Trapping of Small Organisms Moving Randomly

Miller, James R.; Adams, Christopher G.; Weston, Paul A.; Schenker, Jeffrey H.

doi:10.1007/978-3-319-12994-5

Cited by 55 publications

(66 citation statements)

References 37 publications

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“…Calculation of T fer for cardinal direction experiments, where number released did not increase with distance, was performed by dividing mean annulus area × spT fer (proportion caught at a defined distance) by mean annulus area (see Table 5.1 example of Miller et al 2015). …”

Section: Methodsmentioning

confidence: 99%

“…Here we put into practice the techniques for interpreting catch and measuring maximum dispersive distance and pheromone plume reach recently proposed and detailed by Miller et al (2015). Moreover, these experiments were conducted at both high and low codling moth densities, so as to represent conditions encountered by growers spraying often or little.…”

Section: Study Objectives and Rationale For Using Codling Moth For Prmentioning

confidence: 99%

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

Adams¹,

Schenker²,

McGhee³

et al. 2017

J Econ Entomol

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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 trap’s 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.

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Section: Methodsmentioning

confidence: 99%

Section: Study Objectives and Rationale For Using Codling Moth For Prmentioning

confidence: 99%

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

Adams¹,

Schenker²,

McGhee³

et al. 2017

J Econ Entomol

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“…Animals such as lions, hawks, and predatory fishes are thought to fulfill this expectation via saltatory patterns of movement involving both cruising the habitat for prey and ambushing [ 8 ], all the while relying heavily on learning and memory. On the other hand, simpler animals like microorganisms [ 9 , 10 ], nematodes [ 11 , 12 ], and insects [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ] may rely only on random walks (flights) [ 20 , 21 , 22 ] to bring them into contact with sensory cues from a resource that then orient the last legs of a foraging excursion [ 18 ]. An intriguing feature of biological random walks is their time-averaged symmetry in the frequency and severity of left vs. right turns between locomotory steps such that the resulting frequency histogram becomes smoothly Gaussian, e.g., Figure 7a of [ 23 ], Figure 2 of [ 24 ], and Figure 2b of [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…(intensifying distant “search”) when these cues are no longer detected [ 5 , 27 , 28 , 29 , 30 ]. Spread of turn angle distributions during foraging is a critical biological trait [ 5 , 18 , 31 ], shown to be heritable [ 32 ]. We suggest that diversifying approaches to its measurement is a worthy research endeavor.…”

Section: Introductionmentioning

confidence: 99%

“…A readily accessible and direct approach to quantifying relative path meander of slow-moving foragers is to measure, and then plot, a frequency histogram for turns between uniform segments of an animal’s foraging track [ 33 ] recorded by, e.g., videography [ 34 , 35 ]. A suitable unit of path length for a walker is one body length [ 18 ], while that for a flyer may be a meter or more [ 36 , 37 ]. Upon slow-motion video play-back, the position of track segment ends can be recorded by digitizing software and then the angle struck by the preceding vs. current segment of track can be measured or computed, using computer software.…”

Section: Introductionmentioning

confidence: 99%

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Path Meander of Male Codling Moths (Cydia pomonella) Foraging for Sex Pheromone Plumes: Field Validation of a Novel Method for Quantifying Path Meander of Random Movers Developed Using Computer Simulations

Adams

Schenker

Weston

et al. 2020

Insects

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Measures of path meander are highly relevant to studies of optimal foraging by animals. However, directly recording paths of small animals such as insects can be difficult because of small size or crepuscular activity. Computer simulations of correlated random walkers demonstrated that the rates of decay in captures across a rectangular grid of traps when movers were released at its corner can be used to produce calibration curves for quantifying path meander indirectly. Simulations using spatial parameters matching those previously documented for male codling moths (Cydia pomonella (L.)) foraging for female pheromone plumes in the field predicted that meander, as measured in circular standard deviation (c.s.d.) of turn angles between track segments, should be ca. 50° and 30° when the target population density is high vs. low, respectively. Thus, if optimized, the mean value measured for C. pomonella populations encountering an unknown target density should fall between these limits. We recorded decay in C. pomonella catch across a 5 × 5 grid of pheromone-baited traps each separated by 15 m on 39 occasions where batches of ca. 800 males were released 10 m outside the corner of trapping grids arranged in five large Michigan apple orchards. This decay constant was translated into mean c.s.d value for path meander using the standard curve generated by the computer simulations. The measured decay constant for C. pomonella males was negative 0.99 ± 0.02 (S.E.M.), which translates to a path meander of 37 ± 2° c.s.d. Thus, the measured path meander of 37° fell between the 50° and 30° values optimal for dense and sparse populations, respectively. In addition to providing a rare documented example of optimal foraging for odor plumes, this research offers proof-of-concept for a novel approach to quantifying path meander of movers that could prove useful across diverse taxa.

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Signal from the noise: model‐based interpretation of variable correspondence between active and passive samplers

Parkos

Kline²,

Trexler

2019

Ecosphere

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Combining information from active and passive sampling of mobile animals is challenging because active‐sampling data are affected by limited detection of rare or sparse taxa, while passive‐sampling data reflect both density and movement. We propose that a model‐based analysis allows information to be combined between these methods to interpret variation in the relationship between active estimates of density and passive measurements of catch per unit effort to yield novel information on activity rates (distance/time). We illustrate where discrepancies arise between active and passive methods and demonstrate the model‐based approach with seasonal surveys of fish assemblages in the Florida Everglades, where data are derived from concurrent sampling with throw traps, an enclosure‐type sampler producing point estimates of density, and drift fences with unbaited minnow traps that measure catch per unit effort (CPUE). We compared incidence patterns generated by active and passive sampling, used hierarchical Bayesian modeling to quantify the detection ability of each method, characterized interspecific and seasonal variation in the relationship between density and passively measured CPUE, and used a predator encounter‐rate model to convert variable CPUE–density relationships into ecological information on activity rates. Activity rate information was used to compare interspecific responses to seasonal hydrology and to quantify spatial variation in non‐native fish activity. Drift fences had higher detection probabilities for rare and sparse species than throw traps, causing discrepancies in the estimated spatial distribution of non‐native species from passively measured CPUE and actively measured density. Detection probability of the passive sampler, but not the active sampler, varied seasonally with changes in water depth. The relationship between CPUE and density was sensitive to fluctuating depth, with most species not having a proportional relationship between CPUE and density until seasonal declines in depth. Activity rate estimates revealed interspecific differences in response to declining depths and identified locations and species with high rates of activity. We propose that variation in catchability from methods that passively measure CPUE can be sources of ecological information on activity. We also suggest that model‐based combining of data types could be a productive approach for analyzing correspondence of incidence and abundance patterns in other applications.

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Trapping of Small Organisms Moving Randomly

Cited by 55 publications

References 37 publications

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

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

Path Meander of Male Codling Moths (Cydia pomonella) Foraging for Sex Pheromone Plumes: Field Validation of a Novel Method for Quantifying Path Meander of Random Movers Developed Using Computer Simulations

Signal from the noise: model‐based interpretation of variable correspondence between active and passive samplers

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