Insect Community Response Following Wildfire in an Eastern North American Pine Barrens

Ecosystem recovery following wildfire is heavily dependent upon fire severity and frequency, as well as factors such as regional topography and connectivity to unburned patches. Insects are an often-overlooked group of organisms impacted by fire and play crucial roles in many ecosystem services. Flying insects are particularly capable of avoiding fire, returning to burned patches following the initial disturbance, making them an important group to study when assessing wildfire impacts. Following a wildfire in July of 2018 at the Altona Flat Rock jack pine barrens in northeastern New York, insects were collected from an unburned reference site and a post-fire site using malaise traps. Samples were collected in the 2018, 2019, and 2020 field seasons. Insect groups were found to have three main responses to the disturbance event: increased abundance post-fire, unchanged abundance post-fire, or reduced abundance post-fire. Several dipteran families and some non-dipteran groups were present in greater abundance in the post-fire study site, such as Diptera Polleniidae, which increased in abundance immediately following the disturbance in 2018. Other fire-adapted taxa exhibited a more delayed positive response in 2019 and 2020. Diversity, particularly among Diptera, increased with time since the disturbance at the post-fire site. Many taxa declined in response to fire disturbance, including Lepidoptera and several Diptera families, most likely due to habitat, moisture, and organic matter requirements. Future studies could prove beneficial in understanding the recovery of this community and informing land management practices.

Performance and stability of fly-inspired, saccade-based hybrid control

In a way analogous to human vision, the fruit fly Drosophila melanogaster and many other flying insects generate smooth and saccadic movements to stabilize and shift their gaze in flight, respectively. It has been hypothesized that this combination of continuous and discrete movements benefits both flight stability and performance, particularly at high frequencies or speeds. Here we develop a hybrid control system model to explore the effects of saccades on the yaw stabilization reflex of D. melanogaster. Inspired from experimental data, the model includes a first order plant, a Proportional-Integral (PI) continuous controller, and a saccadic reset system that fires based on the integrated error of the continuous controller. We explore the gain, delay and switching threshold parameter space to quantify the optimum regions for stability and performance. We show that the addition of saccades to a continuous controller provides benefits to both stability and performance across a range of frequencies. Our model suggests that Drosophila operate near its optimal switching threshold for its experimental gain set. We also show that based on experimental data, D. melanogaster operates in a region that trades off performance and stability. This trade-off increases flight robustness to compensate for environmental uncertainties such as wing damage.

Alternation emerges as a multi-modal strategy for turbulent odor navigation

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, "alternation", a momentary pause to sniff in the air often preceded by the animal rearing on its hind legs or raising its head. Intriguingly, rodents executing an olfactory search task spontaneously exhibit alternation in the presence of airflow, suggesting that alternation may serve an important role during turbulent plume-tracking. To test this hypothesis, we combine fully-resolved numerical simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior commonly observed in flying insects. Alternation is tightly linked with casting and occurs more frequently when the agent is far downwind of the source, where the likelihood of detecting airborne cues is higher relative to cues close to the ground. Casting and alternation emerge as complementary tools for effective exploration when cues are sparse. We develop a model based on marginal value theory to capture the interplay between casting, surging and alternation. More generally, we show how multiple sensorimotor modalities can be fruitfully integrated during complex goal-directed behavior.

The Use of Honeybee Hives May Boost Yields of Some Crops in Nepal

Many pollination-dependent crops worldwide need bees for the highest productivity. If the crops are not pollinated, a pollination deficit will result. Consequently, low yields of fruit set and seed set of cultivated plants may be expected. Here, we evaluated how pollination with honeybee (Apis mellifera) hives may affect the production of the bittergourd (Momordica charantia), buckwheat (Fagopyrum esculentum), and mustard (Brassica campestris) in tons or quintal per hectare in Nepal. Our experimental design involved three treatments in blocks within selected areas: (i) the effect of the honeybees alone (caged with beehives), (ii) free insect access under natural field conditions, and (iii) blocks restraining insect access (caged without beehives). We also assessed the flower visiting insects within crops using pan traps and identifying insect orders. We found that the productivity of bittergourd, buckwheat, and mustard significantly increased in the treatments with beehives inside the cage. To a lesser extent, the treatment with free access to the flying insects enhanced the production of the selected crops. Proportionally, Hymenoptera (mainly bees) was the most common taxon within bittergourd, buckwheat, and mustard crops, followed by Diptera and Lepidoptera. Hence, the provision of beehives in cultivated areas such as those evaluated here could be considered as a complementary strategy for supporting the long-term productivity of these crops in Nepal.

