Pests Are Prohibitively Costly
1.5 Can Traps and Trapping Fill This Need?
1.6 Aims and Approach of This Book
2 Trap Function and Overview of the Trapping Process
2.1 Definition and Functions of Traps
2.2 Overview of the Trapping Process
3 Random Displacement in the Absence of Cues
3.1 The Classical Random Walk
3.2 The Correlated Random Walk
3.3 Outward Dispersion as Influenced by c.s.d.
3.4 Outward Dispersion as Influenced by Time
3.5 Does a Population of Random Walkers Spread Indefinitely Away from the Point of Origin and, If So, Why?
3.6 Maximum Net Outward Dispersion as Influenced by Mover Sample Size
3.7 Patterns in Random-Walker Ending Positions After a Short Period of Dispersion as Influenced by c.s.d
. 3.8 Experimental Analyses of Tracks and Measures of Meander for Individuals
4 Intersections of Movers with Traps
4.1 Ballistic Movers—The Simplest Case
4.2 Random Walkers
4.3 Gain as Influenced by c.s.d. and Run Time
4.4 Optimal c.s.d. as Influenced by Trap or Resource Size
4.5 What Aspect of Plume Geometry Correlates Best with Capture Probability?
4.6 Contrasts of Ellipsoid Plumes with Discoid Plumes
4.7 Setting the Stage for Estimating Plume Reach from Field Experiments Measuring spTfer
5 Interpreting Catch in a Single Trap
5.1 A Simple Trapping Equation
5.2 Converting spTfer into Tfer
5.3 From Where Does most of the Catch Accumulating in a Trap Originate?
5.4 Preparing to Put Eq. (5.1) to Work
5.5 Measures of Variation around Estimates of Absolute Animal Density Derived from Trapping

5.6 Examples of Eq. 5.1 at Work
5.7 Patterns in Tfer Values and Plume Reaches for Organisms Displacing Randomly
5.8 This Single Trap Approach is Ready for Testing and Implementation Where Proven Reliable
5.9 A Caveat
6 Competing Traps
6.1 Definition of Trap Competition
6.2 Complete Competition
6.3 Test for Whether or Not Competition is Complete
6.4 Incomplete Competition
6.5 Trapping Radius Does Not Equate to Competition Threshold
6.6 Equation for Incompletely Competing Traps
6.7 Estimating Mover Numbers and Trapping Area Simultaneously by Competitive Trapping
6.8 Computer Simulations Demonstrating How Absolute Density of Biological Random Walkers Can Be Estimated by Competitive Trapping under Variable Run Times
6.9 Suggested Plan for Employing Competitive Trapping Under Field Conditions
6.10 Summary
7 Experimental Method for Indirect Estimation of c.s.d. for Random Walkers via a Trapping Grid
7.1 The Idea
7.2 Translation of the Idea to Field Tests with Real Organisms
8 Trapping to Achieve Pest Control Directly
8.1 The Idea
8.2 Time-Dependency and Dynamics of Mass Trapping
8.3 Damage Suppression as Influenced by Trap Number and Spacing: Simulations
8.4 Examples of Successful Pest Control by Mass Trapping
8.5 New Approaches to Mass Trapping
9 Automated Systems for Recording, Reporting, and Analyzing Trapping Data
9.1 Need for Such Systems
9.2 History of Insect Trap Automation
9.3 Recent Developments and Future Prospects
9.4 Wrap-Up