Computational Methods for Accurate Evaluation of Pest Insect Population Size

Petrovskaya, Natalia; Embleton, Nina

doi:10.1007/978-3-319-06877-0_8

Cited by 8 publications

(20 citation statements)

References 56 publications

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“…This heuristic argument is in agreement with the results of numerical integration; case (b) of Table 1 shows that the relative error is less than 25%. Interestingly, as was shown in [115], an increase in the number of traps from 3 × 3 to 9 × 9 does not significantly increase the accuracy of integration because small patches of high density remain unresolved on those grids. The above results lead us to the conclusion that, on a grid with a given number of nodes (traps), the accuracy of pest abundance evaluation is to a large extent determined by the properties of the spatial pattern in the density distribution.…”

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 68%

“…This brings to the fore the challenging problem of integration on coarse grids. In recent years, intensive study of numerical integration methods for ecological applications has been carried out [43,44,114,115,116,118,119] and here we briefly summarize our experience and highlight the main results obtained.…”

Section: Single Field Problem: Multiple Trapsmentioning

confidence: 97%

“…Application of numerical integration methods to the problem of insect pest abundance evaluation was first considered in [116] and then further developed in [43,114,115,118,119]. Examples of numerical integration of either field data or ecologically meaningful simulation data can be found in [115,116,117,119] where a detailed discussion of the numerical procedure and the related issues has been provided.…”

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

“…Examples of numerical integration of either field data or ecologically meaningful simulation data can be found in [115,116,117,119] where a detailed discussion of the numerical procedure and the related issues has been provided. Several methods of numerical integration have been considered.…”

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

“…Numerical integration techniques have been applied to field data of ecological monitoring in [115,119] and here we discuss some of their results. Figure 23a shows one of the six cases of different population density distributions of the New Zealand flatworm (Arthurdendyus triangulatus) analyzed in [119] based on the field data collected by Murchie and Harrison [100].…”

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

See 4 more Smart Citations

Multiscale approach to pest insect monitoring: Random walks, pattern formation, synchronization, and networks

Petrovskii

Petrovskaya

Bearup

2014

Physics of Life Reviews

Self Cite

104

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Pest insects pose a significant threat to food production worldwide resulting in annual losses worth hundreds of billions of dollars. Pest control attempts to prevent pest outbreaks that could otherwise destroy a sward. It is good practice in integrated pest management to recommend control actions (usually pesticides application) only when the pest density exceeds a certain threshold. Accurate estimation of pest population density in ecosystems, especially in agro-ecosystems, is therefore very important, and this is the overall goal of the pest insect monitoring. However, this is a complex and challenging task; providing accurate information about pest abundance is hardly possible without taking into account the complexity of ecosystems' dynamics, in particular, the existence of multiple scales. In the case of pest insects, monitoring has three different spatial scales, each of them having their own scale-specific goal and their own approaches to data collection and interpretation. In this paper, we review recent progress in mathematical models and methods applied at each of these scales and show how it helps to improve the accuracy and robustness of pest population density estimation.

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Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 68%

Section: Single Field Problem: Multiple Trapsmentioning

confidence: 97%

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

Section: Evaluation Of Population Abundance On Coarse Gridsmentioning

confidence: 99%

See 3 more Smart Citations

Multiscale approach to pest insect monitoring: Random walks, pattern formation, synchronization, and networks

Petrovskii

Petrovskaya

Bearup

2014

Physics of Life Reviews

Self Cite

104

View full text Add to dashboard Cite

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Accurate Recognition of Spatial Patterns Arising in Spatio-Temporal Dynamics of Invasive Species

Petrovskaya

Zhang

2020

SEMA SIMAI Springer Series

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A Novel Approach to Evaluation of Pest Insect Abundance in the Presence of Noise

Embleton

Petrovskaya

2014

Bull Math Biol

Self Cite

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Evaluation of pest abundance is an important task of integrated pest management. It has recently been shown that evaluation of pest population size from discrete sampling data can be done by using the ideas of numerical integration. Numerical integration of the pest population density function is a computational technique that readily gives us an estimate of the pest population size, where the accuracy of the estimate depends on the number of traps installed in the agricultural field to collect the data. However, in a standard mathematical problem of numerical integration, it is assumed that the data are precise, so that the random error is zero when the data are collected. This assumption does not hold in ecological applications. An inherent random error is often present in field measurements, and therefore it may strongly affect the accuracy of evaluation. In our paper, we offer a novel approach to evaluate the pest insect population size under the assumption that the data about the pest population include a random error. The evaluation is not based on statistical methods but is done using a spatially discrete method of numerical integration where the data obtained by trapping as in pest insect monitoring are converted to values of the population density. It will be discussed in the paper how the accuracy of evaluation differs from the case where the same evaluation method is employed to handle precise data. We also consider how the accuracy of the pest insect abundance evaluation can be affected by noise when the data available from trapping are sparse. In particular, we show that, contrary to intuitive expectations, noise does not have any considerable impact on the accuracy of evaluation when the number of traps is small as is conventional in ecological applications.

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Computational Methods for Accurate Evaluation of Pest Insect Population Size

Cited by 8 publications

References 56 publications

Multiscale approach to pest insect monitoring: Random walks, pattern formation, synchronization, and networks

Multiscale approach to pest insect monitoring: Random walks, pattern formation, synchronization, and networks

Accurate Recognition of Spatial Patterns Arising in Spatio-Temporal Dynamics of Invasive Species

A Novel Approach to Evaluation of Pest Insect Abundance in the Presence of Noise

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