Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes

Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variations in pest ecology but also from the widespread use of categorical land-use maps that do not adequately characterize habitat availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into c..., Our cotton dataset encompassed 1487 field-year replicates of L. hesperus observations across 565 conventionally managed irrigated cotton fields located within 18 ranches (i.e., fields managed by the same organization or grower that may or may not be spatially contiguous). The study site network spanned ~280km of California’s Central Valley, with fields in different ranches separated by an average of 100 km (Interquartile range 31 km). Cotton was usually planted in April (N = 630/872 for which planting date was known). Pesticides were regularly applied to target L. hesperus, most often at peak trap capture (July) and not in the early season studied here (see below). Latitude, longitude, year, and ranch name were available for all fields. Lygus hesperus densities were sampled in Gossypium hirsutum (“upland cotton”) and Gossypium barbadense (“Pima cotton”).
Pest densities were calculated by aggregating 50 swings of a sweep net across the top of the plant canopy. Usually, 6-12 sweep samples..., , # Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes

[https://doi.org/10.5061/dryad.r4xgxd2mz](https://doi.org/10.5061/dryad.r4xgxd2mz)

These data include pest (*Lygus hesperus*) density data in early-season cotton from commercial fields in California between 1997-2008. There are also data at 3 radii around these pest density replicate observations (30 km, 20km, 10km), in which the NLCD crop and non-crop habitat are given as a proportion of the total area, precipitation from DAYMET is reported for the entire radius and split between crop and non-crop habitat and MODIS variables are extracted from Google Earth Engine for the entire radius and split between crop and non-crop habitat at a given radius.

A common replicate ID can be matched across pest observation data and NLCD/DAYMET/MODIS data. Generalized additive models were used to test hypotheses about the proportion, phenology, and productivity of non-crop habitat as predic...