Vertical migration is a geographically and taxonomically widespread behaviour among zooplankton that spans across diel and seasonal timescales. The shorter-term diel vertical migration (DVM) has a periodicity of up to 1 day and was first described by the French naturalist Georges Cuvier in 1817. In 1888, the German marine biologist Carl Chun described the longer-term seasonal vertical migration (SVM), which has a periodicity of ca. 1 year. The proximate control and adaptive significance of DVM have been extensively studied and are well understood. DVM is generally a behaviour controlled by ambient irradiance, which allows herbivorous zooplankton to feed in food-rich shallower waters during the night when light-dependent (visual) predation risk is minimal and take refuge in deeper, darker waters during daytime. However, DVMs of herbivorous zooplankton are followed by their predators, producing complex predator-prey patterns that may be traced across multiple trophic levels. In contrast to DVM, SVM research is relatively young and its causes and consequences are less well understood. During periods of seasonal environmental deterioration, SVM allows zooplankton to evacuate shallower waters seasonally and take refuge in deeper waters often in a state of dormancy. Both DVM and SVM play a significant role in the vertical transport of organic carbon to deeper waters (biological carbon sequestration), and hence in the buffering of global climate change. Although many animal migrations are expected to change under future climate scenarios, little is known about the potential implications of global climate change on zooplankton vertical migrations and its impact on the biological carbon sequestration process. Further, the combined influence of DVM and SVM in determining zooplankton fitness and maintenance of their horizontal (geographic) distributions is not well understood. The contrasting spatial (deep versus shallow) and temporal (diel versus seasonal) scales over which these two migrations occur lead to challenges in studying them at higher spatial, temporal and biological resolution and coverage. Extending the largely population-based vertical migration knowledge base to individual-based studies will be an important way forward. While tracking individual zooplankton in their natural habitats remains a major challenge, conducting trophic-scale, high-resolution, year-round studies that utilise emerging field sampling and observation techniques, molecular genetic tools and computational hardware and software will be the best solution to improve our understanding of zooplankton vertical migrations.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/brv.12715

Two hundred years of zooplankton vertical migration research

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration. 

http://www.cell.com/article/S0169534722001343/pdf

Emerging technologies revolutionise insect ecology and monitoring.

The recent several years we developed the Scheimpflug lidar method. We combined an invention from the 19th century with modern optoelectronics such as diode lasers and CMOS array from the 21st century. The approach exceeds expectations of background suppression, sensitivity and resolution beyond known from time-of-flight lidars. We accomplished multiband elastic atmospheric lidars for resolving single particles and aerosol plumes from 405 nm to 1550 nm. We pursued hyperspectral differential absorption lidar for molecular species. We demonstrated a simple method of inelastic hyperspectral lidar for profiling aquatic environments and vegetation structure. Not least, we have developed polarimetric Scheimpflug lidar with multi-kHz sampling rates for remote modulation spectroscopy and classification of aerofauna. All these advances are thanks to the Scheimpflug principle. Here we give a review of how far we have come and shed light on the limitations and opportunities for future directions. In particular, we show how the biosphere can be resolved with unsurpassed resolution in space and time, and share our expectation on how this can revolutionize ecological analysis and management in relation to agricultural pests, disease vectors and pollinator problematics.

The Scheimpflug lidar method

A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring. 

/pdf/automating-insect-monitoring-using-unsupervised-near-31494oa0.pdf

Automating insect monitoring using unsupervised near-infrared sensors

Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

/pdf/advances-in-automatic-identification-of-flying-insects-using-1sflpc4aqt.pdf

Advances in automatic identification of flying insects using optical sensors and machine learning.

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. However, conventional monitoring methods of trapping and identification are time consuming and thus expensive. Here, we present a network of distributed wireless sensors, recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value of 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional trapping.