A new innovative real-time tracking method for flying insects applicable under natural conditions

Background Sixty percent of all species are insects, yet despite global efforts to monitor animal movement patterns, insects are continuously underrepresented. This striking difference between species richness and the number of species monitored is not due to a lack of interest but rather to the lack of technical solutions. Often the accuracy and speed of established tracking methods is not high enough to record behavior and react to it experimentally in real-time, which applies in particular to small flying animals. Results Our new method of real-time tracking relates to frequencies of solar radiation which are almost completely absorbed by traveling through the atmosphere. For tracking, photoluminescent tags with a peak emission (1400 nm), which lays in such a region of strong absorption through the atmosphere, were attached to the animals. The photoluminescent properties of passivated lead sulphide quantum dots were responsible for the emission of light by the tags and provide a superb signal-to noise ratio. We developed prototype markers with a weight of 12.5 mg and a diameter of 5 mm. Furthermore, we developed a short wave infrared detection system which can record and determine the position of an animal in a heterogeneous environment with a delay smaller than 10 ms. With this method we were able to track tagged bumblebees as well as hawk moths in a flight arena that was placed outside on a natural meadow. Conclusion Our new method eliminates the necessity of a constant or predictable environment for many experimental setups. Furthermore, we postulate that the developed matrix-detector mounted to a multicopter will enable tracking of small flying insects, over medium range distances (>1000m) in the near future because: a) the matrix-detector equipped with an 70 mm interchangeable lens weighs less than 380 g, b) it evaluates the position of an animal in real-time and c) it can directly control and communicate with electronic devices.

Laboratory and Greenhouse Performance of Five Commercial Light Traps for Capturing Mosquitoes in China

ABSTRACT Mosquito light traps for household use are popular because they are small, cheap, user friendly, and environment friendly. At present, there are many variations and specifications of mosquito traps intended for household use on the market. The light traps claim they are powerful, but research and evaluation are lacking. Key parameters such as capture rates in the laboratory and field of 5 popular mosquito traps were evaluated as intended for household use. This study found that in the laboratory experiments, the capture rate of the mosquito traps selected was between 34.7% and 65.0%. Field tests in greenhouses found that the 5 mosquito traps had high catch rates for Culex quinquefasciatus. The percentage of Cx. quinquefasciatus, Aedes albopictus, Anopheles sinensis, and other flying insects captured was 51.76%, 25.29%, 14.12%, and 8.82%, respectively. There was no significant difference in the capture rate of Ae. albopictus and An. sinensis by the 5 mosquito traps in the greenhouse, but a significant difference in the catch rate of Cx. quinquefasciatus. The analysis showed that the fan speed and design of the air guide of the traps are important factors that affect the mosquito catch rate and that the ultraviolet wavelength (395–400 nm) used by the traps did not impact mosquito catch rates. Therefore, the mosquito traps intended for household use can be improved by adjusting the fan speed and optimizing the air guide.

Detection of insect health with deep learning on near-infrared sensor data

Conventional monitoring methods for disease vectors, pollinators or agricultural pests require time-consuming trapping and identification of individual insects. Automated optical sensors that detect backscattered near-infrared modulations created by flying insects are increasingly used to identify and count live insects, but do not inform about the health status of individual insects. Here we show that deep learning in trained convolutional neural networks in conjunction with sensors is a promising emerging method to detect infected insects. Health status was correctly determined in 85.6% of cases as early as two days post infection with a fungal pathogen. The ability to monitor insect health in real-time potentially has wide-reaching implications for preserving pollinator biodiversity and the rapid assessment of disease carrying individuals in vector populations.