Geostatistical Description of the Spatial Distribution of Limonius californicus (Coleoptera: Elateridae) Wireworms in the Northwestern United States, with Comments on Sampling

Williams, Livy; Schotzko, D. J.; McCaffrey, J. P.

doi:10.1093/ee/21.5.983

Cited by 53 publications

(42 citation statements)

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“…The hexagonal grid sampling pattern was chosen because this design has provided better estimates than other sampling designs for assessing the spatial structure of insect populations (Schotzko and 1990, Williams et al 1992). Ten sampling transects =75 m apart were established (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…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). Midgarden et al (1993) applied geostatistical techniques to description of spatially structured, aggregated distributions of counts for corn rootworm adults trapped on sticky boards.…”

Section: Lag Distancementioning

confidence: 99%

“…Because the influence of environmentally mediated mortality factors may vary spatially as well as seasonally, the distributions of agriculturally important insects also vary spatially within a field as well as from one growing season to another. Although the numeric distributions of insect populations may be estimated from statistical indices based on frequency distributions, such as Moran's I statistic or Geary's c (Williams et al 1992), these techniques do not measure spatial relatedness and do not allow correlation of population distributions with spatially variable environmental factors. Temperature-driven predictive models that are available for survival of corn rootworm eggs Gustin 1993, Woodson andEllsbury 1994), development of the soil-dwelling stages (Bergman and Turpin 1986, Jackson and Elliott 1988, Schaafsma et al 1991, and the emergence phenology of adult corn rootworm (Elliott et al 1990) do not account for spatial variation of within-field insect pest populations.…”

mentioning

confidence: 99%

“…calized discontinuity or nugget, Co, is defined by the y-intercept and is interpreted as variability due to experimental error, distributional effects below the sampling scale of the study, and other random effects. The value of the semivariance at the 1st lag distance, termed the experimental nugget, is used as a more conservative estimate of the proportion of variability due to spatial structure (Williams et al 1992). The range is the lag distance beyond which samples are considered spatially independent.…”

mentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Lag Distancementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…The dispersion pattern of insect populations provides useful information for developing effective sampling and scouting programs because dispersion affects sampling accuracy, efÞciency, and precision (Williams et al 1992, Davis 1994. Accordingly, extensive studies have been conducted for the past 40 yr to characterize the dispersion of insect populations by using various indices (Taylor 1961, Lloyd 1967, Iwao and Kuno 1971, Southwood 1978, Kuno 1991, Pedigo and Buntin 1994.…”

mentioning

confidence: 99%

Characterization of the Spatial Dispersion of Corn Root Injury by Corn Rootworms (Coleoptera: Chrysomelidae)

Park¹,

Tollefson²

2005

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The dispersion of root injury to Zea mays L. by corn rootworm, Diabrotica spp., larvae was characterized using geostatistics to determine the appropriate sampling distance to obtain independent estimates of root injury. We also investigated the effect of the root injury levels on the spatial dependence of root injury at three different spatial scales: large-scale study with 25-m sampling distance in three, 8-ha fields; moderate-scale study with 0.2-m sampling distance in six, 2-ha fields; and small-scale study with exhaustive sampling in eight, 12-m(2) plots within cornfields. In the large-scale study, a nugget model best fit the semivariograms, indicating the dispersion of root injury exhibited a random pattern. In the moderate-scale study, all the semivariograms were best fit with an exponential model, and 68 to 96% of the variability was explained by spatial dependence, suggesting a high degree of spatial aggregation of corn root larval injury. The ranges of semivariograms fell within < 4 m, except one field that had the range of 14.7 m. In the small-scale study, the Gaussian, exponential, or spherical models fit the semivariograms best and 50 to 89% of the variability was explained by spatial dependence, suggesting spatial aggregation of root injury. The ranges of semivariograms were from 0.35 to 1.04 m. In the small-scale study, the degree of spatial dependence increased significantly (P < 0.05) as the average root injury increased within the range of root injury observed in the study. These results suggest that the minimum distance between root samples in small-plot insecticide trials should be > 1 m within a row to obtain unbiased estimates of root injury.

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A two‐stage model for incidence and prevalence in point‐level spatial count data

2011

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aWe consider the problem of modeling point-level spatial count data with a large number of zeros. We develop a model that is compatible with scientific assumptions about the underlying data-generating process. We utilize a two-stage spatial generalized linear mixed model framework for the counts, modeling incidence, resulting in 0-1 outcomes, and prevalence, resulting in positive counts, as separate but dependent processes and utilize a Gaussian process model for characterizing the underlying spatial dependence. We describe a Bayesian approach and study several variants of our two-stage model. We fit the models via Markov chain Monte Carlo methods. We study several Markov chain Monte Carlo algorithms, including a version of the Langevin-Hastings algorithm, for exploring the complicated posterior distribution efficiently and recommend an algorithm that is fairly efficient. Finally, we demonstrate the application of our modeling and computational approach on both simulated data and real data from an ecological field survey.

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Geostatistical Description of the Spatial Distribution of Limonius californicus (Coleoptera: Elateridae) Wireworms in the Northwestern United States, with Comments on Sampling

Cited by 53 publications

References 0 publications

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

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

Characterization of the Spatial Dispersion of Corn Root Injury by Corn Rootworms (Coleoptera: Chrysomelidae)

A two‐stage model for incidence and prevalence in point‐level spatial count data

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