Trapping of Small Organisms Moving Randomly: Principles and Applications to Pest Monitoring and Management

ISBN-13: 9783319129938 / Angielski / Miękka / 2015 / 114 str.

James R. Miller; Christopher G. Adams; Jeffrey H. Schenker

Opis

This new book is the first to make logical and important connections between trapping and foraging ecology. It develops and describes--both verbally and mathematically--the underlying principles that determine and define trap-organism interactions. More important, it goes on to explain and illustrate how these principles and relationships can be used to estimate absolute population densities in the landscape and to address an array of important problems relating to the use of trapping for detection, population estimation, and suppression in both research and applied contexts. The breakthrough nature of subject matter described has broad fundamental and applied implications for research for addressing important real-world problems in agriculture, ecology, public health and conservation biology. Monitoring traps baited with potent attractants of animals like insects have long played a critical role in revealing what pests are present and when they are active. However, pest managers have been laboring without the tools necessary for quick and inexpensive determination of absolute pest density, which is the cornerstone of pest management decisions. This book spans the gamut from highly theoretical and fundamental research to very practical applications that will be widely useful across all of agriculture.

Spis treści

TABLE OF CONTENTS

 

Chapter 1. Why Care about Small Animals Moving Randomly

 

This chapter documents the acute need for quick and inexpensive estimation of absolute density of small animals. It explains what alternative tools are available for this purpose and why they are impractical for everyday pest management decisions.

 

Chapter 2. Trap Function and Overview of the Trapping Process

This section defines a trap and articulates the various roles traps play, including increasing the efficiency whereby animals are detected, harvested, and thwarted from being pests (mass trapping).

This section explains that trapping is a process of intersection of moving targets with the trap or with an attractant plume emanating from the trap. Diagrams are offered for typical spatial contexts of trapping. Six steps of trapping are articulated that fall into three main categories: finding the trap (findability), engaging the trapping mechanism (efficiency), and keeping the catch contained in the trap (retention). The concept of random walks is introduced and explanation is provided for why the composite probability for findability, efficiency, and retention is typically very low for random movers. Definitions and examples are offered for the core elements of trapping: plume reach, maximum mover dispersion, and trapping radius and area. A foundational trapping equation is offered that postulates catch is the sum across a trapping area of probability of catch when movers originate at a particular distance from a trap multiplied by the number of movers originating at that given distance.  The key parameters of trapping are identified as a setup for detailed analysis in the following three chapters.

 

Chapter 3. Random Displacement in the Absence of Cues

This section summarizes key features of random walks and details the computer software that author Paul Weston wrote for simulating random walks with manipulable step lengths, meander (amount of turning), and run time.  Diagrams are presented for classical random walks of which diffusing molecules and Brownian motion are exemplars.

This section contrasts tracks of molecules (very high local meander) with those for displacing organisms (low local meander) and explains how the distribution of turn angles comprising a stretch of track can be plotted so as to reveal the spread (circular standard deviation, or c.s.d.) of the normal distribution that results when new headings are selected randomly. 

Chapter 4. The Geometry of Trap Interceptions

This section demonstrates that the proportion of straight-line movers that will be intercepted by a trap upon departing on random headings from a common origin is trap length divided by the circumference of a circle whose radius is the distance from mover origin to the trap. Thus, the relationship between proportion of interceptions and distance from a trap is an inverse function, not a log function as other trapping researchers have just assumed. We then document that the graphical technique of plotting distance of origin from a trap on the x-axis against 1/catch on the y-axis generates a straight line whose slope can be used to calculate trap length or plume reach.

This section establishes that the relationships developed above for trap interceptions of ballistic movers hold also for random walkers. However, when the slope of an inverse plot of release distance vs. 1/proportion caught is used to mathematically extract plume reach, an additional length is registered that we call “gain.”  Gain results from functional track broadening due to meander. Gain is a positive attribute of foraging animals because it increases the efficiency of resource finding during local search.

Chapter 5. Interpreting Catch in the Single Trap

Here we establish that catch in a trap is equal to the proportion of all animals in a trapping area that get caught (designated T_fer) multiplied by the number of animals in that trapping area (designated M_den).

