Biology subjects

August, T.

Publications and source records attributed to August, T..

4 recordsLinked to original sources

bioRxiv2026

Automated Monitoring of Insects system: Building a non-lethal and scalable solution for monitoring of nocturnal insects

There is growing evidence that human-induced climate change and habitat loss are having negative impacts on insect populations. New technologies have a vital role in improving and expanding global biodiversity monitoring capacity to understand where change is happening and to support restoration. Monitoring of insects traditionally needs entomologists in the field, but insect camera traps powered by AI are emerging as a scalable approach to monitoring semi-autonomously. These systems attract, detect, and identify insects using AI algorithms and are being developed by a network of researchers across the world, notably in Europe and North America. The first version of a system for monitoring nocturnal insects was developed by Bjerge et al. 2021. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Here we describe the second generation of the system as an open-source solution. This paper aims to enable anyone to build their own system, and to iterate and improve the design for their needs. This system captures images at set intervals or based on motion detection to monitor insects that are attracted to lights at night. The UKCEH Automated Monitoring of Insects System (UKCEH AMI-system) is an insect camera trap designed using a single board computer, USB camera and attractant lights as the primary components along with peripheral accessories to make an autonomous system capable of long-term deployment in the field. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/704388v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@17ccdc4org.highwire.dtl.DTLVardef@a92179org.highwire.dtl.DTLVardef@1d4aaf8org.highwire.dtl.DTLVardef@1248d07_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2026

Testing the efficacy of artificial flowers as a novel attractant for automated pollinator monitoring

Insect camera traps are a rapidly developing technology, enabling automated monitoring of insects. However, little has been reported on improving the attractants used for daytime flying insects on such cameras. This study compares the attractiveness of novel, 3D printed, artificial flowers with traditional methods of attracting insects (e.g. pan traps and solid coloured paper squares). We hypothesised that artificial flowers would attract a higher abundance and diversity of insects compared to traditional attractants by more accurately mimicking flowers. Additionally, we examined colour preference and average landing duration on the attractants. Artificial flowers, dry pan traps and paper squares, painted in yellow, white, or blue ultraviolet fluorescent paint, were filmed simultaneously to observe wild insect behavioural responses (landings and approaches). The results indicate overall preference for artificial flowers over the two traditional attractants when considering all insect groups together, and overall colour preferences for blue and yellow. When analysing insect groups separately, hoverflies preferred landing on artificial flowers over the other attractants. Bumblebees preferred approaching artificial flowers, and small insects preferred landing and approaching artificial flowers over the other attractants. Other flies preferred landing on pan traps and paper over artificial flowers. Hoverflies, small insects, wasps, and solitary bees responded more to yellow than the other colours, while bumblebees responded more to blue. Comparisons of landing durations revealed that hoverflies spent longer on the artificial flowers than paper. Other flies spent longer on the pan traps and paper. These results show that artificial flowers could offer an efficient attractant for insect camera traps as they attracted a higher abundance of key pollinating insects (hoverflies and bumblebees), and do not have worse attraction rates for the other insect groups (excluding other flies).

ecology↗

bioRxiv2019

Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis

O_LISpecies records from volunteers are a vast and valuable source of information on biodiversity for a wide range of taxonomic groups. Although these citizen science data are opportunistic and unstructured, occupancy analysis can be used to quantify trends in distribution. However, occupancy analysis of unstructured data can be resource-intensive and requires substantial expertise. It is valuable to have simple rules of thumb to efficiently assess the suitability of a dataset for occupancy analysis prior to analysis.\nC_LIO_LIOur analysis was possible due to the production of trends, from our Bayesian occupancy analysis, for 10 967 species from 34 multi-species recording schemes in Great Britain. These schemes had an average of 500 visits to sites per year, and an average of 20% of visited sites received a revisit in a year. Occupancy trend outputs varied in their precision and we used expert elicitation on a subset of outputs to determine a precision threshold above which trends were suitable for further consideration. We then used classification trees with seven metrics to define simple rules explaining when the data would result in outputs that met the precision threshold.\nC_LIO_LIWe found that the suitability of a species data was best described by (i) the number of records of the focal species in the 10% best-recorded years, and (ii) the proportion of recording visits for that taxonomic group with non-detections of the focal species. Surprisingly few data were required to be predicted to meet the precision threshold. Specifically, for 98% confidence that our Bayesian occupancy models would produce outputs meeting the precision threshold, there needed to be [≥]29 records of the focal species in the 10% best-recorded years (equivalent to an average of 12.5 records per year in our dataset), although only [≥]10 records (equivalent to 4.5 records per year) were required for species recorded in less than 1 in 25 visits.\nC_LIO_LIWe applied these rules to regional species data for Great Britain. Data from 32% of the species:region combinations met the precision threshold with 80% confidence, and 14% with 98% confidence. There was great variation between taxonomic groups (e.g. butterflies, moths and dragonflies were well recorded) and region (e.g. south-east England was best recorded).\nC_LIO_LIThese simple criteria provide no indication of the accuracy or representativeness of the trend outputs: this is vital, but needs to be assessed individually. However our criteria do provide a rapid, quantitative assessment of the predicted suitability of existing data for occupancy analysis and could be used to inform the design and implementation of multi-species citizen science recording projects elsewhere in the world.\nC_LI

ecology↗

bioRxiv2019

Autonomous drones are a viable tool for acoustic bat surveys

Acoustic surveys of bats are currently limited by the detection range of ultrasound microphones. This makes it difficult to survey bats at height, over water, or in other hard to reach locations. Drones, also known as UAVs or UASs, are becoming more affordable and feature rich, resulting in more uptake in conservation, primarily for aerial imagery. In this paper we address current limitations to acoustic bat surveys by developing three autonomous drones for surveying bats; a plane, quadcopter and boat. All three are capable of moving autonomously from one waypoint to another while carrying a bat detector to record bats and are all low cost (under US$950) with large operational ranges that make them suited to aerial transect work.\n\nInitial testing highlighted ultrasound noise generated by the drones as a major issue for recording bats from these systems. This was mitigated through iterative design of the microphone placement and the vehicles themselves. Subsequent testing in real world settings, in the presence of bats, demonstrated that bats could be recorded under autonomous navigation and that the ultrasound interference could be reduced to a negligible level.\n\nAutonomous drones offer an exciting new tool in the tool box of bat workers and researchers. They allow us to study areas previously inaccessible on foot, as well as at heights that have previously been inaccessible. We also discuss current limitations to this technology, including legal considerations, hidden costs and potential impacts on bat behaviour.

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