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Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), reached damaging levels in 2003 and 2005 in soybean, Glycine max (L.) Merrill, in most northern U.S. states and Canadian provinces, and it has become one of the most important pests of soybean throughout the North Central region. A common experimental protocol was adopted by participants in six states who provided data from 19 yield-loss experiments conducted over a 3-yr period. Population doubling times for field populations of soybean aphid averaged 6.8 d +/- 0.8 d (mean +/- SEM). The average economic threshold (ET) over all control costs, market values, and yield was 273 +/- 38 (mean +/- 95% confidence interval [CI], range 111-567) aphids per plant. This ET provides a 7-d lead time before aphid populations are expected to exceed the economic injury level (EIL) of 674 +/- 95 (mean +/- 95% CI, range 275-1,399) aphids per plant. Peak aphid density in 18 of the 19 location-years occurred during soybean growth stages R3 (beginning pod formation) to R5 (full size pod) with a single data set having aphid populations peaking at R6 (full size green seed). The ET developed here is strongly supported through soybean growth stage R5. Setting an ET at lower aphid densities increases the risk to producers by treating an aphid population that is growing too slowly to exceed the EIL in 7 d, eliminates generalist predators, and exposes a larger portion of the soybean aphid population to selection by insecticides, which could lead to development of insecticide resistance.

Soybean aphid, Aphis glycines Matsumura, is now widely established in soybean, Glycine max L., production areas of the northern United States and southern Canada and is becoming an important economic pest. Temperature effect on soybean aphid fecundity and survivorship is not well understood. We determined the optimal temperature for soybean aphid growth and reproduction on soybean under controlled conditions. We constructed life tables for soybean aphid at 20, 25, 30, and 35 degrees C with a photoperiod of 16:8 (L:D) h. Population growth rates were greatest at 25 degrees C. As temperature increased, net fecundity, gross fecundity, generation time, and life expectancy decreased. The prereproductive period did not differ between 20 and 30 degrees C; however, at 30 degrees C aphids required more degree-days (base 8.6 degrees C) to develop. Nymphs exposed to 35 degrees C did not complete development, and all individuals died within 11 d. Reproductive periods were significantly different at all temperatures, with aphids reproducing longer and producing more progeny at 20 and 25 degrees C than at 30 or 35 degrees C. Using a modification of the nonlinear Logan model, we estimated upper and optimal developmental thresholds to be 34.9 and 27.8 degrees C, respectively. At 25 degrees C, aphid populations doubled in 1.5 d; at 20 and 30 degrees C, populations doubled in 1.9 d.

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Soybean aphid, Aphis glycines Matsumura, is now widely established in soybean, Glycine max L., production areas of the northern United States and southern Canada and is becoming an important economic pest. Temperature effect on soybean aphid fecundity and survivorship is not well understood. We determined the optimal temperature for soybean aphid growth and reproduction on soybean under controlled conditions. We constructed life tables for soybean aphid at 20, 25, 30, and 35 degrees C with a photoperiod of 16:8 (L:D) h. Population growth rates were greatest at 25 degrees C. As temperature increased, net fecundity, gross fecundity, generation time, and life expectancy decreased. The prereproductive period did not differ between 20 and 30 degrees C; however, at 30 degrees C aphids required more degree-days (base 8.6 degrees C) to develop. Nymphs exposed to 35 degrees C did not complete development, and all individuals died within 11 d. Reproductive periods were significantly different at all temperatures, with aphids reproducing longer and producing more progeny at 20 and 25 degrees C than at 30 or 35 degrees C. Using a modification of the nonlinear Logan model, we estimated upper and optimal developmental thresholds to be 34.9 and 27.8 degrees C, respectively. At 25 degrees C, aphid populations doubled in 1.5 d; at 20 and 30 degrees C, populations doubled in 1.9 d.

These data demonstrate that an IPM approach, combining scouting and foliar-applied insecticide where necessary, remains the best option for treatment of soybean aphids, both in terms of protecting the yield potential of the crop and of break-even probability for producers. Furthermore, we found that thiamethoxam concentrations in foliage are unlikely to effectively manage soybean aphids for most of the pests' activity period across the region. © 2017 Society of Chemical Industry.

Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.

The soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is capable of reducing soybean, Glycine max (L.) Merr., yield up to 40% during severe outbreaks. Frequent sampling, which can be costly and time-consuming, is essential to making informed management decisions. However, one way to decrease sampling effort is to use a reduced sample unit when possible. The objectives of this study were to describe the vertical distribution of soybean aphid within soybean over time and to define node-based sample units of varying sizes by testing the ability of selected units to accurately estimate whole-plant aphid density. Within-plant distribution of soybean aphid changed significantly with time. However, the average nodal position where soybean aphids were found on soybean remained within the top half of the plant at all three locations studied across all sample dates. Consequently, selecting the node with the highest aphid density multiplied by the total number of infested nodes (N(MAX)') was the best predictor of aphids on remaining soybean components in both the original (r2 = 0.855) and validation (r2 = 0.824) data sets. For sample units that included more than a single node to estimate densities, a weighted formula, which incorporated changes observed in the within-plant aphid distribution, improved model performance (higher r2 values) and reduced variability around parameter estimates compared with a node-averaged formula. Our results suggest that smaller sample units provide reliable estimations of whole-plant aphid density throughout the growing season for differently maturing soybean, which is essential to their use in pest management decisions and development of future sampling plans.