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The sampling dynamics of the pink bollworm (Pectinophora gossypiella (Saunders)) were studied during the cotton growing seasons of 1976, 1977, and 1978. Different sampling methods were employed each year to compare sampling intensity and to compare whole plant samples with susceptible boll samples. Analysis of variance data indicated that significant differences in mean boll infestation levels occurred between small areas of a field (ca. 1 acre), but that these differences were masked when larger units (ca. 10 acres) of a field were used as the sample area. From these data it was found that samples yielded the most accurate pink bollworm infestation estimate when taken from as large an area of the field as possible. Data indicated that accuracy improved only 2 to 3 percent when sample size increased from 60 bolls to 100 bolls. Bolls taken from whole plant samples were as accurate as susceptible boll samples if there was an equal number of bolls in each sample. Sample accuracy was nearly stable for fields 20 to 40 acres in size. A sequential sampling plan for pink bollworm was developed using the binomial distribution. Decision levels were established using the 6 and 12 percent boll infestation levels with α and β levels of 0.2. Approximately 50 percent of the sampling time can be saved over conventional 100 boll samples with very little loss of accuracy for a spray-no spray decision based upon an economic threshold value of 15 percent pink bollworm infestation.

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

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Refuges of non-Bt cotton are used to delay Bt resistance in the pink bollworm (Pectinophora gossypiella, Lepidoptera: Gelechiidae), a pest that eats cotton seeds. Contamination of refuges by transgenic Bt cotton could threaten the efficacy of such refuges by increasing the relative survival of larvae that carry alleles for Bt resistance. Here I compared contamination levels in refuges of varying configuration and distance from Bt. I found two types of contamination at low rates in refuges: outcrossing by Bt pollen and adventitious Bt plants. Unexpectedly, outcrossing did not differ between refuge configurations, and did not decrease as distance from Bt fields increased, perhaps because Bt plants in refuges acted as the main Bt pollen source. Bioassays, conducted to evaluate the impacts of contamination on pink bollworm resistance, indicated that Bt plants in refuges may increase the frequency of resistance alleles at a higher rate than outcrossing by Bt plants.

In field experiments, thresholds, spray timings, and bollworm oviposition were conducted to evaluate the current recommended action thresholds for bollworm (Lepidoptera: Noctuidae) in cotton. Based on studies conducted evaluating thresholds, insecticide applications varied across Bt technologies. Bollgard 3 required less sprays when compared to Bollgard II and non-Bt. A second experiment highlighted the importance of timely insecticide applications for managing bollworm populations in Bollgard II cotton based on insect damage. The highest yields were associated with insecticide applications made during the timeframe that cotton is the most susceptible to bollworm damage. The last experiment was conducted to determine if oviposition varied throughout the plant canopy. Oviposition occurred throughout the whole plant, however, the greatest relationship of egg lay was observed in the top three nodes of the plant. From this research, studies would suggest that the current recommended bollworm thresholds in Mississippi cotton production systems, at this time, do not need to be adjusted.

The within-field spatial distribution of F₁, F₂, and F₃ pink bollworm (PBW) (Pectinophora gossypiella Saunders) generations were modeled with Taylor's power law (TPL), Iwao's patchiness regression (IPR), and the geostatistical semivariogram. Kriging interpolation was used to grid data for the generation of isarithmic maps. Distributional patterns and movements within a field are displayed in a time series of three maps depicting density across the field. The sampling protocol was replicated in eight commercial cotton fields in south-central Arizona during 1985 and 1986. Permanent sample stations were situated throughout the fields on a regular grid pattern. Samples were collected during the peak larval population and handled so as to maintain the integrity of site-specific samples (spatially identified by X,Y coordinates). TPL and IPR could not be used satisfactorily to model the F₁ generation. TPL fit the observed F₂ and F₃ data better than IPR. Both methods predicted the F₂ to be more highly aggregated than the F₃. For a given precision, optimum sample size increased when TPL and IPR model parameters were incorporated into sample size formulae relative to a formula which assumed random distribution. Ninety-five percent of the modeled PBW distributions were autocorrelated in 2-dimensional space and shown to conform to regionalized variable theory by the successful application of geostatistics. The semivariogram models are in conceptual agreement with traditional models and represent a worthy alternative to traditional modeling methodology. The semivariogram models have a large nugget effect proportion (average = 67%) which, in combination with low PBW density in commercial fields, limits the applicability of geostatistics in this system. Isarithmic maps showed that F₁ larvae are either localized near a field edge or generally scattered throughout the field. No consistent inter-generational dispersal pattern was identified. The use of systematic grid sampling is most advantageous (relative to random sampling) when density and the spatial dependence of samples is high, or many samples can be taken. Systematic sampling and kriging estimation yielded more precise estimates than random sampling and classical statistics, but the advantage was buffered by low PBW densities and large nugget effect.

