|SEYMOUR, LYNNE - University Of Georgia|
|Bosch, David - Dave|
|SCHMIDT, JASON - University Of Georgia|
|Strickland, Timothy - Tim|
Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2021
Publication Date: 2/17/2021
Citation: Coffin, A.W., Olson, D.M., Seymour, L., Bosch, D.D., Schmidt, J.M., Strickland, T.C. 2021. Responses to environmental variability by herbivorous insects and their natural enemies within a bioenergy crop, Miscanthus x giganteus. PLoS ONE. https://doi.org/10.1371/journal.pone.0246855.
Interpretive Summary: Precision agriculture (PA) is the application of management decisions in space and time based on identifying, quantifying, and responding to the variability that is normally found in the environment. However, knowledge is limited about how crop pests respond to these variable environmental conditions. Research that provides insights on pest/predator relationships within a field can provide important insights for farmers who work with PA systems. Pest insects and their natural enemies were observed across 81 locations within a field of the perennial biofuel feedstock grass, Miscanthus × giganteus, over two years in the Coastal Plain of Georgia, USA and related to environmental factors. Non-spatial statistical models indicated that insect pest abundance increased at higher wind speeds, and decreased with increases in elevation, the proportion of silt and sand in the soil, and field greeness. Seasonal analysis that removed weather variables indicated that terrain and soil variables were significant, and natural enemies and spiders became relevant, showing that, especially in late season, spiders and natural enemy increases were coupled with decreases in insect pests. However, results also showed that relationships were complex. Insect pest abundance decreased when a measure of plant health increased during mid-season, and when spider abundance increased in both early and late seasons. Fine resolution spatial models of herbivorous insect responses to environmental factors, provide an opportunity to learn about the variability within agricultural fields and, with further analysis, has the potential to inform and improve PA and habitat management decisions.
Technical Abstract: Precision agriculture (PA) is the application of management decisions based on identifying, quantifying, and responding to space-time variability. However, knowledge of crop pest responses to within-field environmental variability, and the spatial distribution of their natural enemies, is limited. Quantitative methods providing insights on how pest-predator relationships vary within fields are potentially important tools. In this study, phloem feeders and their natural enemies, were observed over two years across 81 locations within a field of the perennial feedstock grass in Georgia, USA. Geographically weighted regression (GWR) was used to spatially correlate their abundance with environmental factors. Variables included distance to forest edge, Normalized Difference of Vegetation Index (NDVI), slope, aspect, elevation, soil particle size distribution, and weather values. GWR methods were compared with generalized linear regression methods that do not account for spatial information. Non-spatial models indicated positive relationships between phloem-feeder abundance and wind speed, but negative relationships between elevation, proportions of silt and sand, and NDVI. With data partitioned into three seasonal groups, terrain and soil variables remained significant, and natural enemies and spiders became relevant. Results from GWR indicated that magnitudes and directions of responses varied within the field, and that relationships differed among seasons. Strong negative relationships between response and explanatory factors occurred: with NDVI during mid-season; with percent silt, during mid-, and late seasons; and with spider abundance during early and late seasons. In GWR models, slope, elevation, and aspect were mostly positive indicating further that associations with elevation depended on whether models incorporated spatial information or not. By using spatially explicit models, the analysis provided a complex, nuanced understanding of within-field relationships between phloem feeders and environmental covariates. This approach provides an opportunity to learn about the variability within agricultural fields and, with further analysis, has potential to inform and improve PA and habitat management decisions.