This section demonstrates how a data set on the proportion of animals caught from a specified distance from a trap (spT_fer) can be correctly converted into mean overall proportion of movers from the whole trapping area that get caught (T_fer). Then we show how absolute animal density can be calculated using the simple trapping equation once catch number and T_fer are known.  Examples are given from simulations and real-world field experiments with insects that validate this simple trapping equation.

Chapter 6. Competing Traps

We define traps as competing when the presence of one or more traps reduces catch in a given trap below what would have been measured if the additional trap or traps were not present.

Complete competition occurs when traps are so closely deployed that they split arriving animals equally. Then the familiar mathematics of playing card probabilities apply.

Chapter 7. Proposed Experimental Method for Measuring C.S.D. of Random Walkers Via a Trapping-Grid

Our research with grids of traps and patterns of catch across them suggested that the c.s.d. employed by foraging animals might be revealed through such analysis. We go on to explore the interactions of simulated movers with a regularly spaced 5 x 5 grid of traps. Indeed, a useful pattern of catch was generated when a population of movers was released at a point just outside of and nearby a corner trap. Catch fell with the negative exponential of distance from the corner. These negative exponents were found by simulations to be tightly linearly correlated with mover c.s.d. Thus, a standard curve was produced that should prove useful in translating field catches of real animals into c.s.d. estimates, something that was entirely out of reach until now.

This section gives step-by-step instruction on how the above method for c.s.d. estimation can be conducted in the field with animals like insects. Field experiments testing this procedure for moth pests of fruit are currently underway.

Chapter 8. Trapping to Achieve Pest Control Directly

This section defines the purpose of mass trapping and its vocabulary.

Here we explain why efficacy of behavioral control tactics is pest-density dependent even though suppression of catch in a monitoring trap (the standard measure of effect for behavioral controls) is pest-density independent. This knowledge resolves long-standing confusion about why % catch suppression is not well correlated with realized crop protection.

Chapter 9. Automated Systems for Recording, Reporting, and Analyzing Trapping Data

This chapter opens with the observation that profit margins in agriculture are so thin that the labor required to deploy and service standard pest monitoring traps is becoming prohibitive. We argue that automation of monitoring systems is appropriate and inevitable in the face of rapidly evolving and ever-cheaper information technologies.

This section briefly traces the evolution of automated traps beginning with mechanical devices shifting the collection vessel so that the timing of catch was revealed. A leap occurred in the evolution trap automation when in the mid-1980s D. E. Hendricks used an infrared sensor to electronically record the arrivals of moths in a pheromone-baited trap. He then adopted existing technology to wirelessly transmit the data to a central station for analysis. Others have advanced automated trapping to where the records of insect arrivals are highly accurate and the data are being immediately and accurately transmitted by adaptations of the ever-improving and now ubiquitous cell-phone system at affordable pricing.

References Cited:

Bibliography (offers a chronology of publications with key concepts of trapping)

O autorze

Dr. James R. Miller serves as Distinguished Professor of Entomology at Michigan State University. Dr. Miller’s research centers on insect reproductive physiology, behavior, and chemical ecology. Current basic research projects address mechanisms of moth pheromone disruption, sensory physiology of pheromone reception and host-plant acceptance by herbivorous Diptera.

Blurb

Monitoring traps baited with potent attractants of animals like insects have long played a critical role in revealing what pests are present and when they are active. However, pest managers have been laboring without the tools necessary for quick and inexpensive determination of absolute pest density, which is the cornerstone of pest management decisions. This book fills that gap by demonstrating how catch numbers from monitoring traps can be translated into measures of pests per given area of crop and accurate projections of damage.

An unhappy fact of life from the human perspective is that small animals such as insects, mites, mollusks, and nematodes compete severely for our food and fiber. Moreover, some of these creatures vector diseases such as malaria, which kills more than a million humans each year. An imperative of a civilized world is that pests be accurately monitored so that control measures like pesticides are used only when the benefits clearly outweigh the risks to the health of humans, non-target organisms, and the environment.