Christopher G. Adams

General principles of attraction and competitive attraction as revealed by large-cage studies of moths responding to sex pheromone

Knowledge of how insects are actually affected by sex pheromones deployed throughout a crop so as to disrupt mating has lacked a mechanistic framework sufficient for guiding optimization of this environmentally friendly pest-control tactic. Major hypotheses are competitive attraction, desensitization, and camouflage. Working with codling moths, Cydia pomonella, in field cages millions of times larger than laboratory test tubes and at substrate concentrations trillions of times less than those typical for enzymes, we nevertheless demonstrate that mating disruption sufficiently parallels enzyme (ligand) –substrate interactions so as to justify adoption of conceptual and analytical tools of biochemical kinetics. By doing so, we prove that commercial dispensers of codling moth pheromone first competitively attract and then deactivate males probably for the remainder of a night. No evidence was found for camouflage. We generated and now validate simple algebraic equations for attraction and competitive attraction that will guide optimization and broaden implementation of behavioral manipulations of pests. This analysis system also offers a unique approach to quantifying animal foraging behaviors and could find applications across the natural and social sciences.

Maximizing Information Yield From Pheromone-Baited Monitoring Traps: Estimating Plume Reach, Trapping Radius, and Absolute Density of Cydia pomonella (Lepidoptera: Tortricidae) in Michigan Apple

Abstract Novel methods of data analysis were used to interpret codling moth (Cydia pomonella) catch data from central-trap, multiple-release experiments using a standard codlemone-baited monitoring trap in commercial apple orchards not under mating disruption. The main objectives were to determine consistency and reliability for measures of: 1) the trapping radius, composed of the traps behaviorally effective plume reach and the maximum dispersive distance of a responder population; and 2) the proportion of the population present in the trapping area that is caught. Two moth release designs were used: 1) moth releases at regular intervals in the four cardinal directions, and 2) evenly distributed moth releases across entire approximately 18-ha orchard blocks using both high and low codling moth populations. For both release designs, at high populations, the mean proportion catch was 0.01, and for the even release of low populations, that value was approximately 0.02. Mean maximum dispersive distance for released codling moth males was approximately 260 m. Behaviorally effective plume reach for the standard codling moth trap was < 5 m, and total trapping area for a single trap was approximately 21 ha. These estimates were consistent across three growing seasons and are supported by extraordinarily high replication for this type of field experiment. Knowing the trapping area and mean proportion caught, catch number per single monitoring trap can be translated into absolute pest density using the equation: males per trapping area = catch per trapping area/proportion caught. Thus, catches of 1, 3, 10, and 30 codling moth males per trap translate to approximately 5, 14, 48, and 143 males/ha, respectively, and reflect equal densities of females, because the codling moth sex ratio is 1:1. Combined with life-table data on codling moth fecundity and mortality, along with data on crop yield per trapping area, this fundamental knowledge of how to interpret catch numbers will enable pest managers to make considerably more precise projections of damage and therefore more precise and reliable decisions on whether insecticide applications are justified. The principles and methods established here for estimating absolute codling moth density may be broadly applicable to pests generally and thereby could set a new standard for integrated pest management decisions based on trapping.

Line-Trapping of Codling Moth (Lepidoptera: Tortricidae): A Novel Approach to Improving the Precision of Capture Numbers in Traps Monitoring Pest Density

Abstract This field study of codling moth, Cydia pomonella (L.), response to single versus multiple monitoring traps baited with codlemone demonstrates that precision of a given capture number is alarmingly poor when the population is held constant by releasing moths. Captures as low as zero and as high as 12 males per single trap are to be expected where the catch mode is three. Here, we demonstrate that the frequency of false negatives and overestimated positives for codling moth trapping can be substantially reduced by employing the tactic of line-trapping, where five traps were deployed 4 m apart along a row of apple trees. Codling moth traps spaced closely competed only slightly. Therefore, deploying five traps closely in a line is a sampling technique nearly as good as deploying five traps spaced widely. But line trapping offers a substantial savings in time and therefore cost when servicing aggregated versus distributed traps. As the science of pest management matures by mastering the ability to translate capture numbers into estimates of absolute pest density, it will be important to employ a tactic like line-trapping so as to shrink the troublesome variability associated with capture numbers in single traps that thwarts accurate decisions about if and when to spray. Line-trapping might similarly increase the reliability and utility of density estimates derived from capture numbers in monitoring traps for various pest and beneficial insects.

Evaluation of Off-season Potential Breeding Sources for Spotted Wing Drosophila (Drosophila suzukii Matsumura) in Michigan

Abstract It has been suggested that fruit wastes including dropped and unharvested fruits, and fruit byproducts (i.e., pomace) found in fruit plantings and cideries or wine-making facilities could serve as potential off-season breeding sites for spotted wing Drosophila (Drosophila suzukii Matsumura (Diptera: Drosophilidae)). This idea, however, has yet to be widely tested. The goal of our study was to determine the potential of dropped fruit and fruit wastes as Fall spotted wing Drosophila breeding resources in Michigan, USA. Fruit waste samples were collected from 15 farms across the lower peninsula of Michigan and were evaluated for spotted wing Drosophila and other drosophilid emergence and used in host suitability bioassays. All of the dropped apples, pears, grapes, and raspberries and 40% of apple and 100% of grape fruit pomace evaluated were found to contain spotted wing Drosophila with the highest numbers collected from dropped grapes and pears. Greater spotted wing Drosophila recovery was found in fruit wastes at sites attached with cideries and wine-making facilities and with multiple cultivated fruit crops than sites with no cideries and only one crop. Females oviposited in raspberry, pear, apple, grape, apple pomace and grape pomace samples with the highest rates of reproduction in raspberries. Our results demonstrate that fruit wastes including dropped berry, pomme and stone fruits, as well as fruit compost may be important late season reproductive resources for spotted wing Drosophila.

