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Dispersion and Sampling of Adult Dermacentor andersoni in Rangeland in Western North America
Author(s) -
Kateryn Rochon,
Glen A. Scoles,
Tim Lysyk
Publication year - 2012
Publication title -
journal of medical entomology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.866
H-Index - 99
eISSN - 1938-2928
pISSN - 0022-2585
DOI - 10.1603/me11160
Subject(s) - negative binomial distribution , quadrat , statistics , sample size determination , poisson distribution , biology , abundance (ecology) , sampling (signal processing) , count data , mathematics , ecology , physics , transect , detector , optics
A fixed precision sampling plan was developed for off-host populations of adult Rocky Mountain wood tick, Dermacentor andersoni (Stiles) based on data collected by dragging at 13 locations in Alberta, Canada; Washington; and Oregon. In total, 222 site-date combinations were sampled. Each site-date combination was considered a sample, and each sample ranged in size from 86 to 250 10 m2 quadrats. Analysis of simulated quadrats ranging in size from 10 to 50 m2 indicated that the most precise sample unit was the 10 m2 quadrat. Samples taken when abundance < 0.04 ticks per 10 m2 were more likely to not depart significantly from statistical randomness than samples taken when abundance was greater. Data were grouped into ten abundance classes and assessed for fit to the Poisson and negative binomial distributions. The Poisson distribution fit only data in abundance classes < 0.02 ticks per 10 m2, while the negative binomial distribution fit data from all abundance classes. A negative binomial distribution with common k = 0.3742 fit data in eight of the 10 abundance classes. Both the Taylor and Iwao mean-variance relationships were fit and used to predict sample sizes for a fixed level of precision. Sample sizes predicted using the Taylor model tended to underestimate actual sample sizes, while sample sizes estimated using the Iwao model tended to overestimate actual sample sizes. Using a negative binomial with common k provided estimates of required sample sizes closest to empirically calculated sample sizes.

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