Background: The pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) is one of the world's most important pests of cotton. Insecticide sprays and transgenic cotton producing toxins of the bacterium Bacillus thuringiensis (Bt) are currently used to manage this pest. Bt toxins kill susceptible insects by specifically binding to and destroying midgut cells, but they are not toxic to most other organisms. Pink bollworm is useful as a model for understanding insect responses to Bt toxins, yet advances in understanding at the molecular level have been limited because basic genomic information is lacking for this cosmopolitan pest. Here, we have sequenced, de novo assembled and annotated a comprehensive larval midgut transcriptome from a susceptible strain of pink bollworm. Findings: A de novo transcriptome assembly for the midgut of P. gossypiella was generated containing 46,458 transcripts (average length of 770 bp) derived from 39,874 unigenes. The size of the transcriptome is similar to published midgut transcriptomes of other Lepidoptera and includes up to 91 % annotated contigs. The dataset is publicly available in NCBI and GigaDB as a resource for researchers. Conclusions: Foundational knowledge of protein-coding genes from the pink bollworm midgut is critical for understanding how this important insect pest functions. The transcriptome data presented here represent the first large-scale molecular resource for this species, and may be used for deciphering relevant midgut proteins critical for xenobiotic detoxification, nutrient digestion and allocation, as well as for the discovery of protein receptors important for Bt intoxication.

Helicoverpa zea is one of the most costly insect pests of food and fiber crops throughout the Americas. Pyrethroid insecticides are widely applied for control as they are effective and relatively inexpensive; however, resistance threatens sustainability because alternative insecticides are often more expensive or less effective. Pyrethroid resistance has been identified since 1990 and monitoring has utilized cypermethrin in the adult vial test, but resistance mechanisms have not yet been elucidated at the molecular level. Here we examined field-collected H. zea males resistant to cypermethrin for target site and metabolic resistance mechanisms. We report the cDNA sequence of the H. zea sodium channel a-subunit homologous to the Drosophila para gene and identified known resistance-conferring mutations L1029H and V421M, along with two novel mutations at the V421 residue, V421A and V421G. An additional mutation, I951V, may be the first example of a pyrethroid resistance mutation caused by RNA-editing. We identified other specimens with significantly higher transcriptional expression levels of cytochrome P450 genes CYP6B8 and CYP6B9 compared to the susceptible, ranging from a factor of 3.7 to 34.9 and 5.6 to 39.6, respectively. In addition, we investigated if differences in insect growth stage and pyrethroid structure affect our ability to predict resistance in the adult vial test. Vial bioassays with cypermethrin, esfenvalerate, and bifenthrin were conducted on third instars and male moths from a susceptible laboratory colony and the F1 generation of a resistant field population. For the resistant population, vial assays using either growth stage gave similar resistance ratios for each of the three pyrethroids, respectively, proving the adult vial test accurately reflects larval resistance. However, resistance ratios varied considerably depending on the pyrethroid used, so values obtained with one pyrethroid may not be predictive of another. This dissertation is the first to identify molecular mechanisms associated with H. zea pyrethroid resistance. Our results suggest carefully chosen pyrethroid structures diagnostic for specific resistance mechanisms could improve regional monitoring programs and development of high throughput assays to detect the resistance mechanisms used in tandem with traditional monitoring may greatly improve our ability to identify and predict resistance and make better control recommendations.