COS 163-5 - Landscape phenology and productivity predict mirid bug abundance in cotton fields

Background/Question/Methods

The value of land-use intensity as a predictor of insect pest abundance has been hotly debated, but functional variation between insect species and their host environment has made broad comparisons difficult. Comparisons of functionally similar species in the same crop on an international scale are needed to quantify the drivers of variability over time and space. Miridae are major pests of cotton crops worldwide. They cause yield loss by piercing cotton squares and damaging boll set. Mirids are known to move large distances. Homogenous agricultural landscapes result in earlier migration into cotton fields and increased overall abundance. Historic data of bi-weekly monitoring of mirid pest density in cotton fields are used in this analysis from three different continents between 2000 and 2015 consisting of 4536 field-years in California, 1367 field-years in Spain and 373 field-years in Australia. We quantify the effect of direct measures of landscape productivity, derived from satellite remote sensing data, on mirid bug abundance in cotton. We test whether vegetation phenology (e.g. timing of peak greenness) on the landscape scale is important for explaining variation of pest abundance. We test whether these metrics are equally important for predicting mirid bug pest density in cotton across three continents.

Results/Conclusions

Our cross-continent decadal data support previous location-specific findings that higher early season mirid abundance results in lower cotton yield. Early season mirid abundance was compared to EVI index and vegetation phenology metrics at the 2km, 10km and 30km scales. Early-season mirid abundance in cotton fields declines with later landscape peak greenness and higher EVI amplitude at the 30km scale. A higher proportion of natural (non-agricultural) landscape is also correlated with lower early-season mirid abundance. Phenological vegetation metrics are significantly correlated and show similar patterns, though the timing of peak greenness is the most predictive for early-season mirid abundance. These results have important implications for landscape management decisions and in the context of global climate change which often benefits agricultural pests over their predators. Furthermore, it shows the ecologically predictive value of earth observations in predicting invertebrate abundance and movement even in complex landscapes.