Automated Systems for Recording, Reporting, and Analyzing Trapping Data

The substantial costs of labor to deploy and tend monitoring traps for agricultural pests provide strong impetus for the development and adoption of automated trapping systems. Early steps in this direction included mechanical devices to switch collecting vessels to determine when during a diel cycle pests were active. With the development of modern electronics, infrared beam detectors were incorporated into insect traps and technologies were borrowed from the electronics industry to automatically collect and transmit the trapping data from remote locations. The automated trapping systems currently penetrating the marketplace use cell phone technologies to transmit captured data as image files. We anticipate that such automated systems will be widely adopted as part of an integrated pest management (IPM) service industry collecting and then analyzing trapping data enabling the growers to make more precise and economical pest management decisions. The principles of trapping and catch interpretations reported in this book will assist these developments.

Evaluation of Nasonov Pheromone Dispensers for Pollinator Attraction in Apple, Blueberry, and Cherry

Abstract Declines in the number of commercial honey bees (Apis mellifera L.) (Hymenoptera: Apidae) and some wild bee species around the world threaten fruit, nut, and vegetable production and have prompted interest in developing methods for gaining efficiencies in pollination services. One possible approach would be to deploy attractants within the target crop to increase the number of floral visits. In this study, we evaluate two new pollinator attractants, Polynate and SPLAT Bloom, for their ability to increase pollinator visitation and fruit set in apple (Malus pumila Mill.), highbush blueberry (Vaccinium sp. L.), and tart cherry (Prunus cerasus L.). Polynate is a plastic twintube dispenser loaded with a mixture of floral scent and Nasonov pheromone. SPLAT Bloom contains the same chemical formula as Polynate, but is applied as a 3 g wax dollop directly onto the tree or bush. The objectives of this study were to determine if Polynate and SPLAT Bloom increase the number of honey bee foragers and fruit set in apples, highbush blueberries, and tart cherries. We conducted replicated evaluations of 32 fields or orchards with and without putative attractants over three growing seasons. Both products failed to provide a measurable increase in pollinator visits or fruit set in these crops, indicating no return on investment for either product.

Trap Function and Overview of the Trapping Process

Traps are devices that delimit the displacement of previously free-ranging entities in space through time. Examples of traps used for prey capture by organisms other than humans are spider webs, ant-lion traps, and the Venus fly trap. Traps offer efficiencies to their makers by concentrating the target organisms. They can also remove pests from the system, thereby offering direct control. A key feature of all trapping is intersection of a trap with its targets at some point in space. Because traps are typically stationary, it is their targets that must move so as to either approach the trap by a chance encounter or be lured there after chance encounters with attractive cues emitted by the trap. For random walkers, the sampling radius of a trap is comprised by the maximum net dispersive distance of the target organisms over the trapping interval plus attractive plume reach. Key parameters influencing the number of targets caught from any specified distance of origin from the trap are the probabilities that the trap is found (findability) and that the organism is captured after arriving at the trap (efficiency) and retained (retention) until harvested. Total catch is given by findability × efficiency × retention per distance multiplied by the number of animals present at the given distance.

Interpreting Catch in a Single Trap

Trap findability × efficiency × retention averaged across the set of animals populating a trapping area can be abbreviated as T fer . Then, catch per trapping area is given simply as T fer multiplied by animals per trapping area. We call the proportion of movers originating at a specified distance from the trap specific Tfer and abbreviate it as spTfer. T fer has apparently not yet been measured directly for any animal. But, it can be calculated from spT fer values arising from a single-trap, multiple-release experiment. A simple way to calculate T fer when the trapping area is comprised of annuli bounded by the release distances is to sum the values for spT fer × respective annulus area and then divide this sum by total annulus area. Once T fer and trapping area are known, organisms per trapping area can be calculated as catch divided by T fer . The precision of abundance estimates and mover density derived from a catch number rises in accordance with plume reach. This chapter details four specific examples of how spT fer data from field experiments on invertebrates can best be analyzed for plume reach, trapping area, and T fer , and how these values can then be translated into estimates of absolute animal density for sharpening pest management decisions.

Why Care About Trapping Small Organisms Moving Randomly

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.

Trapping to Achieve Pest Control Directly

In certain cases, mass trapping alone can reduce pest populations to tolerable levels. The time required for doing so increases with pest density. Thus, this pest management tactic may work satisfactorily at low pest densities but fail at high pest densities. Computer simulations reveal that the impact of trapping on pest damage is exceedingly local for random walkers. Control is achieved only when the traps are deployed in an array that: (i) rapidly clears pests from a given area, and (ii) holds that area from recolonization. Thus, control by mass trapping shares common attributes with military actions where “clear-and-hold” happens to be the dominant counter-insurgency tactic. A regular grid of traps is an effective deployment pattern. Computer simulations suggest that the optimal spacing for such traps is ca. 1.5 times the reach of the trap’s plume. Literature examples of successful mass trapping of insects and small vertebrates are highlighted. All require appreciable labor. If mass trapping is to be widely adopted in modern agriculture, the traps may need to be miniaturized to reduce costs and their deployment automated.