Abstract Background Recent declines of honeybees and simplifications of wild bee communities, at least partly attributed to changes of agricultural landscapes, have worried both the public and the scientific community. To understand how wild and managed bees respond to landscape structure it is essential to investigate their spatial use of foraging habitats. However, such studies are challenging since the foraging behaviour of bees differs between species and can be highly dynamic. Consequently, the necessary data collection is laborious using conventional methods and there is a need for novel methods that allow for automated and continuous monitoring of bees. In this work, we deployed an entomological lidar in a homogenous white clover seed crop and profiled the activity of honeybees and other ambient insects in relation to a cluster of beehives. Results In total, 566,609 insect observations were recorded by the lidar. The total measured range distribution was separated into three groups, out of which two were centered around the beehives and considered to be honeybees, while the remaining group was considered to be wild insects. The validity of this model in separating honeybees from wild insects was verified by the average wing modulation frequency spectra in the dominating range interval for each group. The temporal variation in measured activity of the assumed honeybee observations was well correlated with honeybee activity indirectly estimated using hive scales as well as directly observed using transect counts. Additional insight regarding the three-dimensional distribution of bees close to the hive was provided by alternating the beam between two heights, revealing a “funnel like” distribution around the beehives, widening with height. Conclusions We demonstrate how lidar can record very high numbers of insects during a short time period. In this work, a spatial model, derived from the detection limit of the lidar and two Gaussian distributions of honeybees centered around their hives was sufficient to reproduce the observations of honeybees and background insects. This methodology can in the future provide valuable new information on how external factors influence pollination services and foraging habitat selection and range of both managed bees and wild pollinators. 

/pdf/scheimpflug-lidar-range-profiling-of-bee-activity-patterns-2qd2xc16.pdf

Scheimpflug lidar range profiling of bee activity patterns and spatial distributions

/pdf/scheimpflug-lidar-range-profiling-of-bee-activity-patterns-r4xtx966.pdf

This thesis work has been focused on evaluation and further development of a hyperspectral LIDAR (Light Detection And Ranging) system made by Ljungholm et. al. [1] for monitoring of aquatic fauna, and in particular zooplankton. The project, which was a ”proof-of-concept”, showed the possibility to create a relatively compact and low-cost LIDAR with both spectral, temporal and range resolution. Experiments were developed with the help of the Department of Limnology, Lund University and performed with the LIDAR system. The dispersal rate of the zooplankton genus Daphnia in a water tank was measured, and was found to be largely dependent on background light and laser duty cycle of the LIDAR. The intrusive degree of the laser was determined to be large, since the zooplankton genus Daphnia was attracted to the laser light source, but reduced by employing a low duty cycle. The laser intensity stabilisation was ensured by a ramp time before each duty cycle. Range resolution measurements showed that the modelled resolution was not achieved, and this was determined to be due to defocusing and misalignment of the LIDAR system. The defocusing problem also lead to a weakening of the signal by approximately a factor 102, and artifacts were introduced on the detector. Measurements on the interaction kinetics between the Daphnia and the midge larvae Chaoborus were performed, and species distinction was made by labelling the diﬀerent species with diﬀerent dyes and recording the wavelength spectrum. No interaction statistics could be deduced from the results, mainly due to the inactivity of Chaoborus. However, the dyeing of the aquatic fauna using marker pens were shown to be an easy way to label diﬀerent species for spectral distinction. Lastly, 3D-scanning of a terrestrial plant was performed, with the aid of a simple robot acting as a whiskbroom LIDAR. The results were promising and the chlorophyll was readily distinguished from the branch, although the range resolution and uneven movement of the robot resulted in a relatively low image resolution. (Less)

Klas Rydhmer

Papers

Advances in automatic identification of flying insects using optical sensors and machine learning.

Automating insect monitoring using unsupervised near-infrared sensors

Scheimpflug lidar range profiling of bee activity patterns and spatial distributions

Scheimpflug lidar range profiling of bee activity patterns and spatial distributions

Applied hyperspectral LIDAR for monitoring fauna dispersal in aquatic environments