Location
Logan, UT
Start Date
5-17-2022 3:30 PM
Description
Principal response curve (PRC) analysis was applied to an assessment of the ecological impact of the genetically-modified (GM), insect-resistant, cotton MON 88702 on predatory Hemiptera communities in the field. The field community was represented by ten taxa collected ten times across the season at six sites, in which individual taxa were not observed in at least 25% of the time (unique site x collection combinations). These complete absences and those nearly so, called sparse subsets of the data in this investigation, were the result of geoclimatic and seasonal variations, which are both independent of the treatment effect for which the PRC analysis is intended. If the sparse subsets were included in the analysis, the treatment effect would be underestimated. Here, a modified analysis is proposed to remove those sparse subsets and to be performed on the incomplete data. In the application to MON 88702, four components (PRC1-4) were significant at the 5% level by the modified method, when more than 50% of the data were excluded due to no- or low responses, and five (PRC1-5) by the classical method. While PRC1-2 was highly consistent between two methods, PRC3-5 was largely different because of sparse subsets of the data. Differences in results between two methods demonstrate that excluding sparse subsets prevented the bias in the estimation of the treatment effect and the relationship with the community from confounding with the environmental variation that caused the sparse data. In this regard, the modification should be considered as a supplement of the classical PRC analysis and recommended when abundance data have sparse subsets.
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Included in
Principal Response Curve Analysis of Arthropod Community Abundance Data with Sparse Subsets
Logan, UT
Principal response curve (PRC) analysis was applied to an assessment of the ecological impact of the genetically-modified (GM), insect-resistant, cotton MON 88702 on predatory Hemiptera communities in the field. The field community was represented by ten taxa collected ten times across the season at six sites, in which individual taxa were not observed in at least 25% of the time (unique site x collection combinations). These complete absences and those nearly so, called sparse subsets of the data in this investigation, were the result of geoclimatic and seasonal variations, which are both independent of the treatment effect for which the PRC analysis is intended. If the sparse subsets were included in the analysis, the treatment effect would be underestimated. Here, a modified analysis is proposed to remove those sparse subsets and to be performed on the incomplete data. In the application to MON 88702, four components (PRC1-4) were significant at the 5% level by the modified method, when more than 50% of the data were excluded due to no- or low responses, and five (PRC1-5) by the classical method. While PRC1-2 was highly consistent between two methods, PRC3-5 was largely different because of sparse subsets of the data. Differences in results between two methods demonstrate that excluding sparse subsets prevented the bias in the estimation of the treatment effect and the relationship with the community from confounding with the environmental variation that caused the sparse data. In this regard, the modification should be considered as a supplement of the classical PRC analysis and recommended when abundance data have sparse subsets.