Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Entomology, Soils and Plant Sciences

Committee Member

Francis Reay-Jones

Committee Member

Kendall Kirk

Committee Member

Brandon Peoples


Site-specific management of insect pests of field crops has the potential to decrease control costs and environmental impacts associated with traditional pest management tactics, but the success of these programs relies on the accurate characterization of arthropod distributions within a crop. Although the expense of the fine-scale spatial sampling required for management zone identification in fields may offset the overall reduction in costs achieved with site-specific pest management, the correlation of arthropod counts with ground-based and remotely sensed field attribute data could help to make site-specific pest management programs more profitable. In this study, we chose to determine how insect pests and natural enemies in soybean were associated with abiotic and biotic variables collected with ground-based and remote sensing technologies. Arthropods were grid-sampled from July-October in two soybean fields at the Clemson University Edisto Research and Education Center in Blackville, SC, in 2017 and 2018 using drop-cloth, sweep-net, and pitfall trap sampling methods. During each sampling event, or calendar week, arthropod and soybean plant data (Normalized Difference Vegetation Index [NDVI], plant heights, and defoliation) were collected for each grid point for a given field. Fields were further characterized through the collection of elevation and soil apparent electrical conductivity (soil ECa) data for all grid points. Spatial Analysis by Distance Indices (SADIE) was used to analyze how the sweep-net collected larvae of three major lepidopteran pests [velvetbean caterpillar, Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae), soybean looper, Chrysodeixis includens (Walker) (Lepidoptera: Noctuidae), and green cloverworm, Hypena scabra (Lepidoptera: Erebidae) (Fabricius)] were spatially associated with defoliation, NDVI, and plant height in soybean, and how the pitfall trap collected predatory Carolina metallic tiger beetle, Tetracha carolina (Linnaeus) (Coleoptera: Carabidae), and punctured tiger beetle, Cicindelidia punctulata (Olivier) (Coleoptera: Carabidae), were associated with abiotic (elevation and soil ECa) and biotic (Cydnidae adults and nymphs, Elateridae adults, and Gryllotalpidae adults and nymphs) variables within the crop. Negative binomial, zero-inflated models were used to estimate presence and drop-cloth counts of arthropod taxa based on distance from the field edge, NDVI, soybean plant height, soil ECa, elevation, and calendar week. Although aggregations of insect taxa, as identified by SADIE, were limited for sweep-net and pitfall-trap datasets, significant spatial overlap (42% of the total significant associations among insects and field variables) was observed for C. punctulata and T. carolina from pitfall-trap datasets, while 14% and 6% of paired plant-insect sweep-net datasets were significantly associated or dissociated, respectively. Cicindelines collected from pitfall traps were found to have more significant associations and dissociations with Elateridae than any other herbivorous taxa, and more significant dissociations with soil ECa than with elevation. NDVI was found to be more associated with sweep-net collected pest distributions than soybean plant heights and defoliation estimates, and the majority of all plant-insect associations and dissociations occurred in the first four weeks of sampling (late July-early August). Among all variables from drop-cloth datasets, calendar week was the most reliable predictor of arthropod counts, as it was a significant predictor for a majority of all taxa. Additionally, counts for a majority of drop-cloth collected pestiferous taxa were significantly associated with distance from the field edge, elevation, soybean plant height, and NDVI. Given that the knowledge of the ecological interactions specific to a given species are critical to the development of practical management applications for that species, the identification of ground-based (e.g. soil ECa) and remotely sensed variables (e.g. NDVI) that can be associated with the in-field distributions of important soybean pests and natural enemies represents the first step towards the implementation of site-specific pest management in this crop. Results from this study advocate for the relationship between distributions of pests and natural enemies and important biotic and abiotic variables to be further investigated to better determine the strength of the correlations across years and sites.



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