Geostatistical Description of the Spatial Distribution of Lygus hesperus (Heteroptera: Miridae) in Lentils

Schotzko, D. J.; O’Keeffe, L. E.

doi:10.1093/jee/82.5.1277

Cited by 61 publications

(21 citation statements)

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“…An asymptotic function indicates an aggregated insect distribution and the range represents approximately the extension of hot spots (areas of aggregation); on the contrary, linear functions indicate a uniform/random distribution, with the random component increasing with the increase of the variance variability; when the slope is near to zero we obtain a pure nugget effect, indicating a complete lack of any autocorrelation and a pure random distribution (Schotzko & O'Keefe 1989).…”

Section: Analysis Of Spatial Patternsmentioning

confidence: 91%

Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

Sciarretta¹,

Trematerra²

2014

Plant Prot. Sci.

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Sciarretta A., Trematerra P. (2014): Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests. Plant Protect. Sci., 50: 97-110.Spatial heterogeneity in agricultural systems is recognised as an important source of variability to be investigated. In the evolution of Integrated Pest Management (IPM), patterns and processes that influence spatio-temporal dynamics in insect populations tend to assume more importance compared to the classical theory. Geostatistics represent a valuable tool to investigate the spatial pattern of insect populations and to support pest control. After an explanation of the geostatistical analysis, in the present paper we provided an overview of practical applications in managing pests, focusing on fruit orchards and vineyards. The utility of geostatistical tools is illustrated with examples taken from field studies, with attention to the analysis of spatial patterns, monitoring schemes, use of traps, scale issues, precision targeting, and risk assessment maps. Potential approaches in the context of IPM are discussed in relation to future perspectives.

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Section: Analysis Of Spatial Patternsmentioning

confidence: 91%

Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

Sciarretta¹,

Trematerra²

2014

Plant Prot. Sci.

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“…Kemp et al (1989) use statewide grasshopper (Orthoptera: Acrididae) counts in three regions of Montana to create, with kriging, region-specific macroscale hazard maps of grasshopper densities. Schotzko and O'Keeffe (1989) model the spatial variability of the Lygus bug Lygus hesperus in an agricultural field and then use kriging to map that organism's density.…”

Section: There Is More To Geostatistics Than the Variogram And Krigingmentioning

confidence: 99%

Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence

Rossi

Mulla

Journel

et al. 1992

Ecological Monographs

1,078

814

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Abstract. Geostatistics brings to ecology novel tools for the interpretation of spatial patterns of organisms, of the numerous environmental components with which they interact, and of the joint spatial dependence between organisms and their environment. The purpose of this paper is to use data from the ecological literature as well as from original research to provide a comprehensive and easily understood analysis ofgeostatistics' manner of modeling and methods. The traditional geostatistical tool, the variogram, a tool that is beginning to be used in ecology, is shown to provide an incomplete and misleading summary of spatial pattern when local means and variances change. Use of the non-ergodic covariance and correlogram provides a more effective description of lag-to-lag spatial dependence because the changing local means and variances are accounted for. Indicator transformations capture the spatial patterns of nominal ecological variables like gene frequencies and the presence/absence of an organism and of subgroups of a population like large or small individuals. Robust variogram measures are shown to be useful in data sets that contain many data outliers. Appropriate removal of outliers reveals latent spatial dependence and patterns. Cross-variograms, cross-covariances, and cross-correlograms define the joint spatial dependence between co-occurring organisms. The results of all of these analyses bring new insights into the spatial relations of organisms in their environment.

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“…Using geostatistical techniques, Schotzko and O'Keeffe (1989) described within-field spatial nature of nymphal and adult populations of the lygus bug, Lygus hesperus Knight. Williams et al (1992) demonstrated the aggregated and spatially variable structure of distributions of the soil-dwelling stages of the sugarbeet wireworm, Limonius cali/amicus (Mannerheim).…”

Section: Lag Distancementioning

confidence: 99%

Geostatistical Characterization of the Spatial Distribution of Adult Corn Rootworm (Coleoptera: Chrysomelidae) Emergence

Ellsbury¹,

Woodson²,

Clay³

et al. 1998

Environmental Entomology

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Geostatistical Description of the Spatial Distribution of Lygus hesperus (Heteroptera: Miridae) in Lentils

Cited by 61 publications

References 0 publications

Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence

Geostatistical Characterization of the Spatial Distribution of Adult Corn Rootworm (Coleoptera: Chrysomelidae) Emergence

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