DOI: 10.5061/dryad.2z34tmq02
title: Monitoring Eastern flower thrips and soybean thrips (Thysanoptera: Thripidae) and the generalist predator, insidious flower bug (Hemiptera: Anthocoridae) in the American Midwest Suction Trap Network

description: Eastern flower thrips (Frankliniella tritici) and soybean thrips
 (Neohydatothrips variabilis) are vectors of soybean vein necrosis virus
 (SVNV) and have increased in importance since the detection of the SVNV in
 2008 in Arkansas. Understanding the factors that influence the timing and
 extent of these insects’ activity, along with their predators such as the
 insidious flower bug (Orius insidiosus), could contribute to improved
 management. Here, we compiled monitoring data between 2020-2023 from the
 American Midwest Suction Trap Network and examined associations between
 the timing of early and late activity, cumulative activity density,
 weather, landscape composition, and host plant phenology. We found that
 the activity of Eastern flower thrips began earlier, and insidious flower
 bug activity occurred later when conditions were warmer. In contrast, the
 activity of soybean thrips began earlier when there was higher edge
 density in the landscape but was not coincident with the timing of soybean
 bloom, suggesting an important role for non-crop host plants in early
 activity of these insects. Despite becoming active later, soybean thrips
 had greater cumulative activity density where it was warmer, a discordance
 suggesting the importance of migration in their overall abundance. Suction
 trap captures might therefore reflect the influences of local conditions
 as well as migratory movements on soybean thrips activity. Soybean thrips
 and insidious flower bug cumulative activity densities were also found to
 be positively correlated, suggesting that insidious flower bugs may be
 opportunistically utilizing soybean thrips as prey. Continued regional
 monitoring of these insects could be used to improve vector management.
description: Insect data The methodology for monitoring
 <em>F. tritici</em>, <em>N. variabilis</em> and
 <em>O. insidiosus</em> with suction traps was the same as that
 established in 2005 for monitoring aphids previously described by
 Lagos-Kutz et al. (2020). Briefly, the suction traps capture insects
 flying over a 5.8 m vertical stack that pulls in air at a constant rate of
 60 m3 per minute between the hours of 7:00 AM and 8:00 PM according to
 each location’s standard time. Suction trap samples were collected weekly
 from 22 May to 23 October in 2020, 21 May to 22 October in 2021, 20 May to
 21 October in 2022, and 19 May to 20 October in 2023. They were mailed to
 the USDA Soybean Diseases and Pests Laboratory at the University of
 Illinois for sample processing, identification, and counting.
 We ultimately compiled insect counts from 33 suction traps distributed
 across 10 states in the central U.S.: Illinois (5 traps), Indiana (5),
 Iowa (4), Kansas (1), Michigan (4), Minnesota (4), Missouri (1), Nebraska
 (2) and Wisconsin (7) (Table 1). Selected specimens were deposited at the
 Illinois Natural History Survey-Insect Collection.
 Insect phenology data We summarized insect
 phenology using two metrics that describe the timing of early and late
 activity: the day of year when 10% (DOY10) and 90% (DOY90) of insect
 detections occurred. To do so, for each insect species we calculated the
 cumulative proportion of counts for each site<em>year, then used a
 two-parameter logistic regression model with the “nplr” R package (Commo
 and Bot 2016) to estimate when the DOY10 and DOY90 of counts had
 accumulated. In some instances where a two-parameter logistic regression
 model could not be solved (two out of 121 site</em>years for Eastern
 flower thrips), a three-parameter logistic regression model was used.
 Using DOY10 and DOY90 instead of the day of first and last detection
 avoids artificial truncation of phenology metrics due to timing of
 sampling (e.g., if a large proportion of captured insects were detected on
 the first sampling date, this would artificially truncate “day of first
 capture”, while DOY10 would be less affected). In five of 121
 site<em>year cases, low insect counts (mostly zeroes) precluded
 estimation of DOY10 or DOY90, and these site</em>years were removed
 from subsequent analysis. We also removed one site*year from models
 explaining DOY10s due to exceptionally high counts early in the sampling
 period resulting in an unrealistic (&lt;0) DOY10 estimate.
 Weather data We curated weather data to assess
 how precipitation, temperature, and wind direction might influence timing
 of thrips detections. Daily precipitation and temperature data were
 obtained from the PRISM Climate Group (PRISM 2024). Values of daily
 precipitation and mean temperature were extracted and averaged from within
 a 1 km radius of suction trap sites. Cumulative precipitation was then
 calculated as the sum of precipitation between January 1 and the last week
 of sampling in each site*year. Mean temperature data were converted to
 daily 1degree day accumulation according to the equation:
 𝐷𝐷 =(𝑇𝑚𝑎𝑥 + 𝑇𝑚𝑖𝑛/2) ― 𝑇𝑏𝑎𝑠𝑒 where 𝑇𝑚𝑎𝑥 is the
 maximum temperature in a day, 𝑇𝑚𝑖𝑛 is the minimum temperature in a day,
 and 𝑇𝑏𝑎𝑠𝑒 is the lower developmental threshold of 10oC (Keough et al.
 2018). Cumulative degree days were then summed between January 1 and the
 last week of sampling at each site*year. Data on the v (north/south) wind
 component were obtained from NCEP North American Regional Reanalysis (NCEP
 NARR 2024). The v wind component was extracted and averaged from within a
 1 km radius of suction trap sites. Average values &lt; 0 indicate
 winds generally coming from the north, while values &gt; 0 indicate
 winds generally coming from the south. We used functions available in the
 ‘raster’, ‘terra’, and ‘ncdf4’ R packages to curate all spatial data
 (Hijmans 2023a, 2023b, Pierce 2023). Environmental covariates were
 visualized using the R package ‘tmap’ (Tennekes 2018).
 Land cover data We obtained land cover data to
 assess how selected crop and non-crop thrips habitat might influence the
 timing of thrips detections. Land cover data was collected from the United
 States Department of Agriculture – National Statistics Service Cropland
 Data Layer (USDA-NASS 2024). We summarized the proportion of land cover
 within a 1 km radius of each suction trap site that was soybean (includes
 soybean, double-crop winter wheat / soybean, double-crop soybeans,
 double-crop soybean / oats, double-crop corn / soybean, and double-crop
 barley / soybean), as well as the edge density. We only considered the
 proportion of soybean because exploratory analyses revealed strong
 correlations with other land cover classes (corn, forest) that precluded
 their inclusion in models (described under Statistical analysis). Edge
 density was calculated as the proportion of edges between 30 × 30 m pixels
 that occurred between different land cover classes using the lsm_l_ed()
 function from the ‘landscapemetrics’ R package (Hesselbarth et al. 2019).
 We included edge density as a proxy for weed habitat, which has been a
 hypothesized source of spring-colonizing thrips populations (Bloomingdale
 et al. 2017, Keough et al. 2018). Crop phenology
 data To assess how thrips activity may be related to
 soybean phenology, we curated state-level reports of crop progress from
 2020-2023 (https://usda.library.cornell.edu/concern/publications/8336h188j?locale=en#release-items). We specifically gathered data on the percentage of the soybean crops that are blooming, and the percentage of the soybean crops that are senescing. We then used two-parameter logistic regression regression models to estimate the day of year when 10% of soybeans were blooming (soybean bloom DOY10) and when 10% of soybeans were senescing (soybean senescence DOY10). We expected that if insect activity was linked to soybean phenology in the area around a suction trap, the beginning of insect detections should roughly coincide with soybean blooming, and the ending of insect detections should roughly coincide with soybean senescence.  Statistical analysis Statistical analysis involved three sets of generalized linear models with a Gaussian error distribution for each insect species (<em>F. tritici</em>, <em>N. variabilis</em> and <em>O. insidiosus</em>), implemented with glmmTMB function available in the ‘glmmTMB’ R package (Brooks et al. 2017). The first set considered how landscape covariates, soybean phenology, sampling period, and weather might influence the DOY10 of insect detections. These models included as fixed effects the predictor variables of year (categorical), day of year when sampling began (ranging from Julian day 139 to 210 among site<em>years), cumulative degree days, cumulative precipitation, average north/south wind component the week when the modeled insect was first detected, soybean bloom DOY10, proportion soybean land cover, and edge density. In this and all model sets, non-categorical covariates were z-score transformed (subtract mean, divide by standard deviation) to put them on a common scale prior to modeling. All models also included “site” as a random intercept to account for overall differences in timing of insect activity among sites. The second model set considered how landscape covariates, soybean phenology, sampling period, and weather might influence the DOY90 of insect detections. These models included as fixed effects the predictor variables of year (categorical), day of year when sampling ended (ranging from Julian day 260 to 297 among site</em>years), cumulative degree days, cumulative precipitation, soybean senescence DOY10, proportion soybean land cover, and edge density. The last model set considered how landscape covariates, sampling period, weather, and other insect activity density might influence the cumulative activity density (total number of insects captured in a site<em>year) of each focal insect. These models included as fixed effects the predictor variables of year (categorical), number of weeks sampled (ranging from 12 to 23 site</em>years), cumulative degree days, cumulative precipitation, proportion of soybean land cover, and edge density. For <em>F. tritici</em> and <em>N. variabilis</em> models, the total count of <em>O. insidiosus</em> (z-score transformed) was also included as a fixed effect. For the <em>O. insidiosus</em> model, total count of <em>F. tritici</em> and <em>N. variabilis</em> (both z-score transformed) were also included as fixed effects. We checked for multicollinearity among covariates using the check_collinearity function in the ‘performance’ R package (Lüdecke et al. 2021), and variance inflation factors were all &lt; 5. Generalized linear models were implemented with the glmmTMB function available in the ‘glmmTMB’ R package (Brooks et al. 2017). For data visualization and effect size estimation, parameter estimates were visualized using the ‘sjPlot’ R package (Lüdecke 2023).
description: # Monitoring Eastern flower thrips and soybean thrips (Thysanoptera:
 Thripidae) and the generalist predator, insidious flower bug (Hemiptera:
 Anthocoridae) in the American Midwest Suction Trap Network Dataset DOI:
 [10.5061/dryad.2z34tmq02](https://doi.org/10.5061/dryad.2z34tmq02) ##
 Description of the data and file structure The methodology for monitoring
 *F. tritici*, *N. variabilis* and *O. insidiosus* with suction traps was
 the same as that established in 2005 for monitoring aphids previously
 described by Lagos-Kutz et al. (2020). Briefly, the suction traps capture
 insects flying over a 5.8 m vertical stack that pulls in air at a constant
 rate of 60 m3 per minute between the hours of 7:00 AM and 8:00
 PM according to each location’s standard time. Suction trap samples were
 collected weekly from 22 May to 23 October in 2020, 21 May to 22 October
 in 2021, 20 May to 21 October in 2022, and 19 May to 20 October in 2023.
 They were mailed to the USDA Soybean Diseases and Pests Laboratory at the
 University of Illinois for sample processing, identification, and
 counting. We ultimately compiled insect counts from 33 suction traps
 distributed across 10 states in the central U.S.: Illinois (5 traps),
 Indiana (5), Iowa (4), Kansas (1), Michigan (4), Minnesota (4), Missouri
 (1), Nebraska (2) and Wisconsin (7) (Table 1). Selected specimens were
 deposited at the Illinois Natural History Survey-Insect Collection. ###
 Files and variables #### File: Dryad_Thrips_Orius_Lagos-Kutz_2025.xlsx
 **Description:** The Excel file contains 3 sheets, each per species
 included in this studied: Soybean thrips, Eastern flower thrips, and
 insidious flower bugs. ##### Variables * States, locations/state, year,
 months, and days, counts, * Missing values were marked as n.s. meaning no
 sample was collected. ## Access information Other publicly accessible
 locations of the data: * The soybean thrips data for year 2023 has been
 published
 in [https://suctiontrapnetwork.org/data/](https://suctiontrapnetwork.org/data/)